Using AMQ Streams on RHEL

Red Hat AMQ Streams 2.5

Configure and manage a deployment of AMQ Streams 2.5 on Red Hat Enterprise Linux

Abstract

Configure the operators and Kafka components deployed with AMQ Streams to build a large-scale messaging network.

Making open source more inclusive

Red Hat is committed to replacing problematic language in our code, documentation, and web properties. We are beginning with these four terms: master, slave, blacklist, and whitelist. Because of the enormity of this endeavor, these changes will be implemented gradually over several upcoming releases. For more details, see our CTO Chris Wright’s message.

Chapter 1. Overview of AMQ Streams

Red Hat AMQ Streams is a massively-scalable, distributed, and high-performance data streaming platform based on the Apache ZooKeeper and Apache Kafka projects.

The main components comprise:

Kafka Broker

Messaging broker responsible for delivering records from producing clients to consuming clients.

Apache ZooKeeper is a core dependency for Kafka, providing a cluster coordination service for highly reliable distributed coordination.

Kafka Streams API
API for writing stream processor applications.
Producer and Consumer APIs
Java-based APIs for producing and consuming messages to and from Kafka brokers.
Kafka Bridge
AMQ Streams Kafka Bridge provides a RESTful interface that allows HTTP-based clients to interact with a Kafka cluster.
Kafka Connect
A toolkit for streaming data between Kafka brokers and other systems using Connector plugins.
Kafka MirrorMaker
Replicates data between two Kafka clusters, within or across data centers.
Kafka Exporter
An exporter used in the extraction of Kafka metrics data for monitoring.

A cluster of Kafka brokers is the hub connecting all these components. The broker uses Apache ZooKeeper for storing configuration data and for cluster coordination. Before running Apache Kafka, an Apache ZooKeeper cluster has to be ready.

Figure 1.1. AMQ Streams architecture

AMQ Streams architecture

1.1. Using the Kafka Bridge to connect with a Kafka cluster

You can use the AMQ Streams Kafka Bridge API to create and manage consumers and send and receive records over HTTP rather than the native Kafka protocol.

When you set up the Kafka Bridge you configure HTTP access to the Kafka cluster. You can then use the Kafka Bridge to produce and consume messages from the cluster, as well as performing other operations through its REST interface.

Additional resources

1.2. Document conventions

User-replaced values

User-replaced values, also known as replaceables, are shown in italics with angle brackets (< >). Underscores ( _ ) are used for multi-word values. If the value refers to code or commands, monospace is also used.

For example, in the following code, you will want to replace <bootstrap_address> and <topic_name> with your own address and topic name:

bin/kafka-console-consumer.sh --bootstrap-server <bootstrap_address> --topic <topic_name> --from-beginning

Chapter 2. FIPS support

Federal Information Processing Standards (FIPS) are standards for computer security and interoperability. To use FIPS with AMQ Streams, you must have a FIPS-compliant OpenJDK (Open Java Development Kit) installed on your system. If your RHEL system is FIPS-enabled, OpenJDK automatically switches to FIPS mode when running AMQ Streams. This ensures that AMQ Streams uses the FIPS-compliant security libraries provided by OpenJDK.

Minimum password length

When running in the FIPS mode, SCRAM-SHA-512 passwords need to be at least 32 characters long. If you have a Kafka cluster with custom configuration that uses a password length that is less than 32 characters, you need to update your configuration. If you have any users with passwords shorter than 32 characters, you need to regenerate a password with the required length.

2.1. Installing AMQ Streams with FIPS mode enabled

Enable FIPS mode before you install AMQ Streams on RHEL. Red Hat recommends installing RHEL with FIPS mode enabled, as opposed to enabling FIPS mode later. Enabling FIPS mode during the installation ensures that the system generates all keys with FIPS-approved algorithms and continuous monitoring tests in place.

With RHEL running in FIPS mode, you must ensure that the AMQ Streams configuration is FIPS-compliant. Additionally, your Java implementation must also be FIPS-compliant.

Note

Running AMQ Streams on RHEL in FIPS mode requires a FIPS-compliant JDK. We recommend using OpenJDK 17.0.6 or newer.

Procedure

  1. Install RHEL in FIPS mode.

    For further information, see the information on security hardening in the RHEL documentation.

  2. Proceed with the installation of AMQ Streams.
  3. Configure AMQ Streams to use FIPS-compliant algorithms and protocols.

    If used, ensure that the following configuration is compliant:

    • SSL cipher suites and TLS versions must be supported by the JDK framework.
    • SCRAM-SHA-512 passwords must be at least 32 characters long.
Important

Make sure that your installation environment and AMQ Streams configuration remains compliant as FIPS requirements change.

Chapter 3. Getting started

AMQ Streams is distributed in a ZIP file. The ZIP files contain installation artifacts for the following components:

  • Apache Kafka
  • Apache ZooKeeper
  • Apache Kafka Connect
  • Apache Kafka MirrorMaker
Note

The Kafka Bridge has separate installation files. For information on installing and using the Kafka Bridge, see Using the AMQ Streams Kafka Bridge.

3.1. Installation environment

AMQ Streams runs in Red Hat Enterprise Linux. The host (node) can be a physical or virtual machine (VM). Use the installation files provided with AMQ Streams to install Kafka components. You can install Kafka in a single-node or multi-node environment.

Single-node environment
A single-node Kafka cluster runs one or more Kafka brokers and ZooKeeper instances on a single host. This configuration is not suitable for a production environment.
Multi-node environment
A multi-node Kafka cluster runs one or more Kafka brokers and ZooKeeper instances on a multiple hosts.

We recommended that you run Kafka and other Kafka components, such as Kafka Connect, on separate hosts. By running the components in this way, its easier to maintain and upgrade each component.

Kafka clients establish a connection to the Kafka cluster using the bootstrap.servers configuration property. If you are using Kafka Connect, for example, the Kafka Connect configuration properties must include a bootstrap.servers value that specifies the hostname and port of the hosts where the Kafka brokers are running. If the Kafka cluster is running on more than one host with multiple Kafka brokers, you specify a hostname and port for each broker. Each Kafka broker is identified by a broker.id.

3.1.1. Supported environment versions

AMQ Streams must be running in a supported version of Red Hat Enterprise Linux. The environment must also be using a supported JVM version. For more information, see Supported Configurations.

3.1.2. Data storage considerations

An efficient data storage infrastructure is essential to the optimal performance of AMQ Streams.

Block storage is required. File storage, such as NFS, does not work with Kafka.

Choose from one of the following options for your block storage:

  • Cloud-based block storage solutions, such as Amazon Elastic Block Store (EBS)
  • Local storage
  • Storage Area Network (SAN) volumes accessed by a protocol such as Fibre Channel or iSCSI

3.1.3. File systems

Kafka uses a file system for storing messages. AMQ Streams is compatible with the XFS and ext4 file systems, which are commonly used with Kafka. Consider the underlying architecture and requirements of your deployment when choosing and setting up your file system.

For more information, refer to Filesystem Selection in the Kafka documentation.

3.1.4. Apache Kafka and ZooKeeper storage

Use separate disks for Apache Kafka and ZooKeeper.

Kafka supports JBOD (Just a Bunch of Disks) storage, a data storage configuration of multiple disks or volumes. JBOD provides increased data storage for Kafka brokers. It can also improve performance.

Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with ZooKeeper, which requires fast, low latency data access.

Note

You do not need to provision replicated storage because Kafka and ZooKeeper both have built-in data replication.

Additional resources

3.2. Downloading AMQ Streams

A ZIP file distribution of AMQ Streams is available for download from the Red Hat website. You can download the latest version of Red Hat AMQ Streams from the AMQ Streams software downloads page.

  • For Kafka and other Kafka components, download the amq-streams-<version>-bin.zip file
  • For Kafka Bridge, download the amq-streams-<version>-bridge-bin.zip file.

    For installation instructions, see Using the AMQ Streams Kafka Bridge.

3.3. Installing Kafka

Use the AMQ Streams ZIP files to install Kafka on Red Hat Enterprise Linux. You can install Kafka in a single-node or multi-node environment. In this procedure, a single Kafka broker and ZooKeeper instance are installed on a single host (node).

The AMQ Streams installation files include the binaries for running other Kafka components, like Kafka Connect, Kafka MirrorMaker 2, and Kafka Bridge. In a single-node environment, you can run these components from the same host where you installed Kafka. However, we recommended that you add the installation files and run other Kafka components on separate hosts.

Note

If you are using a multi-node environment, you install Kafka brokers and ZooKeeper instances on more than one host. Repeat the installation steps for each host. To identify each ZooKeeper instance and broker, you add a unique ID in the configuration. For more information, see Chapter 4, Running a multi-node environment.

Prerequisites

Procedure

Install Kafka with ZooKeeper on your host.

  1. Add a new kafka user and group:

    groupadd kafka
    useradd -g kafka kafka
    passwd kafka
  2. Extract and move the contents of the amq-streams-<version>-bin.zip file into the /opt/kafka directory:

    unzip amq-streams-<version>-bin.zip -d /opt
    mv /opt/kafka*redhat* /opt/kafka

    This step requires admin privileges.

  3. Change the ownership of the /opt/kafka directory to the kafka user:

    chown -R kafka:kafka /opt/kafka
  4. Create directory /var/lib/zookeeper for storing ZooKeeper data and set its ownership to the kafka user:

    mkdir /var/lib/zookeeper
    chown -R kafka:kafka /var/lib/zookeeper
  5. Create directory /var/lib/kafka for storing Kafka data and set its ownership to the kafka user:

    mkdir /var/lib/kafka
    chown -R kafka:kafka /var/lib/kafka

    You can now run a default configuration of Kafka as a single-node cluster.

    You can also use the installation to run other Kafka components, like Kafka Connect, on the same host.

    To run other components, specify the hostname and port to connect to the Kafka broker using the bootstrap.servers property in the component configuration.

    Example bootstrap servers configuration pointing to a single Kafka broker on the same host

    bootstrap.servers=localhost:9092

    However, we recommend installing and running Kafka components on separate hosts.

  6. (Optional) Install Kafka components on separate hosts.

    1. Extract the installation files to the /opt/kafka directory on each host.
    2. Change the ownership of the /opt/kafka directory to the kafka user.
    3. Add bootstrap.servers configuration that connects the component to the host (or hosts in a multi-node environment) running the Kafka brokers.

      Example bootstrap servers configuration pointing to Kafka brokers on different hosts

      bootstrap.servers=kafka0.<host_ip_address>:9092,kafka1.<host_ip_address>:9092,kafka2.<host_ip_address>:9092

      You can use this configuration for Kafka Connect, MirrorMaker 2, and the Kafka Bridge.

3.4. Running a single-node Kafka cluster

This procedure shows how to run a basic AMQ Streams cluster consisting of a single Apache ZooKeeper node and a single Apache Kafka node, both running on the same host. The default configuration files are used for ZooKeeper and Kafka.

Warning

A single node AMQ Streams cluster does not provide reliability and high availability and is suitable only for development purposes.

Prerequisites

  • AMQ Streams is installed on the host

Running the cluster

  1. Edit the ZooKeeper configuration file /opt/kafka/config/zookeeper.properties. Set the dataDir option to /var/lib/zookeeper/:

    dataDir=/var/lib/zookeeper/
  2. Edit the Kafka configuration file /opt/kafka/config/server.properties. Set the log.dirs option to /var/lib/kafka/:

    log.dirs=/var/lib/kafka/
  3. Switch to the kafka user:

    su - kafka
  4. Start ZooKeeper:

    /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties
  5. Check that ZooKeeper is running:

    jcmd | grep zookeeper

    Returns:

    number org.apache.zookeeper.server.quorum.QuorumPeerMain /opt/kafka/config/zookeeper.properties
  6. Start Kafka:

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  7. Check that Kafka is running:

    jcmd | grep kafka

    Returns:

    number kafka.Kafka /opt/kafka/config/server.properties

3.5. Sending and receiving messages from a topic

This procedure describes how to start the Kafka console producer and consumer clients and use them to send and receive several messages.

A new topic is automatically created in step one. Topic auto-creation is controlled using the auto.create.topics.enable configuration property (set to true by default). Alternatively, you can configure and create topics before using the cluster. For more information, see Topics.

Procedure

  1. Start the Kafka console producer and configure it to send messages to a new topic:

    /opt/kafka/bin/kafka-console-producer.sh --broker-list <bootstrap_address> --topic <topic-name>

    For example:

    /opt/kafka/bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-topic
  2. Enter several messages into the console. Press Enter to send each individual message to your new topic:

    >message 1
    >message 2
    >message 3
    >message 4

    When Kafka creates a new topic automatically, you might receive a warning that the topic does not exist:

    WARN Error while fetching metadata with correlation id 39 :
    {4-3-16-topic1=LEADER_NOT_AVAILABLE} (org.apache.kafka.clients.NetworkClient)

    The warning should not reappear after you send further messages.

  3. In a new terminal window, start the Kafka console consumer and configure it to read messages from the beginning of your new topic.

    /opt/kafka/bin/kafka-console-consumer.sh --bootstrap-server <bootstrap_address> --topic <topic-name> --from-beginning

    For example:

    /opt/kafka/bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic my-topic --from-beginning

    The incoming messages display in the consumer console.

  4. Switch to the producer console and send additional messages. Check that they display in the consumer console.
  5. Stop the Kafka console producer and then the consumer by pressing Ctrl+C.

3.6. Stopping the AMQ Streams services

You can stop the Kafka and ZooKeeper services by running a script. All connections to the Kafka and ZooKeeper services will be terminated.

Prerequisites

  • AMQ Streams is installed on the host
  • ZooKeeper and Kafka are up and running

Procedure

  1. Stop the Kafka broker.

    su - kafka
    /opt/kafka/bin/kafka-server-stop.sh
  2. Confirm that the Kafka broker is stopped.

    jcmd | grep kafka
  3. Stop ZooKeeper.

    su - kafka
    /opt/kafka/bin/zookeeper-server-stop.sh

Chapter 4. Running a multi-node environment

A multi-node environment comprises a number of nodes that operate as a cluster. You can have a cluster of replicated ZooKeeper nodes and a cluster of broker nodes, with topic replication across the brokers.

Multi-node environments offer stability and availability.

4.1. Running a multi-node ZooKeeper cluster

Configure and run ZooKeeper as a multi-node cluster.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as ZooKeeper cluster nodes.

Running the cluster

  1. Create the myid file in /var/lib/zookeeper/. Enter ID 1 for the first ZooKeeper node, 2 for the second ZooKeeper node, and so on.

    su - kafka
    echo "<NodeID>" > /var/lib/zookeeper/myid

    For example:

    su - kafka
    echo "1" > /var/lib/zookeeper/myid
  2. Edit the ZooKeeper /opt/kafka/config/zookeeper.properties configuration file for the following:

    • Set the option dataDir to /var/lib/zookeeper/.
    • Configure the initLimit and syncLimit options.
    • Configure the reconfigEnabled and standaloneEnabled options.
    • Add a list of all ZooKeeper nodes. The list should include also the current node.

      Example configuration for a node of ZooKeeper cluster with five members

      tickTime=2000
      dataDir=/var/lib/zookeeper/
      initLimit=5
      syncLimit=2
      reconfigEnabled=true
      standaloneEnabled=false
      listener.security.protocol.map=PLAINTEXT:PLAINTEXT,REPLICATION:PLAINTEXT
      
      server.1=172.17.0.1:2888:3888:participant;172.17.0.1:2181
      server.2=172.17.0.2:2888:3888:participant;172.17.0.2:2181
      server.3=172.17.0.3:2888:3888:participant;172.17.0.3:2181
      server.4=172.17.0.4:2888:3888:participant;172.17.0.4:2181
      server.5=172.17.0.5:2888:3888:participant;172.17.0.5:2181

  3. Start ZooKeeper with the default configuration file.

    su - kafka
    /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties
  4. Verify that ZooKeeper is running.

    jcmd | grep zookeeper

    Returns:

    number org.apache.zookeeper.server.quorum.QuorumPeerMain /opt/kafka/config/zookeeper.properties
  5. Repeat this procedure on all the nodes of the cluster.
  6. Verify that all nodes are members of the cluster by sending a stat command to each of the nodes using the ncat utility.

    Use ncat stat to check the node status

    echo stat | ncat localhost 2181

    To use four-letter word commands, like stat, you need to specify 4lw.commands.whitelist=* in zookeeper.properties.

    The output shows that a node is either a leader or follower.

    Example output from the ncat command

    ZooKeeper version: 3.4.13-2d71af4dbe22557fda74f9a9b4309b15a7487f03, built on 06/29/2018 00:39 GMT
    Clients:
     /0:0:0:0:0:0:0:1:59726[0](queued=0,recved=1,sent=0)
    
    Latency min/avg/max: 0/0/0
    Received: 2
    Sent: 1
    Connections: 1
    Outstanding: 0
    Zxid: 0x200000000
    Mode: follower
    Node count: 4

4.2. Running a multi-node Kafka cluster

Configure and run Kafka as a multi-node cluster.

Prerequisites

Running the cluster

For each Kafka broker in your AMQ Streams cluster:

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file as follows:

    • Set the broker.id field to 0 for the first broker, 1 for the second broker, and so on.
    • Configure the details for connecting to ZooKeeper in the zookeeper.connect option.
    • Configure the Kafka listeners.
    • Set the directories where the commit logs should be stored in the logs.dir directory.

      Here we see an example configuration for a Kafka broker:

      broker.id=0
      zookeeper.connect=zoo1.my-domain.com:2181,zoo2.my-domain.com:2181,zoo3.my-domain.com:2181
      listeners=REPLICATION://:9091,PLAINTEXT://:9092
      listener.security.protocol.map=PLAINTEXT:PLAINTEXT,REPLICATION:PLAINTEXT
      inter.broker.listener.name=REPLICATION
      log.dirs=/var/lib/kafka

      In a typical installation where each Kafka broker is running on identical hardware, only the broker.id configuration property will differ between each broker config.

  2. Start the Kafka broker with the default configuration file.

    su - kafka
    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  3. Verify that the Kafka broker is running.

    jcmd | grep Kafka

    Returns:

    number kafka.Kafka /opt/kafka/config/server.properties
  4. Verify that all nodes are members of the Kafka cluster by sending a dump command to one of the ZooKeeper nodes using the ncat utility.

    Use ncat dump to check all Kafka brokers registered in ZooKeeper

    echo dump | ncat zoo1.my-domain.com 2181

    To use four-letter word commands, like dump, you need to specify 4lw.commands.whitelist=* in zookeeper.properties.

    The output must contain all Kafka brokers you just configured and started.

    Example output from the ncat command for a Kafka cluster with 3 nodes

    SessionTracker dump:
    org.apache.zookeeper.server.quorum.LearnerSessionTracker@28848ab9
    ephemeral nodes dump:
    Sessions with Ephemerals (3):
    0x20000015dd00000:
            /brokers/ids/1
    0x10000015dc70000:
            /controller
            /brokers/ids/0
    0x10000015dc70001:
            /brokers/ids/2

4.3. Performing a graceful rolling restart of Kafka brokers

This procedure shows how to do a graceful rolling restart of brokers in a multi-node cluster. A rolling restart is usually required following an upgrade or change to the Kafka cluster configuration properties.

Note

Some broker configurations do not need a restart of the broker. For more information, see Updating Broker Configs in the Apache Kafka documentation.

After you perform a restart of a broker, check for under-replicated topic partitions to make sure that replica partitions have caught up.

You can only perform a graceful restart, with no loss of availability, if you are replicating topics and ensuring that at least one replica is in sync. For a multi-node cluster, the standard approach is to have a topic replication factor of at least 3 and a minimum number of in-sync replicas set to 1 less than the replication factor. If you are using acks=all in your producer configuration for data durability, check that the broker you restarted is in sync with all the partitions it’s replicating before restarting the next broker.

Single-node clusters are unavailable during a restart, since all partitions are on the same broker.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • A ZooKeeper cluster is configured and running.
  • The Kafka cluster is operating as expected.

    Check for under-replicated partitions or any other issues affecting broker operation. The steps in this procedure describe how to check for under-replicated partitions.

Procedure

Perform the following steps on each Kafka broker. Complete the steps on the first broker before moving on to the next. Perform the steps on the broker that’s the active controller last. Otherwise, the active controller needs to change on more than one restart.

  1. Stop the Kafka broker:

    /opt/kafka/bin/kafka-server-stop.sh
  2. Make any changes to the broker configuration that require a restart after completion.

    For further information, see the following:

  3. Restart the Kafka broker:

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  4. Check that Kafka is running:

    jcmd | grep kafka

    Returns:

    number kafka.Kafka /opt/kafka/config/server.properties
  5. Verify that all nodes are members of the Kafka cluster by sending a dump command to one of the ZooKeeper nodes using the ncat utility.

    Use ncat dump to check all Kafka brokers registered in ZooKeeper

    echo dump | ncat zoo1.my-domain.com 2181

    To use four-letter word commands, like dump, you need to specify 4lw.commands.whitelist=* in zookeeper.properties.

    The output must contain the Kafka broker you started.

    Example output from the ncat command for a Kafka cluster with 3 nodes

    SessionTracker dump:
    org.apache.zookeeper.server.quorum.LearnerSessionTracker@28848ab9
    ephemeral nodes dump:
    Sessions with Ephemerals (3):
    0x20000015dd00000:
            /brokers/ids/1
    0x10000015dc70000:
            /controller
            /brokers/ids/0
    0x10000015dc70001:
            /brokers/ids/2

  6. Wait until the broker has zero under-replicated partitions. You can check from the command line or use metrics.

    • Use the kafka-topics.sh command with the --under-replicated-partitions parameter:

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server <bootstrap_address>  --describe --under-replicated-partitions

      For example:

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --describe --under-replicated-partitions

      The command provides a list of topics with under-replicated partitions in a cluster.

      Topics with under-replicated partitions

      Topic: topic3 Partition: 4 Leader: 2 Replicas: 2,3 Isr: 2
      Topic: topic3 Partition: 5 Leader: 3 Replicas: 1,2 Isr: 1
      Topic: topic1 Partition: 1 Leader: 3 Replicas: 1,3 Isr: 3
      # …

      Under-replicated partitions are listed if the ISR (in-sync replica) count is less than the number of replicas. If a list is not returned, there are no under-replicated partitions.

    • Use the UnderReplicatedPartitions metric:

      kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions

      The metric provides a count of partitions where replicas have not caught up. You wait until the count is zero.

      Tip

      Use the Kafka Exporter to create an alert when there are one or more under-replicated partitions for a topic.

Checking logs when restarting

If a broker fails to start, check the application logs for information. You can also check the status of a broker shutdown and restart in the /opt/kafka/logs/server.log application log.

Log for the successful shutdown of a broker

# ...
[2022-06-08 14:32:29,885] INFO Terminating process due to signal SIGTERM (org.apache.kafka.common.utils.LoggingSignalHandler)
[2022-06-08 14:32:29,886] INFO [KafkaServer id=0] shutting down (kafka.server.KafkaServer)
[2022-06-08 14:32:29,887] INFO [KafkaServer id=0] Starting controlled shutdown (kafka.server.KafkaServer)
[2022-06-08 14:32:29,896] INFO [KafkaServer id=0] Controlled shutdown request returned successfully after 6ms (kafka.server.KafkaServer)
# ...

Log for the successful restart of a broker

# ...
[2022-06-08 14:39:35,245] INFO [KafkaServer id=0] started (kafka.server.KafkaServer)
# ...

Chapter 5. Running Kafka in KRaft mode (development preview)

When you run AMQ Streams in KRaft (Kafka Raft metadata) mode, Kafka clusters are managed by an internal quorum of controllers instead of ZooKeeper.

Apache Kafka is in the process of phasing out the need for ZooKeeper. KRaft mode is now available to try. You can deploy a Kafka cluster in KRaft mode without ZooKeeper.

Caution

KRaft mode is experimental, intended only for development and testing, and must not be enabled for a production environment.

Currently, the KRaft mode in AMQ Streams has the following major limitations:

  • Moving from Kafka clusters with ZooKeeper to KRaft clusters or the other way around is not supported.
  • Upgrades and downgrades of Apache Kafka versions are not supported.
  • JBOD storage with multiple disks is not supported.
  • Many configuration options are still in development.

5.1. Using AMQ Streams with Kafka in KRaft mode

If you use Kafka in KRaft mode, you do not need to use ZooKeeper for cluster coordination or storing metadata. Kafka coordinates the cluster itself using brokers that act as controllers. Kafka also stores the metadata used to track the status of brokers and partitions.

To identify a cluster, create an ID. The ID is used when creating logs for the brokers you add to the cluster.

In the configuration of each broker node, specify the following:

  • A node ID
  • Broker roles
  • A list of brokers (or voters) that act as controllers

A broker performs the role of broker, controller, or both.

Broker role
A broker, sometimes referred to as a node or server, orchestrates the storage and passing of messages.
Controller role
A controller coordinates the cluster and manages the tracking metadata.

You can use combined broker and controller nodes, though you might want to separate these functions. Brokers performing combined roles can be more efficient in simpler deployments.

You specify a list of controllers, configured as voters, using the node ID and connection details (hostname and port) for each controller.

5.2. Running a Kafka cluster in KRaft mode

Configure and run Kafka in KRaft mode. You can run Kafka in KRaft mode if you are using a single-node or multi-node Kafka cluster. Run a minimum of three broker and controller nodes for stability and availability.

You set roles for brokers so that they can also be controllers. You apply broker configuration, including the setting of roles, using a configuration properties file. Broker configuration differs according to role. KRaft provides three example broker configuration properties files.

  • /opt/kafka/config/kraft/broker.properties has example configuration for a broker role
  • /opt/kafka/config/kraft/controller.properties has example configuration for a controller role
  • /opt/kafka/config/kraft/server.properties has example configuration for a dual role

You can base your broker configuration on these example properties files. In this procedure, the example server.properties configuration is used.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.

Procedure

  1. Generate an ID for the Kafka cluster using the kafka-storage tool:

    /opt/kafka/bin/kafka-storage.sh random-uuid

    The command returns an ID. A cluster ID is required in KRaft mode.

  2. Create a configuration properties file for each broker in the cluster.

    You can base the file on the examples provided with Kafka.

    1. Specify a role as broker, `controller, or broker, controller

      For example, specify process.roles=broker, controller for a dual role.

    2. Specify a unique node.id for each node in the cluster starting from 0.

      For example, node.id=1.

    3. Specify a list of controller.quorum.voters in the format <node_id>@<hostname:port>.

      For example, controller.quorum.voters=1@localhost:9093.

  3. Set up log directories for each node in your Kafka cluster:

    /opt/kafka/bin/kafka-storage.sh format -t <uuid> -c /opt/kafka/config/kraft/server.properties

    Returns:

    Formatting /tmp/kraft-combined-logs

    Replace <uuid> with the cluster ID you generated. Use the same ID for each node in your cluster.

    Apply the broker configuration using the properties file you created for the broker.

    The default log directory location specified in the server.properties configuration file is /tmp/kraft-combined-logs. You can add a comma-separated list to set up multiple log directories.

  4. Start each Kafka broker.

    /opt/kafka/bin/kafka-server-start.sh /opt/kafka/config/kraft/server.properties
  5. Check that Kafka is running:

    jcmd | grep kafka

    Returns:

    number kafka.Kafka /opt/kafka/config/kraft/server.properties

You can now create topics, and send and receive messages from the brokers.

For brokers passing messages, you can use topic replication across the brokers in a cluster for data durability. Configure topics to have a replication factor of at least three and a minimum number of in-sync replicas set to 1 less than the replication factor. For more information, see Section 7.7, “Creating a topic”.

Chapter 6. Configuring an AMQ Streams on RHEL deployment

Use the Kafka and ZooKeeper properties files to configure AMQ Streams.

ZooKeeper
/kafka/config/zookeeper.properties
Kafka
/kafka/config/server.properties

The properties files are in the Java format, with each property on separate line in the following format:

<option> = <value>

Lines starting with # or ! will be treated as comments and will be ignored by AMQ Streams components.

# This is a comment

Values can be split into multiple lines by using \ directly before the newline / carriage return.

sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required \
    username="bob" \
    password="bobs-password";

After you save the changes in the properties files, you need to restart the Kafka broker or ZooKeeper. In a multi-node environment, you will need to repeat the process on each node in the cluster.

6.1. Using standard Kafka configuration properties

Use standard Kafka configuration properties to configure Kafka components.

The properties provide options to control and tune the configuration of the following Kafka components:

  • Brokers
  • Topics
  • Clients (producers and consumers)
  • Admin client
  • Kafka Connect
  • Kafka Streams

Broker and client parameters include options to configure authorization, authentication and encryption.

Note

For AMQ Streams on OpenShift, some configuration properties are managed entirely by AMQ Streams and cannot be changed.

For further information on Kafka configuration properties and how to use the properties to tune your deployment, see the following guides:

6.2. Loading configuration values from environment variables

Use the Environment Variables Configuration Provider plugin to load configuration data from environment variables. You can use the Environment Variables Configuration Provider, for example, to load certificates or JAAS configuration from environment variables.

You can use the provider to load configuration data for all Kafka components, including producers and consumers. Use the provider, for example, to provide the credentials for Kafka Connect connector configuration.

Prerequisites

  • AMQ Streams is downloaded and installed on the host
  • Environment Variables Configuration Provider JAR file

    The JAR file is available from the AMQ Streams archive

Procedure

  1. Add the Environment Variables Configuration Provider JAR file to the Kafka libs directory.
  2. Initialize the Environment Variables Configuration Provider in the configuration properties file of the Kafka component. For example, to initialize the provider for Kafka, add the configuration to the server.properties file.

    Configuration to enable the Environment Variables Configuration Provider

    config.providers=env
    config.providers.env.class=io.strimzi.kafka.EnvVarConfigProvider

  3. Add configuration to the properties file to load data from environment variables.

    Configuration to load data from an environment variable

    option=${env:<MY_ENV_VAR_NAME>}

    Use capitalized or upper-case environment variable naming conventions, such as MY_ENV_VAR_NAME.

  4. Save the changes.
  5. Restart the Kafka component.

    For information on restarting brokers in a multi-node cluster, see Section 4.3, “Performing a graceful rolling restart of Kafka brokers”.

6.3. Configuring ZooKeeper

Kafka uses ZooKeeper to store configuration data and for cluster coordination. It is strongly recommended to run a cluster of replicated ZooKeeper instances.

6.3.1. Basic configuration

The most important ZooKeeper configuration options are:

tickTime
ZooKeeper’s basic time unit in milliseconds. It is used for heartbeats and session timeouts. For example, minimum session timeout will be two ticks.
dataDir
The directory where ZooKeeper stores its transaction logs and snapshots of its in-memory database. This should be set to the /var/lib/zookeeper/ directory that was created during installation.
clientPort
Port number where clients can connect. Defaults to 2181.

An example ZooKeeper configuration file named config/zookeeper.properties is located in the AMQ Streams installation directory. It is recommended to place the dataDir directory on a separate disk device to minimize the latency in ZooKeeper.

ZooKeeper configuration file should be located in /opt/kafka/config/zookeeper.properties. A basic example of the configuration file can be found below. The configuration file has to be readable by the kafka user.

tickTime=2000
dataDir=/var/lib/zookeeper/
clientPort=2181

6.3.2. ZooKeeper cluster configuration

In most production environments, we recommend you deploy a cluster of replicated ZooKeeper instances. A stable and highly available ZooKeeper cluster is important for running for a reliable ZooKeeper service. ZooKeeper clusters are also referred to as ensembles.

ZooKeeper clusters usually consist of an odd number of nodes. ZooKeeper requires that a majority of the nodes in the cluster are up and running. For example:

  • In a cluster with three nodes, at least two of the nodes must be up and running. This means it can tolerate one node being down.
  • In a cluster consisting of five nodes, at least three nodes must be available. This means it can tolerate two nodes being down.
  • In a cluster consisting of seven nodes, at least four nodes must be available. This means it can tolerate three nodes being down.

Having more nodes in the ZooKeeper cluster delivers better resiliency and reliability of the whole cluster.

ZooKeeper can run in clusters with an even number of nodes. The additional node, however, does not increase the resiliency of the cluster. A cluster with four nodes requires at least three nodes to be available and can tolerate only one node being down. Therefore it has exactly the same resiliency as a cluster with only three nodes.

Ideally, the different ZooKeeper nodes should be located in different data centers or network segments. Increasing the number of ZooKeeper nodes increases the workload spent on cluster synchronization. For most Kafka use cases, a ZooKeeper cluster with 3, 5 or 7 nodes should be sufficient.

Warning

A ZooKeeper cluster with 3 nodes can tolerate only 1 unavailable node. This means that if a cluster node crashes while you are doing maintenance on another node your ZooKeeper cluster will be unavailable.

Replicated ZooKeeper configuration supports all configuration options supported by the standalone configuration. Additional options are added for the clustering configuration:

initLimit
Amount of time to allow followers to connect and sync to the cluster leader. The time is specified as a number of ticks (see the tickTime option for more details).
syncLimit
Amount of time for which followers can be behind the leader. The time is specified as a number of ticks (see the tickTime option for more details).
reconfigEnabled
Enables or disables dynamic reconfiguration. Must be enabled in order to add or remove servers to a ZooKeeper cluster.
standaloneEnabled
Enables or disables standalone mode, where ZooKeeper runs with only one server.

In addition to the options above, every configuration file should contain a list of servers which should be members of the ZooKeeper cluster. The server records should be specified in the format server.id=hostname:port1:port2, where:

id
The ID of the ZooKeeper cluster node.
hostname
The hostname or IP address where the node listens for connections.
port1
The port number used for intra-cluster communication.
port2
The port number used for leader election.

The following is an example configuration file of a ZooKeeper cluster with three nodes:

tickTime=2000
dataDir=/var/lib/zookeeper/
initLimit=5
syncLimit=2
reconfigEnabled=true
standaloneEnabled=false

server.1=172.17.0.1:2888:3888:participant;172.17.0.1:2181
server.2=172.17.0.2:2888:3888:participant;172.17.0.2:2181
server.3=172.17.0.3:2888:3888:participant;172.17.0.3:2181
Tip

To use four letter word commands, specify 4lw.commands.whitelist=* in zookeeper.properties.

myid files

Each node in the ZooKeeper cluster must be assigned a unique ID. Each node’s ID must be configured in a myid file and stored in the dataDir folder, like /var/lib/zookeeper/. The myid files should contain only a single line with the written ID as text. The ID can be any integer from 1 to 255. You must manually create this file on each cluster node. Using this file, each ZooKeeper instance will use the configuration from the corresponding server. line in the configuration file to configure its listeners. It will also use all other server. lines to identify other cluster members.

In the above example, there are three nodes, so each one will have a different myid with values 1, 2, and 3 respectively.

6.3.3. Authentication

By default, ZooKeeper does not use any form of authentication and allows anonymous connections. However, it supports Java Authentication and Authorization Service (JAAS) which can be used to set up authentication using Simple Authentication and Security Layer (SASL). ZooKeeper supports authentication using the DIGEST-MD5 SASL mechanism with locally stored credentials.

6.3.3.1. Authentication with SASL

JAAS is configured using a separate configuration file. It is recommended to place the JAAS configuration file in the same directory as the ZooKeeper configuration (/opt/kafka/config/). The recommended file name is zookeeper-jaas.conf. When using a ZooKeeper cluster with multiple nodes, the JAAS configuration file has to be created on all cluster nodes.

JAAS is configured using contexts. Separate parts such as the server and client are always configured with a separate context. The context is a configuration option and has the following format:

ContextName {
       param1
       param2;
};

SASL Authentication is configured separately for server-to-server communication (communication between ZooKeeper instances) and client-to-server communication (communication between Kafka and ZooKeeper). Server-to-server authentication is relevant only for ZooKeeper clusters with multiple nodes.

Server-to-Server authentication

For server-to-server authentication, the JAAS configuration file contains two parts:

  • The server configuration
  • The client configuration

When using DIGEST-MD5 SASL mechanism, the QuorumServer context is used to configure the authentication server. It must contain all the usernames to be allowed to connect together with their passwords in an unencrypted form. The second context, QuorumLearner, has to be configured for the client which is built into ZooKeeper. It also contains the password in an unencrypted form. An example of the JAAS configuration file for DIGEST-MD5 mechanism can be found below:

QuorumServer {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       user_zookeeper="123456";
};

QuorumLearner {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       username="zookeeper"
       password="123456";
};

In addition to the JAAS configuration file, you must enable the server-to-server authentication in the regular ZooKeeper configuration file by specifying the following options:

quorum.auth.enableSasl=true
quorum.auth.learnerRequireSasl=true
quorum.auth.serverRequireSasl=true
quorum.auth.learner.loginContext=QuorumLearner
quorum.auth.server.loginContext=QuorumServer
quorum.cnxn.threads.size=20

Use the KAFKA_OPTS environment variable to pass the JAAS configuration file to the ZooKeeper server as a Java property:

su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

For more information about server-to-server authentication, see ZooKeeper wiki.

Client-to-Server authentication

Client-to-server authentication is configured in the same JAAS file as the server-to-server authentication. However, unlike the server-to-server authentication, it contains only the server configuration. The client part of the configuration has to be done in the client. For information on how to configure a Kafka broker to connect to ZooKeeper using authentication, see the Kafka installation section.

Add the Server context to the JAAS configuration file to configure client-to-server authentication. For DIGEST-MD5 mechanism it configures all usernames and passwords:

Server {
    org.apache.zookeeper.server.auth.DigestLoginModule required
    user_super="123456"
    user_kafka="123456"
    user_someoneelse="123456";
};

After configuring the JAAS context, enable the client-to-server authentication in the ZooKeeper configuration file by adding the following line:

requireClientAuthScheme=sasl
authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.2=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.3=org.apache.zookeeper.server.auth.SASLAuthenticationProvider

You must add the authProvider.<ID> property for every server that is part of the ZooKeeper cluster.

Use the KAFKA_OPTS environment variable to pass the JAAS configuration file to the ZooKeeper server as a Java property:

su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

For more information about configuring ZooKeeper authentication in Kafka brokers, see Section 6.4.5, “ZooKeeper authentication”.

6.3.3.2. Enabling server-to-server authentication using DIGEST-MD5

This procedure describes how to enable authentication using the SASL DIGEST-MD5 mechanism between the nodes of the ZooKeeper cluster.

Prerequisites

  • AMQ Streams is installed on the host
  • ZooKeeper cluster is configured with multiple nodes.

Enabling SASL DIGEST-MD5 authentication

  1. On all ZooKeeper nodes, create or edit the /opt/kafka/config/zookeeper-jaas.conf JAAS configuration file and add the following contexts:

    QuorumServer {
           org.apache.zookeeper.server.auth.DigestLoginModule required
           user_<Username>="<Password>";
    };
    
    QuorumLearner {
           org.apache.zookeeper.server.auth.DigestLoginModule required
           username="<Username>"
           password="<Password>";
    };

    The username and password must be the same in both JAAS contexts. For example:

    QuorumServer {
           org.apache.zookeeper.server.auth.DigestLoginModule required
           user_zookeeper="123456";
    };
    
    QuorumLearner {
           org.apache.zookeeper.server.auth.DigestLoginModule required
           username="zookeeper"
           password="123456";
    };
  2. On all ZooKeeper nodes, edit the /opt/kafka/config/zookeeper.properties ZooKeeper configuration file and set the following options:

    quorum.auth.enableSasl=true
    quorum.auth.learnerRequireSasl=true
    quorum.auth.serverRequireSasl=true
    quorum.auth.learner.loginContext=QuorumLearner
    quorum.auth.server.loginContext=QuorumServer
    quorum.cnxn.threads.size=20
  3. Restart all ZooKeeper nodes one by one. To pass the JAAS configuration to ZooKeeper, use the KAFKA_OPTS environment variable.

    su - kafka
    export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

6.3.3.3. Enabling Client-to-server authentication using DIGEST-MD5

This procedure describes how to enable authentication using the SASL DIGEST-MD5 mechanism between ZooKeeper clients and ZooKeeper.

Prerequisites

Enabling SASL DIGEST-MD5 authentication

  1. On all ZooKeeper nodes, create or edit the /opt/kafka/config/zookeeper-jaas.conf JAAS configuration file and add the following context:

    Server {
        org.apache.zookeeper.server.auth.DigestLoginModule required
        user_super="<SuperUserPassword>"
        user<Username1>_="<Password1>" user<USername2>_="<Password2>";
    };

    The super automatically has administrator priviledges. The file can contain multiple users, but only one additional user is required by the Kafka brokers. The recommended name for the Kafka user is kafka.

    The following example shows the Server context for client-to-server authentication:

    Server {
        org.apache.zookeeper.server.auth.DigestLoginModule required
        user_super="123456"
        user_kafka="123456";
    };
  2. On all ZooKeeper nodes, edit the /opt/kafka/config/zookeeper.properties ZooKeeper configuration file and set the following options:

    requireClientAuthScheme=sasl
    authProvider.<IdOfBroker1>=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
    authProvider.<IdOfBroker2>=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
    authProvider.<IdOfBroker3>=org.apache.zookeeper.server.auth.SASLAuthenticationProvider

    The authProvider.<ID> property has to be added for every node which is part of the ZooKeeper cluster. An example three-node ZooKeeper cluster configuration must look like the following:

    requireClientAuthScheme=sasl
    authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
    authProvider.2=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
    authProvider.3=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
  3. Restart all ZooKeeper nodes one by one. To pass the JAAS configuration to ZooKeeper, use the KAFKA_OPTS environment variable.

    su - kafka
    export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

6.3.4. Authorization

ZooKeeper supports access control lists (ACLs) to protect data stored inside it. Kafka brokers can automatically configure the ACL rights for all ZooKeeper records they create so no other ZooKeeper user can modify them.

For more information about enabling ZooKeeper ACLs in Kafka brokers, see Section 6.4.7, “ZooKeeper authorization”.

6.3.5. TLS

ZooKeeper supports TLS for encryption or authentication.

6.3.6. Additional configuration options

You can set the following additional ZooKeeper configuration options based on your use case:

maxClientCnxns
The maximum number of concurrent client connections to a single member of the ZooKeeper cluster.
autopurge.snapRetainCount
Number of snapshots of ZooKeeper’s in-memory database which will be retained. Default value is 3.
autopurge.purgeInterval
The time interval in hours for purging snapshots. The default value is 0 and this option is disabled.

All available configuration options can be found in the ZooKeeper documentation.

6.4. Configuring Kafka

Kafka uses a properties file to store static configuration. The recommended location for the configuration file is /opt/kafka/config/server.properties. The configuration file has to be readable by the kafka user.

AMQ Streams ships an example configuration file that highlights various basic and advanced features of the product. It can be found under config/server.properties in the AMQ Streams installation directory.

This chapter explains the most important configuration options.

6.4.1. ZooKeeper

Kafka brokers need ZooKeeper to store some parts of their configuration as well as to coordinate the cluster (for example to decide which node is a leader for which partition). Connection details for the ZooKeeper cluster are stored in the configuration file. The field zookeeper.connect contains a comma-separated list of hostnames and ports of members of the zookeeper cluster.

For example:

zookeeper.connect=zoo1.my-domain.com:2181,zoo2.my-domain.com:2181,zoo3.my-domain.com:2181

Kafka will use these addresses to connect to the ZooKeeper cluster. With this configuration, all Kafka znodes will be created directly in the root of ZooKeeper database. Therefore, such a ZooKeeper cluster could be used only for a single Kafka cluster. To configure multiple Kafka clusters to use single ZooKeeper cluster, specify a base (prefix) path at the end of the ZooKeeper connection string in the Kafka configuration file:

zookeeper.connect=zoo1.my-domain.com:2181,zoo2.my-domain.com:2181,zoo3.my-domain.com:2181/my-cluster-1

6.4.2. Listeners

Listeners are used to connect to Kafka brokers. Each Kafka broker can be configured to use multiple listeners. Each listener requires a different configuration so it can listen on a different port or network interface.

To configure listeners, edit the listeners property in the configuration file (/opt/kafka/config/server.properties). Add listeners to the listeners property as a comma-separated list. Configure each property as follows:

<listenerName>://<hostname>:<port>

If <hostname> is empty, Kafka uses the java.net.InetAddress.getCanonicalHostName() class as the hostname.

Example configuration for multiple listeners

listeners=internal-1://:9092,internal-2://:9093,replication://:9094

When a Kafka client wants to connect to a Kafka cluster, it first connects to the bootstrap server, which is one of the cluster nodes. The bootstrap server provides the client with a list of all the brokers in the cluster, and the client connects to each one individually. The list of brokers is based on the configured listeners.

Advertised listeners

Optionally, you can use the advertised.listeners property to provide the client with a different set of listener addresses than those given in the listeners property. This is useful if additional network infrastructure, such as a proxy, is between the client and the broker, or an external DNS name is being used instead of an IP address.

The advertised.listeners property is formatted in the same way as the listeners property.

Example configuration for advertised listeners

listeners=internal-1://:9092,internal-2://:9093
advertised.listeners=internal-1://my-broker-1.my-domain.com:1234,internal-2://my-broker-1.my-domain.com:1235

Note

The names of the advertised listeners must match those listed in the listeners property.

Inter-broker listeners

Inter-broker listeners are used for communication between Kafka brokers. Inter-broker communication is required for:

  • Coordinating workloads between different brokers
  • Replicating messages between partitions stored on different brokers
  • Handling administrative tasks from the controller, such as partition leadership changes

The inter-broker listener can be assigned to a port of your choice. When multiple listeners are configured, you can define the name of the inter-broker listener in the inter.broker.listener.name property.

Here, the inter-broker listener is named as REPLICATION:

listeners=REPLICATION://0.0.0.0:9091
inter.broker.listener.name=REPLICATION

Control plane listeners

By default, communication between the controller and other brokers uses the inter-broker listener. The controller is responsible for coordinating administrative tasks, such as partition leadership changes.

You can enable a dedicated control plane listener for controller connections. The control plane listener can be assigned to a port of your choice.

To enable the control plane listener, configure the control.plane.listener.name property with a listener name:

listeners=CONTROLLER://0.0.0.0:9090,REPLICATION://0.0.0.0:9091
...
control.plane.listener.name=CONTROLLER

Enabling the control plane listener might improve cluster performance because controller communications are not delayed by data replication across brokers. Data replication continues through the inter-broker listener.

If control.plane.listener is not configured, controller connections use the inter-broker listener.

6.4.3. Commit logs

Apache Kafka stores all records it receives from producers in commit logs. The commit logs contain the actual data, in the form of records, that Kafka needs to deliver. These are not the application log files which record what the broker is doing.

Log directories

You can configure log directories using the log.dirs property file to store commit logs in one or multiple log directories. It should be set to /var/lib/kafka directory created during installation:

log.dirs=/var/lib/kafka

For performance reasons, you can configure log.dirs to multiple directories and place each of them on a different physical device to improve disk I/O performance. For example:

log.dirs=/var/lib/kafka1,/var/lib/kafka2,/var/lib/kafka3

6.4.4. Broker ID

Broker ID is a unique identifier for each broker in the cluster. You can assign an integer greater than or equal to 0 as broker ID. The broker ID is used to identify the brokers after restarts or crashes and it is therefore important that the id is stable and does not change over time. The broker ID is configured in the broker properties file:

broker.id=1

6.4.5. ZooKeeper authentication

By default, connections between ZooKeeper and Kafka are not authenticated. However, Kafka and ZooKeeper support Java Authentication and Authorization Service (JAAS) which can be used to set up authentication using Simple Authentication and Security Layer (SASL). ZooKeeper supports authentication using the DIGEST-MD5 SASL mechanism with locally stored credentials.

6.4.5.1. JAAS Configuration

SASL authentication for ZooKeeper connections has to be configured in the JAAS configuration file. By default, Kafka will use the JAAS context named Client for connecting to ZooKeeper. The Client context should be configured in the /opt/kafka/config/jass.conf file. The context has to enable the PLAIN SASL authentication, as in the following example:

Client {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    username="kafka"
    password="123456";
};

6.4.5.2. Enabling ZooKeeper authentication

This procedure describes how to enable authentication using the SASL DIGEST-MD5 mechanism when connecting to ZooKeeper.

Prerequisites

  • Client-to-server authentication is enabled in ZooKeeper

Enabling SASL DIGEST-MD5 authentication

  1. On all Kafka broker nodes, create or edit the /opt/kafka/config/jaas.conf JAAS configuration file and add the following context:

    Client {
        org.apache.kafka.common.security.plain.PlainLoginModule required
        username="<Username>"
        password="<Password>";
    };

    The username and password should be the same as configured in ZooKeeper.

    Following example shows the Client context:

    Client {
        org.apache.kafka.common.security.plain.PlainLoginModule required
        username="kafka"
        password="123456";
    };
  2. Restart all Kafka broker nodes one by one. To pass the JAAS configuration to Kafka brokers, use the KAFKA_OPTS environment variable.

    su - kafka
    export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

    For information on restarting brokers in a multi-node cluster, see Section 4.3, “Performing a graceful rolling restart of Kafka brokers”.

Additional resources

6.4.6. Authorization

Authorization in Kafka brokers is implemented using authorizer plugins.

In this section we describe how to use the AclAuthorizer plugin provided with Kafka.

Alternatively, you can use your own authorization plugins. For example, if you are using OAuth 2.0 token-based authentication, you can use OAuth 2.0 authorization.

6.4.6.1. Simple ACL authorizer

Authorizer plugins, including AclAuthorizer, are enabled through the authorizer.class.name property:

authorizer.class.name=kafka.security.auth.SimpleAclAuthorizer

A fully-qualified name is required for the chosen authorizer. For AclAuthorizer, the fully-qualified name is kafka.security.auth.SimpleAclAuthorizer.

6.4.6.1.1. ACL rules

AclAuthorizer uses ACL rules to manage access to Kafka brokers.

ACL rules are defined in the format:

Principal P is allowed / denied operation O on Kafka resource R from host H

For example, a rule might be set so that user:

John can view the topic comments from host 127.0.0.1

Host is the IP address of the machine that John is connecting from.

In most cases, the user is a producer or consumer application:

Consumer01 can write to the consumer group accounts from host 127.0.0.1

If ACL rules are not present

If ACL rules are not present for a given resource, all actions are denied. This behavior can be changed by setting the property allow.everyone.if.no.acl.found to true in the Kafka configuration file /opt/kafka/config/server.properties.

6.4.6.1.2. Principals

A principal represents the identity of a user. The format of the ID depends on the authentication mechanism used by clients to connect to Kafka:

  • User:ANONYMOUS when connected without authentication.
  • User:<username> when connected using simple authentication mechanisms, such as PLAIN or SCRAM.

    For example User:admin or User:user1.

  • User:<DistinguishedName> when connected using TLS client authentication.

    For example User:CN=user1,O=MyCompany,L=Prague,C=CZ.

  • User:<Kerberos username> when connected using Kerberos.

The DistinguishedName is the distinguished name from the client certificate.

The Kerberos username is the primary part of the Kerberos principal, which is used by default when connecting using Kerberos. You can use the sasl.kerberos.principal.to.local.rules property to configure how the Kafka principal is built from the Kerberos principal.

6.4.6.1.3. Authentication of users

To use authorization, you need to have authentication enabled and used by your clients. Otherwise, all connections will have the principal User:ANONYMOUS.

For more information on methods of authentication, see Encryption and authentication.

6.4.6.1.4. Super users

Super users are allowed to take all actions regardless of the ACL rules.

Super users are defined in the Kafka configuration file using the property super.users.

For example:

super.users=User:admin,User:operator
6.4.6.1.5. Replica broker authentication

When authorization is enabled, it is applied to all listeners and all connections. This includes the inter-broker connections used for replication of data between brokers. If enabling authorization, therefore, ensure that you use authentication for inter-broker connections and give the users used by the brokers sufficient rights. For example, if authentication between brokers uses the kafka-broker user, then super user configuration must include the username super.users=User:kafka-broker.

6.4.6.1.6. Supported resources

You can apply Kafka ACLs to these types of resource:

  • Topics
  • Consumer groups
  • The cluster
  • TransactionId
  • DelegationToken
6.4.6.1.7. Supported operations

AclAuthorizer authorizes operations on resources.

Fields with X in the following table mark the supported operations for each resource.

Table 6.1. Supported operations for a resource

 TopicsConsumer GroupsCluster

Read

X

X

 

Write

X

  

Create

  

X

Delete

X

  

Alter

X

  

Describe

X

X

X

ClusterAction

  

X

All

X

X

X

6.4.6.1.8. ACL management options

ACL rules are managed using the bin/kafka-acls.sh utility, which is provided as part of the Kafka distribution package.

Use kafka-acls.sh parameter options to add, list and remove ACL rules, and perform other functions.

The parameters require a double-hyphen convention, such as --add.

OptionTypeDescriptionDefault

add

Action

Add ACL rule.

 

remove

Action

Remove ACL rule.

 

list

Action

List ACL rules.

 

authorizer

Action

Fully-qualified class name of the authorizer.

kafka.security.auth.SimpleAclAuthorizer

authorizer-properties

Configuration

Key/value pairs passed to the authorizer for initialization.

For AclAuthorizer, the example values are: zookeeper.connect=zoo1.my-domain.com:2181.

 

bootstrap-server

Resource

Host/port pairs to connect to the Kafka cluster.

Use this option or the authorizer option, not both.

command-config

Resource

Configuration property file to pass to the Admin Client, which is used in conjunction with the bootstrap-server parameter.

 

cluster

Resource

Specifies a cluster as an ACL resource.

 

topic

Resource

Specifies a topic name as an ACL resource.

An asterisk (*) used as a wildcard translates to all topics.

A single command can specify multiple --topic options.

 

group

Resource

Specifies a consumer group name as an ACL resource.

A single command can specify multiple --group options.

 

transactional-id

Resource

Specifies a transactional ID as an ACL resource.

Transactional delivery means that all messages sent by a producer to multiple partitions must be successfully delivered or none of them.

An asterisk (*) used as a wildcard translates to all IDs.

 

delegation-token

Resource

Specifies a delegation token as an ACL resource.

An asterisk (*) used as a wildcard translates to all tokens.

 

resource-pattern-type

Configuration

Specifies a type of resource pattern for the add parameter or a resource pattern filter value for the list or remove parameters.

Use literal or prefixed as the resource pattern type for a resource name.

Use any or match as resource pattern filter values, or a specific pattern type filter.

literal

allow-principal

Principal

Principal added to an allow ACL rule.

A single command can specify multiple --allow-principal options.

 

deny-principal

Principal

Principal added to a deny ACL rule.

A single command can specify multiple --deny-principal options.

 

principal

Principal

Principal name used with the list parameter to return a list of ACLs for the principal.

A single command can specify multiple --principal options.

 

allow-host

Host

IP address that allows access to the principals listed in --allow-principal.

Hostnames or CIDR ranges are not supported.

If --allow-principal is specified, defaults to * meaning "all hosts".

deny-host

Host

IP address that denies access to the principals listed in --deny-principal.

Hostnames or CIDR ranges are not supported.

if --deny-principal is specified, defaults to * meaning "all hosts".

operation

Operation

Allows or denies an operation.

A single command can specify multipleMultiple --operation options can be specified in single command.

All

producer

Shortcut

A shortcut to allow or deny all operations needed by a message producer (WRITE and DESCRIBE on topic, CREATE on cluster).

 

consumer

Shortcut

A shortcut to allow or deny all operations needed by a message consumer (READ and DESCRIBE on topic, READ on consumer group).

 

idempotent

Shortcut

A shortcut to enable idempotence when used with the --producer parameter, so that messages are delivered exactly once to a partition.

Idepmotence is enabled automatically if the producer is authorized to send messages based on a specific transactional ID.

 

force

Shortcut

A shortcut to accept all queries and do not prompt.

 

6.4.6.2. Enabling authorization

This procedure describes how to enable the AclAuthorizer plugin for authorization in Kafka brokers.

Prerequisites

Procedure

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file to use the AclAuthorizer.

    authorizer.class.name=kafka.security.auth.SimpleAclAuthorizer
  2. (Re)start the Kafka brokers.

6.4.6.3. Adding ACL rules

When using the AclAuthorizer plugin to control access to Kafka brokers based on Access Control Lists (ACLs), you can add new ACL rules using the kafka-acls.sh utility.

Prerequisites

Procedure

  • Run kafka-acls.sh with the --add option.

    Examples:

  • Allow user1 and user2 access to read from myTopic using the MyConsumerGroup consumer group.

    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --add --operation Read --topic myTopic --allow-principal User:user1 --allow-principal User:user2
    
    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --add --operation Describe --topic myTopic --allow-principal User:user1 --allow-principal User:user2
    
    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --add --operation Read --operation Describe --group MyConsumerGroup --allow-principal User:user1 --allow-principal User:user2
  • Deny user1 access to read myTopic from IP address host 127.0.0.1.

    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --add --operation Describe --operation Read --topic myTopic --group MyConsumerGroup --deny-principal User:user1 --deny-host 127.0.0.1
  • Add user1 as the consumer of myTopic with MyConsumerGroup.

    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --add --consumer --topic myTopic --group MyConsumerGroup --allow-principal User:user1

Additional resources

6.4.6.4. Listing ACL rules

When using the AclAuthorizer plugin to control access to Kafka brokers based on Access Control Lists (ACLs), you can list existing ACL rules using the kafka-acls.sh utility.

Prerequisites

Procedure

  • Run kafka-acls.sh with the --list option.

    For example:

    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --list --topic myTopic
    
    Current ACLs for resource `Topic:myTopic`:
    
    User:user1 has Allow permission for operations: Read from hosts: *
    User:user2 has Allow permission for operations: Read from hosts: *
    User:user2 has Deny permission for operations: Read from hosts: 127.0.0.1
    User:user1 has Allow permission for operations: Describe from hosts: *
    User:user2 has Allow permission for operations: Describe from hosts: *
    User:user2 has Deny permission for operations: Describe from hosts: 127.0.0.1

Additional resources

6.4.6.5. Removing ACL rules

When using the AclAuthorizer plugin to control access to Kafka brokers based on Access Control Lists (ACLs), you can remove existing ACL rules using the kafka-acls.sh utility.

Prerequisites

Procedure

  • Run kafka-acls.sh with the --remove option.

    Examples:

  • Remove the ACL allowing Allow user1 and user2 access to read from myTopic using the MyConsumerGroup consumer group.

    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --remove --operation Read --topic myTopic --allow-principal User:user1 --allow-principal User:user2
    
    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --remove --operation Describe --topic myTopic --allow-principal User:user1 --allow-principal User:user2
    
    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --remove --operation Read --operation Describe --group MyConsumerGroup --allow-principal User:user1 --allow-principal User:user2
  • Remove the ACL adding user1 as the consumer of myTopic with MyConsumerGroup.

    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --remove --consumer --topic myTopic --group MyConsumerGroup --allow-principal User:user1
  • Remove the ACL denying user1 access to read myTopic from IP address host 127.0.0.1.

    opt/kafka/bin/kafka-acls.sh --bootstrap-server localhost:9092 --remove --operation Describe --operation Read --topic myTopic --group MyConsumerGroup --deny-principal User:user1 --deny-host 127.0.0.1

Additional resources

6.4.7. ZooKeeper authorization

When authentication is enabled between Kafka and ZooKeeper, you can use ZooKeeper Access Control List (ACL) rules to automatically control access to Kafka’s metadata stored in ZooKeeper.

6.4.7.1. ACL Configuration

Enforcement of ZooKeeper ACL rules is controlled by the zookeeper.set.acl property in the config/server.properties Kafka configuration file.

The property is disabled by default and enabled by setting to true:

zookeeper.set.acl=true

If ACL rules are enabled, when a znode is created in ZooKeeper only the Kafka user who created it can modify or delete it. All other users have read-only access.

Kafka sets ACL rules only for newly created ZooKeeper znodes. If the ACLs are only enabled after the first start of the cluster, the zookeeper-security-migration.sh tool can set ACLs on all existing znodes.

Confidentiality of data in ZooKeeper

Data stored in ZooKeeper includes:

  • Topic names and their configuration
  • Salted and hashed user credentials when SASL SCRAM authentication is used.

But ZooKeeper does not store any records sent and received using Kafka. The data stored in ZooKeeper is assumed to be non-confidential.

If the data is to be regarded as confidential (for example because topic names contain customer IDs), the only option available for protection is isolating ZooKeeper on the network level and allowing access only to Kafka brokers.

6.4.7.2. Enabling ZooKeeper ACLs for a new Kafka cluster

This procedure describes how to enable ZooKeeper ACLs in Kafka configuration for a new Kafka cluster. Use this procedure only before the first start of the Kafka cluster. For enabling ZooKeeper ACLs in a cluster that is already running, see Section 6.4.7.3, “Enabling ZooKeeper ACLs in an existing Kafka cluster”.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • ZooKeeper cluster is configured and running.
  • Client-to-server authentication is enabled in ZooKeeper.
  • ZooKeeper authentication is enabled in the Kafka brokers.
  • Kafka brokers have not yet been started.

Procedure

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file to set the zookeeper.set.acl field to true on all cluster nodes.

    zookeeper.set.acl=true
  2. Start the Kafka brokers.

6.4.7.3. Enabling ZooKeeper ACLs in an existing Kafka cluster

This procedure describes how to enable ZooKeeper ACLs in Kafka configuration for a Kafka cluster that is running. Use the zookeeper-security-migration.sh tool to set ZooKeeper ACLs on all existing znodes. The zookeeper-security-migration.sh is available as part of AMQ Streams, and can be found in the bin directory.

Prerequisites

Enabling the ZooKeeper ACLs

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file to set the zookeeper.set.acl field to true on all cluster nodes.

    zookeeper.set.acl=true
  2. Restart all Kafka brokers one by one.

    For information on restarting brokers in a multi-node cluster, see Section 4.3, “Performing a graceful rolling restart of Kafka brokers”.

  3. Set the ACLs on all existing ZooKeeper znodes using the zookeeper-security-migration.sh tool.

    su - kafka
    cd /opt/kafka
    KAFKA_OPTS="-Djava.security.auth.login.config=./config/jaas.conf"; ./bin/zookeeper-security-migration.sh --zookeeper.acl=secure --zookeeper.connect=<ZooKeeperURL>
    exit

    For example:

    su - kafka
    cd /opt/kafka
    KAFKA_OPTS="-Djava.security.auth.login.config=./config/jaas.conf"; ./bin/zookeeper-security-migration.sh --zookeeper.acl=secure --zookeeper.connect=zoo1.my-domain.com:2181
    exit

6.4.8. Encryption and authentication

AMQ Streams supports encryption and authentication, which is configured as part of the listener configuration.

6.4.8.1. Listener configuration

Encryption and authentication in Kafka brokers is configured per listener. For more information about Kafka listener configuration, see Section 6.4.2, “Listeners”.

Each listener in the Kafka broker is configured with its own security protocol. The configuration property listener.security.protocol.map defines which listener uses which security protocol. It maps each listener name to its security protocol. Supported security protocols are:

PLAINTEXT
Listener without any encryption or authentication.
SSL
Listener using TLS encryption and, optionally, authentication using TLS client certificates.
SASL_PLAINTEXT
Listener without encryption but with SASL-based authentication.
SASL_SSL
Listener with TLS-based encryption and SASL-based authentication.

Given the following listeners configuration:

listeners=INT1://:9092,INT2://:9093,REPLICATION://:9094

the listener.security.protocol.map might look like this:

listener.security.protocol.map=INT1:SASL_PLAINTEXT,INT2:SASL_SSL,REPLICATION:SSL

This would configure the listener INT1 to use unencrypted connections with SASL authentication, the listener INT2 to use encrypted connections with SASL authentication and the REPLICATION interface to use TLS encryption (possibly with TLS client authentication). The same security protocol can be used multiple times. The following example is also a valid configuration:

listener.security.protocol.map=INT1:SSL,INT2:SSL,REPLICATION:SSL

Such a configuration would use TLS encryption and TLS authentication for all interfaces. The following chapters will explain in more detail how to configure TLS and SASL.

6.4.8.2. TLS Encryption

Kafka supports TLS for encrypting communication with Kafka clients.

In order to use TLS encryption and server authentication, a keystore containing private and public keys has to be provided. This is usually done using a file in the Java Keystore (JKS) format. A path to this file is set in the ssl.keystore.location property. The ssl.keystore.password property should be used to set the password protecting the keystore. For example:

ssl.keystore.location=/path/to/keystore/server-1.jks
ssl.keystore.password=123456

In some cases, an additional password is used to protect the private key. Any such password can be set using the ssl.key.password property.

Kafka is able to use keys signed by certification authorities as well as self-signed keys. Using keys signed by certification authorities should always be the preferred method. In order to allow clients to verify the identity of the Kafka broker they are connecting to, the certificate should always contain the advertised hostname(s) as its Common Name (CN) or in the Subject Alternative Names (SAN).

It is possible to use different SSL configurations for different listeners. All options starting with ssl. can be prefixed with listener.name.<NameOfTheListener>., where the name of the listener has to be always in lower case. This will override the default SSL configuration for that specific listener. The following example shows how to use different SSL configurations for different listeners:

listeners=INT1://:9092,INT2://:9093,REPLICATION://:9094
listener.security.protocol.map=INT1:SSL,INT2:SSL,REPLICATION:SSL

# Default configuration - will be used for listeners INT1 and INT2
ssl.keystore.location=/path/to/keystore/server-1.jks
ssl.keystore.password=123456

# Different configuration for listener REPLICATION
listener.name.replication.ssl.keystore.location=/path/to/keystore/server-1.jks
listener.name.replication.ssl.keystore.password=123456

Additional TLS configuration options

In addition to the main TLS configuration options described above, Kafka supports many options for fine-tuning the TLS configuration. For example, to enable or disable TLS / SSL protocols or cipher suites:

ssl.cipher.suites
List of enabled cipher suites. Each cipher suite is a combination of authentication, encryption, MAC and key exchange algorithms used for the TLS connection. By default, all available cipher suites are enabled.
ssl.enabled.protocols
List of enabled TLS / SSL protocols. Defaults to TLSv1.2,TLSv1.1,TLSv1.

6.4.8.3. Enabling TLS encryption

This procedure describes how to enable encryption in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.

Procedure

  1. Generate TLS certificates for all Kafka brokers in your cluster. The certificates should have their advertised and bootstrap addresses in their Common Name or Subject Alternative Name.
  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Change the listener.security.protocol.map field to specify the SSL protocol for the listener where you want to use TLS encryption.
    • Set the ssl.keystore.location option to the path to the JKS keystore with the broker certificate.
    • Set the ssl.keystore.password option to the password you used to protect the keystore.

      For example:

      listeners=UNENCRYPTED://:9092,ENCRYPTED://:9093,REPLICATION://:9094
      listener.security.protocol.map=UNENCRYPTED:PLAINTEXT,ENCRYPTED:SSL,REPLICATION:PLAINTEXT
      ssl.keystore.location=/path/to/keystore/server-1.jks
      ssl.keystore.password=123456
  3. (Re)start the Kafka brokers

6.4.8.4. Authentication

For authentication, you can use:

  • TLS client authentication based on X.509 certificates on encrypted connections
  • A supported Kafka SASL (Simple Authentication and Security Layer) mechanism
  • OAuth 2.0 token based authentication
6.4.8.4.1. TLS client authentication

TLS client authentication can be used only on connections which are already using TLS encryption. To use TLS client authentication, a truststore with public keys can be provided to the broker. These keys can be used to authenticate clients connecting to the broker. The truststore should be provided in Java Keystore (JKS) format and should contain public keys of the certification authorities. All clients with public and private keys signed by one of the certification authorities included in the truststore will be authenticated. The location of the truststore is set using field ssl.truststore.location. In case the truststore is password protected, the password should be set in the ssl.truststore.password property. For example:

ssl.truststore.location=/path/to/keystore/server-1.jks
ssl.truststore.password=123456

Once the truststore is configured, TLS client authentication has to be enabled using the ssl.client.auth property. This property can be set to one of three different values:

none
TLS client authentication is switched off. (Default value)
requested
TLS client authentication is optional. Clients will be asked to authenticate using TLS client certificate but they can choose not to.
required
Clients are required to authenticate using TLS client certificate.

When a client authenticates using TLS client authentication, the authenticated principal name is the distinguished name from the authenticated client certificate. For example, a user with a certificate which has a distinguished name CN=someuser will be authenticated with the following principal CN=someuser,OU=Unknown,O=Unknown,L=Unknown,ST=Unknown,C=Unknown. When TLS client authentication is not used and SASL is disabled, the principal name will be ANONYMOUS.

6.4.8.4.2. SASL authentication

SASL authentication is configured using Java Authentication and Authorization Service (JAAS). JAAS is also used for authentication of connections between Kafka and ZooKeeper. JAAS uses its own configuration file. The recommended location for this file is /opt/kafka/config/jaas.conf. The file has to be readable by the kafka user. When running Kafka, the location of this file is specified using Java system property java.security.auth.login.config. This property has to be passed to Kafka when starting the broker nodes:

KAFKA_OPTS="-Djava.security.auth.login.config=/path/to/my/jaas.config"; bin/kafka-server-start.sh

SASL authentication is supported both through plain unencrypted connections as well as through TLS connections. SASL can be enabled individually for each listener. To enable it, the security protocol in listener.security.protocol.map has to be either SASL_PLAINTEXT or SASL_SSL.

SASL authentication in Kafka supports several different mechanisms:

PLAIN
Implements authentication based on username and passwords. Usernames and passwords are stored locally in Kafka configuration.
SCRAM-SHA-256 and SCRAM-SHA-512
Implements authentication using Salted Challenge Response Authentication Mechanism (SCRAM). SCRAM credentials are stored centrally in ZooKeeper. SCRAM can be used in situations where ZooKeeper cluster nodes are running isolated in a private network.
GSSAPI
Implements authentication against a Kerberos server.
Warning

The PLAIN mechanism sends the username and password over the network in an unencrypted format. It should be therefore only be used in combination with TLS encryption.

The SASL mechanisms are configured via the JAAS configuration file. Kafka uses the JAAS context named KafkaServer. After they are configured in JAAS, the SASL mechanisms have to be enabled in the Kafka configuration. This is done using the sasl.enabled.mechanisms property. This property contains a comma-separated list of enabled mechanisms:

sasl.enabled.mechanisms=PLAIN,SCRAM-SHA-256,SCRAM-SHA-512

In case the listener used for inter-broker communication is using SASL, the property sasl.mechanism.inter.broker.protocol has to be used to specify the SASL mechanism which it should use. For example:

sasl.mechanism.inter.broker.protocol=PLAIN

The username and password which will be used for the inter-broker communication has to be specified in the KafkaServer JAAS context using the field username and password.

SASL PLAIN

To use the PLAIN mechanism, the usernames and password which are allowed to connect are specified directly in the JAAS context. The following example shows the context configured for SASL PLAIN authentication. The example configures three different users:

  • admin
  • user1
  • user2
KafkaServer {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    user_admin="123456"
    user_user1="123456"
    user_user2="123456";
};

The JAAS configuration file with the user database should be kept in sync on all Kafka brokers.

When SASL PLAIN is also used for inter-broker authentication, the username and password properties should be included in the JAAS context:

KafkaServer {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    username="admin"
    password="123456"
    user_admin="123456"
    user_user1="123456"
    user_user2="123456";
};

SASL SCRAM

SCRAM authentication in Kafka consists of two mechanisms: SCRAM-SHA-256 and SCRAM-SHA-512. These mechanisms differ only in the hashing algorithm used - SHA-256 versus stronger SHA-512. To enable SCRAM authentication, the JAAS configuration file has to include the following configuration:

KafkaServer {
    org.apache.kafka.common.security.scram.ScramLoginModule required;
};

When enabling SASL authentication in the Kafka configuration file, both SCRAM mechanisms can be listed. However, only one of them can be chosen for the inter-broker communication. For example:

sasl.enabled.mechanisms=SCRAM-SHA-256,SCRAM-SHA-512
sasl.mechanism.inter.broker.protocol=SCRAM-SHA-512

User credentials for the SCRAM mechanism are stored in ZooKeeper. The kafka-configs.sh tool can be used to manage them. For example, run the following command to add user user1 with password 123456:

bin/kafka-configs.sh --bootstrap-server localhost:9092 --alter --add-config 'SCRAM-SHA-256=[password=123456],SCRAM-SHA-512=[password=123456]' --entity-type users --entity-name user1

To delete a user credential use:

bin/kafka-configs.sh --bootstrap-server localhost:9092 --alter --delete-config 'SCRAM-SHA-512' --entity-type users --entity-name user1

SASL GSSAPI

The SASL mechanism used for authentication using Kerberos is called GSSAPI. To configure Kerberos SASL authentication, the following configuration should be added to the JAAS configuration file:

KafkaServer {
    com.sun.security.auth.module.Krb5LoginModule required
    useKeyTab=true
    storeKey=true
    keyTab="/etc/security/keytabs/kafka_server.keytab"
    principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
};

The domain name in the Kerberos principal has to be always in upper case.

In addition to the JAAS configuration, the Kerberos service name needs to be specified in the sasl.kerberos.service.name property in the Kafka configuration:

sasl.enabled.mechanisms=GSSAPI
sasl.mechanism.inter.broker.protocol=GSSAPI
sasl.kerberos.service.name=kafka

Multiple SASL mechanisms

Kafka can use multiple SASL mechanisms at the same time. The different JAAS configurations can be all added to the same context:

KafkaServer {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    user_admin="123456"
    user_user1="123456"
    user_user2="123456";

    com.sun.security.auth.module.Krb5LoginModule required
    useKeyTab=true
    storeKey=true
    keyTab="/etc/security/keytabs/kafka_server.keytab"
    principal="kafka/kafka1.hostname.com@EXAMPLE.COM";

    org.apache.kafka.common.security.scram.ScramLoginModule required;
};

When multiple mechanisms are enabled, clients will be able to choose the mechanism which they want to use.

6.4.8.5. Enabling TLS client authentication

This procedure describes how to enable TLS client authentication in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • TLS encryption is enabled.

Procedure

  1. Prepare a JKS truststore containing the public key of the certification authority used to sign the user certificates.
  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Set the ssl.truststore.location option to the path to the JKS truststore with the certification authority of the user certificates.
    • Set the ssl.truststore.password option to the password you used to protect the truststore.
    • Set the ssl.client.auth option to required.

      For example:

      ssl.truststore.location=/path/to/truststore.jks
      ssl.truststore.password=123456
      ssl.client.auth=required
  3. (Re)start the Kafka brokers

6.4.8.6. Enabling SASL PLAIN authentication

This procedure describes how to enable SASL PLAIN authentication in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.

Procedure

  1. Edit or create the /opt/kafka/config/jaas.conf JAAS configuration file. This file should contain all your users and their passwords. Make sure this file is the same on all Kafka brokers.

    For example:

    KafkaServer {
        org.apache.kafka.common.security.plain.PlainLoginModule required
        user_admin="123456"
        user_user1="123456"
        user_user2="123456";
    };
  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Change the listener.security.protocol.map field to specify the SASL_PLAINTEXT or SASL_SSL protocol for the listener where you want to use SASL PLAIN authentication.
    • Set the sasl.enabled.mechanisms option to PLAIN.

      For example:

      listeners=INSECURE://:9092,AUTHENTICATED://:9093,REPLICATION://:9094
      listener.security.protocol.map=INSECURE:PLAINTEXT,AUTHENTICATED:SASL_PLAINTEXT,REPLICATION:PLAINTEXT
      sasl.enabled.mechanisms=PLAIN
  3. (Re)start the Kafka brokers using the KAFKA_OPTS environment variable to pass the JAAS configuration to Kafka brokers.

    su - kafka
    export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

6.4.8.7. Enabling SASL SCRAM authentication

This procedure describes how to enable SASL SCRAM authentication in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.

Procedure

  1. Edit or create the /opt/kafka/config/jaas.conf JAAS configuration file. Enable the ScramLoginModule for the KafkaServer context. Make sure this file is the same on all Kafka brokers.

    For example:

    KafkaServer {
        org.apache.kafka.common.security.scram.ScramLoginModule required;
    };
  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Change the listener.security.protocol.map field to specify the SASL_PLAINTEXT or SASL_SSL protocol for the listener where you want to use SASL SCRAM authentication.
    • Set the sasl.enabled.mechanisms option to SCRAM-SHA-256 or SCRAM-SHA-512.

      For example:

      listeners=INSECURE://:9092,AUTHENTICATED://:9093,REPLICATION://:9094
      listener.security.protocol.map=INSECURE:PLAINTEXT,AUTHENTICATED:SASL_PLAINTEXT,REPLICATION:PLAINTEXT
      sasl.enabled.mechanisms=SCRAM-SHA-512
  3. (Re)start the Kafka brokers using the KAFKA_OPTS environment variable to pass the JAAS configuration to Kafka brokers.

    su - kafka
    export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

6.4.8.8. Adding SASL SCRAM users

This procedure describes how to add new users for authentication using SASL SCRAM.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • SASL SCRAM authentication is enabled.

Procedure

  • Use the kafka-configs.sh tool to add new SASL SCRAM users.

    bin/kafka-configs.sh --bootstrap-server <broker_address> --alter --add-config 'SCRAM-SHA-512=[password=<Password>]' --entity-type users --entity-name <Username>

    For example:

    bin/kafka-configs.sh --bootstrap-server localhost:9092 --alter --add-config 'SCRAM-SHA-512=[password=123456]' --entity-type users --entity-name user1

6.4.8.9. Deleting SASL SCRAM users

This procedure describes how to remove users when using SASL SCRAM authentication.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • SASL SCRAM authentication is enabled.

Procedure

  • Use the kafka-configs.sh tool to delete SASL SCRAM users.

    /opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --alter --delete-config 'SCRAM-SHA-512' --entity-type users --entity-name <Username>

    For example:

    /opt/kafka/bin/kafka-configs.sh --bootstrap-server localhost:9092 --alter --delete-config 'SCRAM-SHA-512' --entity-type users --entity-name user1

6.4.9. Using OAuth 2.0 token-based authentication

AMQ Streams supports the use of OAuth 2.0 authentication using the OAUTHBEARER and PLAIN mechanisms.

OAuth 2.0 enables standardized token-based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources.

You can configure OAuth 2.0 authentication, then OAuth 2.0 authorization.

Kafka brokers and clients both need to be configured to use OAuth 2.0. OAuth 2.0 authentication can also be used in conjunction with simple or OPA-based Kafka authorization.

Using OAuth 2.0 authentication, application clients can access resources on application servers (called resource servers) without exposing account credentials.

The application client passes an access token as a means of authenticating, which application servers can also use to determine the level of access to grant. The authorization server handles the granting of access and inquiries about access.

In the context of AMQ Streams:

  • Kafka brokers act as OAuth 2.0 resource servers
  • Kafka clients act as OAuth 2.0 application clients

Kafka clients authenticate to Kafka brokers. The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.

For a deployment of AMQ Streams, OAuth 2.0 integration provides:

  • Server-side OAuth 2.0 support for Kafka brokers
  • Client-side OAuth 2.0 support for Kafka MirrorMaker, Kafka Connect, and the Kafka Bridge

AMQ Streams on RHEL includes two OAuth 2.0 libraries:

kafka-oauth-client
Provides a custom login callback handler class named io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler. To handle the OAUTHBEARER authentication mechanism, use the login callback handler with the OAuthBearerLoginModule provided by Apache Kafka.
kafka-oauth-common
A helper library that provides some of the functionality needed by the kafka-oauth-client library.

The provided client libraries also have dependencies on some additional third-party libraries, such as: keycloak-core, jackson-databind, and slf4j-api.

We recommend using a Maven project to package your client to ensure that all the dependency libraries are included. Dependency libraries might change in future versions.

Additional resources

6.4.9.1. OAuth 2.0 authentication mechanisms

AMQ Streams supports the OAUTHBEARER and PLAIN mechanisms for OAuth 2.0 authentication. Both mechanisms allow Kafka clients to establish authenticated sessions with Kafka brokers. The authentication flow between clients, the authorization server, and Kafka brokers is different for each mechanism.

We recommend that you configure clients to use OAUTHBEARER whenever possible. OAUTHBEARER provides a higher level of security than PLAIN because client credentials are never shared with Kafka brokers. Consider using PLAIN only with Kafka clients that do not support OAUTHBEARER.

You configure Kafka broker listeners to use OAuth 2.0 authentication for connecting clients. If necessary, you can use the OAUTHBEARER and PLAIN mechanisms on the same oauth listener. The properties to support each mechanism must be explicitly specified in the oauth listener configuration.

OAUTHBEARER overview

To use OAUTHBEARER, set sasl.enabled.mechanisms to OAUTHBEARER in the OAuth authentication listener configuration for the Kafka broker. For detailed configuration, see Section 6.4.9.2, “OAuth 2.0 Kafka broker configuration”.

listener.name.client.sasl.enabled.mechanisms=OAUTHBEARER

Many Kafka client tools use libraries that provide basic support for OAUTHBEARER at the protocol level. To support application development, AMQ Streams provides an OAuth callback handler for the upstream Kafka Client Java libraries (but not for other libraries). Therefore, you do not need to write your own callback handlers. An application client can use the callback handler to provide the access token. Clients written in other languages, such as Go, must use custom code to connect to the authorization server and obtain the access token.

With OAUTHBEARER, the client initiates a session with the Kafka broker for credentials exchange, where credentials take the form of a bearer token provided by the callback handler. Using the callbacks, you can configure token provision in one of three ways:

  • Client ID and Secret (by using the OAuth 2.0 client credentials mechanism)
  • A long-lived access token, obtained manually at configuration time
  • A long-lived refresh token, obtained manually at configuration time
Note

OAUTHBEARER authentication can only be used by Kafka clients that support the OAUTHBEARER mechanism at the protocol level.

PLAIN overview

To use PLAIN, add PLAIN to the value of sasl.enabled.mechanisms.

listener.name.client.sasl.enabled.mechanisms=OAUTHBEARER,PLAIN

PLAIN is a simple authentication mechanism used by all Kafka client tools. To enable PLAIN to be used with OAuth 2.0 authentication, AMQ Streams provides OAuth 2.0 over PLAIN server-side callbacks.

With the AMQ Streams implementation of PLAIN, the client credentials are not stored in ZooKeeper. Instead, client credentials are handled centrally behind a compliant authorization server, similar to when OAUTHBEARER authentication is used.

When used with the OAuth 2.0 over PLAIN callbacks, Kafka clients authenticate with Kafka brokers using either of the following methods:

  • Client ID and secret (by using the OAuth 2.0 client credentials mechanism)
  • A long-lived access token, obtained manually at configuration time

For both methods, the client must provide the PLAIN username and password properties to pass credentials to the Kafka broker. The client uses these properties to pass a client ID and secret or username and access token.

Client IDs and secrets are used to obtain access tokens.

Access tokens are passed as password property values. You pass the access token with or without an $accessToken: prefix.

  • If you configure a token endpoint (oauth.token.endpoint.uri) in the listener configuration, you need the prefix.
  • If you don’t configure a token endpoint (oauth.token.endpoint.uri) in the listener configuration, you don’t need the prefix. The Kafka broker interprets the password as a raw access token.

If the password is set as the access token, the username must be set to the same principal name that the Kafka broker obtains from the access token. You can specify username extraction options in your listener using the oauth.username.claim, oauth.fallback.username.claim, oauth.fallback.username.prefix, and oauth.userinfo.endpoint.uri properties. The username extraction process also depends on your authorization server; in particular, how it maps client IDs to account names.

Note

OAuth over PLAIN does not support passing a username and password (password grants) using the (deprecated) OAuth 2.0 password grant mechanism.

6.4.9.1.1. Configuring OAuth 2.0 with properties or variables

You can configure OAuth 2.0 settings using Java Authentication and Authorization Service (JAAS) properties or environment variables.

  • JAAS properties are configured in the server.properties configuration file, and passed as key-values pairs of the listener.name.LISTENER-NAME.oauthbearer.sasl.jaas.config property.
  • If using environment variables, you still need to provide the listener.name.LISTENER-NAME.oauthbearer.sasl.jaas.config property in the server.properties file, but you can omit the other JAAS properties.

    You can use capitalized or upper-case environment variable naming conventions.

The AMQ Streams OAuth 2.0 libraries use properties that start with:

6.4.9.2. OAuth 2.0 Kafka broker configuration

Kafka broker configuration for OAuth 2.0 authentication involves:

  • Creating the OAuth 2.0 client in the authorization server
  • Configuring OAuth 2.0 authentication in the Kafka cluster
Note

In relation to the authorization server, Kafka brokers and Kafka clients are both regarded as OAuth 2.0 clients.

6.4.9.2.1. OAuth 2.0 client configuration on an authorization server

To configure a Kafka broker to validate the token received during session initiation, the recommended approach is to create an OAuth 2.0 client definition in an authorization server, configured as confidential, with the following client credentials enabled:

  • Client ID of kafka-broker (for example)
  • Client ID and secret as the authentication mechanism
Note

You only need to use a client ID and secret when using a non-public introspection endpoint of the authorization server. The credentials are not typically required when using public authorization server endpoints, as with fast local JWT token validation.

6.4.9.2.2. OAuth 2.0 authentication configuration in the Kafka cluster

To use OAuth 2.0 authentication in the Kafka cluster, you enable an OAuth authentication listener configuration for your Kafka cluster, in the Kafka server.properties file. A minimum configuration is required. You can also configure a TLS listener, where TLS is used for inter-broker communication.

You can configure the broker for token validation by the authorization server using one of the following methods:

  • Fast local token validation: a JWKS endpoint in combination with signed JWT-formatted access tokens
  • Introspection endpoint

You can configure OAUTHBEARER or PLAIN authentication, or both.

The following example shows a minimum configuration that applies a global listener configuration, which means that inter-broker communication goes through the same listener as application clients.

The example also shows an OAuth 2.0 configuration for a specific listener, where you specify listener.name.LISTENER-NAME.sasl.enabled.mechanisms instead of sasl.enabled.mechanisms. LISTENER-NAME is the case-insensitive name of the listener. Here, we name the listener CLIENT, so the property name is listener.name.client.sasl.enabled.mechanisms.

The example uses OAUTHBEARER authentication.

Example: Minimum listener configuration for OAuth 2.0 authentication using a JWKS endpoint

sasl.enabled.mechanisms=OAUTHBEARER 1
listeners=CLIENT://0.0.0.0:9092 2
listener.security.protocol.map=CLIENT:SASL_PLAINTEXT 3
listener.name.client.sasl.enabled.mechanisms=OAUTHBEARER 4
sasl.mechanism.inter.broker.protocol=OAUTHBEARER 5
inter.broker.listener.name=CLIENT 6
listener.name.client.oauthbearer.sasl.server.callback.handler.class=io.strimzi.kafka.oauth.server.JaasServerOauthValidatorCallbackHandler 7
listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ 8
  oauth.valid.issuer.uri="https://<oauth_server_address>" \ 9
  oauth.jwks.endpoint.uri="https://<oauth_server_address>/jwks" \ 10
  oauth.username.claim="preferred_username"  \ 11
  oauth.client.id="kafka-broker" \ 12
  oauth.client.secret="kafka-secret" \ 13
  oauth.token.endpoint.uri="https://<oauth_server_address>/token" ; 14
listener.name.client.oauthbearer.sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler 15
listener.name.client.oauthbearer.connections.max.reauth.ms=3600000 16

1
Enables the OAUTHBEARER mechanism for credentials exchange over SASL.
2
Configures a listener for client applications to connect to. The system hostname is used as an advertised hostname, which clients must resolve in order to reconnect. The listener is named CLIENT in this example.
3
Specifies the channel protocol for the listener. SASL_SSL is for TLS. SASL_PLAINTEXT is used for an unencrypted connection (no TLS), but there is risk of eavesdropping and interception at the TCP connection layer.
4
Specifies the OAUTHBEARER mechanism for the CLIENT listener. The client name (CLIENT) is usually specified in uppercase in the listeners property, in lowercase for listener.name properties (listener.name.client), and in lowercase when part of a listener.name.client.* property.
5
Specifies the OAUTHBEARER mechanism for inter-broker communication.
6
Specifies the listener for inter-broker communication. The specification is required for the configuration to be valid.
7
Configures OAuth 2.0 authentication on the client listener.
8
Configures authentication settings for client and inter-broker communication. The oauth.client.id, oauth.client.secret, and auth.token.endpoint.uri properties relate to inter-broker configuration.
9
A valid issuer URI. Only access tokens issued by this issuer will be accepted. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME.
10
The JWKS endpoint URL. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/certs.
11
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The value will depend on the authentication flow and the authorization server used. If required, you can use a JsonPath expression like "['user.info'].['user.id']" to retrieve the username from nested JSON attributes within a token.
12
Client ID of the Kafka broker, which is the same for all brokers. This is the client registered with the authorization server as kafka-broker.
13
Secret for the Kafka broker, which is the same for all brokers.
14
The OAuth 2.0 token endpoint URL to your authorization server. For production, always use https:// urls. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/token.
15
Enables (and is only required for) OAuth 2.0 authentication for inter-broker communication.
16
(Optional) Enforces session expiry when a token expires, and also activates the Kafka re-authentication mechanism. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.

The following example shows a minimum configuration for a TLS listener, where TLS is used for inter-broker communication.

Example: TLS listener configuration for OAuth 2.0 authentication

listeners=REPLICATION://kafka:9091,CLIENT://kafka:9092 1
listener.security.protocol.map=REPLICATION:SSL,CLIENT:SASL_PLAINTEXT 2
listener.name.client.sasl.enabled.mechanisms=OAUTHBEARER
inter.broker.listener.name=REPLICATION
listener.name.replication.ssl.keystore.password=<keystore_password> 3
listener.name.replication.ssl.truststore.password=<truststore_password>
listener.name.replication.ssl.keystore.type=JKS
listener.name.replication.ssl.truststore.type=JKS
listener.name.replication.ssl.secure.random.implementation=SHA1PRNG 4
listener.name.replication.ssl.endpoint.identification.algorithm=HTTPS 5
listener.name.replication.ssl.keystore.location=<path_to_keystore> 6
listener.name.replication.ssl.truststore.location=<path_to_truststore> 7
listener.name.replication.ssl.client.auth=required 8
listener.name.client.oauthbearer.sasl.server.callback.handler.class=io.strimzi.kafka.oauth.server.JaasServerOauthValidatorCallbackHandler
listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ 9
  oauth.valid.issuer.uri="https://<oauth_server_address>" \
  oauth.jwks.endpoint.uri="https://<oauth_server_address>/jwks" \
  oauth.username.claim="preferred_username" ;

1
Separate configurations are required for inter-broker communication and client applications.
2
Configures the REPLICATION listener to use TLS, and the CLIENT listener to use SASL over an unencrypted channel. The client could use an encrypted channel (SASL_SSL) in a production environment.
3
The ssl. properties define the TLS configuration.
4
Random number generator implementation. If not set, the Java platform SDK default is used.
5
Hostname verification. If set to an empty string, the hostname verification is turned off. If not set, the default value is HTTPS, which enforces hostname verification for server certificates.
6
Path to the keystore for the listener.
7
Path to the truststore for the listener.
8
Specifies that clients of the REPLICATION listener have to authenticate with a client certificate when establishing a TLS connection (used for inter-broker connectivity).
9
Configures the CLIENT listener for OAuth 2.0. Connectivity with the authorization server should use secure HTTPS connections.

The following example shows a minimum configuration for OAuth 2.0 authentication using the PLAIN authentication mechanism for credentials exchange over SASL. Fast local token validation is used.

Example: Minimum listener configuration for PLAIN authentication

listeners=CLIENT://0.0.0.0:9092 1
listener.security.protocol.map=CLIENT:SASL_PLAINTEXT 2
listener.name.client.sasl.enabled.mechanisms=OAUTHBEARER,PLAIN 3
sasl.mechanism.inter.broker.protocol=OAUTHBEARER 4
inter.broker.listener.name=CLIENT 5
listener.name.client.oauthbearer.sasl.server.callback.handler.class=io.strimzi.kafka.oauth.server.JaasServerOauthValidatorCallbackHandler 6
listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ 7
  oauth.valid.issuer.uri="http://<auth_server>/auth/realms/<realm>" \ 8
  oauth.jwks.endpoint.uri="https://<auth_server>/auth/realms/<realm>/protocol/openid-connect/certs" \ 9
  oauth.username.claim="preferred_username"  \ 10
  oauth.client.id="kafka-broker" \ 11
  oauth.client.secret="kafka-secret" \ 12
  oauth.token.endpoint.uri="https://<oauth_server_address>/token" ; 13
listener.name.client.oauthbearer.sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler 14
listener.name.client.plain.sasl.server.callback.handler.class=io.strimzi.kafka.oauth.server.plain.JaasServerOauthOverPlainValidatorCallbackHandler 15
listener.name.client.plain.sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required \ 16
  oauth.valid.issuer.uri="https://<oauth_server_address>" \ 17
  oauth.jwks.endpoint.uri="https://<oauth_server_address>/jwks" \ 18
  oauth.username.claim="preferred_username"  \ 19
  oauth.token.endpoint.uri="http://<auth_server>/auth/realms/<realm>/protocol/openid-connect/token" ; 20
connections.max.reauth.ms=3600000 21

1
Configures a listener (named CLIENT in this example) for client applications to connect to. The system hostname is used as an advertised hostname, which clients must resolve in order to reconnect. Because this is the only configured listener, it is also used for inter-broker communication.
2
Configures the example CLIENT listener to use SASL over an unencrypted channel. In a production environment, the client should use an encrypted channel (SASL_SSL) in order to guard against eavesdropping and interception at the TCP connection layer.
3
Enables the PLAIN authentication mechanism for credentials exchange over SASL as well as OAUTHBEARER. OAUTHBEARER is also specified because it is required for inter-broker communication. Kafka clients can choose which mechanism to use to connect.
4
Specifies the OAUTHBEARER authentication mechanism for inter-broker communication.
5
Specifies the listener (named CLIENT in this example) for inter-broker communication. Required for the configuration to be valid.
6
Configures the server callback handler for the OAUTHBEARER mechanism.
7
Configures authentication settings for client and inter-broker communication using the OAUTHBEARER mechanism. The oauth.client.id, oauth.client.secret, and oauth.token.endpoint.uri properties relate to inter-broker configuration.
8
A valid issuer URI. Only access tokens from this issuer are accepted. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME
9
The JWKS endpoint URL. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/certs
10
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The value will depend on the authentication flow and the authorization server used. If required, you can use a JsonPath expression like "['user.info'].['user.id']" to retrieve the username from nested JSON attributes within a token.
11
Client ID of the Kafka broker, which is the same for all brokers. This is the client registered with the authorization server as kafka-broker.
12
Secret for the Kafka broker (the same for all brokers).
13
The OAuth 2.0 token endpoint URL to your authorization server. For production, always use https:// urls. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/token
14
Enables OAuth 2.0 authentication for inter-broker communication.
15
Configures the server callback handler for PLAIN authentication.
16
Configures authentication settings for client communication using PLAIN authentication.

oauth.token.endpoint.uri is an optional property that enables OAuth 2.0 over PLAIN using the OAuth 2.0 client credentials mechanism.

17
A valid issuer URI. Only access tokens from this issuer are accepted. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME
18
The JWKS endpoint URL. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/certs
19
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The value will depend on the authentication flow and the authorization server used. If required, you can use a JsonPath expression like "['user.info'].['user.id']" to retrieve the username from nested JSON attributes within a token.
20
The OAuth 2.0 token endpoint URL to your authorization server. Additional configuration for the PLAIN mechanism. If specified, clients can authenticate over PLAIN by passing an access token as the password using an $accessToken: prefix.

For production, always use https:// urls. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/token.

21
(Optional) Enforces session expiry when a token expires, and also activates the Kafka re-authentication mechanism. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
6.4.9.2.3. Fast local JWT token validation configuration

Fast local JWT token validation checks a JWT token signature locally.

The local check ensures that a token:

  • Conforms to type by containing a (typ) claim value of Bearer for an access token
  • Is valid (not expired)
  • Has an issuer that matches a validIssuerURI

You specify a valid issuer URI when you configure the listener, so that any tokens not issued by the authorization server are rejected.

The authorization server does not need to be contacted during fast local JWT token validation. You activate fast local JWT token validation by specifying a JWKs endpoint URI exposed by the OAuth 2.0 authorization server. The endpoint contains the public keys used to validate signed JWT tokens, which are sent as credentials by Kafka clients.

Note

All communication with the authorization server should be performed using HTTPS.

For a TLS listener, you can configure a certificate truststore and point to the truststore file.

Example properties for fast local JWT token validation

listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
  oauth.valid.issuer.uri="https://<oauth_server_address>" \ 1
  oauth.jwks.endpoint.uri="https://<oauth_server_address>/jwks" \ 2
  oauth.jwks.refresh.seconds="300" \ 3
  oauth.jwks.refresh.min.pause.seconds="1" \ 4
  oauth.jwks.expiry.seconds="360" \ 5
  oauth.username.claim="preferred_username" \ 6
  oauth.ssl.truststore.location="<path_to_truststore_p12_file>" \ 7
  oauth.ssl.truststore.password="<truststore_password>" \ 8
  oauth.ssl.truststore.type="PKCS12" ; 9

1
A valid issuer URI. Only access tokens issued by this issuer will be accepted. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME.
2
The JWKS endpoint URL. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/certs.
3
The period between endpoint refreshes (default 300).
4
The minimum pause in seconds between consecutive attempts to refresh JWKS public keys. When an unknown signing key is encountered, the JWKS keys refresh is scheduled outside the regular periodic schedule with at least the specified pause since the last refresh attempt. The refreshing of keys follows the rule of exponential backoff, retrying on unsuccessful refreshes with ever increasing pause, until it reaches oauth.jwks.refresh.seconds. The default value is 1.
5
The duration the JWKs certificates are considered valid before they expire. Default is 360 seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates.
6
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The value will depend on the authentication flow and the authorization server used. If required, you can use a JsonPath expression like "['user.info'].['user.id']" to retrieve the username from nested JSON attributes within a token.
7
The location of the truststore used in the TLS configuration.
8
Password to access the truststore.
9
The truststore type in PKCS #12 format.
6.4.9.2.4. OAuth 2.0 introspection endpoint configuration

Token validation using an OAuth 2.0 introspection endpoint treats a received access token as opaque. The Kafka broker sends an access token to the introspection endpoint, which responds with the token information necessary for validation. Importantly, it returns up-to-date information if the specific access token is valid, and also information about when the token expires.

To configure OAuth 2.0 introspection-based validation, you specify an introspection endpoint URI rather than the JWKs endpoint URI specified for fast local JWT token validation. Depending on the authorization server, you typically have to specify a client ID and client secret, because the introspection endpoint is usually protected.

Example properties for an introspection endpoint

listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
  oauth.introspection.endpoint.uri="https://<oauth_server_address>/introspection" \ 1
  oauth.client.id="kafka-broker" \ 2
  oauth.client.secret="kafka-broker-secret" \ 3
  oauth.ssl.truststore.location="<path_to_truststore_p12_file>" \ 4
  oauth.ssl.truststore.password="<truststore_password>" \ 5
  oauth.ssl.truststore.type="PKCS12" \ 6
  oauth.username.claim="preferred_username" ; 7

1
The OAuth 2.0 introspection endpoint URI. For example, https://AUTH-SERVER-ADDRESS/auth/realms/REALM-NAME/protocol/openid-connect/token/introspect.
2
Client ID of the Kafka broker.
3
Secret for the Kafka broker.
4
The location of the truststore used in the TLS configuration.
5
Password to access the truststore.
6
The truststore type in PKCS #12 format.
7
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The value will depend on the authentication flow and the authorization server used. If required, you can use a JsonPath expression like "['user.info'].['user.id']" to retrieve the username from nested JSON attributes within a token.

6.4.9.3. Session re-authentication for Kafka brokers

You can configure OAuth listeners to use Kafka session re-authentication for OAuth 2.0 sessions between Kafka clients and Kafka brokers. This mechanism enforces the expiry of an authenticated session between the client and the broker after a defined period of time. When a session expires, the client immediately starts a new session by reusing the existing connection rather than dropping it.

Session re-authentication is disabled by default. You can enable it in the server.properties file. Set the connections.max.reauth.ms property for a TLS listener with OAUTHBEARER or PLAIN enabled as the SASL mechanism.

You can specify session re-authentication per listener. For example:

listener.name.client.oauthbearer.connections.max.reauth.ms=3600000

Session re-authentication must be supported by the Kafka client libraries used by the client.

Session re-authentication can be used with fast local JWT or introspection endpoint token validation.

Client re-authentication

When the broker’s authenticated session expires, the client must re-authenticate to the existing session by sending a new, valid access token to the broker, without dropping the connection.

If token validation is successful, a new client session is started using the existing connection. If the client fails to re-authenticate, the broker will close the connection if further attempts are made to send or receive messages. Java clients that use Kafka client library 2.2 or later automatically re-authenticate if the re-authentication mechanism is enabled on the broker.

Session re-authentication also applies to refresh tokens, if used. When the session expires, the client refreshes the access token by using its refresh token. The client then uses the new access token to re-authenticate over the existing connection.

Session expiry for OAUTHBEARER and PLAIN

When session re-authentication is configured, session expiry works differently for OAUTHBEARER and PLAIN authentication.

For OAUTHBEARER and PLAIN, using the client ID and secret method:

  • The broker’s authenticated session will expire at the configured connections.max.reauth.ms.
  • The session will expire earlier if the access token expires before the configured time.

For PLAIN using the long-lived access token method:

  • The broker’s authenticated session will expire at the configured connections.max.reauth.ms.
  • Re-authentication will fail if the access token expires before the configured time. Although session re-authentication is attempted, PLAIN has no mechanism for refreshing tokens.

If connections.max.reauth.ms is not configured, OAUTHBEARER and PLAIN clients can remain connected to brokers indefinitely, without needing to re-authenticate. Authenticated sessions do not end with access token expiry. However, this can be considered when configuring authorization, for example, by using keycloak authorization or installing a custom authorizer.

6.4.9.4. OAuth 2.0 Kafka client configuration

A Kafka client is configured with either:

  • The credentials required to obtain a valid access token from an authorization server (client ID and Secret)
  • A valid long-lived access token or refresh token, obtained using tools provided by an authorization server

The only information ever sent to the Kafka broker is an access token. The credentials used to authenticate with the authorization server to obtain the access token are never sent to the broker.

When a client obtains an access token, no further communication with the authorization server is needed.

The simplest mechanism is authentication with a client ID and Secret. Using a long-lived access token, or a long-lived refresh token, adds more complexity because there is an additional dependency on authorization server tools.

Note

If you are using long-lived access tokens, you may need to configure the client in the authorization server to increase the maximum lifetime of the token.

If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client exchanges either:

  • Client ID and Secret
  • Client ID, refresh token, and (optionally) a secret
  • Username and password, with client ID and (optionally) a secret

6.4.9.5. OAuth 2.0 client authentication flows

OAuth 2.0 authentication flows depend on the underlying Kafka client and Kafka broker configuration. The flows must also be supported by the authorization server used.

The Kafka broker listener configuration determines how clients authenticate using an access token. The client can pass a client ID and secret to request an access token.

If a listener is configured to use PLAIN authentication, the client can authenticate with a client ID and secret or username and access token. These values are passed as the username and password properties of the PLAIN mechanism.

Listener configuration supports the following token validation options:

  • You can use fast local token validation based on JWT signature checking and local token introspection, without contacting an authorization server. The authorization server provides a JWKS endpoint with public certificates that are used to validate signatures on the tokens.
  • You can use a call to a token introspection endpoint provided by an authorization server. Each time a new Kafka broker connection is established, the broker passes the access token received from the client to the authorization server. The Kafka broker checks the response to confirm whether or not the token is valid.
Note

An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible.

Kafka client credentials can also be configured for the following types of authentication:

  • Direct local access using a previously generated long-lived access token
  • Contact with the authorization server for a new access token to be issued (using a client ID and a secret, or a refresh token, or a username and a password)
6.4.9.5.1. Example client authentication flows using the SASL OAUTHBEARER mechanism

You can use the following communication flows for Kafka authentication using the SASL OAUTHBEARER mechanism.

Client using client ID and secret, with broker delegating validation to authorization server

Client using client ID and secret with broker delegating validation to authorization server

  1. The Kafka client requests an access token from the authorization server using a client ID and secret, and optionally a refresh token. Alternatively, the client may authenticate using a username and a password.
  2. The authorization server generates a new access token.
  3. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server using its own client ID and secret.
  5. A Kafka client session is established if the token is valid.

Client using client ID and secret, with broker performing fast local token validation

Client using client ID and secret with broker performing fast local token validation

  1. The Kafka client authenticates with the authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token. Alternatively, the client may authenticate using a username and a password.
  2. The authorization server generates a new access token.
  3. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. The Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.

Client using long-lived access token, with broker delegating validation to authorization server

Client using long-lived access token with broker delegating validation to authorization server

  1. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server, using its own client ID and secret.
  3. A Kafka client session is established if the token is valid.

Client using long-lived access token, with broker performing fast local validation

Client using long-lived access token with broker performing fast local validation

  1. The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. The Kafka broker validates the access token locally using a JWT token signature check and local token introspection.
Warning

Fast local JWT token signature validation is suitable only for short-lived tokens as there is no check with the authorization server if a token has been revoked. Token expiration is written into the token, but revocation can happen at any time, so cannot be accounted for without contacting the authorization server. Any issued token would be considered valid until it expires.

6.4.9.5.2. Example client authentication flows using the SASL PLAIN mechanism

You can use the following communication flows for Kafka authentication using the OAuth PLAIN mechanism.

Client using a client ID and secret, with the broker obtaining the access token for the client

Client using a client ID and secret with the broker obtaining the access token for the client

  1. The Kafka client passes a clientId as a username and a secret as a password.
  2. The Kafka broker uses a token endpoint to pass the clientId and secret to the authorization server.
  3. The authorization server returns a fresh access token or an error if the client credentials are not valid.
  4. The Kafka broker validates the token in one of the following ways:

    1. If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if the token validation is successful.
    2. If local token introspection is used, a request is not made to the authorization server. The Kafka broker validates the access token locally using a JWT token signature check.

Client using a long-lived access token without a client ID and secret

Client using a long-lived access token without a client ID and secret

  1. The Kafka client passes a username and password. The password provides the value of an access token that was obtained manually and configured before running the client.
  2. The password is passed with or without an $accessToken: string prefix depending on whether or not the Kafka broker listener is configured with a token endpoint for authentication.

    1. If the token endpoint is configured, the password should be prefixed by $accessToken: to let the broker know that the password parameter contains an access token rather than a client secret. The Kafka broker interprets the username as the account username.
    2. If the token endpoint is not configured on the Kafka broker listener (enforcing a no-client-credentials mode), the password should provide the access token without the prefix. The Kafka broker interprets the username as the account username. In this mode, the client doesn’t use a client ID and secret, and the password parameter is always interpreted as a raw access token.
  3. The Kafka broker validates the token in one of the following ways:

    1. If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if token validation is successful.
    2. If local token introspection is used, there is no request made to the authorization server. Kafka broker validates the access token locally using a JWT token signature check.

6.4.9.6. Configuring OAuth 2.0 authentication

OAuth 2.0 is used for interaction between Kafka clients and AMQ Streams components.

In order to use OAuth 2.0 for AMQ Streams, you must:

6.4.9.6.1. Configuring Red Hat Single Sign-On as an OAuth 2.0 authorization server

This procedure describes how to deploy Red Hat Single Sign-On as an authorization server and configure it for integration with AMQ Streams.

The authorization server provides a central point for authentication and authorization, and management of users, clients, and permissions. Red Hat Single Sign-On has a concept of realms where a realm represents a separate set of users, clients, permissions, and other configuration. You can use a default master realm, or create a new one. Each realm exposes its own OAuth 2.0 endpoints, which means that application clients and application servers all need to use the same realm.

To use OAuth 2.0 with AMQ Streams, you use a deployment of Red Hat Single Sign-On to create and manage authentication realms.

Note

If you already have Red Hat Single Sign-On deployed, you can skip the deployment step and use your current deployment.

Before you begin

You will need to be familiar with using Red Hat Single Sign-On.

For installation and administration instructions, see:

Prerequisites

  • AMQ Streams and Kafka are running

For the Red Hat Single Sign-On deployment:

Procedure

  1. Install Red Hat Single Sign-On.

    You can install from a ZIP file or by using an RPM.

  2. Log in to the Red Hat Single Sign-On Admin Console to create the OAuth 2.0 policies for AMQ Streams.

    Login details are provided when you deploy Red Hat Single Sign-On.

  3. Create and enable a realm.

    You can use an existing master realm.

  4. Adjust the session and token timeouts for the realm, if required.
  5. Create a client called kafka-broker.
  6. From the Settings tab, set:

    • Access Type to Confidential
    • Standard Flow Enabled to OFF to disable web login for this client
    • Service Accounts Enabled to ON to allow this client to authenticate in its own name
  7. Click Save before continuing.
  8. From the Credentials tab, take a note of the secret for using in your AMQ Streams Kafka cluster configuration.
  9. Repeat the client creation steps for any application client that will connect to your Kafka brokers.

    Create a definition for each new client.

    You will use the names as client IDs in your configuration.

What to do next

After deploying and configuring the authorization server, configure the Kafka brokers to use OAuth 2.0.

6.4.9.6.2. Configuring OAuth 2.0 support for Kafka brokers

This procedure describes how to configure Kafka brokers so that the broker listeners are enabled to use OAuth 2.0 authentication using an authorization server.

We advise use of OAuth 2.0 over an encrypted interface through configuration of TLS listeners. Plain listeners are not recommended.

Configure the Kafka brokers using properties that support your chosen authorization server, and the type of authorization you are implementing.

Before you start

For more information on the configuration and authentication of Kafka broker listeners, see:

For a description of the properties used in the listener configuration, see:

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed

Procedure

  1. Configure the Kafka broker listener configuration in the server.properties file.

    For example, using the OAUTHBEARER mechanism:

    sasl.enabled.mechanisms=OAUTHBEARER
    listeners=CLIENT://0.0.0.0:9092
    listener.security.protocol.map=CLIENT:SASL_PLAINTEXT
    listener.name.client.sasl.enabled.mechanisms=OAUTHBEARER
    sasl.mechanism.inter.broker.protocol=OAUTHBEARER
    inter.broker.listener.name=CLIENT
    listener.name.client.oauthbearer.sasl.server.callback.handler.class=io.strimzi.kafka.oauth.server.JaasServerOauthValidatorCallbackHandler
    listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required ;
    listener.name.client.oauthbearer.sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
  2. Configure broker connection settings as part of the listener.name.client.oauthbearer.sasl.jaas.config.

    The examples here show connection configuration options.

    Example 1: Local token validation using a JWKS endpoint configuration

    listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.valid.issuer.uri="https://<oauth_server_address>/auth/realms/<realm_name>" \
      oauth.jwks.endpoint.uri="https://<oauth_server_address>/auth/realms/<realm_name>/protocol/openid-connect/certs" \
      oauth.jwks.refresh.seconds="300" \
      oauth.jwks.refresh.min.pause.seconds="1" \
      oauth.jwks.expiry.seconds="360" \
      oauth.username.claim="preferred_username" \
      oauth.ssl.truststore.location="<path_to_truststore_p12_file>" \
      oauth.ssl.truststore.password="<truststore_password>" \
      oauth.ssl.truststore.type="PKCS12" ;
    listener.name.client.oauthbearer.connections.max.reauth.ms=3600000

    Example 2: Delegating token validation to the authorization server through the OAuth 2.0 introspection endpoint

    listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      oauth.introspection.endpoint.uri="https://<oauth_server_address>/auth/realms/<realm_name>/protocol/openid-connect/introspection" \
      # ...

  3. If required, configure access to the authorization server.

    This step is normally required for a production environment, unless a technology like service mesh is used to configure secure channels outside containers.

    1. Provide a custom truststore for connecting to a secured authorization server. SSL is always required for access to the authorization server.

      Set properties to configure the truststore.

      For example:

      listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        # ...
        oauth.client.id="kafka-broker" \
        oauth.client.secret="kafka-broker-secret" \
        oauth.ssl.truststore.location="<path_to_truststore_p12_file>" \
        oauth.ssl.truststore.password="<truststore_password>" \
        oauth.ssl.truststore.type="PKCS12" ;
    2. If the certificate hostname does not match the access URL hostname, you can turn off certificate hostname validation:

      oauth.ssl.endpoint.identification.algorithm=""

      The check ensures that client connection to the authorization server is authentic. You may wish to turn off the validation in a non-production environment.

  4. Configure additional properties according to your chosen authentication flow:

    listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      # ...
      oauth.token.endpoint.uri="https://<oauth_server_address>/auth/realms/<realm_name>/protocol/openid-connect/token" \ 1
      oauth.custom.claim.check="@.custom == 'custom-value'" \ 2
      oauth.scope="<scope>" \ 3
      oauth.check.audience="true" \ 4
      oauth.audience="<audience>" \ 5
      oauth.valid.issuer.uri="https://https://<oauth_server_address>/auth/<realm_name>" \ 6
      oauth.client.id="kafka-broker" \ 7
      oauth.client.secret="kafka-broker-secret" \ 8
      oauth.connect.timeout.seconds=60 \ 9
      oauth.read.timeout.seconds=60 \ 10
      oauth.http.retries=2 \ 11
      oauth.http.retry.pause.millis=300 \ 12
      oauth.groups.claim="$.groups" \ 13
      oauth.groups.claim.delimiter="," ; 14
    1
    The OAuth 2.0 token endpoint URL to your authorization server. For production, always use https:// urls. Required when KeycloakAuthorizer is used, or an OAuth 2.0 enabled listener is used for inter-broker communication.
    2
    (Optional) Custom claim checking. A JsonPath filter query that applies additional custom rules to the JWT access token during validation. If the access token does not contain the necessary data, it is rejected. When using the introspection endpoint method, the custom check is applied to the introspection endpoint response JSON.
    3
    (Optional) A scope parameter passed to the token endpoint. A scope is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
    4
    (Optional) Audience checking. If your authorization server provides an aud (audience) claim, and you want to enforce an audience check, set ouath.check.audience to true. Audience checks identify the intended recipients of tokens. As a result, the Kafka broker will reject tokens that do not have its clientId in their aud claims. Default is false.
    5
    (Optional) An audience parameter passed to the token endpoint. An audience is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId and secret. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
    6
    A valid issuer URI. Only access tokens issued by this issuer will be accepted. (Always required.)
    7
    The configured client ID of the Kafka broker, which is the same for all brokers. This is the client registered with the authorization server as kafka-broker. Required when an introspection endpoint is used for token validation, or when KeycloakAuthorizer is used.
    8
    The configured secret for the Kafka broker, which is the same for all brokers. When the broker must authenticate to the authorization server, either a client secret, access token or a refresh token has to be specified.
    9
    (Optional) The connect timeout in seconds when connecting to the authorization server. The default value is 60.
    10
    (Optional) The read timeout in seconds when connecting to the authorization server. The default value is 60.
    11
    The maximum number of times to retry a failed HTTP request to the authorization server. The default value is 0, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for the oauth.connect.timeout.seconds and oauth.read.timeout.seconds options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive.
    12
    The time to wait before attempting another retry of a failed HTTP request to the authorization server. By default, this time is set to zero, meaning that no pause is applied. This is because many issues that cause failed requests are per-request network glitches or proxy issues that can be resolved quickly. However, if your authorization server is under stress or experiencing high traffic, you may want to set this option to a value of 100 ms or more to reduce the load on the server and increase the likelihood of successful retries.
    13
    A JsonPath query used to extract groups information from JWT token or introspection endpoint response. Not set by default. This can be used by a custom authorizer to make authorization decisions based on user groups.
    14
    A delimiter used to parse groups information when returned as a single delimited string. The default value is ',' (comma).
  5. Depending on how you apply OAuth 2.0 authentication, and the type of authorization server being used, add additional configuration settings:

    listener.name.client.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
      # ...
      oauth.check.issuer=false \ 1
      oauth.fallback.username.claim="<client_id>" \ 2
      oauth.fallback.username.prefix="<client_account>" \ 3
      oauth.valid.token.type="bearer" \ 4
      oauth.userinfo.endpoint.uri="https://<oauth_server_address>/auth/realms/<realm_name>/protocol/openid-connect/userinfo" ; 5
    1
    If your authorization server does not provide an iss claim, it is not possible to perform an issuer check. In this situation, set oauth.check.issuer to false and do not specify a oauth.valid.issuer.uri. Default is true.
    2
    An authorization server may not provide a single attribute to identify both regular users and clients. When a client authenticates in its own name, the server might provide a client ID. When a user authenticates using a username and password, to obtain a refresh token or an access token, the server might provide a username attribute in addition to a client ID. Use this fallback option to specify the username claim (attribute) to use if a primary user ID attribute is not available. If required, you can use a JsonPath expression like "['client.info'].['client.id']" to retrieve the fallback username from nested JSON attributes within a token.
    3
    In situations where oauth.fallback.username.claim is applicable, it may also be necessary to prevent name collisions between the values of the username claim, and those of the fallback username claim. Consider a situation where a client called producer exists, but also a regular user called producer exists. In order to differentiate between the two, you can use this property to add a prefix to the user ID of the client.
    4
    (Only applicable when using oauth.introspection.endpoint.uri) Depending on the authorization server you are using, the introspection endpoint may or may not return the token type attribute, or it may contain different values. You can specify a valid token type value that the response from the introspection endpoint has to contain.
    5
    (Only applicable when using oauth.introspection.endpoint.uri) The authorization server may be configured or implemented in such a way to not provide any identifiable information in an introspection endpoint response. In order to obtain the user ID, you can configure the URI of the userinfo endpoint as a fallback. The oauth.fallback.username.claim, oauth.fallback.username.claim, and oauth.fallback.username.prefix settings are applied to the response of the userinfo endpoint.
6.4.9.6.3. Configuring Kafka Java clients to use OAuth 2.0

Configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers. Add a callback plugin to your client pom.xml file, then configure your client for OAuth 2.0.

Specify the following in your client configuration:

  • A SASL (Simple Authentication and Security Layer) security protocol:

    • SASL_SSL for authentication over TLS encrypted connections
    • SASL_PLAINTEXT for authentication over unencrypted connections

      Use SASL_SSL for production and SASL_PLAINTEXT for local development only. When using SASL_SSL, additional ssl.truststore configuration is needed. The truststore configuration is required for secure connection (https://) to the OAuth 2.0 authorization server. To verify the OAuth 2.0 authorization server, add the CA certificate for the authorization server to the truststore in your client configuration. You can configure a truststore in PEM or PKCS #12 format.

  • A Kafka SASL mechanism:

    • OAUTHBEARER for credentials exchange using a bearer token
    • PLAIN to pass client credentials (clientId + secret) or an access token
  • A JAAS (Java Authentication and Authorization Service) module that implements the SASL mechanism:

    • org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule implements the OAUTHBEARER mechanism
    • org.apache.kafka.common.security.plain.PlainLoginModule implements the PLAIN mechanism
  • SASL authentication properties, which support the following authentication methods:

    • OAuth 2.0 client credentials
    • OAuth 2.0 password grant (deprecated)
    • Access token
    • Refresh token

Add the SASL authentication properties as JAAS configuration (sasl.jaas.config). How you configure the authentication properties depends on the authentication method you are using to access the OAuth 2.0 authorization server. In this procedure, the properties are specified in a properties file, then loaded into the client configuration.

Note

You can also specify authentication properties as environment variables, or as Java system properties. For Java system properties, you can set them using setProperty and pass them on the command line using the -D option.

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Add the client library with OAuth 2.0 support to the pom.xml file for the Kafka client:

    <dependency>
     <groupId>io.strimzi</groupId>
     <artifactId>kafka-oauth-client</artifactId>
     <version>0.13.0.redhat-00008</version>
    </dependency>
  2. Configure the client properties by specifying the following configuration in a properties file:

    • The security protocol
    • The SASL mechanism
    • The JAAS module and authentication properties according to the method being used

      For example, we can add the following to a client.properties file:

      Client credentials mechanism properties

      security.protocol=SASL_SSL 1
      sasl.mechanism=OAUTHBEARER 2
      ssl.truststore.location=/tmp/truststore.p12 3
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \ 4
        oauth.client.id="<client_id>" \ 5
        oauth.client.secret="<client_secret>" \ 6
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \ 7
        oauth.ssl.truststore.password="$STOREPASS" \ 8
        oauth.ssl.truststore.type="PKCS12" \ 9
        oauth.scope="<scope>" \ 10
        oauth.audience="<audience>" ; 11

      1
      SASL_SSL security protocol for TLS-encrypted connections. Use SASL_PLAINTEXT over unencrypted connections for local development only.
      2
      The SASL mechanism specified as OAUTHBEARER or PLAIN.
      3
      The truststore configuration for secure access to the Kafka cluster.
      4
      URI of the authorization server token endpoint.
      5
      Client ID, which is the name used when creating the client in the authorization server.
      6
      Client secret created when creating the client in the authorization server.
      7
      The location contains the public key certificate (truststore.p12) for the authorization server.
      8
      The password for accessing the truststore.
      9
      The truststore type.
      10
      (Optional) The scope for requesting the token from the token endpoint. An authorization server may require a client to specify the scope.
      11
      (Optional) The audience for requesting the token from the token endpoint. An authorization server may require a client to specify the audience.

      Password grants mechanism properties

      security.protocol=SASL_SSL
      sasl.mechanism=OAUTHBEARER
      ssl.truststore.location=/tmp/truststore.p12
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \
        oauth.client.id="<client_id>" \ 1
        oauth.client.secret="<client_secret>" \ 2
        oauth.password.grant.username="<username>" \ 3
        oauth.password.grant.password="<password>" \ 4
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \
        oauth.ssl.truststore.password="$STOREPASS" \
        oauth.ssl.truststore.type="PKCS12" \
        oauth.scope="<scope>" \
        oauth.audience="<audience>" ;

      1
      Client ID, which is the name used when creating the client in the authorization server.
      2
      (Optional) Client secret created when creating the client in the authorization server.
      3
      Username for password grant authentication. OAuth password grant configuration (username and password) uses the OAuth 2.0 password grant method. To use password grants, create a user account for a client on your authorization server with limited permissions. The account should act like a service account. Use in environments where user accounts are required for authentication, but consider using a refresh token first.
      4
      Password for password grant authentication.
      Note

      SASL PLAIN does not support passing a username and password (password grants) using the OAuth 2.0 password grant method.

      Access token properties

      security.protocol=SASL_SSL
      sasl.mechanism=OAUTHBEARER
      ssl.truststore.location=/tmp/truststore.p12
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \
        oauth.access.token="<access_token>" ; 1
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \
        oauth.ssl.truststore.password="$STOREPASS" \
        oauth.ssl.truststore.type="PKCS12" \

      1
      Long-lived access token for Kafka clients.

      Refresh token properties

      security.protocol=SASL_SSL
      sasl.mechanism=OAUTHBEARER
      ssl.truststore.location=/tmp/truststore.p12
      ssl.truststore.password=$STOREPASS
      ssl.truststore.type=PKCS12
      sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
        oauth.token.endpoint.uri="<token_endpoint_url>" \
        oauth.client.id="<client_id>" \ 1
        oauth.client.secret="<client_secret>" \ 2
        oauth.refresh.token="<refresh_token>" ; 3
        oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \
        oauth.ssl.truststore.password="$STOREPASS" \
        oauth.ssl.truststore.type="PKCS12" \

      1
      Client ID, which is the name used when creating the client in the authorization server.
      2
      (Optional) Client secret created when creating the client in the authorization server.
      3
      Long-lived refresh token for Kafka clients.
  3. Input the client properties for OAUTH 2.0 authentication into the Java client code.

    Example showing input of client properties

    Properties props = new Properties();
    try (FileReader reader = new FileReader("client.properties", StandardCharsets.UTF_8)) {
      props.load(reader);
    }

  4. Verify that the Kafka client can access the Kafka brokers.

6.4.10. Using OAuth 2.0 token-based authorization

If you are using OAuth 2.0 with Red Hat Single Sign-On for token-based authentication, you can also use Red Hat Single Sign-On to configure authorization rules to constrain client access to Kafka brokers. Authentication establishes the identity of a user. Authorization decides the level of access for that user.

AMQ Streams supports the use of OAuth 2.0 token-based authorization through Red Hat Single Sign-On Authorization Services, which allows you to manage security policies and permissions centrally.

Security policies and permissions defined in Red Hat Single Sign-On are used to grant access to resources on Kafka brokers. Users and clients are matched against policies that permit access to perform specific actions on Kafka brokers.

Kafka allows all users full access to brokers by default, and also provides the AclAuthorizer plugin to configure authorization based on Access Control Lists (ACLs).

ZooKeeper stores ACL rules that grant or deny access to resources based on username. However, OAuth 2.0 token-based authorization with Red Hat Single Sign-On offers far greater flexibility on how you wish to implement access control to Kafka brokers. In addition, you can configure your Kafka brokers to use OAuth 2.0 authorization and ACLs.

6.4.10.1. OAuth 2.0 authorization mechanism

OAuth 2.0 authorization in AMQ Streams uses Red Hat Single Sign-On server Authorization Services REST endpoints to extend token-based authentication with Red Hat Single Sign-On by applying defined security policies on a particular user, and providing a list of permissions granted on different resources for that user. Policies use roles and groups to match permissions to users. OAuth 2.0 authorization enforces permissions locally based on the received list of grants for the user from Red Hat Single Sign-On Authorization Services.

6.4.10.1.1. Kafka broker custom authorizer

A Red Hat Single Sign-On authorizer (KeycloakAuthorizer) is provided with AMQ Streams. To be able to use the Red Hat Single Sign-On REST endpoints for Authorization Services provided by Red Hat Single Sign-On, you configure a custom authorizer on the Kafka broker.

The authorizer fetches a list of granted permissions from the authorization server as needed, and enforces authorization locally on the Kafka Broker, making rapid authorization decisions for each client request.

6.4.10.2. Configuring OAuth 2.0 authorization support

This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Red Hat Single Sign-On Authorization Services.

Before you begin

Consider the access you require or want to limit for certain users. You can use a combination of Red Hat Single Sign-On groups, roles, clients, and users to configure access in Red Hat Single Sign-On.

Typically, groups are used to match users based on organizational departments or geographical locations. And roles are used to match users based on their function.

With Red Hat Single Sign-On, you can store users and groups in LDAP, whereas clients and roles cannot be stored this way. Storage and access to user data may be a factor in how you choose to configure authorization policies.

Note

Super users always have unconstrained access to a Kafka broker regardless of the authorization implemented on the Kafka broker.

Prerequisites

  • AMQ Streams must be configured to use OAuth 2.0 with Red Hat Single Sign-On for token-based authentication. You use the same Red Hat Single Sign-On server endpoint when you set up authorization.
  • You need to understand how to manage policies and permissions for Red Hat Single Sign-On Authorization Services, as described in the Red Hat Single Sign-On documentation.

Procedure

  1. Access the Red Hat Single Sign-On Admin Console or use the Red Hat Single Sign-On Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.
  2. Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.
  3. Bind the permissions to users and clients by assigning them roles and groups.
  4. Configure the Kafka brokers to use Red Hat Single Sign-On authorization.

    Add the following to the Kafka server.properties configuration file to install the authorizer in Kafka:

    authorizer.class.name=io.strimzi.kafka.oauth.server.authorizer.KeycloakAuthorizer
    principal.builder.class=io.strimzi.kafka.oauth.server.OAuthKafkaPrincipalBuilder
  5. Add configuration for the Kafka brokers to access the authorization server and Authorization Services.

    Here we show example configuration added as additional properties to server.properties, but you can also define them as environment variables using capitalized or upper-case naming conventions.

    strimzi.authorization.token.endpoint.uri="https://<auth_server_address>/auth/realms/REALM-NAME/protocol/openid-connect/token" 1
    strimzi.authorization.client.id="kafka" 2
    1
    The OAuth 2.0 token endpoint URL to Red Hat Single Sign-On. For production, always use https:// urls.
    2
    The client ID of the OAuth 2.0 client definition in Red Hat Single Sign-On that has Authorization Services enabled. Typically, kafka is used as the ID.
  6. (Optional) Add configuration for specific Kafka clusters.

    For example:

    strimzi.authorization.kafka.cluster.name="kafka-cluster" 1
    1
    The name of a specific Kafka cluster. Names are used to target permissions, making it possible to manage multiple clusters within the same Red Hat Single Sign-On realm. The default value is kafka-cluster.
  7. (Optional) Delegate to simple authorization.

    For example:

    strimzi.authorization.delegate.to.kafka.acl="false" 1
    1
    Delegate authorization to Kafka AclAuthorizer if access is denied by Red Hat Single Sign-On Authorization Services policies. The default is false.
  8. (Optional) Add configuration for TLS connection to the authorization server.

    For example:

    strimzi.authorization.ssl.truststore.location=<path_to_truststore> 1
    strimzi.authorization.ssl.truststore.password=<my_truststore_password> 2
    strimzi.authorization.ssl.truststore.type=JKS 3
    strimzi.authorization.ssl.secure.random.implementation=SHA1PRNG 4
    strimzi.authorization.ssl.endpoint.identification.algorithm=HTTPS 5
    1
    The path to the truststore that contain the certificates.
    2
    The password for the truststore.
    3
    The truststore type. If not set, the default Java keystore type is used.
    4
    Random number generator implementation. If not set, the Java platform SDK default is used.
    5
    Hostname verification. If set to an empty string, the hostname verification is turned off. If not set, the default value is HTTPS, which enforces hostname verification for server certificates.
  9. (Optional) Configure the refresh of grants from the authorization server. The grants refresh job works by enumerating the active tokens and requesting the latest grants for each.

    For example:

    strimzi.authorization.grants.refresh.period.seconds="120" 1
    strimzi.authorization.grants.refresh.pool.size="10" 2
    strimzi.authorization.grants.max.idle.time.seconds="300" 3
    strimzi.authorization.grants.gc.period.seconds="300" 4
    strimzi.authorization.reuse.grants="false" 5
    1
    Specifies how often the list of grants from the authorization server is refreshed (once per minute by default). To turn grants refresh off for debugging purposes, set to "0".
    2
    Specifies the size of the thread pool (the degree of parallelism) used by the grants refresh job. The default value is "5".
    3
    The time, in seconds, after which an idle grant in the cache can be evicted. The default value is 300.
    4
    The time, in seconds, between consecutive runs of a job that cleans stale grants from the cache. The default value is 300.
    5
    Controls whether the latest grants are fetched for a new session. When disabled, grants are retrieved from Red Hat Single Sign-On and cached for the user. The default value is true.
  10. (Optional) Configure network timeouts when communicating with the authorization server.

    For example:

    strimzi.authorization.connect.timeout.seconds="60" 1
    strimzi.authorization.read.timeout.seconds="60" 2
    strimzi.authorization.http.retries="2" 3
    1
    The connect timeout in seconds when connecting to the Red Hat Single Sign-On token endpoint. The default value is 60.
    2
    The read timeout in seconds when connecting to the Red Hat Single Sign-On token endpoint. The default value is 60.
    3
    The maximum number of times to retry (without pausing) a failed HTTP request to the authorization server. The default value is 0, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for the strimzi.authorization.connect.timeout.seconds and strimzi.authorization.read.timeout.seconds options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive.
  11. (Optional) Enable OAuth 2.0 metrics for token validation and authorization.

    For example:

    oauth.enable.metrics="true" 1
    1
    Controls whether to enable or disable OAuth metrics. The default value is false.
  12. Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, making sure they have the necessary access, or do not have the access they are not supposed to have.

6.4.11. Using OPA policy-based authorization

Open Policy Agent (OPA) is an open-source policy engine. You can integrate OPA with AMQ Streams to act as a policy-based authorization mechanism for permitting client operations on Kafka brokers.

When a request is made from a client, OPA will evaluate the request against policies defined for Kafka access, then allow or deny the request.

Note

Red Hat does not support the OPA server.

Additional resources

6.4.11.1. Defining OPA policies

Before integrating OPA with AMQ Streams, consider how you will define policies to provide fine-grained access controls.

You can define access control for Kafka clusters, consumer groups and topics. For instance, you can define an authorization policy that allows write access from a producer client to a specific broker topic.

For this, the policy might specify the:

  • User principal and host address associated with the producer client
  • Operations allowed for the client
  • Resource type (topic) and resource name the policy applies to

Allow and deny decisions are written into the policy, and a response is provided based on the request and client identification data provided.

In our example the producer client would have to satisfy the policy to be allowed to write to the topic.

6.4.11.2. Connecting to the OPA

To enable Kafka to access the OPA policy engine to query access control policies, , you configure a custom OPA authorizer plugin (kafka-authorizer-opa-VERSION.jar) in your Kafka server.properties file.

When a request is made by a client, the OPA policy engine is queried by the plugin using a specified URL address and a REST endpoint, which must be the name of the defined policy.

The plugin provides the details of the client request — user principal, operation, and resource — in JSON format to be checked against the policy. The details will include the unique identity of the client; for example, taking the distinguished name from the client certificate if TLS authentication is used.

OPA uses the data to provide a response — either true or false — to the plugin to allow or deny the request.

6.4.11.3. Configuring OPA authorization support

This procedure describes how to configure Kafka brokers to use OPA authorization.

Before you begin

Consider the access you require or want to limit for certain users. You can use a combination of users and Kafka resources to define OPA policies.

It is possible to set up OPA to load user information from an LDAP data source.

Note

Super users always have unconstrained access to a Kafka broker regardless of the authorization implemented on the Kafka broker.

Prerequisites

Procedure

  1. Write the OPA policies required for authorizing client requests to perform operations on the Kafka brokers.

    See Defining OPA policies.

    Now configure the Kafka brokers to use OPA.

  2. Install the OPA authorizer plugin for Kafka.

    See Connecting to the OPA.

    Make sure that the plugin files are included in the Kafka classpath.

  3. Add the following to the Kafka server.properties configuration file to enable the OPA plugin:

    authorizer.class.name: com.bisnode.kafka.authorization.OpaAuthorizer
  4. Add further configuration to server.properties for the Kafka brokers to access the OPA policy engine and policies.

    For example:

    opa.authorizer.url=https://OPA-ADDRESS/allow 1
    opa.authorizer.allow.on.error=false 2
    opa.authorizer.cache.initial.capacity=50000 3
    opa.authorizer.cache.maximum.size=50000 4
    opa.authorizer.cache.expire.after.seconds=600000 5
    super.users=User:alice;User:bob 6
    1
    (Required) The OAuth 2.0 token endpoint URL for the policy the authorizer plugin will query. In this example, the policy is called allow.
    2
    Flag to specify whether a client is allowed or denied access by default if the authorizer plugin fails to connect with the OPA policy engine.
    3
    Initial capacity in bytes of the local cache. The cache is used so that the plugin does not have to query the OPA policy engine for every request.
    4
    Maximum capacity in bytes of the local cache.
    5
    Time in milliseconds that the local cache is refreshed by reloading from the OPA policy engine.
    6
    A list of user principals treated as super users, so that they are always allowed without querying the Open Policy Agent policy.

    Refer to the Open Policy Agent website for information on authentication and authorization options.

  5. Verify the configured permissions by accessing Kafka brokers using clients that have and do not have the correct authorization.

Chapter 7. Creating and managing topics

Messages in Kafka are always sent to or received from a topic. This chapter describes how to create and manage Kafka topics.

7.1. Partitions and replicas

Messages in Kafka are always sent to or received from a topic. A topic is always split into one or more partitions. Partitions act as shards. That means that every message sent by a producer is always written only into a single partition. Thanks to the sharding of messages into different partitions, topics are easy to scale horizontally.

Each partition can have one or more replicas, which will be stored on different brokers in the cluster. When creating a topic you can configure the number of replicas using the replication factor. Replication factor defines the number of copies which will be held within the cluster. One of the replicas for given partition will be elected as a leader. The leader replica will be used by the producers to send new messages and by the consumers to consume messages. The other replicas will be follower replicas. The followers replicate the leader.

If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so the load is well balanced within the cluster.

Note

The replication factor determines the number of replicas including the leader and the followers. For example, if you set the replication factor to 3, then there will one leader and two follower replicas.

7.2. Message retention

The message retention policy defines how long the messages will be stored on the Kafka brokers. It can be defined based on time, partition size or both.

For example, you can define that the messages should be kept:

  • For 7 days
  • Until the parition has 1GB of messages. Once the limit is reached, the oldest messages will be removed.
  • For 7 days or until the 1GB limit has been reached. Whatever limit comes first will be used.
Warning

Kafka brokers store messages in log segments. The messages which are past their retention policy will be deleted only when a new log segment is created. New log segments are created when the previous log segment exceeds the configured log segment size. Additionally, users can request new segments to be created periodically.

Additionally, Kafka brokers support a compacting policy.

For a topic with the compacted policy, the broker will always keep only the last message for each key. The older messages with the same key will be removed from the partition. Because compacting is a periodically executed action, it does not happen immediately when the new message with the same key are sent to the partition. Instead it might take some time until the older messages are removed.

For more information about the message retention configuration options, see Section 7.5, “Topic configuration”.

7.3. Topic auto-creation

When a producer or consumer tries to send messages to or receive messages from a topic that does not exist, Kafka will, by default, automatically create that topic. This behavior is controlled by the auto.create.topics.enable configuration property which is set to true by default.

To disable it, set auto.create.topics.enable to false in the Kafka broker configuration file:

auto.create.topics.enable=false

7.4. Topic deletion

Kafka offers the possibility to disable deletion of topics. This is configured through the delete.topic.enable property, which is set to true by default (that is, deleting topics is possible). When this property is set to false it will be not possible to delete topics and all attempts to delete topic will return success but the topic will not be deleted.

delete.topic.enable=false

7.5. Topic configuration

Auto-created topics will use the default topic configuration which can be specified in the broker properties file. However, when creating topics manually, their configuration can be specified at creation time. It is also possible to change a topic’s configuration after it has been created. The main topic configuration options for manually created topics are:

cleanup.policy
Configures the retention policy to delete or compact. The delete policy will delete old records. The compact policy will enable log compaction. The default value is delete. For more information about log compaction, see Kafka website.
compression.type
Specifies the compression which is used for stored messages. Valid values are gzip, snappy, lz4, uncompressed (no compression) and producer (retain the compression codec used by the producer). The default value is producer.
max.message.bytes
The maximum size of a batch of messages allowed by the Kafka broker, in bytes. The default value is 1000012.
min.insync.replicas
The minimum number of replicas which must be in sync for a write to be considered successful. The default value is 1.
retention.ms
Maximum number of milliseconds for which log segments will be retained. Log segments older than this value will be deleted. The default value is 604800000 (7 days).
retention.bytes
The maximum number of bytes a partition will retain. Once the partition size grows over this limit, the oldest log segments will be deleted. Value of -1 indicates no limit. The default value is -1.
segment.bytes
The maximum file size of a single commit log segment file in bytes. When the segment reaches its size, a new segment will be started. The default value is 1073741824 bytes (1 gibibyte).

The defaults for auto-created topics can be specified in the Kafka broker configuration using similar options:

log.cleanup.policy
See cleanup.policy above.
compression.type
See compression.type above.
message.max.bytes
See max.message.bytes above.
min.insync.replicas
See min.insync.replicas above.
log.retention.ms
See retention.ms above.
log.retention.bytes
See retention.bytes above.
log.segment.bytes
See segment.bytes above.
default.replication.factor
Default replication factor for automatically created topics. Default value is 1.
num.partitions
Default number of partitions for automatically created topics. Default value is 1.

7.6. Internal topics

Internal topics are created and used internally by the Kafka brokers and clients. Kafka has several internal topics. These are used to store consumer offsets (__consumer_offsets) or transaction state (__transaction_state). These topics can be configured using dedicated Kafka broker configuration options starting with prefix offsets.topic. and transaction.state.log.. The most important configuration options are:

offsets.topic.replication.factor
Number of replicas for __consumer_offsets topic. The default value is 3.
offsets.topic.num.partitions
Number of partitions for __consumer_offsets topic. The default value is 50.
transaction.state.log.replication.factor
Number of replicas for __transaction_state topic. The default value is 3.
transaction.state.log.num.partitions
Number of partitions for __transaction_state topic. The default value is 50.
transaction.state.log.min.isr
Minimum number of replicas that must acknowledge a write to __transaction_state topic to be considered successful. If this minimum cannot be met, then the producer will fail with an exception. The default value is 2.

7.7. Creating a topic

Use the kafka-topics.sh tool to manage topics. kafka-topics.sh is part of the AMQ Streams distribution and is found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running

Creating a topic

  1. Create a topic using the kafka-topics.sh utility and specify the following:

    • Host and port of the Kafka broker in the --bootstrap-server option.
    • The new topic to be created in the --create option.
    • Topic name in the --topic option.
    • The number of partitions in the --partitions option.
    • Topic replication factor in the --replication-factor option.

      You can also override some of the default topic configuration options using the option --config. This option can be used multiple times to override different options.

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server <broker_address> --create --topic <TopicName> --partitions <NumberOfPartitions> --replication-factor <ReplicationFactor> --config <Option1>=<Value1> --config <Option2>=<Value2>

      Example of the command to create a topic named mytopic

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --create --topic mytopic --partitions 50 --replication-factor 3 --config cleanup.policy=compact --config min.insync.replicas=2

  2. Verify that the topic exists using kafka-topics.sh.

    /opt/kafka/bin/kafka-topics.sh --bootstrap-server <broker_address> --describe --topic <TopicName>

    Example of the command to describe a topic named mytopic

    /opt/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --describe --topic mytopic

Additional resources

7.8. Listing and describing topics

The kafka-topics.sh tool can be used to list and describe topics. kafka-topics.sh is part of the AMQ Streams distribution and can be found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running
  • Topic mytopic exists

Describing a topic

  1. Describe a topic using the kafka-topics.sh utility and specify the following:

    • Host and port of the Kafka broker in the --bootstrap-server option.
    • Use the --describe option to specify that you want to describe a topic.
    • Topic name must be specified in the --topic option.
    • When the --topic option is omitted, it will describe all available topics.

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server <broker_address> --describe --topic <TopicName>

      Example of the command to describe a topic named mytopic

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --describe --topic mytopic

      The command lists all partitions and replicas which belong to this topic. It also lists all topic configuration options.

7.9. Modifying a topic configuration

The kafka-configs.sh tool can be used to modify topic configurations. kafka-configs.sh is part of the AMQ Streams distribution and can be found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running
  • Topic mytopic exists

Modify topic configuration

  1. Use the kafka-configs.sh tool to get the current configuration.

    • Specify the host and port of the Kafka broker in the --bootstrap-server option.
    • Set the --entity-type as topic and --entity-name to the name of your topic.
    • Use --describe option to get the current configuration.

      /opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --entity-type topics --entity-name <TopicName> --describe

      Example of the command to get configuration of a topic named mytopic

      /opt/kafka/bin/kafka-configs.sh --bootstrap-server localhost:9092 --entity-type topics --entity-name mytopic --describe

  2. Use the kafka-configs.sh tool to change the configuration.

    • Specify the host and port of the Kafka broker in the --bootstrap-server option.
    • Set the --entity-type as topic and --entity-name to the name of your topic.
    • Use --alter option to modify the current configuration.
    • Specify the options you want to add or change in the option --add-config.

      /opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --entity-type topics --entity-name <TopicName> --alter --add-config <Option>=<Value>

      Example of the command to change configuration of a topic named mytopic

      /opt/kafka/bin/kafka-configs.sh --bootstrap-server localhost:9092 --entity-type topics --entity-name mytopic --alter --add-config min.insync.replicas=1

  3. Use the kafka-configs.sh tool to delete an existing configuration option.

    • Specify the host and port of the Kafka broker in the --bootstrap-server option.
    • Set the --entity-type as topic and --entity-name to the name of your topic.
    • Use --delete-config option to remove existing configuration option.
    • Specify the options you want to remove in the option --remove-config.

      /opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --entity-type topics --entity-name <TopicName> --alter --delete-config <Option>

      Example of the command to change configuration of a topic named mytopic

      /opt/kafka/bin/kafka-configs.sh --bootstrap-server localhost:9092 --entity-type topics --entity-name mytopic --alter --delete-config min.insync.replicas

7.10. Deleting a topic

The kafka-topics.sh tool can be used to manage topics. kafka-topics.sh is part of the AMQ Streams distribution and can be found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running
  • Topic mytopic exists

Deleting a topic

  1. Delete a topic using the kafka-topics.sh utility.

    • Host and port of the Kafka broker in the --bootstrap-server option.
    • Use the --delete option to specify that an existing topic should be deleted.
    • Topic name must be specified in the --topic option.

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server <broker_address> --delete --topic <TopicName>

      Example of the command to create a topic named mytopic

      /opt/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --delete --topic mytopic

  2. Verify that the topic was deleted using kafka-topics.sh.

    /opt/kafka/bin/kafka-topics.sh --bootstrap-server <broker_address> --list

    Example of the command to list all topics

    /opt/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --list

Additional resources

Chapter 8. Using AMQ Streams with Kafka Connect

Use Kafka Connect to stream data between Kafka and external systems. Kafka Connect provides a framework for moving large amounts of data while maintaining scalability and reliability. Kafka Connect is typically used to integrate Kafka with database, storage, and messaging systems that are external to your Kafka cluster.

Kafka Connect runs in standalone or distributed modes.

Standalone mode
In standalone mode, Kafka Connect runs on a single node. Standalone mode is intended for development and testing.
Distributed mode
In distributed mode, Kafka Connect runs across one or more worker nodes and the workloads are distributed among them. Distributed mode is intended for production.

Kafka Connect uses connector plugins that implement connectivity for different types of external systems. There are two types of connector plugins: sink and source. Sink connectors stream data from Kafka to external systems. Source connectors stream data from external systems into Kafka.

You can also use the Kafka Connect REST API to create, manage, and monitor connector instances.

Connector configuration specifies details such as the source or sink connectors and the Kafka topics to read from or write to. How you manage the configuration depends on whether you are running Kafka Connect in standalone or distributed mode.

  • In standalone mode, you can provide the connector configuration as JSON through the Kafka Connect REST API or you can use properties files to define the configuration.
  • In distributed mode, you can only provide the connector configuration as JSON through the Kafka Connect REST API.

Handling high volumes of messages

You can tune the configuration to handle high volumes of messages. For more information, see Chapter 11, Handling high volumes of messages.

8.1. Using Kafka Connect in standalone mode

In Kafka Connect standalone mode, connectors run on the same node as the Kafka Connect worker process, which runs as a single process in a single JVM. This means that the worker process and connectors share the same resources, such as CPU, memory, and disk.

8.1.1. Configuring Kafka Connect in standalone mode

To configure Kafka Connect in standalone mode, edit the config/connect-standalone.properties configuration file. The following options are the most important.

bootstrap.servers
A list of Kafka broker addresses used as bootstrap connections to Kafka. For example, kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092.
key.converter
The class used to convert message keys to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
value.converter
The class used to convert message payloads to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
offset.storage.file.filename
Specifies the file in which the offset data is stored.

Connector plugins open client connections to the Kafka brokers using the bootstrap address. To configure these connections, use the standard Kafka producer and consumer configuration options prefixed by producer. or consumer..

8.1.2. Running Kafka Connect in standalone mode

Configure and run Kafka Connect in standalone mode.

Prerequisites

  • AMQ Streams is installed and a Kafka cluster is running.
  • You have specified connector configuration in properties files.

    You can also use the Kafka Connect REST API to manage connectors.

Procedure

  1. Edit the /opt/kafka/config/connect-standalone.properties Kafka Connect configuration file and set bootstrap.server to point to your Kafka brokers. For example:

    bootstrap.servers=kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092
  2. Start Kafka Connect with the configuration file and specify one or more connector configurations.

    su - kafka
    /opt/kafka/bin/connect-standalone.sh /opt/kafka/config/connect-standalone.properties connector1.properties
    [connector2.properties ...]
  3. Verify that Kafka Connect is running.

    jcmd | grep ConnectStandalone

8.2. Using Kafka Connect in distributed mode

In distributed mode, Kafka Connect runs as a cluster of worker processes, with each worker running on a separate node. Connectors can run on any worker in the cluster, allowing for greater scalability and fault tolerance. The connectors are managed by the workers, which coordinate with each other to distribute the work and ensure that each connector is running on a single node at any given time.

8.2.1. Configuring Kafka Connect in distributed mode

To configure Kafka Connect in distributed mode, edit the config/connect-distributed.properties configuration file. The following options are the most important.

bootstrap.servers
A list of Kafka broker addresses used as bootstrap connections to Kafka. For example, kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092.
key.converter
The class used to convert message keys to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
value.converter
The class used to convert message payloads to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
group.id
The name of the distributed Kafka Connect cluster. This must be unique and must not conflict with another consumer group ID. The default value is connect-cluster.
config.storage.topic
The Kafka topic used to store connector configurations. The default value is connect-configs.
offset.storage.topic
The Kafka topic used to store offsets. The default value is connect-offset.
status.storage.topic
The Kafka topic used for worker node statuses. The default value is connect-status.

AMQ Streams includes an example configuration file for Kafka Connect in distributed mode – see config/connect-distributed.properties in the AMQ Streams installation directory.

Connector plugins open client connections to the Kafka brokers using the bootstrap address. To configure these connections, use the standard Kafka producer and consumer configuration options prefixed by producer. or consumer..

8.2.2. Running Kafka Connect in distributed mode

Configure and run Kafka Connect in distributed mode.

Prerequisites

  • AMQ Streams is installed and a Kafka cluster is running.

Running the cluster

  1. Edit the /opt/kafka/config/connect-distributed.properties Kafka Connect configuration file on all Kafka Connect worker nodes.

    • Set the bootstrap.server option to point to your Kafka brokers.
    • Set the group.id option.
    • Set the config.storage.topic option.
    • Set the offset.storage.topic option.
    • Set the status.storage.topic option.

      For example:

      bootstrap.servers=kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092
      group.id=my-group-id
      config.storage.topic=my-group-id-configs
      offset.storage.topic=my-group-id-offsets
      status.storage.topic=my-group-id-status
  2. Start the Kafka Connect workers with the /opt/kafka/config/connect-distributed.properties configuration file on all Kafka Connect nodes.

    su - kafka
    /opt/kafka/bin/connect-distributed.sh /opt/kafka/config/connect-distributed.properties
  3. Verify that Kafka Connect is running.

    jcmd | grep ConnectDistributed
  4. Use the Kafka Connect REST API to manage connectors.

8.3. Managing connectors

The Kafka Connect REST API provides endpoints for creating, updating, and deleting connectors directly. You can also use the API to check the status of connectors or change logging levels. When you create a connector through the API, you provide the configuration details for the connector as part of the API call.

You can also add and manage connectors as plugins. Plugins are packaged as JAR files that contain the classes to implement the connectors through the Kafka Connect API. You just need to specify the plugin in the classpath or add it to a plugin path for Kafka Connect to run the connector plugin on startup.

In addition to using the Kafka Connect REST API or plugins to manage connectors, you can also add connector configuration using properties files when running Kafka Connect in standalone mode. To do this, you simply specify the location of the properties file when starting the Kafka Connect worker process. The properties file should contain the configuration details for the connector, including the connector class, source and destination topics, and any required authentication or serialization settings.

8.3.1. Limiting access to the Kafka Connect API

The Kafka Connect REST API can be accessed by anyone who has authenticated access and knows the endpoint URL, which includes the hostname/IP address and port number. It is crucial to restrict access to the Kafka Connect API only to trusted users to prevent unauthorized actions and potential security issues.

For improved security, we recommend configuring the following properties for the Kafka Connect API:

  • (Kafka 3.4 or later) org.apache.kafka.disallowed.login.modules to specifically exclude insecure login modules
  • connector.client.config.override.policy set to NONE to prevent connector configurations from overriding the Kafka Connect configuration and the consumers and producers it uses

8.3.2. Configuring connectors

Use the Kafka Connect REST API or properties files to create, manage, and monitor connector instances. You can use the REST API when using Kafka Connect in standalone or distributed mode. You can use properties files when using Kafka Connect in standalone mode.

8.3.2.1. Using the Kafka Connect REST API to manage connectors

When using the Kafka Connect REST API, you can create connectors dynamically by sending PUT or POST HTTP requests to the Kafka Connect REST API, specifying the connector configuration details in the request body.

Tip

When you use the PUT command, it’s the same command for starting and updating connectors.

The REST interface listens on port 8083 by default and supports the following endpoints:

GET /connectors
Return a list of existing connectors.
POST /connectors
Create a connector. The request body has to be a JSON object with the connector configuration.
GET /connectors/<connector_name>
Get information about a specific connector.
GET /connectors/<connector_name>/config
Get configuration of a specific connector.
PUT /connectors/<connector_name>/config
Update the configuration of a specific connector.
GET /connectors/<connector_name>/status
Get the status of a specific connector.
GET /connectors/<connector_name>/tasks
Get a list of tasks for a specific connector
GET /connectors/<connector_name>/tasks/<task_id>/status
Get the status of a task for a specific connector
PUT /connectors/<connector_name>/pause
Pause the connector and all its tasks. The connector will stop processing any messages.
PUT /connectors/<connector_name>/resume
Resume a paused connector.
POST /connectors/<connector_name>/restart
Restart a connector in case it has failed.
POST /connectors/<connector_name>/tasks/<task_id>/restart
Restart a specific task.
DELETE /connectors/<connector_name>
Delete a connector.
GET /connectors/<connector_name>/topics
Get the topics for a specific connector.
PUT /connectors/<connector_name>/topics/reset
Empty the set of active topics for a specific connector.
GET /connector-plugins
Get a list of all supported connector plugins.
PUT /connector-plugins/<connector_type>/config/validate
Validate connector configuration.

8.3.2.2. Specifying connector configuration properties

To configure a Kafka Connect connector, you need to specify the configuration details for source or sink connectors. There are two ways to do this: through the Kafka Connect REST API, using JSON to provide the configuration, or by using properties files to define the configuration properties. The specific configuration options available for each type of connector may differ, but both methods provide a flexible way to specify the necessary settings.

The following options apply to all connectors:

name
The name of the connector, which must be unique within the current Kafka Connect instance.
connector.class
The class of the connector plug-in. For example, org.apache.kafka.connect.file.FileStreamSinkConnector.
tasks.max
The maximum number of tasks that the specified connector can use. Tasks enable the connector to perform work in parallel. The connector might create fewer tasks than specified.
key.converter
The class used to convert message keys to and from Kafka format. This overrides the default value set by the Kafka Connect configuration. For example, org.apache.kafka.connect.json.JsonConverter.
value.converter
The class used to convert message payloads to and from Kafka format. This overrides the default value set by the Kafka Connect configuration. For example, org.apache.kafka.connect.json.JsonConverter.

You must set at least one of the following options for sink connectors:

topics
A comma-separated list of topics used as input.
topics.regex
A Java regular expression of topics used as input.

For all other options, see the connector properties in the Apache Kafka documentation.

Note

AMQ Streams includes the example connector configuration files config/connect-file-sink.properties and config/connect-file-source.properties in the AMQ Streams installation directory.

8.3.3. Creating connectors using the Kafka Connect API

Use the Kafka Connect REST API to create a connector to use with Kafka Connect.

Prerequisites

  • A Kafka Connect installation.

Procedure

  1. Prepare a JSON payload with the connector configuration. For example:

    {
      "name": "my-connector",
      "config": {
      "connector.class": "org.apache.kafka.connect.file.FileStreamSinkConnector",
        "tasks.max": "1",
        "topics": "my-topic-1,my-topic-2",
        "file": "/tmp/output-file.txt"
      }
    }
  2. Send a POST request to <KafkaConnectAddress>:8083/connectors to create the connector. The following example uses curl:

    curl -X POST -H "Content-Type: application/json" --data @sink-connector.json http://connect0.my-domain.com:8083/connectors
  3. Verify that the connector was deployed by sending a GET request to <KafkaConnectAddress>:8083/connectors. The following example uses curl:

    curl http://connect0.my-domain.com:8083/connectors

8.3.4. Deleting connectors using the Kafka Connect API

Use the Kafka Connect REST API to delete a connector from Kafka Connect.

Prerequisites

  • A Kafka Connect installation.

Deleting connectors

  1. Verify that the connector exists by sending a GET request to <KafkaConnectAddress>:8083/connectors/<ConnectorName>. The following example uses curl:

    curl http://connect0.my-domain.com:8083/connectors
  2. To delete the connector, send a DELETE request to <KafkaConnectAddress>:8083/connectors. The following example uses curl:

    curl -X DELETE http://connect0.my-domain.com:8083/connectors/my-connector
  3. Verify that the connector was deleted by sending a GET request to <KafkaConnectAddress>:8083/connectors. The following example uses curl:

    curl http://connect0.my-domain.com:8083/connectors

8.3.5. Adding connector plugins

Kafka provides example connectors to use as a starting point for developing connectors. The following example connectors are included with AMQ Streams:

FileStreamSink
Reads data from Kafka topics and writes the data to a file.
FileStreamSource
Reads data from a file and sends the data to Kafka topics.

Both connectors are contained in the libs/connect-file-<kafka_version>.redhat-<build>.jar plugin.

To use the connector plugins in Kafka Connect, you can add them to the classpath or specify a plugin path in the Kafka Connect properties file and copy the plugins to the location.

Specifying the example connectors in the classpath

CLASSPATH=/opt/kafka/libs/connect-file-<kafka_version>.redhat-<build>.jar opt/kafka/bin/connect-distributed.sh

Setting a plugin path

plugin.path=/opt/kafka/connector-plugins,/opt/connectors

The plugin.path configuration option can contain a comma-separated list of paths.

You can add more connector plugins if needed. Kafka Connect searches for and runs connector plugins at startup.

Note

When running Kafka Connect in distributed mode, plugins must be made available on all worker nodes.

Chapter 9. Using AMQ Streams with MirrorMaker 2

Use MirrorMaker 2 to replicate data between two or more active Kafka clusters, within or across data centers.

To configure MirrorMaker 2, edit the config/connect-mirror-maker.properties configuration file. If required, you can enable distributed tracing for MirrorMaker 2.

Handling high volumes of messages

You can tune the configuration to handle high volumes of messages. For more information, see Chapter 11, Handling high volumes of messages.

Note

MirrorMaker 2 has features not supported by the previous version of MirrorMaker. However, you can configure MirrorMaker 2 to be used in legacy mode.

9.1. Configuring active/active or active/passive modes

You can use MirrorMaker 2 in active/passive or active/active cluster configurations.

active/active cluster configuration
An active/active configuration has two active clusters replicating data bidirectionally. Applications can use either cluster. Each cluster can provide the same data. In this way, you can make the same data available in different geographical locations. As consumer groups are active in both clusters, consumer offsets for replicated topics are not synchronized back to the source cluster.
active/passive cluster configuration
An active/passive configuration has an active cluster replicating data to a passive cluster. The passive cluster remains on standby. You might use the passive cluster for data recovery in the event of system failure.

The expectation is that producers and consumers connect to active clusters only. A MirrorMaker 2 cluster is required at each target destination.

9.1.1. Bidirectional replication (active/active)

The MirrorMaker 2 architecture supports bidirectional replication in an active/active cluster configuration.

Each cluster replicates the data of the other cluster using the concept of source and remote topics. As the same topics are stored in each cluster, remote topics are automatically renamed by MirrorMaker 2 to represent the source cluster. The name of the originating cluster is prepended to the name of the topic.

Figure 9.1. Topic renaming

MirrorMaker 2 bidirectional architecture

By flagging the originating cluster, topics are not replicated back to that cluster.

The concept of replication through remote topics is useful when configuring an architecture that requires data aggregation. Consumers can subscribe to source and remote topics within the same cluster, without the need for a separate aggregation cluster.

9.1.2. Unidirectional replication (active/passive)

The MirrorMaker 2 architecture supports unidirectional replication in an active/passive cluster configuration.

You can use an active/passive cluster configuration to make backups or migrate data to another cluster. In this situation, you might not want automatic renaming of remote topics.

You can override automatic renaming by adding IdentityReplicationPolicy to the source connector configuration. With this configuration applied, topics retain their original names.

9.2. Configuring MirrorMaker 2 connectors

Use MirrorMaker 2 connector configuration for the internal connectors that orchestrate the synchronization of data between Kafka clusters.

MirrorMaker 2 consists of the following connectors:

MirrorSourceConnector
The source connector replicates topics from a source cluster to a target cluster. It also replicates ACLs and is necessary for the MirrorCheckpointConnector to run.
MirrorCheckpointConnector
The checkpoint connector periodically tracks offsets. If enabled, it also synchronizes consumer group offsets between the source and target cluster.
MirrorHeartbeatConnector
The heartbeat connector periodically checks connectivity between the source and target cluster.

The following table describes connector properties and the connectors you configure to use them.

Table 9.1. MirrorMaker 2 connector configuration properties

PropertysourceConnectorcheckpointConnectorheartbeatConnector
admin.timeout.ms
Timeout for admin tasks, such as detecting new topics. Default is 60000 (1 minute).

replication.policy.class
Policy to define the remote topic naming convention. Default is org.apache.kafka.connect.mirror.DefaultReplicationPolicy.

replication.policy.separator
The separator used for topic naming in the target cluster. By default, the separator is set to a dot (.). Separator configuration is only applicable to the DefaultReplicationPolicy replication policy class, which defines remote topic names. The IdentityReplicationPolicy class does not use the property as topics retain their original names.

consumer.poll.timeout.ms
Timeout when polling the source cluster. Default is 1000 (1 second).

 
offset-syncs.topic.location
The location of the offset-syncs topic, which can be the source (default) or target cluster.

 
topic.filter.class
Topic filter to select the topics to replicate. Default is org.apache.kafka.connect.mirror.DefaultTopicFilter.

 
config.property.filter.class
Topic filter to select the topic configuration properties to replicate. Default is org.apache.kafka.connect.mirror.DefaultConfigPropertyFilter.

  
config.properties.exclude
Topic configuration properties that should not be replicated. Supports comma-separated property names and regular expressions.

  
offset.lag.max
Maximum allowable (out-of-sync) offset lag before a remote partition is synchronized. Default is 100.

  
offset-syncs.topic.replication.factor
Replication factor for the internal offset-syncs topic. Default is 3.

  
refresh.topics.enabled
Enables check for new topics and partitions. Default is true.

  
refresh.topics.interval.seconds
Frequency of topic refresh. Default is 600 (10 minutes). By default, a check for new topics in the source cluster is made every 10 minutes. You can change the frequency by adding refresh.topics.interval.seconds to the source connector configuration.

  
replication.factor
The replication factor for new topics. Default is 2.

  
sync.topic.acls.enabled
Enables synchronization of ACLs from the source cluster. Default is true. For more information, see Section 9.5, “ACL rules synchronization”.

  
sync.topic.acls.interval.seconds
Frequency of ACL synchronization. Default is 600 (10 minutes).

  
sync.topic.configs.enabled
Enables synchronization of topic configuration from the source cluster. Default is true.

  
sync.topic.configs.interval.seconds
Frequency of topic configuration synchronization. Default 600 (10 minutes).

  
checkpoints.topic.replication.factor
Replication factor for the internal checkpoints topic. Default is 3.
 

 
emit.checkpoints.enabled
Enables synchronization of consumer offsets to the target cluster. Default is true.
 

 
emit.checkpoints.interval.seconds
Frequency of consumer offset synchronization. Default is 60 (1 minute).
 

 
group.filter.class
Group filter to select the consumer groups to replicate. Default is org.apache.kafka.connect.mirror.DefaultGroupFilter.
 

 
refresh.groups.enabled
Enables check for new consumer groups. Default is true.
 

 
refresh.groups.interval.seconds
Frequency of consumer group refresh. Default is 600 (10 minutes).
 

 
sync.group.offsets.enabled
Enables synchronization of consumer group offsets to the target cluster __consumer_offsets topic. Default is false.
 

 
sync.group.offsets.interval.seconds
Frequency of consumer group offset synchronization. Default is 60 (1 minute).
 

 
emit.heartbeats.enabled
Enables connectivity checks on the target cluster. Default is true.
  

emit.heartbeats.interval.seconds
Frequency of connectivity checks. Default is 1 (1 second).
  

heartbeats.topic.replication.factor
Replication factor for the internal heartbeats topic. Default is 3.
  

9.2.1. Changing the location of the consumer group offsets topic

MirrorMaker 2 tracks offsets for consumer groups using internal topics.

offset-syncs topic
The offset-syncs topic maps the source and target offsets for replicated topic partitions from record metadata.
checkpoints topic
The checkpoints topic maps the last committed offset in the source and target cluster for replicated topic partitions in each consumer group.

As they are used internally by MirrorMaker 2, you do not interact directly with these topics.

MirrorCheckpointConnector emits checkpoints for offset tracking. Offsets for the checkpoints topic are tracked at predetermined intervals through configuration. Both topics enable replication to be fully restored from the correct offset position on failover.

The location of the offset-syncs topic is the source cluster by default. You can use the offset-syncs.topic.location connector configuration to change this to the target cluster. You need read/write access to the cluster that contains the topic. Using the target cluster as the location of the offset-syncs topic allows you to use MirrorMaker 2 even if you have only read access to the source cluster.

9.2.2. Synchronizing consumer group offsets

The __consumer_offsets topic stores information on committed offsets for each consumer group. Offset synchronization periodically transfers the consumer offsets for the consumer groups of a source cluster into the consumer offsets topic of a target cluster.

Offset synchronization is particularly useful in an active/passive configuration. If the active cluster goes down, consumer applications can switch to the passive (standby) cluster and pick up from the last transferred offset position.

To use topic offset synchronization, enable the synchronization by adding sync.group.offsets.enabled to the checkpoint connector configuration, and setting the property to true. Synchronization is disabled by default.

When using the IdentityReplicationPolicy in the source connector, it also has to be configured in the checkpoint connector configuration. This ensures that the mirrored consumer offsets will be applied for the correct topics.

Consumer offsets are only synchronized for consumer groups that are not active in the target cluster. If the consumer groups are in the target cluster, the synchronization cannot be performed and an UNKNOWN_MEMBER_ID error is returned.

If enabled, the synchronization of offsets from the source cluster is made periodically. You can change the frequency by adding sync.group.offsets.interval.seconds and emit.checkpoints.interval.seconds to the checkpoint connector configuration. The properties specify the frequency in seconds that the consumer group offsets are synchronized, and the frequency of checkpoints emitted for offset tracking. The default for both properties is 60 seconds. You can also change the frequency of checks for new consumer groups using the refresh.groups.interval.seconds property, which is performed every 10 minutes by default.

Because the synchronization is time-based, any switchover by consumers to a passive cluster will likely result in some duplication of messages.

Note

If you have an application written in Java, you can use the RemoteClusterUtils.java utility to synchronize offsets through the application. The utility fetches remote offsets for a consumer group from the checkpoints topic.

9.2.3. Deciding when to use the heartbeat connector

The heartbeat connector emits heartbeats to check connectivity between source and target Kafka clusters. An internal heartbeat topic is replicated from the source cluster, which means that the heartbeat connector must be connected to the source cluster. The heartbeat topic is located on the target cluster, which allows it to do the following:

  • Identify all source clusters it is mirroring data from
  • Verify the liveness and latency of the mirroring process

This helps to make sure that the process is not stuck or has stopped for any reason. While the heartbeat connector can be a valuable tool for monitoring the mirroring processes between Kafka clusters, it’s not always necessary to use it. For example, if your deployment has low network latency or a small number of topics, you might prefer to monitor the mirroring process using log messages or other monitoring tools. If you decide not to use the heartbeat connector, simply omit it from your MirrorMaker 2 configuration.

9.2.4. Aligning the configuration of MirrorMaker 2 connectors

To ensure that MirrorMaker 2 connectors work properly, make sure to align certain configuration settings across connectors. Specifically, ensure that the following properties have the same value across all applicable connectors:

  • replication.policy.class
  • replication.policy.separator
  • offset-syncs.topic.location
  • topic.filter.class

For example, the value for replication.policy.class must be the same for the source, checkpoint, and heartbeat connectors. Mismatched or missing settings cause issues with data replication or offset syncing, so it’s essential to keep all relevant connectors configured with the same settings.

9.3. Connector producer and consumer configuration

MirrorMaker 2 connectors use internal producers and consumers. If needed, you can configure these producers and consumers to override the default settings.

Important

Producer and consumer configuration options depend on the MirrorMaker 2 implementation, and may be subject to change.

Producer and consumer configuration applies to all connectors. You specify the configuration in the config/connect-mirror-maker.properties file.

Use the properties file to override any default configuration for the producers and consumers in the following format:

  • <source_cluster_name>.consumer.<property>
  • <source_cluster_name>.producer.<property>
  • <target_cluster_name>.consumer.<property>
  • <target_cluster_name>.producer.<property>

The following example shows how you configure the producers and consumers. Though the properties are set for all connectors, some configuration properties are only relevant to certain connectors.

Example configuration for connector producers and consumers

clusters=cluster-1,cluster-2

# ...
cluster-1.consumer.fetch.max.bytes=52428800
cluster-2.producer.batch.size=327680
cluster-2.producer.linger.ms=100
cluster-2.producer.request.timeout.ms=30000

9.4. Specifying a maximum number of tasks

Connectors create the tasks that are responsible for moving data in and out of Kafka. Each connector comprises one or more tasks that are distributed across a group of worker pods that run the tasks. Increasing the number of tasks can help with performance issues when replicating a large number of partitions or synchronizing the offsets of a large number of consumer groups.

Tasks run in parallel. Workers are assigned one or more tasks. A single task is handled by one worker pod, so you don’t need more worker pods than tasks. If there are more tasks than workers, workers handle multiple tasks.

You can specify the maximum number of connector tasks in your MirrorMaker configuration using the tasks.max property. Without specifying a maximum number of tasks, the default setting is a single task.

The heartbeat connector always uses a single task.

The number of tasks that are started for the source and checkpoint connectors is the lower value between the maximum number of possible tasks and the value for tasks.max. For the source connector, the maximum number of tasks possible is one for each partition being replicated from the source cluster. For the checkpoint connector, the maximum number of tasks possible is one for each consumer group being replicated from the source cluster. When setting a maximum number of tasks, consider the number of partitions and the hardware resources that support the process.

If the infrastructure supports the processing overhead, increasing the number of tasks can improve throughput and latency. For example, adding more tasks reduces the time taken to poll the source cluster when there is a high number of partitions or consumer groups.

tasks.max configuration for MirrorMaker connectors

clusters=cluster-1,cluster-2
# ...
tasks.max = 10

By default, MirrorMaker 2 checks for new consumer groups every 10 minutes. You can adjust the refresh.groups.interval.seconds configuration to change the frequency. Take care when adjusting lower. More frequent checks can have a negative impact on performance.

9.5. ACL rules synchronization

If AclAuthorizer is being used, ACL rules that manage access to brokers also apply to remote topics. Users that can read a source topic can read its remote equivalent.

Note

OAuth 2.0 authorization does not support access to remote topics in this way.

9.6. Running MirrorMaker 2 in dedicated mode

Use MirrorMaker 2 to synchronize data between Kafka clusters through configuration. This procedure shows how to configure and run a dedicated single-node MirrorMaker 2 cluster. Dedicated clusters use Kafka Connect worker nodes to mirror data between Kafka clusters. At present, MirrorMaker 2 in dedicated mode only works with a single worker node.

Note

It is also possible to run MirrorMaker 2 in distributed mode. In distributed mode, MirrorMaker 2 runs as connectors in a Kafka Connect cluster. Kafka provides MirrorMaker source connectors for data replication. If you wish to use the connectors instead of running a dedicated MirrorMaker cluster, the connectors must be configured in the Kafka Connect cluster. For more information, refer to the Apache Kafka documentation.

The previous version of MirrorMaker continues to be supported, by running MirrorMaker 2 in legacy mode.

The configuration must specify:

  • Each Kafka cluster
  • Connection information for each cluster, including TLS authentication
  • The replication flow and direction

    • Cluster to cluster
    • Topic to topic
  • Replication rules
  • Committed offset tracking intervals

This procedure describes how to implement MirrorMaker 2 by creating the configuration in a properties file, then passing the properties when using the MirrorMaker script file to set up the connections.

You can specify the topics and consumer groups you wish to replicate from a source cluster. You specify the names of the source and target clusters, then specify the topics and consumer groups to replicate.

In the following example, topics and consumer groups are specified for replication from cluster 1 to 2.

Example configuration to replicate specific topics and consumer groups

clusters=cluster-1,cluster-2
cluster-1->cluster-2.topics = topic-1, topic-2
cluster-1->cluster-2.groups = group-1, group-2

You can provide a list of names or use a regular expression. By default, all topics and consumer groups are replicated if you do not set these properties. You can also replicate all topics and consumer groups by using .* as a regular expression. However, try to specify only the topics and consumer groups you need to avoid causing any unnecessary extra load on the cluster.

Before you begin

A sample configuration properties file is provided in ./config/connect-mirror-maker.properties.

Prerequisites

  • You need AMQ Streams installed on the hosts of each Kafka cluster node you are replicating.

Procedure

  1. Open the sample properties file in a text editor, or create a new one, and edit the file to include connection information and the replication flows for each Kafka cluster.

    The following example shows a configuration to connect two clusters, cluster-1 and cluster-2, bidirectionally. Cluster names are configurable through the clusters property.

    Example MirrorMaker 2 configuration

    clusters=cluster-1,cluster-2 1
    
    cluster-1.bootstrap.servers=<cluster_name>-kafka-bootstrap-<project_name_one>:443 2
    cluster-1.security.protocol=SSL 3
    cluster-1.ssl.truststore.password=<truststore_name>
    cluster-1.ssl.truststore.location=<path_to_truststore>/truststore.cluster-1.jks_
    cluster-1.ssl.keystore.password=<keystore_name>
    cluster-1.ssl.keystore.location=<path_to_keystore>/user.cluster-1.p12
    
    cluster-2.bootstrap.servers=<cluster_name>-kafka-bootstrap-<project_name_two>:443 4
    cluster-2.security.protocol=SSL 5
    cluster-2.ssl.truststore.password=<truststore_name>
    cluster-2.ssl.truststore.location=<path_to_truststore>/truststore.cluster-2.jks_
    cluster-2.ssl.keystore.password=<keystore_name>
    cluster-2.ssl.keystore.location=<path_to_keystore>/user.cluster-2.p12
    
    cluster-1->cluster-2.enabled=true 6
    cluster-2->cluster-1.enabled=true 7
    cluster-1->cluster-2.topics=.* 8
    cluster-2->cluster-1.topics=topic-1, topic-2 9
    cluster-1->cluster-2.groups=.* 10
    cluster-2->cluster-1.groups=group-1, group-2 11
    
    replication.policy.separator=- 12
    sync.topic.acls.enabled=false 13
    refresh.topics.interval.seconds=60 14
    refresh.groups.interval.seconds=60 15

    1
    Each Kafka cluster is identified with its alias.
    2
    Connection information for cluster-1, using the bootstrap address and port 443. Both clusters use port 443 to connect to Kafka using OpenShift Routes.
    3
    The ssl. properties define TLS configuration for cluster-1.
    4
    Connection information for cluster-2.
    5
    The ssl. properties define the TLS configuration for cluster-2.
    6
    Replication flow enabled from cluster-1 to cluster-2.
    7
    Replication flow enabled from cluster-2 to cluster-1.
    8
    Replication of all topics from cluster-1 to cluster-2. The source connector replicates the specified topics. The checkpoint connector tracks offsets for the specified topics.
    9
    Replication of specific topics from cluster-2 to cluster-1.
    10
    Replication of all consumer groups from cluster-1 to cluster-2. The checkpoint connector replicates the specified consumer groups.
    11
    Replication of specific consumer groups from cluster-2 to cluster-1.
    12
    Defines the separator used for the renaming of remote topics.
    13
    When enabled, ACLs are applied to synchronized topics. The default is false.
    14
    The period between checks for new topics to synchronize.
    15
    The period between checks for new consumer groups to synchronize.
  2. OPTION: If required, add a policy that overrides the automatic renaming of remote topics. Instead of prepending the name with the name of the source cluster, the topic retains its original name.

    This optional setting is used for active/passive backups and data migration.

    replication.policy.class=org.apache.kafka.connect.mirror.IdentityReplicationPolicy
  3. OPTION: If you want to synchronize consumer group offsets, add configuration to enable and manage the synchronization:

    refresh.groups.interval.seconds=60
    sync.group.offsets.enabled=true 1
    sync.group.offsets.interval.seconds=60 2
    emit.checkpoints.interval.seconds=60 3
    1
    Optional setting to synchronize consumer group offsets, which is useful for recovery in an active/passive configuration. Synchronization is not enabled by default.
    2
    If the synchronization of consumer group offsets is enabled, you can adjust the frequency of the synchronization.
    3
    Adjusts the frequency of checks for offset tracking. If you change the frequency of offset synchronization, you might also need to adjust the frequency of these checks.
  4. Start ZooKeeper and Kafka in the target clusters:

    su - kafka
    /opt/kafka/bin/zookeeper-server-start.sh -daemon \
    /opt/kafka/config/zookeeper.properties
    /opt/kafka/bin/kafka-server-start.sh -daemon \
    /opt/kafka/config/server.properties
  5. Start MirrorMaker with the cluster connection configuration and replication policies you defined in your properties file:

    /opt/kafka/bin/connect-mirror-maker.sh \
    /opt/kafka/config/connect-mirror-maker.properties

    MirrorMaker sets up connections between the clusters.

  6. For each target cluster, verify that the topics are being replicated:

    /opt/kafka/bin/kafka-topics.sh --bootstrap-server <broker_address> --list

9.7. Using MirrorMaker 2 in legacy mode

This procedure describes how to configure MirrorMaker 2 to use it in legacy mode. Legacy mode supports the previous version of MirrorMaker.

The MirrorMaker script /opt/kafka/bin/kafka-mirror-maker.sh can run MirrorMaker 2 in legacy mode.

Important

Kafka MirrorMaker 1 (referred to as just MirrorMaker in the documentation) has been deprecated in Apache Kafka 3.0.0 and will be removed in Apache Kafka 4.0.0. As a result, Kafka MirrorMaker 1 has been deprecated in AMQ Streams as well. Kafka MirrorMaker 1 will be removed from AMQ Streams when we adopt Apache Kafka 4.0.0. As a replacement, use MirrorMaker 2 with the IdentityReplicationPolicy.

Prerequisites

You need the properties files you currently use with the legacy version of MirrorMaker.

  • /opt/kafka/config/consumer.properties
  • /opt/kafka/config/producer.properties

Procedure

  1. Edit the MirrorMaker consumer.properties and producer.properties files to turn off MirrorMaker 2 features.

    For example:

    replication.policy.class=org.apache.kafka.mirror.LegacyReplicationPolicy 1
    
    refresh.topics.enabled=false 2
    refresh.groups.enabled=false
    emit.checkpoints.enabled=false
    emit.heartbeats.enabled=false
    sync.topic.configs.enabled=false
    sync.topic.acls.enabled=false
    1
    Emulate the previous version of MirrorMaker.
    2
    MirrorMaker 2 features disabled, including the internal checkpoint and heartbeat topics
  2. Save the changes and restart MirrorMaker with the properties files you used with the previous version of MirrorMaker:

    su - kafka /opt/kafka/bin/kafka-mirror-maker.sh \
    --consumer.config /opt/kafka/config/consumer.properties \
    --producer.config /opt/kafka/config/producer.properties \
    --num.streams=2

    The consumer properties provide the configuration for the source cluster and the producer properties provide the target cluster configuration.

    MirrorMaker sets up connections between the clusters.

  3. Start ZooKeeper and Kafka in the target cluster:

    su - kafka
    /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties
    su - kafka
    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  4. For the target cluster, verify that the topics are being replicated:

    /opt/kafka/bin/kafka-topics.sh --bootstrap-server <broker_address> --list

Chapter 10. Configuring logging for Kafka components

Configure the logging levels of Kafka components directly in the configuration properties. You can also change the broker levels dynamically for Kafka brokers, Kafka Connect, and MirrorMaker 2.

Increasing the log level detail, such as from INFO to DEBUG, can aid in troubleshooting a Kafka cluster. However, more verbose logs may also negatively impact performance and make it more difficult to diagnose issues.

10.1. Configuring Kafka logging properties

Kafka components use the Log4j framework for error logging. By default, logging configuration is read from the classpath or config directory using the following properties files:

  • log4j.properties for Kafka and ZooKeeper
  • connect-log4j.properties for Kafka Connect and MirrorMaker 2

If they are not set explicitly, loggers inherit the log4j.rootLogger logging level configuration in each file. You can change the logging level in these files. You can also add and set logging levels for other loggers.

You can change the location and name of logging properties file using the KAFKA_LOG4J_OPTS environment variable, which is used by the start script for the component.

Passing the name and location of the logging properties file used by Kafka brokers

su - kafka
export KAFKA_LOG4J_OPTS="-Dlog4j.configuration=file:/my/path/to/log4j.properties"; \
/opt/kafka/bin/kafka-server-start.sh \
/opt/kafka/config/server.properties

Passing the name and location of the logging properties file used by ZooKeeper

su - kafka
export KAFKA_LOG4J_OPTS="-Dlog4j.configuration=file:/my/path/to/log4j.properties"; \
/opt/kafka/bin/zookeeper-server-start.sh -daemon \
/opt/kafka/config/zookeeper.properties

Passing the name and location of the logging properties file used by Kafka Connect

su - kafka
export KAFKA_LOG4J_OPTS="-Dlog4j.configuration=file:/my/path/to/connect-log4j.properties"; \
/opt/kafka/bin/connect-distributed.sh \
/opt/kafka/config/connect-distributed.properties

Passing the name and location of the logging properties file used by MirrorMaker 2

su - kafka
export KAFKA_LOG4J_OPTS="-Dlog4j.configuration=file:/my/path/to/connect-log4j.properties"; \
/opt/kafka/bin/connect-mirror-maker.sh \
/opt/kafka/config/connect-mirror-maker.properties

10.2. Dynamically change logging levels for Kafka broker loggers

Kafka broker logging is provided by broker loggers in each broker. Dynamically change the logging level for broker loggers at runtime without having to restart the broker.

You can also reset broker loggers dynamically to their default logging levels.

Procedure

  1. Switch to the kafka user:

    su - kafka
  2. List all the broker loggers for a broker by using the kafka-configs.sh tool:

    /opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --describe --entity-type broker-loggers --entity-name BROKER-ID

    For example, for broker 0:

    /opt/kafka/bin/kafka-configs.sh --bootstrap-server localhost:9092 --describe --entity-type broker-loggers --entity-name 0

    This returns the logging level for each logger: TRACE, DEBUG, INFO, WARN, ERROR, or FATAL.

    For example:

    #...
    kafka.controller.ControllerChannelManager=INFO sensitive=false synonyms={}
    kafka.log.TimeIndex=INFO sensitive=false synonyms={}
  3. Change the logging level for one or more broker loggers. Use the --alter and --add-config options and specify each logger and its level as a comma-separated list in double quotes.

    /opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --alter --add-config "LOGGER-ONE=NEW-LEVEL,LOGGER-TWO=NEW-LEVEL" --entity-type broker-loggers --entity-name BROKER-ID

    For example, for broker 0:

    /opt/kafka/bin/kafka-configs.sh --bootstrap-server localhost:9092 --alter --add-config "kafka.controller.ControllerChannelManager=WARN,kafka.log.TimeIndex=WARN" --entity-type broker-loggers --entity-name 0

    If successful this returns:

    Completed updating config for broker: 0.

Resetting a broker logger

You can reset one or more broker loggers to their default logging levels by using the kafka-configs.sh tool. Use the --alter and --delete-config options and specify each broker logger as a comma-separated list in double quotes:

/opt/kafka/bin/kafka-configs.sh --bootstrap-server localhost:9092 --alter --delete-config "LOGGER-ONE,LOGGER-TWO" --entity-type broker-loggers --entity-name BROKER-ID

Additional resources

10.3. Dynamically change logging levels for Kafka Connect and MirrorMaker 2

Dynamically change logging levels for Kafka Connect workers or MirrorMaker 2 connectors at runtime without having to restart.

Use the Kafka Connect API to change the log level temporarily for a worker or connector logger. The Kafka Connect API provides an admin/loggers endpoint to get or modify logging levels. When you change the log level using the API, the logger configuration in the connect-log4j.properties configuration file does not change. If required, you can permanently change the logging levels in the configuration file.

Note

You can only change the logging level of MirrorMaker 2 at runtime when in distributed or standalone mode. Dedicated MirrorMaker 2 clusters have no Kafka Connect REST API, so changing the logging level is not possible.

The default listener for the Kafka Connect API is on port 8083, which is used in this procedure. You can change or add more listeners, and also enable TLS authentication, using admin.listeners configuration.

Example listener configuration for the admin endpoint

admin.listeners=https://localhost:8083
admin.listeners.https.ssl.truststore.location=/path/to/truststore.jks
admin.listeners.https.ssl.truststore.password=123456
admin.listeners.https.ssl.keystore.location=/path/to/keystore.jks
admin.listeners.https.ssl.keystore.password=123456

If you do not want the admin endpoint to be available, you can disable it in the configuration by specifying an empty string.

Example listener configuration to disable the admin endpoint

admin.listeners=

Prerequisites

Procedure

  1. Switch to the kafka user:

    su - kafka
  2. Check the current logging level for the loggers configured in the connect-log4j.properties file:

    $ cat /opt/kafka/config/connect-log4j.properties
    
    # ...
    log4j.rootLogger=INFO, stdout, connectAppender
    # ...
    log4j.logger.org.apache.zookeeper=ERROR
    log4j.logger.org.reflections=ERROR

    Use a curl command to check the logging levels from the admin/loggers endpoint of the Kafka Connect API:

    curl -s http://localhost:8083/admin/loggers/ | jq
    
    {
      "org.apache.zookeeper": {
        "level": "ERROR"
      },
      "org.reflections": {
        "level": "ERROR"
      },
      "root": {
        "level": "INFO"
      }
    }

    jq prints the output in JSON format. The list shows standard org and root level loggers, plus any specific loggers with modified logging levels.

    If you configure TLS (Transport Layer Security) authentication for the admin.listeners configuration in Kafka Connect, then the address of the loggers endpoint is the value specified for admin.listeners with the protocol as https, such as https://localhost:8083.

    You can also get the log level of a specific logger:

    curl -s http://localhost:8083/admin/loggers/org.apache.kafka.connect.mirror.MirrorCheckpointConnector | jq
    
    {
      "level": "INFO"
    }
  3. Use a PUT method to change the log level for a logger:

    curl -Ss -X PUT -H 'Content-Type: application/json' -d '{"level": "TRACE"}' http://localhost:8083/admin/loggers/root
    
    {
      # ...
    
      "org.reflections": {
        "level": "TRACE"
      },
      "org.reflections.Reflections": {
        "level": "TRACE"
      },
      "root": {
        "level": "TRACE"
      }
    }

    If you change the root logger, the logging level for loggers that used the root logging level by default are also changed.

Chapter 11. Handling high volumes of messages

If your AMQ Streams deployment needs to handle a high volume of messages, you can use configuration options to optimize for throughput and latency.

Kafka producer and consumer configuration can help control the size and frequency of requests to Kafka brokers. For more information on the configuration options, see the following:

You can also use the same configuration options with the producers and consumers used by the Kafka Connect runtime source connectors (including MirrorMaker 2) and sink connectors.

Source connectors
  • Producers from the Kafka Connect runtime send messages to the Kafka cluster.
  • For MirrorMaker 2, since the source system is Kafka, consumers retrieve messages from a source Kafka cluster.
Sink connectors
  • Consumers from the Kafka Connect runtime retrieve messages from the Kafka cluster.

For consumer configuration (consumer.*), you might increase the amount of data fetched in a single fetch request to reduce latency. You increase the fetch request size using the fetch.max.bytes and max.partition.fetch.bytes properties. You can also set a maximum limit on the number of messages returned from the consumer buffer using the max.poll.records property.

For producer configuration (producer.*), you might increase the size of the message batches sent in a single produce request. You increase the batch size using the batch.size property. A larger batch size reduces the number of outstanding messages ready to be sent and the size of the backlog in the message queue. Messages being sent to the same partition are batched together. A produce request is sent to the target cluster when the batch size is reached. By increasing the batch size, produce requests are delayed and more messages are added to the batch and sent to brokers at the same time. This can improve throughput when you have just a few topic partitions that handle large numbers of messages.

Consider the number and size of the records that the producer handles for a suitable producer batch size.

Use linger.ms to add a wait time in milliseconds to delay produce requests when producer load decreases. The delay means that more records can be added to batches if they are under the maximum batch size.

For Kafka Connect source connectors, the data streaming pipeline to the target Kafka cluster is as follows:

Data streaming pipeline for Kafka Connect source connector

external data source → (Kafka Connect tasks) source message queue → producer buffer → target Kafka topic

For Kafka Connect sink connectors, the data streaming pipeline to the target external data source is as follows:

Data streaming pipeline for Kafka Connect sink connector

source Kafka topic → (Kafka Connect tasks) sink message queue → consumer buffer → external data source

For MirrorMaker 2, the data mirroring pipeline to the target Kafka cluster is as follows:

Data mirroring pipeline for MirrorMaker 2

source Kafka topic → (Kafka Connect tasks) source message queue → producer buffer → target Kafka topic

The producer sends messages in its buffer to topics in the target Kafka cluster. While this is happening, Kafka Connect tasks continue to poll the data source to add messages to the source message queue.

The size of the producer buffer for the source connector is set using the buffer.memory property. Tasks wait for a specified timeout period (offset.flush.timeout.ms) before the buffer is flushed. This should be enough time for the sent messages to be acknowledged by the brokers and offset data committed. The source task does not wait for the producer to empty the message queue before committing offsets, except during shutdown.

If the producer is unable to keep up with the throughput of messages in the source message queue, buffering is blocked until there is space available in the buffer within a time period bounded by max.block.ms. Any unacknowledged messages still in the buffer are sent during this period. New messages are not added to the buffer until these messages are acknowledged and flushed.

You can try the following configuration changes to keep the underlying source message queue of outstanding messages at a manageable size:

  • Increasing the default value in milliseconds of the offset.flush.timeout.ms
  • Ensuring that there are enough CPU and memory resources
  • Increasing the number of tasks that run in parallel by doing the following:

    • Increasing the number of tasks that run in parallel using the tasks.max property
    • Increasing the number of nodes for the workers that run tasks

Consider the number of tasks that can run in parallel according to the available CPU and memory resources and number of worker nodes. You might need to keep adjusting the configuration values until they have the desired effect.

11.1. Configuring Kafka Connect for high-volume messages

Kafka Connect fetches data from the source external data system and hands it to the Kafka Connect runtime producers so that it’s replicated to the target cluster.

The following example shows configuration for a Kafka Connect source connector.

Example source connector configuration for handling high volumes of messages

# ...
producer.batch.size=327680
producer.linger.ms=100
# ...
tasks.max = 2

Consumer configuration is added for the sink connector.

Example sink connector configuration for handling high volumes of messages

# ...
consumer.fetch.max.bytes=52428800
consumer.max.partition.fetch.bytes=1048576
consumer.max.poll.records=500
# ...
tasks.max = 2

11.2. Configuring MirrorMaker 2 for high-volume messages

MirrorMaker 2 fetches data from the source cluster and hands it to the Kafka Connect runtime producers so that it’s replicated to the target cluster.

The following example shows the configuration for MirrorMaker 2. The configuration relates to the consumer that fetches messages from the source and the producer that sends messages to the target Kafka cluster.

Example MirrorMaker 2 configuration for handling high volumes of messages

clusters=cluster-1,cluster-2
# ...
cluster-2.producer.batch.size=327680
cluster-2.producer.linger.ms=100
cluster-1.consumer.fetch.max.bytes=52428800
cluster-1.consumer.max.partition.fetch.bytes=1048576
cluster-1.consumer.max.poll.records=500
# ...
tasks.max = 2

Chapter 12. Setting limits on brokers using the Kafka Static Quota plugin

Important

The Kafka Static Quota plugin is a Technology Preview only. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

Use the Kafka Static Quota plugin to set throughput and storage limits on brokers in your Kafka cluster. You enable the plugin and set limits by adding properties to the Kafka configuration file. You can set a byte-rate threshold and storage quotas to put limits on the clients interacting with your brokers.

You can set byte-rate thresholds for producer and consumer bandwidth. The total limit is distributed across all clients accessing the broker. For example, you can set a byte-rate threshold of 40 MBps for producers. If two producers are running, they are each limited to a throughput of 20 MBps.

Storage quotas throttle Kafka disk storage limits between a soft limit and hard limit. The limits apply to all available disk space. Producers are slowed gradually between the soft and hard limit. The limits prevent disks filling up too quickly and exceeding their capacity. Full disks can lead to issues that are hard to rectify. The hard limit is the maximum storage limit.

Note

For JBOD storage, the limit applies across all disks. If a broker is using two 1 TB disks and the quota is 1.1 TB, one disk might fill and the other disk will be almost empty.

Prerequisites

Procedure

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file.

    The plugin properties are shown in this example configuration.

    Example Kafka Static Quota plugin configuration

    # ...
    client.quota.callback.class=io.strimzi.kafka.quotas.StaticQuotaCallback 1
    client.quota.callback.static.produce=1000000 2
    client.quota.callback.static.fetch=1000000 3
    client.quota.callback.static.storage.soft=400000000000 4
    client.quota.callback.static.storage.hard=500000000000 5
    client.quota.callback.static.storage.check-interval=5 6
    # ...

    1
    Loads the Kafka Static Quota plugin.
    2
    Sets the producer byte-rate threshold. 1 MBps in this example.
    3
    Sets the consumer byte-rate threshold. 1 MBps in this example.
    4
    Sets the lower soft limit for storage. 400 GB in this example.
    5
    Sets the higher hard limit for storage. 500 GB in this example.
    6
    Sets the interval in seconds between checks on storage. 5 seconds in this example. You can set this to 0 to disable the check.
  2. Start the Kafka broker with the default configuration file.

    su - kafka
    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  3. Verify that the Kafka broker is running.

    jcmd | grep Kafka

Chapter 13. Adding and removing Kafka brokers and ZooKeeper nodes

In a Kafka cluster, managing the addition and removal of brokers and ZooKeeper nodes is critical to maintaining a stable and scalable system. When you add to the number of available brokers, you can configure the default replication factor and minimum in-sync replicas for topics across the brokers. You can use dynamic reconfiguration to add and remove ZooKeeper nodes from an ensemble without disruption.

13.1. Scaling clusters by adding or removing brokers

Scaling Kafka clusters by adding brokers can increase the performance and reliability of the cluster. Adding more brokers increases available resources, allowing the cluster to handle larger workloads and process more messages. It can also improve fault tolerance by providing more replicas and backups. Conversely, removing underutilized brokers can reduce resource consumption and improve efficiency. Scaling must be done carefully to avoid disruption or data loss. By redistributing partitions across all brokers in the cluster, the resource utilization of each broker is reduced, which can increase the overall throughput of the cluster.

Note

To increase the throughput of a Kafka topic, you can increase the number of partitions for that topic. This allows the load of the topic to be shared between different brokers in the cluster. However, if every broker is constrained by a specific resource (such as I/O), adding more partitions will not increase the throughput. In this case, you need to add more brokers to the cluster.

Adding brokers when running a multi-node Kafka cluster affects the number of brokers in the cluster that act as replicas. The actual replication factor for topics is determined by settings for the default.replication.factor and min.insync.replicas, and the number of available brokers. For example, a replication factor of 3 means that each partition of a topic is replicated across three brokers, ensuring fault tolerance in the event of a broker failure.

Example replica configuration

default.replication.factor = 3
min.insync.replicas = 2

When you add or remove brokers, Kafka does not automatically reassign partitions. The best way to do this is using Cruise Control. You can use Cruise Control’s add-brokers and remove-brokers modes when scaling a cluster up or down.

  • Use the add-brokers mode after scaling up a Kafka cluster to move partition replicas from existing brokers to the newly added brokers.
  • Use the remove-brokers mode before scaling down a Kafka cluster to move partition replicas off the brokers that are going to be removed.
Note

When scaling down brokers, you cannot specify which specific pod to remove from the cluster. Instead, the broker removal process starts from the highest numbered pod.

13.2. Adding nodes to a ZooKeeper cluster

Use dynamic reconfiguration to add nodes from a ZooKeeper cluster without stopping the entire cluster. Dynamic Reconfiguration allows ZooKeeper to change the membership of a set of nodes that make up the ZooKeeper cluster without interruption.

Prerequisites

  • Dynamic reconfiguration is enabled in the ZooKeeper configuration file (reconfigEnabled=true).
  • ZooKeeper authentication is enabled and you can access the new server using the authentication mechanism.

Procedure

Perform the following steps for each ZooKeeper server you are adding, one at a time:

  1. Add a server to the ZooKeeper cluster as described in Section 4.1, “Running a multi-node ZooKeeper cluster” and then start ZooKeeper.
  2. Note the IP address and configured access ports of the new server.
  3. Start a zookeeper-shell session for the server. Run the following command from a machine that has access to the cluster (this might be one of the ZooKeeper nodes or your local machine, if it has access).

    su - kafka
    /opt/kafka/bin/zookeeper-shell.sh <ip-address>:<zk-port>
  4. In the shell session, with the ZooKeeper node running, enter the following line to add the new server to the quorum as a voting member:

    reconfig -add server.<positive-id> = <address1>:<port1>:<port2>[:role];[<client-port-address>:]<client-port>

    For example:

    reconfig -add server.4=172.17.0.4:2888:3888:participant;172.17.0.4:2181

    Where <positive-id> is the new server ID 4.

    For the two ports, <port1> 2888 is for communication between ZooKeeper servers, and <port2> 3888 is for leader election.

    The new configuration propagates to the other servers in the ZooKeeper cluster; the new server is now a full member of the quorum.

13.3. Removing nodes from a ZooKeeper cluster

Use dynamic reconfiguration to remove nodes from a ZooKeeper cluster without stopping the entire cluster. Dynamic Reconfiguration allows ZooKeeper to change the membership of a set of nodes that make up the ZooKeeper cluster without interruption.

Prerequisites

  • Dynamic reconfiguration is enabled in the ZooKeeper configuration file (reconfigEnabled=true).
  • ZooKeeper authentication is enabled and you can access the new server using the authentication mechanism.

Procedure

Perform the following steps, one at a time, for each ZooKeeper server you remove:

  1. Log in to the zookeeper-shell on one of the servers that will be retained after the scale down (for example, server 1).

    Note

    Access the server using the authentication mechanism configured for the ZooKeeper cluster.

  2. Remove a server, for example server 5.

    reconfig -remove 5
  3. Deactivate the server that you removed.

Chapter 14. Using Cruise Control for cluster rebalancing

Cruise Control is an open source system for automating Kafka operations, such as monitoring cluster workload, rebalancing a cluster based on predefined constraints, and detecting and fixing anomalies. It consists of four main components—​the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—​and a REST API for client interactions.

You can use Cruise Control to rebalance a Kafka cluster. Cruise Control for AMQ Streams on Red Hat Enterprise Linux is provided as a separate zipped distribution.

AMQ Streams utilizes the REST API to support the following Cruise Control features:

  • Generating optimization proposals from optimization goals.
  • Rebalancing a Kafka cluster based on an optimization proposal.

    Optimization goals

    An optimization goal describes a specific objective to achieve from a rebalance. For example, a goal might be to distribute topic replicas across brokers more evenly. You can change what goals to include through configuration. A goal is defined as a hard goal or soft goal. You can add hard goals through Cruise Control deployment configuration. You also have main, default, and user-provided goals that fit into each of these categories.

    • Hard goals are preset and must be satisfied for an optimization proposal to be successful.
    • Soft goals do not need to be satisfied for an optimization proposal to be successful. They can be set aside if it means that all hard goals are met.
    • Main goals are inherited from Cruise Control. Some are preset as hard goals. Main goals are used in optimization proposals by default.
    • Default goals are the same as the main goals by default. You can specify your own set of default goals.
    • User-provided goals are a subset of default goals that are configured for generating a specific optimization proposal.
    Optimization proposals

    Optimization proposals comprise the goals you want to achieve from a rebalance. You generate an optimization proposal to create a summary of proposed changes and the results that are possible with the rebalance. The goals are assessed in a specific order of priority. You can then choose to approve or reject the proposal. You can reject the proposal to run it again with an adjusted set of goals.

    You can generate and approve an optimization proposal by making a request to one of the following API endpoints.

    • /rebalance endpoint to run a full rebalance.
    • /add_broker endpoint to rebalance after adding brokers when scaling up a Kafka cluster.
    • /remove_broker endpoint to rebalance before removing brokers when scaling down a Kafka cluster.

You configure optimization goals through a configuration properties file. AMQ Streams provides example properties files for Cruise Control.

Other Cruise Control features are not currently supported, including self healing, notifications, write-your-own goals, and changing the topic replication factor.

14.1. Cruise Control components and features

Cruise Control consists of four main components—​the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—​and a REST API for client interactions. AMQ Streams utilizes the REST API to support the following Cruise Control features:

  • Generating optimization proposals from optimization goals.
  • Rebalancing a Kafka cluster based on an optimization proposal.
Optimization goals

An optimization goal describes a specific objective to achieve from a rebalance. For example, a goal might be to distribute topic replicas across brokers more evenly. You can change what goals to include through configuration. A goal is defined as a hard goal or soft goal. You can add hard goals through Cruise Control deployment configuration. You also have main, default, and user-provided goals that fit into each of these categories.

  • Hard goals are preset and must be satisfied for an optimization proposal to be successful.
  • Soft goals do not need to be satisfied for an optimization proposal to be successful. They can be set aside if it means that all hard goals are met.
  • Main goals are inherited from Cruise Control. Some are preset as hard goals. Main goals are used in optimization proposals by default.
  • Default goals are the same as the main goals by default. You can specify your own set of default goals.
  • User-provided goals are a subset of default goals that are configured for generating a specific optimization proposal.
Optimization proposals

Optimization proposals comprise the goals you want to achieve from a rebalance. You generate an optimization proposal to create a summary of proposed changes and the results that are possible with the rebalance. The goals are assessed in a specific order of priority. You can then choose to approve or reject the proposal. You can reject the proposal to run it again with an adjusted set of goals.

You can generate an optimization proposal in one of three modes.

  • full is the default mode and runs a full rebalance.
  • add-brokers is the mode you use after adding brokers when scaling up a Kafka cluster.
  • remove-brokers is the mode you use before removing brokers when scaling down a Kafka cluster.

Other Cruise Control features are not currently supported, including self healing, notifications, write-your-own goals, and changing the topic replication factor.

Additional resources

14.2. Downloading Cruise Control

A ZIP file distribution of Cruise Control is available for download from the Red Hat website. You can download the latest version of Red Hat AMQ Streams from the AMQ Streams software downloads page.

Procedure

  1. Download the latest version of the Red Hat AMQ Streams Cruise Control archive from the Red Hat Customer Portal.
  2. Create the /opt/cruise-control directory:

    sudo mkdir /opt/cruise-control
  3. Extract the contents of the Cruise Control ZIP file to the new directory:

    unzip amq-streams-<version>-cruise-control-bin.zip -d /opt/cruise-control
  4. Change the ownership of the /opt/cruise-control directory to the kafka user:

    sudo chown -R kafka:kafka /opt/cruise-control

14.3. Deploying the Cruise Control Metrics Reporter

Before starting Cruise Control, you must configure the Kafka brokers to use the provided Cruise Control Metrics Reporter. The file for the Metrics Reporter is supplied with the AMQ Streams installation artifacts.

When loaded at runtime, the Metrics Reporter sends metrics to the __CruiseControlMetrics topic, one of three auto-created topics. Cruise Control uses these metrics to create and update the workload model and to calculate optimization proposals.

Prerequisites

  • You are logged in to Red Hat Enterprise Linux as the kafka user.
  • Kafka and ZooKeeper are running.

Procedure

For each broker in the Kafka cluster and one at a time:

  1. Stop the Kafka broker:

    /opt/kafka/bin/kafka-server-stop.sh
  2. In the Kafka configuration file (/opt/kafka/config/server.properties) configure the Cruise Control Metrics Reporter:

    1. Add the CruiseControlMetricsReporter class to the metric.reporters configuration option. Do not remove any existing Metrics Reporters.

      metric.reporters=com.linkedin.kafka.cruisecontrol.metricsreporter.CruiseControlMetricsReporter
    2. Add the following configuration options and values:

      cruise.control.metrics.topic.auto.create=true
      cruise.control.metrics.topic.num.partitions=1
      cruise.control.metrics.topic.replication.factor=1

      These options enable the Cruise Control Metrics Reporter to create the __CruiseControlMetrics topic with a log cleanup policy of DELETE. For more information, see Auto-created topics and Log cleanup policy for Cruise Control Metrics topic.

  3. Configure SSL, if required.

    1. In the Kafka configuration file (/opt/kafka/config/server.properties) configure SSL between the Cruise Control Metrics Reporter and the Kafka broker by setting the relevant client configuration properties.

      The Metrics Reporter accepts all standard producer-specific configuration properties with the cruise.control.metrics.reporter prefix. For example: cruise.control.metrics.reporter.ssl.truststore.password.

    2. In the Cruise Control properties file (/opt/cruise-control/config/cruisecontrol.properties) configure SSL between the Kafka broker and the Cruise Control server by setting the relevant client configuration properties.

      Cruise Control inherits SSL client property options from Kafka and uses those properties for all Cruise Control server clients.

  4. Restart the Kafka broker:

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

    For information on restarting brokers in a multi-node cluster, see Section 4.3, “Performing a graceful rolling restart of Kafka brokers”.

  5. Repeat steps 1-5 for the remaining brokers.

14.4. Configuring and starting Cruise Control

Configure the properties used by Cruise Control and then start the Cruise Control server using the kafka-cruise-control-start.sh script. The server is hosted on a single machine for the whole Kafka cluster.

Three topics are auto-created when Cruise Control starts. For more information, see Auto-created topics.

Prerequisites

Procedure

  1. Edit the Cruise Control properties file (/opt/cruise-control/config/cruisecontrol.properties).
  2. Configure the properties shown in the following example configuration:

    # The Kafka cluster to control.
    bootstrap.servers=localhost:9092 1
    
    # The replication factor of Kafka metric sample store topic
    sample.store.topic.replication.factor=2 2
    
    # The configuration for the BrokerCapacityConfigFileResolver (supports JBOD, non-JBOD, and heterogeneous CPU core capacities)
    #capacity.config.file=config/capacity.json
    #capacity.config.file=config/capacityCores.json
    capacity.config.file=config/capacityJBOD.json 3
    
    # The list of goals to optimize the Kafka cluster for with pre-computed proposals
    default.goals={List of default optimization goals} 4
    
    # The list of supported goals
    goals={list of main optimization goals} 5
    
    # The list of supported hard goals
    hard.goals={List of hard goals} 6
    
    # How often should the cached proposal be expired and recalculated if necessary
    proposal.expiration.ms=60000 7
    
    # The zookeeper connect of the Kafka cluster
    zookeeper.connect=localhost:2181 8
    1
    Host and port numbers of the Kafka broker (always port 9092).
    2
    Replication factor of the Kafka metric sample store topic. If you are evaluating Cruise Control in a single-node Kafka and ZooKeeper cluster, set this property to 1. For production use, set this property to 2 or more.
    3
    The configuration file that sets the maximum capacity limits for broker resources. Use the file that applies to your Kafka deployment configuration. For more information, see Capacity configuration.
    4
    Comma-separated list of default optimization goals, using fully-qualified domain names (FQDNs). A number of main optimization goals (see 5) are already set as default optimization goals; you can add or remove goals if desired. For more information, see Section 14.5, “Optimization goals overview”.
    5
    Comma-separated list of main optimization goals, using FQDNs. To completely exclude goals from being used to generate optimization proposals, remove them from the list. For more information, see Section 14.5, “Optimization goals overview”.
    6
    Comma-separated list of hard goals, using FQDNs. Seven of the main optimization goals are already set as hard goals; you can add or remove goals if desired. For more information, see Section 14.5, “Optimization goals overview”.
    7
    The interval, in milliseconds, for refreshing the cached optimization proposal that is generated from the default optimization goals. For more information, see Section 14.6, “Optimization proposals overview”.
    8
    Host and port numbers of the ZooKeeper connection (always port 2181).
  3. Start the Cruise Control server. The server starts on port 9092 by default; optionally, specify a different port.

    cd /opt/cruise-control/
    ./kafka-cruise-control-start.sh config/cruisecontrol.properties <port_number>
  4. To verify that Cruise Control is running, send a GET request to the /state endpoint of the Cruise Control server:

    curl 'http://HOST:PORT/kafkacruisecontrol/state'

Auto-created topics

The following table shows the three topics that are automatically created when Cruise Control starts. These topics are required for Cruise Control to work properly and must not be deleted or changed.

Table 14.1. Auto-created topics

Auto-created topicCreated byFunction

__CruiseControlMetrics

Cruise Control Metrics Reporter

Stores the raw metrics from the Metrics Reporter in each Kafka broker.

__KafkaCruiseControlPartitionMetricSamples

Cruise Control

Stores the derived metrics for each partition. These are created by the Metric Sample Aggregator.

__KafkaCruiseControlModelTrainingSamples

Cruise Control

Stores the metrics samples used to create the Cluster Workload Model.

To ensure that log compaction is disabled in the auto-created topics, make sure that you configure the Cruise Control Metrics Reporter as described in Section 14.3, “Deploying the Cruise Control Metrics Reporter”. Log compaction can remove records that are needed by Cruise Control and prevent it from working properly.

14.5. Optimization goals overview

Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals.

14.5.1. Goals order of priority

AMQ Streams on Red Hat Enterprise Linux supports all the optimization goals developed in the Cruise Control project. The supported goals, in the default descending order of priority, are as follows:

  1. Rack-awareness
  2. Minimum number of leader replicas per broker for a set of topics
  3. Replica capacity
  4. Capacity: Disk capacity, Network inbound capacity, Network outbound capacity
  5. CPU capacity
  6. Replica distribution
  7. Potential network output
  8. Resource distribution: Disk utilization distribution, Network inbound utilization distribution, Network outbound utilization distribution
  9. Leader bytes-in rate distribution
  10. Topic replica distribution
  11. CPU usage distribution
  12. Leader replica distribution
  13. Preferred leader election
  14. Kafka Assigner disk usage distribution
  15. Intra-broker disk capacity
  16. Intra-broker disk usage

For more information on each optimization goal, see Goals in the Cruise Control Wiki.

14.5.2. Goals configuration in the Cruise Control properties file

You configure optimization goals in the cruisecontrol.properties file in the cruise-control/config/ directory. Cruise Control has configurations for hard optimization goals that must be satisfied, as well as main, default, and user-provided optimization goals.

You can specify the following types of optimization goal in the following configuration:

  • Main goals — cruisecontrol.properties file
  • Hard goals — cruisecontrol.properties file
  • Default goals — cruisecontrol.properties file
  • User-provided goals — runtime parameters

Optionally, user-provided optimization goals are set at runtime as parameters in requests to the /rebalance endpoint.

Optimization goals are subject to any capacity limits on broker resources.

14.5.3. Hard and soft optimization goals

Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals. You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations.

Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by the Analyzer and is not sent to the user.

Note

For example, you might have a soft goal to distribute a topic’s replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met.

In Cruise Control, the following main optimization goals are preset as hard goals:

RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal

To change the hard goals, edit the hard.goals property of the cruisecontrol.properties file and specify the goals using their fully-qualified domain names.

Increasing the number of hard goals reduces the likelihood that Cruise Control will calculate and generate valid optimization proposals.

14.5.4. Main optimization goals

The main optimization goals are available to all users. Goals that are not listed in the main optimization goals are not available for use in Cruise Control operations.

The following main optimization goals are preset in the goals property of the cruisecontrol.properties file in descending priority order:

RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal

To reduce complexity, we recommend that you do not change the preset main optimization goals, unless you need to completely exclude one or more goals from being used to generate optimization proposals. The priority order of the main optimization goals can be modified, if desired, in the configuration for default optimization goals.

To modify the preset main optimization goals, specify a list of goals in the goals property in descending priority order. Use fully-qualified domain names as shown in the cruisecontrol.properties file.

You must specify at least one main goal, or Cruise Control will crash.

Note

If you change the preset main optimization goals, you must ensure that the configured hard.goals are a subset of the main optimization goals that you configured. Otherwise, errors will occur when generating optimization proposals.

14.5.5. Default optimization goals

Cruise Control uses the default optimization goals list to generate the cached optimization proposal. For more information, see Section 14.6, “Optimization proposals overview”.

You can override the default optimization goals at runtime by setting user-provided optimization goals.

The following default optimization goals are preset in the default.goals property of the cruisecontrol.properties file in descending priority order:

RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal

You must specify at least one default goal, or Cruise Control will crash.

To modify the default optimization goals, specify a list of goals in the default.goals property in descending priority order. Default goals must be a subset of the main optimization goals; use fully-qualified domain names.

14.5.6. User-provided optimization goals

User-provided optimization goals narrow down the configured default goals for a particular optimization proposal. You can set them, as required, as parameters in HTTP requests to the /rebalance endpoint. For more information, see Section 14.9, “Generating optimization proposals”.

User-provided optimization goals can generate optimization proposals for different scenarios. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization. So, you send a request to the /rebalance endpoint containing a single goal for leader replica distribution.

User-provided optimization goals must:

To ignore the configured hard goals in an optimization proposal, add the skip_hard_goals_check=true parameter to the request.

Additional resources

14.6. Optimization proposals overview

An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers.

Each optimization proposal is based on the set of optimization goals that was used to generate it, subject to any configured capacity limits on broker resources.

All optimization proposals are estimates of the impact of a proposed rebalance. You can approve or reject a proposal. You cannot approve a cluster rebalance without first generating the optimization proposal.

You can run the optimization proposal using one of the following endpoints:

  • /rebalance
  • /add_broker
  • /remove_broker

14.6.1. Rebalancing endpoints

You specify a rebalancing endpoint when you send a POST request to generate an optimization proposal.

/rebalance
The /rebalance endpoint runs a full rebalance by moving replicas across all the brokers in the cluster.
/add_broker
The add_broker endpoint is used after scaling up a Kafka cluster by adding one or more brokers. Normally, after scaling up a Kafka cluster, new brokers are used to host only the partitions of newly created topics. If no new topics are created, the newly added brokers are not used and the existing brokers remain under the same load. By using the add_broker endpoint immediately after adding brokers to the cluster, the rebalancing operation moves replicas from existing brokers to the newly added brokers. You specify the new brokers as a brokerid list in the POST request.
/remove_broker
The /remove_broker endpoint is used before scaling down a Kafka cluster by removing one or more brokers. If you scale down a Kafka cluster, brokers are shut down even if they host replicas. This can lead to under-replicated partitions and possibly result in some partitions being under their minimum ISR (in-sync replicas). To avoid this potential problem, the /remove_broker endpoint moves replicas off the brokers that are going to be removed. When these brokers are not hosting replicas anymore, you can safely run the scaling down operation. You specify the brokers you’re removing as a brokerid list in the POST request.

In general, use the /rebalance endpoint to rebalance a Kafka cluster by spreading the load across brokers. Use the /add-broker endpoint and /remove_broker endpoint only if you want to scale your cluster up or down and rebalance the replicas accordingly.

The procedure to run a rebalance is actually the same across the three different endpoints. The only difference is with listing brokers that have been added or will be removed to the request.

14.6.2. Approving or rejecting an optimization proposal

An optimization proposal summary shows the proposed scope of changes. The summary is returned in a response to a HTTP request through the Cruise Control API.

When you make a POST request to the /rebalance endpoint, an optimization proposal summary is returned in the response.

Returning an optimization proposal summary

curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/rebalance'

Use the summary to decide whether to approve or reject an optimization proposal.

Approving an optimization proposal
You approve the optimization proposal by making a POST request to the /rebalance endpoint and setting the dryrun parameter to false (default true). Cruise Control applies the proposal to the Kafka cluster and starts a cluster rebalance operation.
Rejecting an optimization proposal
If you choose not to approve an optimization proposal, you can change the optimization goals or update any of the rebalance performance tuning options, and then generate another proposal. You can resend a request without the dryrun parameter to generate a new optimization proposal.

Use the optimization proposal to assess the movements required for a rebalance. For example, a summary describes inter-broker and intra-broker movements. Inter-broker rebalancing moves data between separate brokers. Intra-broker rebalancing moves data between disks on the same broker when you are using a JBOD storage configuration. Such information can be useful even if you don’t go ahead and approve the proposal.

You might reject an optimization proposal, or delay its approval, because of the additional load on a Kafka cluster when rebalancing.

In the following example, the proposal suggests the rebalancing of data between separate brokers. The rebalance involves the movement of 55 partition replicas, totaling 12MB of data, across the brokers. Though the inter-broker movement of partition replicas has a high impact on performance, the total amount of data is not large. If the total data was much larger, you could reject the proposal, or time when to approve the rebalance to limit the impact on the performance of the Kafka cluster.

Rebalance performance tuning options can help reduce the impact of data movement. If you can extend the rebalance period, you can divide the rebalance into smaller batches. Fewer data movements at a single time reduces the load on the cluster.

Example optimization proposal summary

Optimization has 55 inter-broker replica (12 MB) moves, 0 intra-broker
replica (0 MB) moves and 24 leadership moves with a cluster model of 5
recent windows and 100.000% of the partitions covered.
Excluded Topics: [].
Excluded Brokers For Leadership: [].
Excluded Brokers For Replica Move: [].
Counts: 3 brokers 343 replicas 7 topics.
On-demand Balancedness Score Before (78.012) After (82.912).
Provision Status: RIGHT_SIZED.

The proposal will also move 24 partition leaders to different brokers. This requires a change to the ZooKeeper configuration, which has a low impact on performance.

The balancedness scores are measurements of the overall balance of the Kafka Cluster before and after the optimization proposal is approved. A balancedness score is based on optimization goals. If all goals are satisfied, the score is 100. The score is reduced for each goal that will not be met. Compare the balancedness scores to see whether the Kafka cluster is less balanced than it could be following a rebalance.

The provision status indicates whether the current cluster configuration supports the optimization goals. Check the provision status to see if you should add or remove brokers.

Table 14.2. Optimization proposal provision status

StatusDescription

RIGHT_SIZED

The cluster has an appropriate number of brokers to satisfy the optimization goals.

UNDER_PROVISIONED

The cluster is under-provisioned and requires more brokers to satisfy the optimization goals.

OVER_PROVISIONED

The cluster is over-provisioned and requires fewer brokers to satisfy the optimization goals.

UNDECIDED

The status is not relevant or it has not yet been decided.

14.6.3. Optimization proposal summary properties

The following table describes the properties contained in an optimization proposal.

Table 14.3. Properties contained in an optimization proposal summary

PropertyDescription

n inter-broker replica (y MB) moves

n: The number of partition replicas that will be moved between separate brokers.

Performance impact during rebalance operation: Relatively high.

y MB: The sum of the size of each partition replica that will be moved to a separate broker.

Performance impact during rebalance operation: Variable. The larger the number of MBs, the longer the cluster rebalance will take to complete.

n intra-broker replica (y MB) moves

n: The total number of partition replicas that will be transferred between the disks of the cluster’s brokers.

Performance impact during rebalance operation: Relatively high, but less than inter-broker replica moves.

y MB: The sum of the size of each partition replica that will be moved between disks on the same broker.

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see inter-broker replica moves).

n excluded topics

The number of topics excluded from the calculation of partition replica/leader movements in the optimization proposal.

You can exclude topics in one of the following ways:

In the cruisecontrol.properties file, specify a regular expression in the topics.excluded.from.partition.movement property.

In a POST request to the /rebalance endpoint, specify a regular expression in the excluded_topics parameter.

Topics that match the regular expression are listed in the response and will be excluded from the cluster rebalance.

n leadership moves

n: The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration.

Performance impact during rebalance operation: Relatively low.

n recent windows

n: The number of metrics windows upon which the optimization proposal is based.

n% of the partitions covered

n%: The percentage of partitions in the Kafka cluster covered by the optimization proposal.

On-demand Balancedness Score Before (nn.yyy) After (nn.yyy)

Measurements of the overall balance of a Kafka Cluster.

Cruise Control assigns a Balancedness Score to every optimization goal based on several factors, including priority (the goal’s position in the list of default.goals or user-provided goals). The On-demand Balancedness Score is calculated by subtracting the sum of the Balancedness Score of each violated soft goal from 100.

The Before score is based on the current configuration of the Kafka cluster. The After score is based on the generated optimization proposal.

14.6.4. Cached optimization proposal

Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster.

The most recent cached optimization proposal is returned when the following goal configurations are used:

  • The default optimization goals
  • User-provided optimization goals that can be met by the current cached proposal

To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the cruisecontrol.properties file. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server.

14.7. Rebalance performance tuning overview

You can adjust several performance tuning options for cluster rebalances. These options control how partition replica and leadership movements in a rebalance are executed, as well as the bandwidth that is allocated to a rebalance operation.

Partition reassignment commands

Optimization proposals are composed of separate partition reassignment commands. When you initiate a proposal, the Cruise Control server applies these commands to the Kafka cluster.

A partition reassignment command consists of either of the following types of operations:

  • Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms:

    • Inter-broker movement: The partition replica is moved to a log directory on a different broker.
    • Intra-broker movement: The partition replica is moved to a different log directory on the same broker.
  • Leadership movement: Involves switching the leader of the partition’s replicas.

Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch.

To configure partition reassignment commands, see Rebalance tuning options.

Replica movement strategies

Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands. By default, Cruise Control uses the BaseReplicaMovementStrategy, which applies the commands in the order in which they were generated. However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments.

Cruise Control provides three alternative replica movement strategies that can be applied to optimization proposals:

  • PrioritizeSmallReplicaMovementStrategy: Order reassignments in ascending size.
  • PrioritizeLargeReplicaMovementStrategy: Order reassignments in descending size.
  • PostponeUrpReplicaMovementStrategy: Prioritize reassignments for replicas of partitions which have no out-of-sync replicas.

These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the next strategy in the sequence to decide the order, and so on.

To configure replica movement strategies, see Rebalance tuning options.

Rebalance tuning options

Cruise Control provides several configuration options for tuning rebalance parameters. These options are set in the following ways:

  • As properties, in the default Cruise Control configuration, in the cruisecontrol.properties file
  • As parameters in POST requests to the /rebalance endpoint

The relevant configurations for both methods are summarized in the following table.

Table 14.4. Rebalance performance tuning configuration

Cruise Control propertiesKafkaRebalance parametersDefaultDescription

num.concurrent.partition.movements.per.broker

concurrent_partition_movements_per_broker

5

The maximum number of inter-broker partition movements in each partition reassignment batch

num.concurrent.intra.broker.partition.movements

concurrent_intra_broker_partition_movements

2

The maximum number of intra-broker partition movements in each partition reassignment batch

num.concurrent.leader.movements

concurrent_leader_movements

1000

The maximum number of partition leadership changes in each partition reassignment batch

default.replication.throttle

replication_throttle

Null (no limit)

The bandwidth (in bytes per second) to assign to partition reassignment

default.replica.movement.strategies

replica_movement_strategies

BaseReplicaMovementStrategy

The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals. There are three strategies: PrioritizeSmallReplicaMovementStrategy, PrioritizeLargeReplicaMovementStrategy, and PostponeUrpReplicaMovementStrategy. For the server setting, use a comma-separated list with the fully qualified names of the strategy class (add com.linkedin.kafka.cruisecontrol.executor.strategy. to the start of each class name). For the rebalance parameters, use a comma-separated list of the class names of the replica movement strategies.

Changing the default settings affects the length of time that the rebalance takes to complete, as well as the load placed on the Kafka cluster during the rebalance. Using lower values reduces the load but increases the amount of time taken, and vice versa.

Additional resources

14.8. Cruise Control configuration

The config/cruisecontrol.properties file contains the configuration for Cruise Control. The file consists of properties in one of the following types:

  • String
  • Number
  • Boolean

You can specify and configure all the properties listed in the Configurations section of the Cruise Control Wiki.

Capacity configuration

Cruise Control uses capacity limits to determine if certain resource-based optimization goals are being broken. An attempted optimization fails if one or more of these resource-based goals is set as a hard goal and then broken. This prevents the optimization from being used to generate an optimization proposal.

You specify capacity limits for Kafka broker resources in one of the following three .json files in cruise-control/config:

  • capacityJBOD.json: For use in JBOD Kafka deployments (the default file).
  • capacity.json: For use in non-JBOD Kafka deployments where each broker has the same number of CPU cores.
  • capacityCores.json: For use in non-JBOD Kafka deployments where each broker has varying numbers of CPU cores.

Set the file in the capacity.config.file property in cruisecontrol.properties. The selected file will be used for broker capacity resolution. For example:

capacity.config.file=config/capacityJBOD.json

Capacity limits can be set for the following broker resources in the described units:

  • DISK: Disk storage in MB
  • CPU: CPU utilization as a percentage (0-100) or as a number of cores
  • NW_IN: Inbound network throughput in KB per second
  • NW_OUT: Outbound network throughput in KB per second

To apply the same capacity limits to every broker monitored by Cruise Control, set capacity limits for broker ID -1. To set different capacity limits for individual brokers, specify each broker ID and its capacity configuration.

Example capacity limits configuration

{
  "brokerCapacities":[
    {
      "brokerId": "-1",
      "capacity": {
        "DISK": "100000",
        "CPU": "100",
        "NW_IN": "10000",
        "NW_OUT": "10000"
      },
      "doc": "This is the default capacity. Capacity unit used for disk is in MB, cpu is in percentage, network throughput is in KB."
    },
    {
      "brokerId": "0",
      "capacity": {
        "DISK": "500000",
        "CPU": "100",
        "NW_IN": "50000",
        "NW_OUT": "50000"
      },
      "doc": "This overrides the capacity for broker 0."
    }
  ]
}

For more information, see Populating the Capacity Configuration File in the Cruise Control Wiki.

Log cleanup policy for Cruise Control Metrics topic

It is important that the auto-created __CruiseControlMetrics topic (see auto-created topics) has a log cleanup policy of DELETE rather than COMPACT. Otherwise, records that are needed by Cruise Control might be removed.

As described in Section 14.3, “Deploying the Cruise Control Metrics Reporter”, setting the following options in the Kafka configuration file ensures that the COMPACT log cleanup policy is correctly set:

  • cruise.control.metrics.topic.auto.create=true
  • cruise.control.metrics.topic.num.partitions=1
  • cruise.control.metrics.topic.replication.factor=1

If topic auto-creation is disabled in the Cruise Control Metrics Reporter (cruise.control.metrics.topic.auto.create=false), but enabled in the Kafka cluster, then the __CruiseControlMetrics topic is still automatically created by the broker. In this case, you must change the log cleanup policy of the __CruiseControlMetrics topic to DELETE using the kafka-configs.sh tool.

  1. Get the current configuration of the __CruiseControlMetrics topic:

    opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --entity-type topics --entity-name __CruiseControlMetrics --describe
  2. Change the log cleanup policy in the topic configuration:

    /opt/kafka/bin/kafka-configs.sh --bootstrap-server <broker_address> --entity-type topics --entity-name __CruiseControlMetrics --alter --add-config cleanup.policy=delete

If topic auto-creation is disabled in both the Cruise Control Metrics Reporter and the Kafka cluster, you must create the __CruiseControlMetrics topic manually and then configure it to use the DELETE log cleanup policy using the kafka-configs.sh tool.

For more information, see Section 7.9, “Modifying a topic configuration”.

Logging configuration

Cruise Control uses log4j1 for all server logging. To change the default configuration, edit the log4j.properties file in /opt/cruise-control/config/log4j.properties.

You must restart the Cruise Control server before the changes take effect.

14.9. Generating optimization proposals

When you make a POST request to the /rebalance endpoint, Cruise Control generates an optimization proposal to rebalance the Kafka cluster based on the optimization goals provided. You can use the results of the optimization proposal to rebalance your Kafka cluster.

You can run the optimization proposal using one of the following endpoints:

  • /rebalance
  • /add_broker
  • /remove_broker

The endpoint you use depends on whether you are rebalancing across all the brokers already running in the Kafka cluster; or you want to rebalance after scaling up or before scaling down your Kafka cluster. For more information, see Rebalancing endpoints with broker scaling.

The optimization proposal is generated as a dry run, unless the dryrun parameter is supplied and set to false. In "dry run mode", Cruise Control generates the optimization proposal and the estimated result, but doesn’t initiate the proposal by rebalancing the cluster.

You can analyze the information returned in the optimization proposal and decide whether to approve it.

Use the following parameters to make requests to the endpoints:

dryrun

type: boolean, default: true

Informs Cruise Control whether you want to generate an optimization proposal only (true), or generate an optimization proposal and perform a cluster rebalance (false).

When dryrun=true (the default), you can also pass the verbose parameter to return more detailed information about the state of the Kafka cluster. This includes metrics for the load on each Kafka broker before and after the optimization proposal is applied, and the differences between the before and after values.

excluded_topics

type: regex

A regular expression that matches the topics to exclude from the calculation of the optimization proposal.

goals

type: list of strings, default: the configured default.goals list

List of user-provided optimization goals to use to prepare the optimization proposal. If goals are not supplied, the configured default.goals list in the cruisecontrol.properties file is used.

skip_hard_goals_check

type: boolean, default: false

By default, Cruise Control checks that the user-provided optimization goals (in the goals parameter) contain all the configured hard goals (in hard.goals). A request fails if you supply goals that are not a subset of the configured hard.goals.

Set skip_hard_goals_check to true if you want to generate an optimization proposal with user-provided optimization goals that do not include all the configured hard.goals.

json

type: boolean, default: false

Controls the type of response returned by the Cruise Control server. If not supplied, or set to false, then Cruise Control returns text formatted for display on the command line. If you want to extract elements of the returned information programmatically, set json=true. This will return JSON formatted text that can be piped to tools such as jq, or parsed in scripts and programs.

verbose

type: boolean, default: false

Controls the level of detail in responses that are returned by the Cruise Control server. Can be used with dryrun=true.

Note

Other parameters are available. For more information, see REST APIs in the Cruise Control Wiki.

Prerequisites

  • Kafka and ZooKeeper are running
  • Cruise Control is running
  • (Optional for scaling up) You have installed new brokers on hosts to include in the rebalance

Procedure

  1. Generate an optimization proposal using a POST request to the /rebalance, /add_broker, or /remove_broker endpoint.

    Example request to /rebalance using default goals

    curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/rebalance'

    The cached optimization proposal is immediately returned.

    Note

    If NotEnoughValidWindows is returned, Cruise Control has not yet recorded enough metrics data to generate an optimization proposal. Wait a few minutes and then resend the request.

    Example request to /rebalance using specified goals

    curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/rebalance?goals=RackAwareGoal,ReplicaCapacityGoal'

    If the request satisfies the supplied goals, the cached optimization proposal is immediately returned. Otherwise, a new optimization proposal is generated using the supplied goals; this takes longer to calculate. You can enforce this behavior by adding the ignore_proposal_cache=true parameter to the request.

    Example request to /rebalance using specified goals without hard goals

    curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/rebalance?goals=RackAwareGoal,ReplicaCapacityGoal,ReplicaDistributionGoal&skip_hard_goal_check=true'

    Example request to /add_broker that includes specified brokers

    curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/add_broker?brokerid=3,4'

    The request includes the IDs of the new brokers only. For example, this request adds brokers with the IDs 3 and 4. Replicas are moved to the new brokers from existing brokers when rebalancing.

    Example request to /remove_broker that excludes specified brokers

    curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/remove_broker?brokerid=3,4'

    The request includes the IDs of the brokers being excluded only. For example, this request excludes brokers with the IDs 3 and 4. Replicas are moved from the brokers being removed to other existing brokers when rebalancing.

    Note

    If a broker that is being removed has excluded topics, replicas are still moved.

  2. Review the optimization proposal contained in the response. The properties describe the pending cluster rebalance operation.

    The proposal contains a high level summary of the proposed optimization, followed by summaries for each default optimization goal, and the expected cluster state after the proposal has executed.

    Pay particular attention to the following information:

    • The Cluster load after rebalance summary. If it meets your requirements, you should assess the impact of the proposed changes using the high level summary.
    • n inter-broker replica (y MB) moves indicates how much data will be moved across the network between brokers. The higher the value, the greater the potential performance impact on the Kafka cluster during the rebalance.
    • n intra-broker replica (y MB) moves indicates how much data will be moved within the brokers themselves (between disks). The higher the value, the greater the potential performance impact on individual brokers (although less than that of n inter-broker replica (y MB) moves).
    • The number of leadership moves. This has a negligible impact on the performance of the cluster during the rebalance.

Asynchronous responses

The Cruise Control REST API endpoints timeout after 10 seconds by default, although proposal generation continues on the server. A timeout might occur if the most recent cached optimization proposal is not ready, or if user-provided optimization goals were specified with ignore_proposal_cache=true.

To allow you to retrieve the optimization proposal at a later time, take note of the request’s unique identifier, which is given in the header of responses from the /rebalance endpoint.

To obtain the response using curl, specify the verbose (-v) option:

curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/rebalance'

Here is an example header:

* Connected to cruise-control-server (::1) port 9090 (#0)
> POST /kafkacruisecontrol/rebalance HTTP/1.1
> Host: cc-host:9090
> User-Agent: curl/7.70.0
> Accept: /
>
* Mark bundle as not supporting multiuse
< HTTP/1.1 200 OK
< Date: Mon, 01 Jun 2020 15:19:26 GMT
< Set-Cookie: JSESSIONID=node01wk6vjzjj12go13m81o7no5p7h9.node0; Path=/
< Expires: Thu, 01 Jan 1970 00:00:00 GMT
< User-Task-ID: 274b8095-d739-4840-85b9-f4cfaaf5c201
< Content-Type: text/plain;charset=utf-8
< Cruise-Control-Version: 2.0.103.redhat-00002
< Cruise-Control-Commit_Id: 58975c9d5d0a78dd33cd67d4bcb497c9fd42ae7c
< Content-Length: 12368
< Server: Jetty(9.4.26.v20200117-redhat-00001)

If an optimization proposal is not ready within the timeout, you can re-submit the POST request, this time including the User-Task-ID of the original request in the header:

curl -v -X POST -H 'User-Task-ID: 274b8095-d739-4840-85b9-f4cfaaf5c201' 'cruise-control-server:9090/kafkacruisecontrol/rebalance'

14.10. Approving an optimization proposal

If you are satisfied with your most recently generated optimization proposal, you can instruct Cruise Control to initiate a cluster rebalance and begin reassigning partitions.

Leave as little time as possible between generating an optimization proposal and initiating the cluster rebalance. If some time has passed since you generated the original optimization proposal, the cluster state might have changed. Therefore, the cluster rebalance that is initiated might be different to the one you reviewed. If in doubt, first generate a new optimization proposal.

Only one cluster rebalance, with a status of "Active", can be in progress at a time.

Prerequisites

Procedure

  1. Send a POST request to the /rebalance, /add_broker, or /remove_broker endpoint with the dryrun=false parameter:

    If you used the /add_broker or /remove_broker endpoint to generate a proposal that included or excluded brokers, use the same endpoint to perform the rebalance with or without the specified brokers.

    Example request to /rebalance

    curl -X POST 'cruise-control-server:9090/kafkacruisecontrol/rebalance?dryrun=false'

    Example request to /add_broker

    curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/add_broker?dryrun=false&brokerid=3,4'

    Example request to /remove_broker

    curl -v -X POST 'cruise-control-server:9090/kafkacruisecontrol/remove_broker?dryrun=false&brokerid=3,4'

    Cruise Control initiates the cluster rebalance and returns the optimization proposal.

  2. Check the changes that are summarized in the optimization proposal. If the changes are not what you expect, you can stop the rebalance.
  3. Check the progress of the cluster rebalance using the /user_tasks endpoint. The cluster rebalance in progress has a status of "Active".

    To view all cluster rebalance tasks executed on the Cruise Control server:

    curl 'cruise-control-server:9090/kafkacruisecontrol/user_tasks'
    
    USER TASK ID      CLIENT ADDRESS  START TIME     STATUS  REQUEST URL
    c459316f-9eb5-482f-9d2d-97b5a4cd294d  0:0:0:0:0:0:0:1       2020-06-01_16:10:29 UTC  Active      POST /kafkacruisecontrol/rebalance?dryrun=false
    445e2fc3-6531-4243-b0a6-36ef7c5059b4  0:0:0:0:0:0:0:1       2020-06-01_14:21:26 UTC  Completed   GET /kafkacruisecontrol/state?json=true
    05c37737-16d1-4e33-8e2b-800dee9f1b01  0:0:0:0:0:0:0:1       2020-06-01_14:36:11 UTC  Completed   GET /kafkacruisecontrol/state?json=true
    aebae987-985d-4871-8cfb-6134ecd504ab  0:0:0:0:0:0:0:1       2020-06-01_16:10:04 UTC
  4. To view the status of a particular cluster rebalance task, supply the user-task-ids parameter and the task ID:

    curl 'cruise-control-server:9090/kafkacruisecontrol/user_tasks?user_task_ids=c459316f-9eb5-482f-9d2d-97b5a4cd294d'

(Optional) Removing brokers when scaling down

After a successful rebalance you can stop any brokers you excluded in order to scale down the Kafka cluster.

  1. Check that each broker being removed does not have any live partitions in its log (log.dirs).

    ls -l <LogDir> | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'

    If a log directory does not match the regular expression \.[a-z0-9]-delete$, active partitions are still present. If you have active partitions, check the rebalance has finished or the configuration for the optimization proposal. You can run the proposal again. Make sure that there are no active partitions before moving on to the next step.

  2. Stop the broker.

    su - kafka
    /opt/kafka/bin/kafka-server-stop.sh
  3. Confirm that the broker has stopped.

    jcmd | grep kafka

14.11. Stopping an active cluster rebalance

You can stop the cluster rebalance that is currently in progress.

This instructs Cruise Control to finish the current batch of partition reassignments and then stop the rebalance. When the rebalance has stopped, completed partition reassignments have already been applied; therefore, the state of the Kafka cluster is different when compared to before the start of the rebalance operation. If further rebalancing is required, you should generate a new optimization proposal.

Note

The performance of the Kafka cluster in the intermediate (stopped) state might be worse than in the initial state.

Prerequisites

  • A cluster rebalance is in progress (indicated by a status of "Active").

Procedure

  • Send a POST request to the /stop_proposal_execution endpoint:

    curl -X POST 'cruise-control-server:9090/kafkacruisecontrol/stop_proposal_execution'

Additional resources

Chapter 15. Using the partition reassignment tool

When scaling a Kafka cluster, you may need to add or remove brokers and update the distribution of partitions or the replication factor of topics. To update partitions and topics, you can use the kafka-reassign-partitions.sh tool.

You can change the replication factor of a topic using the kafka-reassign-partitions.sh tool. The tool can also be used to reassign partitions and balance the distribution of partitions across brokers to improve performance. However, it is recommended to use Cruise Control for automated partition reassignments and cluster rebalancing. Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions. The AMQ Streams Cruise Control integration does not support changing the replication factor of a topic.

15.1. Partition reassignment tool overview

The partition reassignment tool provides the following capabilities for managing Kafka partitions and brokers:

Redistributing partition replicas
Scale your cluster up and down by adding or removing brokers, and move Kafka partitions from heavily loaded brokers to under-utilized brokers. To do this, you must create a partition reassignment plan that identifies which topics and partitions to move and where to move them. Cruise Control is recommended for this type of operation as it automates the cluster rebalancing process.
Scaling topic replication factor up and down
Increase or decrease the replication factor of your Kafka topics. To do this, you must create a partition reassignment plan that identifies the existing replication assignment across partitions and an updated assignment with the replication factor changes.
Changing the preferred leader
Change the preferred leader of a Kafka partition. This can be useful if the current preferred leader is unavailable or if you want to redistribute load across the brokers in the cluster. To do this, you must create a partition reassignment plan that specifies the new preferred leader for each partition by changing the order of replicas.
Changing the log directories to use a specific JBOD volume
Change the log directories of your Kafka brokers to use a specific JBOD volume. This can be useful if you want to move your Kafka data to a different disk or storage device. To do this, you must create a partition reassignment plan that specifies the new log directory for each topic.

15.1.1. Generating a partition reassignment plan

The partition reassignment tool (kafka-reassign-partitions.sh) works by generating a partition assignment plan that specifies which partitions should be moved from their current broker to a new broker.

If you are satisfied with the plan, you can execute it. The tool then does the following:

  • Migrates the partition data to the new broker
  • Updates the metadata on the Kafka brokers to reflect the new partition assignments
  • Triggers a rolling restart of the Kafka brokers to ensure that the new assignments take effect

The partition reassignment tool has three different modes:

--generate
Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you only want to reassign some partitions of some topics.
--execute
Takes a reassignment JSON file and applies it to the partitions and brokers in the cluster. Brokers that gain partitions as a result become followers of the partition leader. For a given partition, once the new broker has caught up and joined the ISR (in-sync replicas) the old broker will stop being a follower and will delete its replica.
--verify
Using the same reassignment JSON file as the --execute step, --verify checks whether all the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any traffic throttles (--throttle) that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.

It is only possible to have one reassignment running in a cluster at any given time, and it is not possible to cancel a running reassignment. If you must cancel a reassignment, wait for it to complete and then perform another reassignment to revert the effects of the first reassignment. The kafka-reassign-partitions.sh will print the reassignment JSON for this reversion as part of its output. Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.

15.1.2. Specifying topics in a partition reassignment JSON file

The kafka-reassign-partitions.sh tool uses a reassignment JSON file that specifies the topics to reassign. You can generate a reassignment JSON file or create a file manually if you want to move specific partitions.

A basic reassignment JSON file has the structure presented in the following example, which describes three partitions belonging to two Kafka topics. Each partition is reassigned to a new set of replicas, which are identified by their broker IDs. The version, topic, partition, and replicas properties are all required.

Example partition reassignment JSON file structure

{
  "version": 1, 1
  "partitions": [ 2
    {
      "topic": "example-topic-1", 3
      "partition": 0, 4
      "replicas": [1, 2, 3] 5
    },
    {
      "topic": "example-topic-1",
      "partition": 1,
      "replicas": [2, 3, 4]
    },
    {
      "topic": "example-topic-2",
      "partition": 0,
      "replicas": [3, 4, 5]
    }
  ]
}

1
The version of the reassignment JSON file format. Currently, only version 1 is supported, so this should always be 1.
2
An array that specifies the partitions to be reassigned.
3
The name of the Kafka topic that the partition belongs to.
4
The ID of the partition being reassigned.
5
An ordered array of the IDs of the brokers that should be assigned as replicas for this partition. The first broker in the list is the leader replica.
Note

Partitions not included in the JSON are not changed.

If you specify only topics using a topics array, the partition reassignment tool reassigns all the partitions belonging to the specified topics.

Example reassignment JSON file structure for reassigning all partitions for a topic

{
  "version": 1,
  "topics": [
    { "topic": "my-topic"}
  ]
}

15.1.3. Reassigning partitions between JBOD volumes

When using JBOD storage in your Kafka cluster, you can reassign the partitions between specific volumes and their log directories (each volume has a single log directory).

To reassign a partition to a specific volume, add log_dirs values for each partition in the reassignment JSON file. Each log_dirs array contains the same number of entries as the replicas array, since each replica should be assigned to a specific log directory. The log_dirs array contains either an absolute path to a log directory or the special value any. The any value indicates that Kafka can choose any available log directory for that replica, which can be useful when reassigning partitions between JBOD volumes.

Example reassignment JSON file structure with log directories

{
  "version": 1,
  "partitions": [
    {
      "topic": "example-topic-1",
      "partition": 0,
      "replicas": [1, 2, 3]
      "log_dirs": ["/var/lib/kafka/data-0/kafka-log1", "any", "/var/lib/kafka/data-1/kafka-log2"]
    },
    {
      "topic": "example-topic-1",
      "partition": 1,
      "replicas": [2, 3, 4]
      "log_dirs": ["any",  "/var/lib/kafka/data-2/kafka-log3", "/var/lib/kafka/data-3/kafka-log4"]
    },
    {
      "topic": "example-topic-2",
      "partition": 0,
      "replicas": [3, 4, 5]
      "log_dirs": ["/var/lib/kafka/data-4/kafka-log5", "any",  "/var/lib/kafka/data-5/kafka-log6"]
    }
  ]
}

15.1.4. Throttling partition reassignment

Partition reassignment can be a slow process because it involves transferring large amounts of data between brokers. To avoid a detrimental impact on clients, you can throttle the reassignment process. Use the --throttle parameter with the kafka-reassign-partitions.sh tool to throttle a reassignment. You specify a maximum threshold in bytes per second for the movement of partitions between brokers. For example, --throttle 5000000 sets a maximum threshold for moving partitions of 50 MBps.

Throttling might cause the reassignment to take longer to complete.

  • If the throttle is too low, the newly assigned brokers will not be able to keep up with records being published and the reassignment will never complete.
  • If the throttle is too high, clients will be impacted.

For example, for producers, this could manifest as higher than normal latency waiting for acknowledgment. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.

15.2. Reassigning partitions after adding brokers

Use a reassignment file generated by the kafka-reassign-partitions.sh tool to reassign partitions after increasing the number of brokers in a Kafka cluster. The reassignment file should describe how partitions are reassigned to brokers in the enlarged Kafka cluster. You apply the reassignment specified in the file to the brokers and then verify the new partition assignments.

This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.

Note

Though you can use the kafka-reassign-partitions.sh tool, Cruise Control is recommended for automated partition reassignments and cluster rebalancing. Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.

Prerequisites

  • An existing Kafka cluster.
  • A new machine with the additional AMQ broker installed.
  • You have created a JSON file to specify how partitions should be reassigned to brokers in the enlarged cluster.

    In this procedure, we are reassigning all partitions for a topic called my-topic. A JSON file named topics.json specifies the topic, and is used to generate a reassignment.json file.

Example JSON file specifies my-topic

{
  "version": 1,
  "topics": [
    { "topic": "my-topic"}
  ]
}

Procedure

  1. Create a configuration file for the new broker using the same settings as for the other brokers in your cluster, except for broker.id, which should be a number that is not already used by any of the other brokers.
  2. Start the new Kafka broker passing the configuration file you created in the previous step as the argument to the kafka-server-start.sh script:

    su - kafka
    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  3. Verify that the Kafka broker is running.

    jcmd | grep Kafka
  4. Repeat the above steps for each new broker.
  5. If you haven’t done so, generate a reassignment JSON file named reassignment.json using the kafka-reassign-partitions.sh tool.

    Example command to generate the reassignment JSON file

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --topics-to-move-json-file topics.json \ 1
      --broker-list 0,1,2,3,4 \ 2
      --generate

    1
    The JSON file that specifies the topic.
    2
    Brokers IDs in the kafka cluster to include in the operation. This assumes broker 4 has been added.

    Example reassignment JSON file showing the current and proposed replica assignment

    Current partition replica assignment
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2],"log_dirs":["any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3],"log_dirs":["any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,0],"log_dirs":["any","any","any"]}]}
    
    Proposed partition reassignment configuration
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2,3],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3,4],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,4,0],"log_dirs":["any","any","any","any"]}]}

    Save a copy of this file locally in case you need to revert the changes later on.

  6. Run the partition reassignment using the --execute option.

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --throttle 5000000 \
      --execute
  7. Verify that the reassignment has completed using the --verify option.

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

15.3. Reassigning partitions before removing brokers

Use a reassignment file generated by the kafka-reassign-partitions.sh tool to reassign partitions before decreasing the number of brokers in a Kafka cluster. The reassignment file must describe how partitions are reassigned to the remaining brokers in the Kafka cluster. You apply the reassignment specified in the file to the brokers and then verify the new partition assignments. Brokers in the highest numbered pods are removed first.

This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.

Note

Though you can use the kafka-reassign-partitions.sh tool, Cruise Control is recommended for automated partition reassignments and cluster rebalancing. Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.

Prerequisites

  • An existing Kafka cluster.
  • You have created a JSON file to specify how partitions should be reassigned to brokers in the reduced cluster.

    In this procedure, we are reassigning all partitions for a topic called my-topic. A JSON file named topics.json specifies the topic, and is used to generate a reassignment.json file.

Example JSON file specifies my-topic

{
  "version": 1,
  "topics": [
    { "topic": "my-topic"}
  ]
}

Procedure

  1. If you haven’t done so, generate a reassignment JSON file named reassignment.json using the kafka-reassign-partitions.sh tool.

    Example command to generate the reassignment JSON file

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --topics-to-move-json-file topics.json \ 1
      --broker-list 0,1,2,3 \ 2
      --generate

    1
    The JSON file that specifies the topic.
    2
    Brokers IDs in the kafka cluster to include in the operation. This assumes broker 4 has been removed.

    Example reassignment JSON file showing the current and proposed replica assignment

    Current partition replica assignment
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[3,4,2,0],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[0,2,3,1],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[1,3,0,4],"log_dirs":["any","any","any","any"]}]}
    
    Proposed partition reassignment configuration
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2],"log_dirs":["any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3],"log_dirs":["any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,0],"log_dirs":["any","any","any"]}]}

    Save a copy of this file locally in case you need to revert the changes later on.

  2. Run the partition reassignment using the --execute option.

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --throttle 5000000 \
      --execute
  3. Verify that the reassignment has completed using the --verify option.

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

  4. Check that each broker being removed does not have any live partitions in its log (log.dirs).

    ls -l <LogDir> | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'

    If a log directory does not match the regular expression \.[a-z0-9]-delete$, active partitions are still present. If you have active partitions, check the reassignment has finished or the configuration in the reassignment JSON file. You can run the reassignment again. Make sure that there are no active partitions before moving on to the next step.

  5. Stop the broker.

    su - kafka
    /opt/kafka/bin/kafka-server-stop.sh
  6. Confirm that the Kafka broker has stopped.

    jcmd | grep kafka

15.4. Changing the replication factor of topics

Use the kafka-reassign-partitions.sh tool to change the replication factor of topics in a Kafka cluster. This can be done using a reassignment file to describe how the topic replicas should be changed.

Prerequisites

  • An existing Kafka cluster.
  • You have created a JSON file to specify the topics to include in the operation.

    In this procedure, a topic called my-topic has 4 replicas and we want to reduce it to 3. A JSON file named topics.json specifies the topic, and is used to generate a reassignment.json file.

Example JSON file specifies my-topic

{
  "version": 1,
  "topics": [
    { "topic": "my-topic"}
  ]
}

Procedure

  1. If you haven’t done so, generate a reassignment JSON file named reassignment.json using the kafka-reassign-partitions.sh tool.

    Example command to generate the reassignment JSON file

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --topics-to-move-json-file topics.json \ 1
      --broker-list 0,1,2,3,4 \ 2
      --generate

    1
    The JSON file that specifies the topic.
    2
    Brokers IDs in the kafka cluster to include in the operation.

    Example reassignment JSON file showing the current and proposed replica assignment

    Current partition replica assignment
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[3,4,2,0],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[0,2,3,1],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[1,3,0,4],"log_dirs":["any","any","any","any"]}]}
    
    Proposed partition reassignment configuration
    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2,3],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3,4],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,4,0],"log_dirs":["any","any","any","any"]}]}

    Save a copy of this file locally in case you need to revert the changes later on.

  2. Edit the reassignment.json to remove a replica from each partition.

    For example use jq to remove the last replica in the list for each partition of the topic:

    Removing the last topic replica for each partition

    jq '.partitions[].replicas |= del(.[-1])' reassignment.json > reassignment.json

    Example reassignment file showing the updated replicas

    {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,4],"log_dirs":["any","any","any","any"]}]}

  3. Make the topic replica change using the --execute option.

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --execute
    Note

    Removing replicas from a broker does not require any inter-broker data movement, so there is no need to throttle replication. If you are adding replicas, then you may want to change the throttle rate.

  4. Verify that the change to the topic replicas has completed using the --verify option.

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --reassignment-json-file reassignment.json \
      --verify

    The reassignment has finished when the --verify command reports that each of the partitions being moved has completed successfully. This final --verify will also have the effect of removing any reassignment throttles.

  5. Run the bin/kafka-topics.sh command with the --describe option to see the results of the change to the topics.

    /opt/kafka/bin/kafka-reassign-partitions.sh \
      --bootstrap-server localhost:9092 \
      --describe

    Results of reducing the number of replicas for a topic

    my-topic  Partition: 0  Leader: 0  Replicas: 0,1,2 Isr: 0,1,2
    my-topic  Partition: 1  Leader: 2  Replicas: 1,2,3 Isr: 1,2,3
    my-topic  Partition: 2  Leader: 3  Replicas: 2,3,4 Isr: 2,3,4

Chapter 16. Using Kerberos (GSSAPI) authentication

AMQ Streams supports the use of the Kerberos (GSSAPI) authentication protocol for secure single sign-on access to your Kafka cluster. GSSAPI is an API wrapper for Kerberos functionality, insulating applications from underlying implementation changes.

Kerberos is a network authentication system that allows clients and servers to authenticate to each other by using symmetric encryption and a trusted third party, the Kerberos Key Distribution Centre (KDC).

16.1. Setting up AMQ Streams to use Kerberos (GSSAPI) authentication

This procedure shows how to configure AMQ Streams so that Kafka clients can access Kafka and ZooKeeper using Kerberos (GSSAPI) authentication.

The procedure assumes that a Kerberos krb5 resource server has been set up on a Red Hat Enterprise Linux host.

The procedure shows, with examples, how to configure:

  1. Service principals
  2. Kafka brokers to use the Kerberos login
  3. ZooKeeper to use Kerberos login
  4. Producer and consumer clients to access Kafka using Kerberos authentication

The instructions describe Kerberos set up for a single ZooKeeper and Kafka installation on a single host, with additional configuration for a producer and consumer client.

Prerequisites

To be able to configure Kafka and ZooKeeper to authenticate and authorize Kerberos credentials, you will need:

  • Access to a Kerberos server
  • A Kerberos client on each Kafka broker host

For more information on the steps to set up a Kerberos server, and clients on broker hosts, see the example Kerberos on RHEL set up configuration.

Add service principals for authentication

From your Kerberos server, create service principals (users) for ZooKeeper, Kafka brokers, and Kafka producer and consumer clients.

Service principals must take the form SERVICE-NAME/FULLY-QUALIFIED-HOST-NAME@DOMAIN-REALM.

  1. Create the service principals, and keytabs that store the principal keys, through the Kerberos KDC.

    For example:

    • zookeeper/node1.example.redhat.com@EXAMPLE.REDHAT.COM
    • kafka/node1.example.redhat.com@EXAMPLE.REDHAT.COM
    • producer1/node1.example.redhat.com@EXAMPLE.REDHAT.COM
    • consumer1/node1.example.redhat.com@EXAMPLE.REDHAT.COM

      The ZooKeeper service principal must have the same hostname as the zookeeper.connect configuration in the Kafka config/server.properties file:

      zookeeper.connect=node1.example.redhat.com:2181

      If the hostname is not the same, localhost is used and authentication will fail.

  2. Create a directory on the host and add the keytab files:

    For example:

    /opt/kafka/krb5/zookeeper-node1.keytab
    /opt/kafka/krb5/kafka-node1.keytab
    /opt/kafka/krb5/kafka-producer1.keytab
    /opt/kafka/krb5/kafka-consumer1.keytab
  3. Ensure the kafka user can access the directory:

    chown kafka:kafka -R /opt/kafka/krb5

Configure ZooKeeper to use a Kerberos Login

Configure ZooKeeper to use the Kerberos Key Distribution Center (KDC) for authentication using the user principals and keytabs previously created for zookeeper.

  1. Create or modify the opt/kafka/config/jaas.conf file to support ZooKeeper client and server operations:

    Client {
        com.sun.security.auth.module.Krb5LoginModule required debug=true
        useKeyTab=true 1
        storeKey=true 2
        useTicketCache=false 3
        keyTab="/opt/kafka/krb5/zookeeper-node1.keytab" 4
        principal="zookeeper/node1.example.redhat.com@EXAMPLE.REDHAT.COM"; 5
    };
    
    Server {
        com.sun.security.auth.module.Krb5LoginModule required debug=true
        useKeyTab=true
        storeKey=true
        useTicketCache=false
        keyTab="/opt/kafka/krb5/zookeeper-node1.keytab"
        principal="zookeeper/node1.example.redhat.com@EXAMPLE.REDHAT.COM";
    };
    
    QuorumServer {
        com.sun.security.auth.module.Krb5LoginModule required debug=true
        useKeyTab=true
        storeKey=true
        keyTab="/opt/kafka/krb5/zookeeper-node1.keytab"
        principal="zookeeper/node1.example.redhat.com@EXAMPLE.REDHAT.COM";
    };
    
    QuorumLearner {
        com.sun.security.auth.module.Krb5LoginModule required debug=true
        useKeyTab=true
        storeKey=true
        keyTab="/opt/kafka/krb5/zookeeper-node1.keytab"
        principal="zookeeper/node1.example.redhat.com@EXAMPLE.REDHAT.COM";
    };
    1
    Set to true to get the principal key from the keytab.
    2
    Set to true to store the principal key.
    3
    Set to true to obtain the Ticket Granting Ticket (TGT) from the ticket cache.
    4
    The keyTab property points to the location of the keytab file copied from the Kerberos KDC. The location and file must be readable by the kafka user.
    5
    The principal property is configured to match the fully-qualified principal name created on the KDC host, which follows the format SERVICE-NAME/FULLY-QUALIFIED-HOST-NAME@DOMAIN-NAME.
  2. Edit opt/kafka/config/zookeeper.properties to use the updated JAAS configuration:

    # ...
    
    requireClientAuthScheme=sasl
    jaasLoginRenew=3600000 1
    kerberos.removeHostFromPrincipal=false 2
    kerberos.removeRealmFromPrincipal=false 3
    quorum.auth.enableSasl=true 4
    quorum.auth.learnerRequireSasl=true 5
    quorum.auth.serverRequireSasl=true
    quorum.auth.learner.loginContext=QuorumLearner 6
    quorum.auth.server.loginContext=QuorumServer
    quorum.auth.kerberos.servicePrincipal=zookeeper/_HOST 7
    quorum.cnxn.threads.size=20
    1
    Controls the frequency for login renewal in milliseconds, which can be adjusted to suit ticket renewal intervals. Default is one hour.
    2
    Dictates whether the hostname is used as part of the login principal name. If using a single keytab for all nodes in the cluster, this is set to true. However, it is recommended to generate a separate keytab and fully-qualified principal for each broker host for troubleshooting.
    3
    Controls whether the realm name is stripped from the principal name for Kerberos negotiations. It is recommended that this setting is set as false.
    4
    Enables SASL authentication mechanisms for the ZooKeeper server and client.
    5
    The RequireSasl properties controls whether SASL authentication is required for quorum events, such as master elections.
    6
    The loginContext properties identify the name of the login context in the JAAS configuration used for authentication configuration of the specified component. The loginContext names correspond to the names of the relevant sections in the opt/kafka/config/jaas.conf file.
    7
    Controls the naming convention to be used to form the principal name used for identification. The placeholder _HOST is automatically resolved to the hostnames defined by the server.1 properties at runtime.
  3. Start ZooKeeper with JVM parameters to specify the Kerberos login configuration:

    su - kafka
    export EXTRA_ARGS="-Djava.security.krb5.conf=/etc/krb5.conf -Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

    If you are not using the default service name (zookeeper), add the name using the -Dzookeeper.sasl.client.username=NAME parameter.

    Note

    If you are using the /etc/krb5.conf location, you do not need to specify -Djava.security.krb5.conf=/etc/krb5.conf when starting ZooKeeper, Kafka, or the Kafka producer and consumer.

Configure the Kafka broker server to use a Kerberos login

Configure Kafka to use the Kerberos Key Distribution Center (KDC) for authentication using the user principals and keytabs previously created for kafka.

  1. Modify the opt/kafka/config/jaas.conf file with the following elements:

    KafkaServer {
        com.sun.security.auth.module.Krb5LoginModule required
        useKeyTab=true
        storeKey=true
        keyTab="/opt/kafka/krb5/kafka-node1.keytab"
        principal="kafka/node1.example.redhat.com@EXAMPLE.REDHAT.COM";
    };
    KafkaClient {
        com.sun.security.auth.module.Krb5LoginModule required debug=true
        useKeyTab=true
        storeKey=true
        useTicketCache=false
        keyTab="/opt/kafka/krb5/kafka-node1.keytab"
        principal="kafka/node1.example.redhat.com@EXAMPLE.REDHAT.COM";
    };
  2. Configure each broker in the Kafka cluster by modifying the listener configuration in the config/server.properties file so the listeners use the SASL/GSSAPI login.

    Add the SASL protocol to the map of security protocols for the listener, and remove any unwanted protocols.

    For example:

    # ...
    broker.id=0
    # ...
    listeners=SECURE://:9092,REPLICATION://:9094 1
    inter.broker.listener.name=REPLICATION
    # ...
    listener.security.protocol.map=SECURE:SASL_PLAINTEXT,REPLICATION:SASL_PLAINTEXT 2
    # ..
    sasl.enabled.mechanisms=GSSAPI 3
    sasl.mechanism.inter.broker.protocol=GSSAPI 4
    sasl.kerberos.service.name=kafka 5
    ...
    1
    Two listeners are configured: a secure listener for general-purpose communications with clients (supporting TLS for communications), and a replication listener for inter-broker communications.
    2
    For TLS-enabled listeners, the protocol name is SASL_PLAINTEXT. For non-TLS-enabled connectors, the protocol name is SASL_PLAINTEXT. If SSL is not required, you can remove the ssl.* properties.
    3
    SASL mechanism for Kerberos authentication is GSSAPI.
    4
    Kerberos authentication for inter-broker communication.
    5
    The name of the service used for authentication requests is specified to distinguish it from other services that may also be using the same Kerberos configuration.
  3. Start the Kafka broker, with JVM parameters to specify the Kerberos login configuration:

    su - kafka
    export KAFKA_OPTS="-Djava.security.krb5.conf=/etc/krb5.conf -Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

    If the broker and ZooKeeper cluster were previously configured and working with a non-Kerberos-based authentication system, it is possible to start the ZooKeeper and broker cluster and check for configuration errors in the logs.

    After starting the broker and Zookeeper instances, the cluster is now configured for Kerberos authentication.

Configure Kafka producer and consumer clients to use Kerberos authentication

Configure Kafka producer and consumer clients to use the Kerberos Key Distribution Center (KDC) for authentication using the user principals and keytabs previously created for producer1 and consumer1.

  1. Add the Kerberos configuration to the producer or consumer configuration file.

    For example:

    /opt/kafka/config/producer.properties

    # ...
    sasl.mechanism=GSSAPI 1
    security.protocol=SASL_PLAINTEXT 2
    sasl.kerberos.service.name=kafka 3
    sasl.jaas.config=com.sun.security.auth.module.Krb5LoginModule required \ 4
        useKeyTab=true  \
        useTicketCache=false \
        storeKey=true  \
        keyTab="/opt/kafka/krb5/producer1.keytab" \
        principal="producer1/node1.example.redhat.com@EXAMPLE.REDHAT.COM";
    # ...

    1
    Configuration for Kerberos (GSSAPI) authentication.
    2
    Kerberos uses the SASL plaintext (username/password) security protocol.
    3
    The service principal (user) for Kafka that was configured in the Kerberos KDC.
    4
    Configuration for the JAAS using the same properties defined in jaas.conf.

    /opt/kafka/config/consumer.properties

    # ...
    sasl.mechanism=GSSAPI
    security.protocol=SASL_PLAINTEXT
    sasl.kerberos.service.name=kafka
    sasl.jaas.config=com.sun.security.auth.module.Krb5LoginModule required \
        useKeyTab=true  \
        useTicketCache=false \
        storeKey=true  \
        keyTab="/opt/kafka/krb5/consumer1.keytab" \
        principal="consumer1/node1.example.redhat.com@EXAMPLE.REDHAT.COM";
    # ...

  2. Run the clients to verify that you can send and receive messages from the Kafka brokers.

    Producer client:

    export KAFKA_HEAP_OPTS="-Djava.security.krb5.conf=/etc/krb5.conf -Dsun.security.krb5.debug=true"; /opt/kafka/bin/kafka-console-producer.sh --producer.config /opt/kafka/config/producer.properties  --topic topic1 --bootstrap-server node1.example.redhat.com:9094

    Consumer client:

    export KAFKA_HEAP_OPTS="-Djava.security.krb5.conf=/etc/krb5.conf -Dsun.security.krb5.debug=true"; /opt/kafka/bin/kafka-console-consumer.sh --consumer.config /opt/kafka/config/consumer.properties  --topic topic1 --bootstrap-server node1.example.redhat.com:9094

Additional resources

Chapter 17. Setting up distributed tracing

Distributed tracing allows you to track the progress of transactions between applications in a distributed system. In a microservices architecture, tracing tracks the progress of transactions between services. Trace data is useful for monitoring application performance and investigating issues with target systems and end-user applications.

In AMQ Streams, tracing facilitates the end-to-end tracking of messages: from source systems to Kafka, and then from Kafka to target systems and applications. It complements the metrics that are available to view in JMX metrics, as well as the component loggers.

Support for tracing is built in to the following Kafka components:

  • Kafka Connect
  • MirrorMaker
  • MirrorMaker 2
  • AMQ Streams Kafka Bridge

Tracing is not supported for Kafka brokers.

You add tracing configuration to the properties file of the component.

To enable tracing, you set environment variables and add the library of the tracing system to the Kafka classpath. For Jaeger tracing, you can add tracing artifacts for the following systems:

  • OpenTelemetry with the Jaeger Exporter
  • OpenTracing with Jaeger
Note

Support for OpenTracing is deprecated. The Jaeger clients are now retired and the OpenTracing project archived. As such, we cannot guarantee their support for future Kafka versions.

To enable tracing in Kafka producers, consumers, and Kafka Streams API applications, you instrument application code. When instrumented, clients generate trace data; for example, when producing messages or writing offsets to the log.

Note

Setting up tracing for applications and systems beyond AMQ Streams is outside the scope of this content.

17.1. Outline of procedures

To set up tracing for AMQ Streams, follow these procedures in order:

Note

For information on enabling tracing for the Kafka Bridge, see Using the AMQ Streams Kafka Bridge.

17.2. Tracing options

Use OpenTelemetry or OpenTracing (deprecated) with the Jaeger tracing system.

OpenTelemetry and OpenTracing provide API specifications that are independent from the tracing or monitoring system.

You use the APIs to instrument application code for tracing.

  • Instrumented applications generate traces for individual requests across the distributed system.
  • Traces are composed of spans that define specific units of work over time.

Jaeger is a tracing system for microservices-based distributed systems.

  • Jaeger implements the tracing APIs and provides client libraries for instrumentation.
  • The Jaeger user interface allows you to query, filter, and analyze trace data.

The Jaeger user interface showing a simple query

The Jaeger user interface showing a simple query

17.3. Environment variables for tracing

Use environment variables when you are enabling tracing for Kafka components or initializing a tracer for Kafka clients.

Tracing environment variables are subject to change. For the latest information, see the OpenTelemetry documentation and OpenTracing documentation.

The following tables describe the key environment variables for setting up a tracer.

Table 17.1. OpenTelemetry environment variables

PropertyRequiredDescription

OTEL_SERVICE_NAME

Yes

The name of the Jaeger tracing service for OpenTelemetry.

OTEL_EXPORTER_JAEGER_ENDPOINT

Yes

The exporter used for tracing.

OTEL_TRACES_EXPORTER

Yes

The exporter used for tracing. Set to otlp by default. If using Jaeger tracing, you need to set this environment variable as jaeger. If you are using another tracing implementation, specify the exporter used.

Table 17.2. OpenTracing environment variables

PropertyRequiredDescription

JAEGER_SERVICE_NAME

Yes

The name of the Jaeger tracer service.

JAEGER_AGENT_HOST

No

The hostname for communicating with the jaeger-agent through the User Datagram Protocol (UDP).

JAEGER_AGENT_PORT

No

The port used for communicating with the jaeger-agent through UDP.

17.4. Enabling tracing for Kafka Connect

Enable distributed tracing for Kafka Connect using configuration properties. Only messages produced and consumed by Kafka Connect itself are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems.

You can enable tracing that uses OpenTelemetry or OpenTracing.

Procedure

  1. Add the tracing artifacts to the opt/kafka/libs directory.
  2. Configure producer and consumer tracing in the relevant Kafka Connect configuration file.

    • If you are running Kafka Connect in standalone mode, edit the /opt/kafka/config/connect-standalone.properties file.
    • If you are running Kafka Connect in distributed mode, edit the /opt/kafka/config/connect-distributed.properties file.

    Add the following tracing interceptor properties to the configuration file:

    Properties for OpenTelemetry

    producer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingProducerInterceptor
    consumer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingConsumerInterceptor

    Properties for OpenTracing

    producer.interceptor.classes=io.opentracing.contrib.kafka.TracingProducerInterceptor
    consumer.interceptor.classes=io.opentracing.contrib.kafka.TracingConsumerInterceptor

    With tracing enabled, you initialize tracing when you run the Kafka Connect script.

  3. Save the configuration file.
  4. Set the environment variables for tracing.
  5. Start Kafka Connect in standalone or distributed mode with the configuration file as a parameter (plus any connector properties):

    Running Kafka Connect in standalone mode

    su - kafka
    /opt/kafka/bin/connect-standalone.sh \
    /opt/kafka/config/connect-standalone.properties \
    connector1.properties \
    [connector2.properties ...]

    Running Kafka Connect in distributed mode

    su - kafka
    /opt/kafka/bin/connect-distributed.sh /opt/kafka/config/connect-distributed.properties

    The internal consumers and producers of Kafka Connect are now enabled for tracing.

17.5. Enabling tracing for MirrorMaker 2

Enable distributed tracing for MirrorMaker 2 by defining the Interceptor properties in the MirrorMaker 2 properties file. Messages are traced between Kafka clusters. The trace data records messages entering and leaving the MirrorMaker 2 component.

You can enable tracing that uses OpenTelemetry or OpenTracing.

Procedure

  1. Add the tracing artifacts to the opt/kafka/libs directory.
  2. Configure producer and consumer tracing in the opt/kafka/config/connect-mirror-maker.properties file.

    Add the following tracing interceptor properties to the configuration file:

    Properties for OpenTelemetry

    header.converter=org.apache.kafka.connect.converters.ByteArrayConverter
    producer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingProducerInterceptor
    consumer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingConsumerInterceptor

    Properties for OpenTracing

    header.converter=org.apache.kafka.connect.converters.ByteArrayConverter
    producer.interceptor.classes=io.opentracing.contrib.kafka.TracingProducerInterceptor
    consumer.interceptor.classes=io.opentracing.contrib.kafka.TracingConsumerInterceptor

    ByteArrayConverter prevents Kafka Connect from converting message headers (containing trace IDs) to base64 encoding. This ensures that messages are the same in both the source and the target clusters.

    With tracing enabled, you initialize tracing when you run the Kafka MirrorMaker 2 script.

  3. Save the configuration file.
  4. Set the environment variables for tracing.
  5. Start MirrorMaker 2 with the producer and consumer configuration files as parameters:

    su - kafka
    /opt/kafka/bin/connect-mirror-maker.sh \
    /opt/kafka/config/connect-mirror-maker.properties

    The internal consumers and producers of MirrorMaker 2 are now enabled for tracing.

17.6. Enabling tracing for MirrorMaker

Enable distributed tracing for MirrorMaker by passing the Interceptor properties as consumer and producer configuration parameters. Messages are traced from the source cluster to the target cluster. The trace data records messages entering and leaving the MirrorMaker component.

You can enable tracing that uses OpenTelemetry or OpenTracing.

Procedure

  1. Add the tracing artifacts to the opt/kafka/libs directory.
  2. Configure producer tracing in the /opt/kafka/config/producer.properties file.

    Add the following tracing interceptor property:

    Producer property for OpenTelemetry

    producer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingProducerInterceptor

    Producer property for OpenTracing

    producer.interceptor.classes=io.opentracing.contrib.kafka.TracingProducerInterceptor

  3. Save the configuration file.
  4. Configure consumer tracing in the /opt/kafka/config/consumer.properties file.

    Add the following tracing interceptor property:

    Consumer property for OpenTelemetry

    consumer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingConsumerInterceptor

    Consumer property for OpenTracing

    consumer.interceptor.classes=io.opentracing.contrib.kafka.TracingConsumerInterceptor

    With tracing enabled, you initialize tracing when you run the Kafka MirrorMaker script.

  5. Save the configuration file.
  6. Set the environment variables for tracing.
  7. Start MirrorMaker with the producer and consumer configuration files as parameters:

    su - kafka
    /opt/kafka/bin/kafka-mirror-maker.sh \
    --producer.config /opt/kafka/config/producer.properties \
    --consumer.config /opt/kafka/config/consumer.properties \
    --num.streams=2

    The internal consumers and producers of MirrorMaker are now enabled for tracing.

17.7. Initializing tracing for Kafka clients

Initialize a tracer, then instrument your client applications for distributed tracing. You can instrument Kafka producer and consumer clients, and Kafka Streams API applications. You can initialize a tracer for OpenTracing or OpenTelemetry.

Configure and initialize a tracer using a set of tracing environment variables.

Procedure

In each client application add the dependencies for the tracer:

  1. Add the Maven dependencies to the pom.xml file for the client application:

    Dependencies for OpenTelemetry

    <dependency>
        <groupId>io.opentelemetry</groupId>
        <artifactId>opentelemetry-sdk-extension-autoconfigure</artifactId>
        <version>1.19.0.redhat-00002</version>
    </dependency>
    <dependency>
      <groupId>io.opentelemetry.instrumentation</groupId>
      <artifactId>opentelemetry-kafka-clients-{OpenTelemetryKafkaClient}</artifactId>
      <version>1.19.0.redhat-00002</version>
    </dependency>
    <dependency>
      <groupId>io.opentelemetry</groupId>
      <artifactId>opentelemetry-exporter-otlp</artifactId>
      <version>1.19.0.redhat-00002</version>
    </dependency>

    Dependencies for OpenTracing

    <dependency>
        <groupId>io.jaegertracing</groupId>
        <artifactId>jaeger-client</artifactId>
        <version>1.8.1.redhat-00002</version>
    </dependency>
    <dependency>
      <groupId>io.opentracing.contrib</groupId>
      <artifactId>opentracing-kafka-client</artifactId>
      <version>0.1.15.redhat-00006</version>
    </dependency>

  2. Define the configuration of the tracer using the tracing environment variables.
  3. Create a tracer, which is initialized with the environment variables:

    Creating a tracer for OpenTelemetry

    OpenTelemetry ot = GlobalOpenTelemetry.get();

    Creating a tracer for OpenTracing

    Tracer tracer = Configuration.fromEnv().getTracer();

  4. Register the tracer as a global tracer:

    GlobalTracer.register(tracer);
  5. Instrument your client:

17.8. Instrumenting producers and consumers for tracing

Instrument application code to enable tracing in Kafka producers and consumers. Use a decorator pattern or interceptors to instrument your Java producer and consumer application code for tracing. You can then record traces when messages are produced or retrieved from a topic.

OpenTelemetry and OpenTracing instrumentation projects provide classes that support instrumentation of producers and consumers.

Decorator instrumentation
For decorator instrumentation, create a modified producer or consumer instance for tracing. Decorator instrumentation is different for OpenTelemetry and OpenTracing.
Interceptor instrumentation
For interceptor instrumentation, add the tracing capability to the consumer or producer configuration. Interceptor instrumentation is the same for OpenTelemetry and OpenTracing.

Prerequisites

  • You have initialized tracing for the client.

    You enable instrumentation in producer and consumer applications by adding the tracing JARs as dependencies to your project.

Procedure

Perform these steps in the application code of each producer and consumer application. Instrument your client application code using either a decorator pattern or interceptors.

  • To use a decorator pattern, create a modified producer or consumer instance to send or receive messages.

    You pass the original KafkaProducer or KafkaConsumer class.

    Example decorator instrumentation for OpenTelemetry

    // Producer instance
    Producer < String, String > op = new KafkaProducer < > (
        configs,
        new StringSerializer(),
        new StringSerializer()
        );
        Producer < String, String > producer = tracing.wrap(op);
    KafkaTracing tracing = KafkaTracing.create(GlobalOpenTelemetry.get());
    producer.send(...);
    
    //consumer instance
    Consumer<String, String> oc = new KafkaConsumer<>(
        configs,
        new StringDeserializer(),
        new StringDeserializer()
        );
        Consumer<String, String> consumer = tracing.wrap(oc);
    consumer.subscribe(Collections.singleton("mytopic"));
    ConsumerRecords<Integer, String> records = consumer.poll(1000);
    ConsumerRecord<Integer, String> record = ...
    SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);

    Example decorator instrumentation for OpenTracing

    //producer instance
    KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);
    TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer, tracer);
    TracingKafkaProducer.send(...)
    
    //consumer instance
    KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);
    TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer, tracer);
    tracingConsumer.subscribe(Collections.singletonList("mytopic"));
    ConsumerRecords<Integer, String> records = tracingConsumer.poll(1000);
    ConsumerRecord<Integer, String> record = ...
    SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);

  • To use interceptors, set the interceptor class in the producer or consumer configuration.

    You use the KafkaProducer and KafkaConsumer classes in the usual way. The TracingProducerInterceptor and TracingConsumerInterceptor interceptor classes take care of the tracing capability.

    Example producer configuration using interceptors

    senderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
        TracingProducerInterceptor.class.getName());
    
    KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);
    producer.send(...);

    Example consumer configuration using interceptors

    consumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG,
        TracingConsumerInterceptor.class.getName());
    
    KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);
    consumer.subscribe(Collections.singletonList("messages"));
    ConsumerRecords<Integer, String> records = consumer.poll(1000);
    ConsumerRecord<Integer, String> record = ...
    SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);

17.9. Instrumenting Kafka Streams applications for tracing

Instrument application code to enable tracing in Kafka Streams API applications. Use a decorator pattern or interceptors to instrument your Kafka Streams API applications for tracing. You can then record traces when messages are produced or retrieved from a topic.

Decorator instrumentation
For decorator instrumentation, create a modified Kafka Streams instance for tracing. The OpenTracing instrumentation project provides a TracingKafkaClientSupplier class that supports instrumentation of Kafka Streams. You create a wrapped instance of the TracingKafkaClientSupplier supplier interface, which provides tracing instrumentation for Kafka Streams. For OpenTelemetry, the process is the same but you need to create a custom TracingKafkaClientSupplier class to provide the support.
Interceptor instrumentation
For interceptor instrumentation, add the tracing capability to the Kafka Streams producer and consumer configuration.

Prerequisites

  • You have initialized tracing for the client.

    You enable instrumentation in Kafka Streams applications by adding the tracing JARs as dependencies to your project.

  • To instrument Kafka Streams with OpenTelemetry, you’ll need to write a custom TracingKafkaClientSupplier.
  • The custom TracingKafkaClientSupplier can extend Kafka’s DefaultKafkaClientSupplier, overriding the producer and consumer creation methods to wrap the instances with the telemetry-related code.

    Example custom TracingKafkaClientSupplier

    private class TracingKafkaClientSupplier extends DefaultKafkaClientSupplier {
        @Override
        public Producer<byte[], byte[]> getProducer(Map<String, Object> config) {
            KafkaTelemetry telemetry = KafkaTelemetry.create(GlobalOpenTelemetry.get());
            return telemetry.wrap(super.getProducer(config));
        }
    
        @Override
        public Consumer<byte[], byte[]> getConsumer(Map<String, Object> config) {
            KafkaTelemetry telemetry = KafkaTelemetry.create(GlobalOpenTelemetry.get());
            return telemetry.wrap(super.getConsumer(config));
        }
    
        @Override
        public Consumer<byte[], byte[]> getRestoreConsumer(Map<String, Object> config) {
            return this.getConsumer(config);
        }
    
        @Override
        public Consumer<byte[], byte[]> getGlobalConsumer(Map<String, Object> config) {
            return this.getConsumer(config);
        }
    }

Procedure

Perform these steps for each Kafka Streams API application.

  • To use a decorator pattern, create an instance of the TracingKafkaClientSupplier supplier interface, then provide the supplier interface to KafkaStreams.

    Example decorator instrumentation

    KafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer);
    KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier);
    streams.start();

  • To use interceptors, set the interceptor class in the Kafka Streams producer and consumer configuration.

    The TracingProducerInterceptor and TracingConsumerInterceptor interceptor classes take care of the tracing capability.

    Example producer and consumer configuration using interceptors

    props.put(StreamsConfig.PRODUCER_PREFIX + ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingProducerInterceptor.class.getName());
    props.put(StreamsConfig.CONSUMER_PREFIX + ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingConsumerInterceptor.class.getName());

17.10. Specifying tracing systems with OpenTelemetry

Instead of the default Jaeger system, you can specify other tracing systems that are supported by OpenTelemetry.

If you want to use another tracing system with OpenTelemetry, do the following:

  1. Add the library of the tracing system to the Kafka classpath.
  2. Add the name of the tracing system as an additional exporter environment variable.

    Additional environment variable when not using Jaeger

    OTEL_SERVICE_NAME=my-tracing-service
    OTEL_TRACES_EXPORTER=zipkin 1
    OTEL_EXPORTER_ZIPKIN_ENDPOINT=http://localhost:9411/api/v2/spans 2

    1
    The name of the tracing system. In this example, Zipkin is specified.
    2
    The endpoint of the specific selected exporter that listens for spans. In this example, a Zipkin endpoint is specified.

Additional resources

17.11. Custom span names

A tracing span is a logical unit of work in Jaeger, with an operation name, start time, and duration. Spans have built-in names, but you can specify custom span names in your Kafka client instrumentation where used.

Specifying custom span names is optional and only applies when using a decorator pattern in producer and consumer client instrumentation or Kafka Streams instrumentation.

17.11.1. Specifying span names for OpenTelemetry

Custom span names cannot be specified directly with OpenTelemetry. Instead, you retrieve span names by adding code to your client application to extract additional tags and attributes.

Example code to extract attributes

//Defines attribute extraction for a producer
private static class ProducerAttribExtractor implements AttributesExtractor < ProducerRecord < ? , ? > , Void > {
    @Override
    public void onStart(AttributesBuilder attributes, ProducerRecord < ? , ? > producerRecord) {
        set(attributes, AttributeKey.stringKey("prod_start"), "prod1");
    }
    @Override
    public void onEnd(AttributesBuilder attributes, ProducerRecord < ? , ? > producerRecord, @Nullable Void unused, @Nullable Throwable error) {
        set(attributes, AttributeKey.stringKey("prod_end"), "prod2");
    }
}
//Defines attribute extraction for a consumer
private static class ConsumerAttribExtractor implements AttributesExtractor < ConsumerRecord < ? , ? > , Void > {
    @Override
    public void onStart(AttributesBuilder attributes, ConsumerRecord < ? , ? > producerRecord) {
        set(attributes, AttributeKey.stringKey("con_start"), "con1");
    }
    @Override
    public void onEnd(AttributesBuilder attributes, ConsumerRecord < ? , ? > producerRecord, @Nullable Void unused, @Nullable Throwable error) {
        set(attributes, AttributeKey.stringKey("con_end"), "con2");
    }
}
//Extracts the attributes
public static void main(String[] args) throws Exception {
        Map < String, Object > configs = new HashMap < > (Collections.singletonMap(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092"));
        System.setProperty("otel.traces.exporter", "jaeger");
        System.setProperty("otel.service.name", "myapp1");
        KafkaTracing tracing = KafkaTracing.newBuilder(GlobalOpenTelemetry.get())
            .addProducerAttributesExtractors(new ProducerAttribExtractor())
            .addConsumerAttributesExtractors(new ConsumerAttribExtractor())
            .build();

17.11.2. Specifying span names for OpenTracing

To specify custom span names for OpenTracing, pass a BiFunction object as an additional argument when instrumenting producers and consumers.

For more information on built-in names and specifying custom span names to instrument client application code in a decorator pattern, see the OpenTracing Apache Kafka client instrumentation.

Chapter 18. Using Kafka Exporter

Kafka Exporter is an open source project to enhance monitoring of Apache Kafka brokers and clients.

Kafka Exporter is provided with AMQ Streams for deployment with a Kafka cluster to extract additional metrics data from Kafka brokers related to offsets, consumer groups, consumer lag, and topics.

The metrics data is used, for example, to help identify slow consumers.

Lag data is exposed as Prometheus metrics, which can then be presented in Grafana for analysis.

If you are already using Prometheus and Grafana for monitoring of built-in Kafka metrics, you can configure Prometheus to also scrape the Kafka Exporter Prometheus endpoint.

Kafka exposes metrics through JMX, which can then be exported as Prometheus metrics. For more information, see Monitoring your cluster using JMX.

18.1. Consumer lag

Consumer lag indicates the difference in the rate of production and consumption of messages. Specifically, consumer lag for a given consumer group indicates the delay between the last message in the partition and the message being currently picked up by that consumer. The lag reflects the position of the consumer offset in relation to the end of the partition log.

This difference is sometimes referred to as the delta between the producer offset and consumer offset, the read and write positions in the Kafka broker topic partitions.

Suppose a topic streams 100 messages a second. A lag of 1000 messages between the producer offset (the topic partition head) and the last offset the consumer has read means a 10-second delay.

The importance of monitoring consumer lag

For applications that rely on the processing of (near) real-time data, it is critical to monitor consumer lag to check that it does not become too big. The greater the lag becomes, the further the process moves from the real-time processing objective.

Consumer lag, for example, might be a result of consuming too much old data that has not been purged, or through unplanned shutdowns.

Reducing consumer lag

Typical actions to reduce lag include:

  • Scaling-up consumer groups by adding new consumers
  • Increasing the retention time for a message to remain in a topic
  • Adding more disk capacity to increase the message buffer

Actions to reduce consumer lag depend on the underlying infrastructure and the use cases AMQ Streams is supporting. For instance, a lagging consumer is less likely to benefit from the broker being able to service a fetch request from its disk cache. And in certain cases, it might be acceptable to automatically drop messages until a consumer has caught up.

18.2. Kafka Exporter alerting rule examples

The sample alert notification rules specific to Kafka Exporter are as follows:

UnderReplicatedPartition
An alert to warn that a topic is under-replicated and the broker is not replicating enough partitions. The default configuration is for an alert if there are one or more under-replicated partitions for a topic. The alert might signify that a Kafka instance is down or the Kafka cluster is overloaded. A planned restart of the Kafka broker may be required to restart the replication process.
TooLargeConsumerGroupLag
An alert to warn that the lag on a consumer group is too large for a specific topic partition. The default configuration is 1000 records. A large lag might indicate that consumers are too slow and are falling behind the producers.
NoMessageForTooLong
An alert to warn that a topic has not received messages for a period of time. The default configuration for the time period is 10 minutes. The delay might be a result of a configuration issue preventing a producer from publishing messages to the topic.

You can adapt alerting rules according to your specific needs.

Additional resources

For more information about setting up alerting rules, see Configuration in the Prometheus documentation.

18.3. Kafka Exporter metrics

Lag information is exposed by Kafka Exporter as Prometheus metrics for presentation in Grafana.

Kafka Exporter exposes metrics data for brokers, topics, and consumer groups.

Table 18.1. Broker metrics output

NameInformation

kafka_brokers

Number of brokers in the Kafka cluster

Table 18.2. Topic metrics output

NameInformation

kafka_topic_partitions

Number of partitions for a topic

kafka_topic_partition_current_offset

Current topic partition offset for a broker

kafka_topic_partition_oldest_offset

Oldest topic partition offset for a broker

kafka_topic_partition_in_sync_replica

Number of in-sync replicas for a topic partition

kafka_topic_partition_leader

Leader broker ID of a topic partition

kafka_topic_partition_leader_is_preferred

Shows 1 if a topic partition is using the preferred broker

kafka_topic_partition_replicas

Number of replicas for this topic partition

kafka_topic_partition_under_replicated_partition

Shows 1 if a topic partition is under-replicated

Table 18.3. Consumer group metrics output

NameInformation

kafka_consumergroup_current_offset

Current topic partition offset for a consumer group

kafka_consumergroup_lag

Current approximate lag for a consumer group at a topic partition

18.4. Running Kafka Exporter

Run Kafka Exporter to expose Prometheus metrics for presentation in a Grafana dashboard.

Download and install the Kafka Exporter package to use the Kafka Exporter with AMQ Streams. You need an AMQ Streams subscription to be able to download and install the package.

This procedure assumes you already have access to a Grafana user interface and Prometheus is deployed and added as a data source.

Procedure

  1. Install the Kafka Exporter package:

    dnf install kafka_exporter
  2. Verify the package has installed:

    dnf info kafka_exporter
  3. Run the Kafka Exporter using appropriate configuration parameter values:

    kafka_exporter --kafka.server=<kafka_bootstrap_address>:9092 --kafka.version=3.5.0  --<my_other_parameters>

    The parameters require a double-hyphen convention, such as --kafka.server.

    Table 18.4. Kafka Exporter configuration parameters

    OptionDescriptionDefault

    kafka.server

    Host/post address of the Kafka server.

    kafka:9092

    kafka.version

    Kafka broker version.

    1.0.0

    group.filter

    A regular expression to specify the consumer groups to include in the metrics.

    .* (all)

    topic.filter

    A regular expression to specify the topics to include in the metrics.

    .* (all)

    sasl.<parameter>

    Parameters to enable and connect to the Kafka cluster using SASL/PLAIN authentication, with user name and password.

    false

    tls.<parameter>

    Parameters to enable connect to the Kafka cluster using TLS authentication, with optional certificate and key.

    false

    web.listen-address

    Port address to expose the metrics.

    :9308

    web.telemetry-path

    Path for the exposed metrics.

    /metrics

    log.level

    Logging configuration, to log messages with a given severity (debug, info, warn, error, fatal) or above.

    info

    log.enable-sarama

    Boolean to enable Sarama logging, a Go client library used by the Kafka Exporter.

    false

    legacy.partitions

    Boolean to enable metrics to be fetched from inactive topic partitions as well as from active partitions. If you want Kafka Exporter to return metrics for inactive partitions, set to true.

    false

    You can use kafka_exporter --help for information on the properties.

  4. Configure Prometheus to monitor the Kafka Exporter metrics.

    For more information on configuring Prometheus, see the Prometheus documentation.

  5. Enable Grafana to present the Kafka Exporter metrics data exposed by Prometheus.

    For more information, see Presenting Kafka Exporter metrics in Grafana.

Updating Kafka Exporter

Use the latest version of Kafka Exporter with your AMQ Streams installation.

To check for updates, use:

dnf check-update

To update Kafka Exporter, use:

dnf update kafka_exporter

18.5. Presenting Kafka Exporter metrics in Grafana

Using Kafka Exporter Prometheus metrics as a data source, you can create a dashboard of Grafana charts.

For example, from the metrics you can create the following Grafana charts:

  • Message in per second (from topics)
  • Message in per minute (from topics)
  • Lag by consumer group
  • Messages consumed per minute (by consumer groups)

When metrics data has been collected for some time, the Kafka Exporter charts are populated.

Use the Grafana charts to analyze lag and to check if actions to reduce lag are having an impact on an affected consumer group. If, for example, Kafka brokers are adjusted to reduce lag, the dashboard will show the Lag by consumer group chart going down and the Messages consumed per minute chart going up.

Chapter 19. Upgrading AMQ Streams and Kafka

Upgrade your Kafka cluster with no downtime. Releases of AMQ Streams for Red Hat Enterprise Linux support one version of Apache Kafka. You upgrade to the latest supported version of Kafka when you install the latest version of AMQ Streams.

19.1. Upgrade prerequisites

Before you begin the upgrade process, make sure you are familiar with any upgrade changes described in the AMQ Streams 2.5 on Red Hat Enterprise Linux Release Notes.

Note

Refer to the documentation supporting a specific version of AMQ Streams for information on how to upgrade to that version.

19.2. Kafka versions

Kafka’s log message format version and inter-broker protocol version specify, respectively, the log format version appended to messages and the version of the Kafka protocol used in a cluster. To ensure the correct versions are used, the upgrade process involves making configuration changes to existing Kafka brokers and code changes to client applications (consumers and producers).

The following table shows the differences between Kafka versions:

Table 19.1. Kafka version differences

AMQ Streams versionKafka versionInter-broker protocol versionLog message format versionZooKeeper version

2.5

3.5.0

3.5

3.5

3.6.4

2.4

3.4.0

3.4

3.4

3.6.3

Note

AMQ Streams 2.5 uses Kafka 3.5.0, but Kafka 3.4.0 is also supported for the purpose of upgrading.

Inter-broker protocol version

In Kafka, the network protocol used for inter-broker communication is called the inter-broker protocol. Each version of Kafka has a compatible version of the inter-broker protocol. The minor version of the protocol typically increases to match the minor version of Kafka, as shown in the preceding table.

The inter-broker protocol version is set cluster wide in the Kafka resource. To change it, you edit the inter.broker.protocol.version property in Kafka.spec.kafka.config.

Log message format version

When a producer sends a message to a Kafka broker, the message is encoded using a specific format. The format can change between Kafka releases, so messages specify which version of the message format they were encoded with.

The properties used to set a specific message format version are as follows:

  • message.format.version property for topics
  • log.message.format.version property for Kafka brokers

From Kafka 3.0.0, the message format version values are assumed to match the inter.broker.protocol.version and don’t need to be set. The values reflect the Kafka version used.

When upgrading to Kafka 3.0.0 or higher, you can remove these settings when you update the inter.broker.protocol.version. Otherwise, set the message format version based on the Kafka version you are upgrading to.

The default value of message.format.version for a topic is defined by the log.message.format.version that is set on the Kafka broker. You can manually set the message.format.version of a topic by modifying its topic configuration.

19.3. Strategies for upgrading clients

Upgrading Kafka clients ensures that they benefit from the features, fixes, and improvements that are introduced in new versions of Kafka. Upgraded clients maintain compatibility with other upgraded Kafka components. The performance and stability of the clients might also be improved.

Consider the best approach for upgrading Kafka clients and brokers to ensure a smooth transition. The chosen upgrade strategy depends on whether you are upgrading brokers or clients first. Since Kafka 3.0, you can upgrade brokers and client independently and in any order. The decision to upgrade clients or brokers first depends on several factors, such as the number of applications that need to be upgraded and how much downtime is tolerable.

If you upgrade clients before brokers, some new features may not work as they are not yet supported by brokers. However, brokers can handle producers and consumers running with different versions and supporting different log message versions.

Upgrading clients when using Kafka versions older than Kafka 3.0

Before Kafka 3.0, you would configure a specific message format for brokers using the log.message.format.version property (or the message.format.version property at the topic level). This allowed brokers to support older Kafka clients that were using an outdated message format. Otherwise, the brokers would need to convert the messages from the older clients, which came with a significant performance cost.

Apache Kafka Java clients have supported the latest message format version since version 0.11. If all of your clients are using the latest message version, you can remove the log.message.format.version or message.format.version overrides when upgrading your brokers.

However, if you still have clients that are using an older message format version, we recommend upgrading your clients first. Start with the consumers, then upgrade the producers before removing the log.message.format.version or message.format.version overrides when upgrading your brokers. This will ensure that all of your clients can support the latest message format version and that the upgrade process goes smoothly.

You can track Kafka client names and versions using this metric:

  • kafka.server:type=socket-server-metrics,clientSoftwareName=<name>,clientSoftwareVersion=<version>,listener=<listener>,networkProcessor=<processor>
Tip

The following Kafka broker metrics help monitor the performance of message down-conversion:

  • kafka.network:type=RequestMetrics,name=MessageConversionsTimeMs,request={Produce|Fetch} provides metrics on the time taken to perform message conversion.
  • kafka.server:type=BrokerTopicMetrics,name={Produce|Fetch}MessageConversionsPerSec,topic=([-.\w]+) provides metrics on the number of messages converted over a period of time.

19.4. Upgrading Kafka brokers and ZooKeeper

Upgrade Kafka brokers and ZooKeeper on a host machine to use the latest version of AMQ Streams. You update the installation files, then configure and restart all Kafka brokers to use a new inter-broker protocol version. After performing these steps, data is transmitted between the Kafka brokers using the new inter-broker protocol version.

Note

From Kafka 3.0.0, message format version values are assumed to match the inter.broker.protocol.version and don’t need to be set. The values reflect the Kafka version used.

Prerequisites

Procedure

For each Kafka broker in your AMQ Streams cluster and one at a time:

  1. Download the AMQ Streams archive from the AMQ Streams software downloads page.

    Note

    If prompted, log in to your Red Hat account.

  2. On the command line, create a temporary directory and extract the contents of the amq-streams-<version>-bin.zip file.

    mkdir /tmp/kafka
    unzip amq-streams-<version>-bin.zip -d /tmp/kafka
  3. If running, stop ZooKeeper and the Kafka broker running on the host.

    /opt/kafka/bin/zookeeper-server-stop.sh
    /opt/kafka/bin/kafka-server-stop.sh
    jcmd | grep zookeeper
    jcmd | grep kafka

    If you are running Kafka on a multi-node cluster, see Section 4.3, “Performing a graceful rolling restart of Kafka brokers”.

  4. Delete the libs and bin directories from your existing installation:

    rm -rf /opt/kafka/libs /opt/kafka/bin
  5. Copy the libs and bin directories from the temporary directory:

    cp -r /tmp/kafka/kafka_<version>/libs /opt/kafka/
    cp -r /tmp/kafka/kafka_<version>/bin /opt/kafka/
  6. If required, update the configuration files in the config directory to reflect any changes in the new versions.
  7. Delete the temporary directory.

    rm -r /tmp/kafka
  8. Edit the /opt/kafka/config/server.properties properties file.

    Set the inter.broker.protocol.version and log.message.format.version properties to the current version.

    For example, the current version is 3.4 if upgrading from Kafka version 3.4.0 to 3.5.0:

    inter.broker.protocol.version=3.4
    log.message.format.version=3.4

    Use the correct version for the Kafka version you are upgrading from (3.3, 3.4, and so on). Leaving the inter.broker.protocol.version unchanged at the current setting ensures that the brokers can continue to communicate with each other throughout the upgrade.

    If the properties are not configured, add them with the current version.

    If you are upgrading from Kafka 3.0.0 or later, you only need to set the inter.broker.protocol.version.

  9. Restart the updated ZooKeeper and Kafka broker:

    /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties
    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

    The Kafka broker and ZooKeeper start using the binaries for the latest Kafka version.

    For information on restarting brokers in a multi-node cluster, see Section 4.3, “Performing a graceful rolling restart of Kafka brokers”.

  10. Verify that the restarted Kafka broker has caught up with the partition replicas it is following.

    Use the kafka-topics.sh tool to ensure that all replicas contained in the broker are back in sync. For instructions, see Listing and describing topics.

    In the next steps, update your Kafka brokers to use the new inter-broker protocol version.

    Update each broker, one at a time.

    Warning

    Downgrading AMQ Streams is not possible after completing the following steps.

  11. Depending on your chosen strategy for upgrading clients, upgrade all client applications to use the new version of the client binaries.
  12. Set the inter.broker.protocol.version property to 3.5 in the /opt/kafka/config/server.properties file:

    inter.broker.protocol.version=3.5
  13. On the command line, stop the Kafka broker that you modified:

    /opt/kafka/bin/kafka-server-stop.sh
  14. Check that Kafka is not running:

    jcmd | grep kafka
  15. Restart the Kafka broker that you modified:

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  16. Check that Kafka is running:

    jcmd | grep kafka
  17. If you are upgrading from a version earlier than Kafka 3.0.0, set the log.message.format.version property to 3.5 in the /opt/kafka/config/server.properties file:

    log.message.format.version=3.5
  18. On the command line, stop the Kafka broker that you modified:

    /opt/kafka/bin/kafka-server-stop.sh
  19. Check that Kafka is not running:

    jcmd | grep kafka
  20. Restart the Kafka broker that you modified:

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  21. Check that Kafka is running:

    jcmd | grep kafka
  22. Verify that the restarted Kafka broker has caught up with the partition replicas it is following.

    Use the kafka-topics.sh tool to ensure that all replicas contained in the broker are back in sync. For instructions, see Listing and describing topics.

  23. If it was used in the upgrade, remove the legacy log.message.format.version configuration from the server.properties file.

19.5. Upgrading Kafka components

Upgrade Kafka components on a host machine to use the latest version of AMQ Streams. You can use the AMQ Streams installation files to upgrade the following components:

  • Kafka Connect
  • MirrorMaker
  • Kafka Bridge (separate ZIP file)

Prerequisites

  • You are logged in to Red Hat Enterprise Linux as the kafka user.
  • You have downloaded the installation files.
  • You have installed Kafka and other Kafka components you are using on separate hosts.

    For more information, see Section 3.1, “Installation environment”.

  • You have upgraded Kafka and ZooKeeper.

    If a Kafka component is running on the same host as Kafka and ZooKeeper, you’ll also need to stop and start Kafka and ZooKeeper when upgrading.

Procedure

For each host running an instance of the Kafka component:

  1. Download the AMQ Streams or Kafka Bridge installation files from the AMQ Streams software downloads page.

    Note

    If prompted, log in to your Red Hat account.

  2. On the command line, create a temporary directory and extract the contents of the amq-streams-<version>-bin.zip file.

    mkdir /tmp/kafka
    unzip amq-streams-<version>-bin.zip -d /tmp/kafka

    For Kafka Bridge, extract the amq-streams-<version>-bridge-bin.zip file.

  3. If running, stop the Kafka component running on the host.
  4. Delete the libs and bin directories from your existing installation:

    rm -rf /opt/kafka/libs /opt/kafka/bin
  5. Copy the libs and bin directories from the temporary directory:

    cp -r /tmp/kafka/kafka_<version>/libs /opt/kafka/
    cp -r /tmp/kafka/kafka_<version>/bin /opt/kafka/
  6. If required, update the configuration files in the config directory to reflect any changes in the new versions.
  7. Delete the temporary directory.

    rm -r /tmp/kafka
  8. Start the Kafka component using the appropriate script and properties files.

    Starting Kafka Connect in standalone mode

    /opt/kafka/bin/connect-standalone.sh \
    /opt/kafka/config/connect-standalone.properties <connector1>.properties
    [<connector2>.properties ...]

    Starting Kafka Connect in distributed mode

    /opt/kafka/bin/connect-distributed.sh \
    /opt/kafka/config/connect-distributed.properties

    Starting MirrorMaker 2 in dedicated mode

    /opt/kafka/bin/connect-mirror-maker.sh \
    /opt/kafka/config/connect-mirror-maker.properties

    Starting Kafka Bridge

    su - kafka
    ./bin/kafka_bridge_run.sh \
    --config-file=<path>/application.properties

  9. Verify that the Kafka component is running, and producing or consuming data as expected.

    Verifying Kafka Connect in standalone mode is running

    jcmd | grep ConnectStandalone

    Verifying Kafka Connect in distributed mode is running

    jcmd | grep ConnectDistributed

    Verifying MirrorMaker 2 in dedicated mode is running

    jcmd | grep mirrorMaker

    Verifying Kafka Bridge is running by checking the log

    HTTP-Kafka Bridge started and listening on port 8080
    HTTP-Kafka Bridge bootstrap servers localhost:9092

Chapter 20. Monitoring your cluster using JMX

Collecting metrics is critical for understanding the health and performance of your Kafka deployment. By monitoring metrics, you can actively identify issues before they become critical and make informed decisions about resource allocation and capacity planning. Without metrics, you may be left with limited visibility into the behavior of your Kafka deployment, which can make troubleshooting more difficult and time-consuming. Setting up metrics can save you time and resources in the long run, and help ensure the reliability of your Kafka deployment.

Kafka brokers, ZooKeeper, Kafka Connect, and Kafka clients use Java Management Extensions (JMX) to actively expose management information. This information primarily consists of metrics that help monitor the performance and condition of the Kafka cluster. Kafka, like other Java applications, relies on managed beans or MBeans to provide this information to monitoring tools and dashboards.

JMX operates at the JVM level, allowing external tools to connect and retrieve management information from the ZooKeeper, Kafka broker, and so on. To connect to the JVM, these tools must be running on the same machine and as the same user by default.

20.1. Enabling the JMX agent

Enable JMX monitoring of Kafka components using JVM system properties. Use the KAFKA_JMX_OPTS environment variable to set the JMX system properties required for enabling JMX monitoring. The scripts that run the Kafka component use these properties.

Procedure

  1. Set the KAFKA_JMX_OPTS environment variable with the JMX properties for enabling JMX monitoring.

    export KAFKA_JMX_OPTS=-Dcom.sun.management.jmxremote=true
      -Dcom.sun.management.jmxremote.port=<port>
      -Dcom.sun.management.jmxremote.authenticate=false
      -Dcom.sun.management.jmxremote.ssl=false

    Replace <port> with the name of the port on which you want the Kafka component to listen for JMX connections.

  2. Add org.apache.kafka.common.metrics.JmxReporter to metric.reporters in the server.properties file.

    metric.reporters=org.apache.kafka.common.metrics.JmxReporter
  3. Start the Kafka component using the appropriate script, such as bin/kafka-server-start.sh for a broker or bin/connect-distributed.sh for Kafka Connect.
Important

It is recommended that you configure authentication and SSL to secure a remote JMX connection. For more information about the system properties needed to do this, see the Oracle documentation.

20.2. Disabling the JMX agent

Disable JMX monitoring for Kafka components by updating the KAFKA_JMX_OPTS environment variable.

Procedure

  1. Set the KAFKA_JMX_OPTS environment variable to disable JMX monitoring.

    export KAFKA_JMX_OPTS=-Dcom.sun.management.jmxremote=false
    Note

    Other JMX properties, like port, authentication, and SSL properties do not need to be specified when disabling JMX monitoring.

  2. Set auto.include.jmx.reporter to false in the Kafka server.properties file.

    auto.include.jmx.reporter=false
    Note

    The auto.include.jmx.reporter property is deprecated. From Kafka 4, the JMXReporter is only enabled if org.apache.kafka.common.metrics.JmxReporter is added to the metric.reporters configuration in the properties file.

  3. Start the Kafka component using the appropriate script, such as bin/kafka-server-start.sh for a broker or bin/connect-distributed.sh for Kafka Connect.

20.3. Metrics naming conventions

When working with Kafka JMX metrics, it’s important to understand the naming conventions used to identify and retrieve specific metrics. Kafka JMX metrics use the following format:

Metrics format

<metric_group>:type=<type_name>,name=<metric_name><other_attribute>=<value>

  • <metric_group> is the name of the metric group
  • <type_name> is the name of the type of metric
  • <metric_name> is the name of the specific metric
  • <other_attribute> represents zero or more additional attributes

For example, the BytesInPerSec metric is a BrokerTopicMetrics type in the kafka.server group:

kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec

In some cases, metrics may include the ID of an entity. For instance, when monitoring a specific client, the metric format includes the client ID:

Metrics for a specific client

kafka.consumer:type=consumer-fetch-manager-metrics,client-id=<client_id>

Similarly, a metric can be further narrowed down to a specific client and topic:

Metrics for a specific client and topic

kafka.consumer:type=consumer-fetch-manager-metrics,client-id=<client_id>,topic=<topic_id>

Understanding these naming conventions will allow you to accurately specify the metrics you want to monitor and analyze.

Note

To view the full list of available JMX metrics for a Strimzi installation, you can use a graphical tool like JConsole. JConsole is a Java Monitoring and Management Console that allows you to monitor and manage Java applications, including Kafka. By connecting to the JVM running the Kafka component using its process ID, the tool’s user interface allows you to view the list of metrics.

20.4. Analyzing Kafka JMX metrics for troubleshooting

JMX provides a way to gather metrics about Kafka brokers for monitoring and managing their performance and resource usage. By analyzing these metrics, common broker issues such as high CPU usage, memory leaks, thread contention, and slow response times can be diagnosed and resolved. Certain metrics can pinpoint the root cause of these issues.

JMX metrics also provide insights into the overall health and performance of a Kafka cluster. They help monitor the system’s throughput, latency, and availability, diagnose issues, and optimize performance. This section explores the use of JMX metrics to help identify common issues and provides insights into the performance of a Kafka cluster.

Collecting and graphing these metrics using tools like Prometheus and Grafana allows you to visualize the information returned. This can be particularly helpful in detecting issues or optimizing performance. Graphing metrics over time can also help with identifying trends and forecasting resource consumption.

20.4.1. Checking for under-replicated partitions

A balanced Kafka cluster is important for optimal performance. In a balanced cluster, partitions and leaders are evenly distributed across all brokers, and I/O metrics reflect this. As well as using metrics, you can use the kafka-topics.sh tool to get a list of under-replicated partitions and identify the problematic brokers. If the number of under-replicated partitions is fluctuating or many brokers show high request latency, this typically indicates a performance issue in the cluster that requires investigation. On the other hand, a steady (unchanging) number of under-replicated partitions reported by many of the brokers in a cluster normally indicates that one of the brokers in the cluster is offline.

Use the describe --under-replicated-partitions option from the kafka-topics.sh tool to show information about partitions that are currently under-replicated in the cluster. These are the partitions that have fewer replicas than the configured replication factor.

If the output is blank, the Kafka cluster has no under-replicated partitions. Otherwise, the output shows replicas that are not in sync or available.

In the following example, only 2 of the 3 replicas are in sync for each partition, with a replica missing from the ISR (in-sync replica).

Returning information on under-replicated partitions from the command line

bin/kafka-topics.sh --bootstrap-server :9092 --describe --under-replicated-partitions

Topic: topic-1 Partition: 0 Leader: 4 Replicas: 4,2,3 Isr: 4,3
Topic: topic-1 Partition: 1 Leader: 3 Replicas: 2,3,4 Isr: 3,4
Topic: topic-1 Partition: 2 Leader: 3 Replicas: 3,4,2 Isr: 3,4

Here are some metrics to check for I/O and under-replicated partitions:

Metrics to check for under-replicated partitions

kafka.server:type=ReplicaManager,name=PartitionCount 1
kafka.server:type=ReplicaManager,name=LeaderCount 2
kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec 3
kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec 4
kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions 5
kafka.server:type=ReplicaManager,name=UnderMinIsrPartitionCount 6

1
Total number of partitions across all topics in the cluster.
2
Total number of leaders across all topics in the cluster.
3
Rate of incoming bytes per second for each broker.
4
Rate of outgoing bytes per second for each broker.
5
Number of under-replicated partitions across all topics in the cluster.
6
Number of partitions below the minimum ISR.

If topic configuration is set for high availability, with a replication factor of at least 3 for topics and a minimum number of in-sync replicas being 1 less than the replication factor, under-replicated partitions can still be usable. Conversely, partitions below the minimum ISR have reduced availability. You can monitor these using the kafka.server:type=ReplicaManager,name=UnderMinIsrPartitionCount metric and the under-min-isr-partitions option from the kafka-topics.sh tool.

Tip

Use Cruise Control to automate the task of monitoring and rebalancing a Kafka cluster to ensure that the partition load is evenly distributed. For more information, see Chapter 14, Using Cruise Control for cluster rebalancing.

20.4.2. Identifying performance problems in a Kafka cluster

Spikes in cluster metrics may indicate a broker issue, which is often related to slow or failing storage devices or compute restraints from other processes. If there is no issue at the operating system or hardware level, an imbalance in the load of the Kafka cluster is likely, with some partitions receiving disproportionate traffic compared to others in the same Kafka topic.

To anticipate performance problems in a Kafka cluster, it’s useful to monitor the RequestHandlerAvgIdlePercent metric. RequestHandlerAvgIdlePercent provides a good overall indicator of how the cluster is behaving. The value of this metric is between 0 and 1. A value below 0.7 indicates that threads are busy 30% of the time and performance is starting to degrade. If the value drops below 50%, problems are likely to occur, especially if the cluster needs to scale or rebalance. At 30%, a cluster is barely usable.

Another useful metric is kafka.network:type=Processor,name=IdlePercent, which you can use to monitor the extent (as a percentage) to which network processors in a Kafka cluster are idle. The metric helps identify whether the processors are over or underutilized.

To ensure optimal performance, set the num.io.threads property equal to the number of processors in the system, including hyper-threaded processors. If the cluster is balanced, but a single client has changed its request pattern and is causing issues, reduce the load on the cluster or increase the number of brokers.

It’s important to note that a single disk failure on a single broker can severely impact the performance of an entire cluster. Since producer clients connect to all brokers that lead partitions for a topic, and those partitions are evenly spread over the entire cluster, a poorly performing broker will slow down produce requests and cause back pressure in the producers, slowing down requests to all brokers. A RAID (Redundant Array of Inexpensive Disks) storage configuration that combines multiple physical disk drives into a single logical unit can help prevent this issue.

Here are some metrics to check the performance of a Kafka cluster:

Metrics to check the performance of a Kafka cluster

kafka.server:type=KafkaRequestHandlerPool,name=RequestHandlerAvgIdlePercent 1
# attributes: OneMinuteRate, FifteenMinuteRate
kafka.server:type=socket-server-metrics,listener=([-.\w]+),networkProcessor=([\d]+) 2
# attributes: connection-creation-rate
kafka.network:type=RequestChannel,name=RequestQueueSize 3
kafka.network:type=RequestChannel,name=ResponseQueueSize 4
kafka.network:type=Processor,name=IdlePercent,networkProcessor=([-.\w]+) 5
kafka.server:type=KafkaServer,name=TotalDiskReadBytes 6
kafka.server:type=KafkaServer,name=TotalDiskWriteBytes 7

1
Average idle percentage of the request handler threads in the Kafka broker’s thread pool. The OneMinuteRate and FifteenMinuteRate attributes show the request rate of the last one minute and fifteen minutes, respectively.
2
Rate at which new connections are being created on a specific network processor of a specific listener in the Kafka broker. The listener attribute refers to the name of the listener, and the networkProcessor attribute refers to the ID of the network processor. The connection-creation-rate attribute shows the rate of connection creation in connections per second.
3
Current size of the request queue.
4
Current sizes of the response queue.
5
Percentage of time the specified network processor is idle. The networkProcessor specifies the ID of the network processor to monitor.
6
Total number of bytes read from disk by a Kafka server.
7
Total number of bytes written to disk by a Kafka server.

20.4.3. Identifying performance problems with a Kafka controller

The Kafka controller is responsible for managing the overall state of the cluster, such as broker registration, partition reassignment, and topic management. Problems with the controller in the Kafka cluster are difficult to diagnose and often fall into the category of bugs in Kafka itself. Controller issues might manifest as broker metadata being out of sync, offline replicas when the brokers appear to be fine, or actions on topics like topic creation not happening correctly.

There are not many ways to monitor the controller, but you can monitor the active controller count and the controller queue size. Monitoring these metrics gives a high-level indicator if there is a problem. Although spikes in the queue size are expected, if this value continuously increases, or stays steady at a high value and does not drop, it indicates that the controller may be stuck. If you encounter this problem, you can move the controller to a different broker, which requires shutting down the broker that is currently the controller.

Here are some metrics to check the performance of a Kafka controller:

Metrics to check the performance of a Kafka controller

kafka.controller:type=KafkaController,name=ActiveControllerCount 1
kafka.controller:type=KafkaController,name=OfflinePartitionsCount 2
kafka.controller:type=ControllerEventManager,name=EventQueueSize 3

1
Number of active controllers in the Kafka cluster. A value of 1 indicates that there is only one active controller, which is the desired state.
2
Number of partitions that are currently offline. If this value is continuously increasing or stays at a high value, there may be a problem with the controller.
3
Size of the event queue in the controller. Events are actions that must be performed by the controller, such as creating a new topic or moving a partition to a new broker. if the value continuously increases or stays at a high value, the controller may be stuck and unable to perform the required actions.

20.4.4. Identifying problems with requests

You can use the RequestHandlerAvgIdlePercent metric to determine if requests are slow. Additionally, request metrics can identify which specific requests are experiencing delays and other issues.

To effectively monitor Kafka requests, it is crucial to collect two key metrics: count and 99th percentile latency, also known as tail latency.

The count metric represents the number of requests processed within a specific time interval. It provides insights into the volume of requests handled by your Kafka cluster and helps identify spikes or drops in traffic.

The 99th percentile latency metric measures the request latency, which is the time taken for a request to be processed. It represents the duration within which 99% of requests are handled. However, it does not provide information about the exact duration for the remaining 1% of requests. In other words, the 99th percentile latency metric tells you that 99% of the requests are handled within a certain duration, and the remaining 1% may take even longer, but the precise duration for this remaining 1% is not known. The choice of the 99th percentile is primarily to focus on the majority of requests and exclude outliers that can skew the results.

This metric is particularly useful for identifying performance issues and bottlenecks related to the majority of requests, but it does not give a complete picture of the maximum latency experienced by a small fraction of requests.

By collecting and analyzing both count and 99th percentile latency metrics, you can gain an understanding of the overall performance and health of your Kafka cluster, as well as the latency of the requests being processed.

Here are some metrics to check the performance of Kafka requests:

Metrics to check the performance of requests

# requests: EndTxn, Fetch, FetchConsumer, FetchFollower, FindCoordinator, Heartbeat, InitProducerId,
# JoinGroup, LeaderAndIsr, LeaveGroup, Metadata, Produce, SyncGroup, UpdateMetadata 1
kafka.network:type=RequestMetrics,name=RequestsPerSec,request=([\w]+) 2
kafka.network:type=RequestMetrics,name=RequestQueueTimeMs,request=([\w]+) 3
kafka.network:type=RequestMetrics,name=TotalTimeMs,request=([\w]+) 4
kafka.network:type=RequestMetrics,name=LocalTimeMs,request=([\w]+) 5
kafka.network:type=RequestMetrics,name=RemoteTimeMs,request=([\w]+) 6
kafka.network:type=RequestMetrics,name=ThrottleTimeMs,request=([\w]+) 7
kafka.network:type=RequestMetrics,name=ResponseQueueTimeMs,request=([\w]+) 8
kafka.network:type=RequestMetrics,name=ResponseSendTimeMs,request=([\w]+) 9
# attributes: Count, 99thPercentile 10

1
Request types to break down the request metrics.
2
Rate at which requests are being processed by the Kafka broker per second.
3
Time (in milliseconds) that a request spends waiting in the broker’s request queue before being processed.
4
Total time (in milliseconds) that a request takes to complete, from the time it is received by the broker to the time the response is sent back to the client.
5
Time (in milliseconds) that a request spends being processed by the broker on the local machine.
6
Time (in milliseconds) that a request spends being processed by other brokers in the cluster.
7
Time (in milliseconds) that a request spends being throttled by the broker. Throttling occurs when the broker determines that a client is sending too many requests too quickly and needs to be slowed down.
8
Time (in milliseconds) that a response spends waiting in the broker’s response queue before being sent back to the client.
9
Time (in milliseconds) that a response takes to be sent back to the client after it has been generated by the broker.
10
For all of the requests metrics, the Count and 99thPercentile attributes show the total number of requests that have been processed and the time it takes for the slowest 1% of requests to complete, respectively.

20.4.5. Using metrics to check the performance of clients

By analyzing client metrics, you can monitor the performance of the Kafka clients (producers and consumers) connected to a broker. This can help identify issues highlighted in broker logs, such as consumers being frequently kicked off their consumer groups, high request failure rates, or frequent disconnections.

Here are some metrics to check the performance of Kafka clients:

Metrics to check the performance of client requests

kafka.consumer:type=consumer-metrics,client-id=([-.\w]+) 1
# attributes: time-between-poll-avg, time-between-poll-max
kafka.consumer:type=consumer-coordinator-metrics,client-id=([-.\w]+) 2
# attributes: heartbeat-response-time-max, heartbeat-rate, join-time-max, join-rate, rebalance-rate-per-hour
kafka.producer:type=producer-metrics,client-id=([-.\w]+) 3
# attributes: buffer-available-bytes, bufferpool-wait-time, request-latency-max, requests-in-flight
# attributes: txn-init-time-ns-total, txn-begin-time-ns-total, txn-send-offsets-time-ns-total, txn-commit-time-ns-total, txn-abort-time-ns-total
# attributes: record-error-total, record-queue-time-avg, record-queue-time-max, record-retry-rate, record-retry-total, record-send-rate, record-send-total

1
(Consumer) Average and maximum time between poll requests, which can help determine if the consumers are polling for messages frequently enough to keep up with the message flow. The time-between-poll-avg and time-between-poll-max attributes show the average and maximum time in milliseconds between successive polls by a consumer, respectively.
2
(Consumer) Metrics to monitor the coordination process between Kafka consumers and the broker coordinator. Attributes relate to the heartbeat, join, and rebalance process.
3
(Producer) Metrics to monitor the performance of Kafka producers. Attributes relate to buffer usage, request latency, in-flight requests, transactional processing, and record handling.

20.4.6. Using metrics to check the performance of topics and partitions

Metrics for topics and partitions can also be helpful in diagnosing issues in a Kafka cluster. You can also use them to debug issues with a specific client when you are unable to collect client metrics.

Here are some metrics to check the performance of a specific topic and partition:

Metrics to check the performance of topics and partitions

#Topic metrics
kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec,topic=([-.\w]+) 1
kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec,topic=([-.\w]+) 2
kafka.server:type=BrokerTopicMetrics,name=FailedFetchRequestsPerSec,topic=([-.\w]+) 3
kafka.server:type=BrokerTopicMetrics,name=FailedProduceRequestsPerSec,topic=([-.\w]+) 4
kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec,topic=([-.\w]+) 5
kafka.server:type=BrokerTopicMetrics,name=TotalFetchRequestsPerSec,topic=([-.\w]+) 6
kafka.server:type=BrokerTopicMetrics,name=TotalProduceRequestsPerSec,topic=([-.\w]+) 7
#Partition metrics
kafka.log:type=Log,name=Size,topic=([-.\w]+),partition=([\d]+)) 8
kafka.log:type=Log,name=NumLogSegments,topic=([-.\w]+),partition=([\d]+)) 9
kafka.log:type=Log,name=LogEndOffset,topic=([-.\w]+),partition=([\d]+)) 10
kafka.log:type=Log,name=LogStartOffset,topic=([-.\w]+),partition=([\d]+)) 11

1
Rate of incoming bytes per second for a specific topic.
2
Rate of outgoing bytes per second for a specific topic.
3
Rate of fetch requests that failed per second for a specific topic.
4
Rate of produce requests that failed per second for a specific topic.
5
Incoming message rate per second for a specific topic.
6
Total rate of fetch requests (successful and failed) per second for a specific topic.
7
Total rate of fetch requests (successful and failed) per second for a specific topic.
8
Size of a specific partition’s log in bytes.
9
Number of log segments in a specific partition.
10
Offset of the last message in a specific partition’s log.
11
Offset of the first message in a specific partition’s log

Additional resources

Appendix A. Using your subscription

AMQ Streams is provided through a software subscription. To manage your subscriptions, access your account at the Red Hat Customer Portal.

Accessing Your Account

  1. Go to access.redhat.com.
  2. If you do not already have an account, create one.
  3. Log in to your account.

Activating a Subscription

  1. Go to access.redhat.com.
  2. Navigate to My Subscriptions.
  3. Navigate to Activate a subscription and enter your 16-digit activation number.

Downloading Zip and Tar Files

To access zip or tar files, use the customer portal to find the relevant files for download. If you are using RPM packages, this step is not required.

  1. Open a browser and log in to the Red Hat Customer Portal Product Downloads page at access.redhat.com/downloads.
  2. Locate the AMQ Streams for Apache Kafka entries in the INTEGRATION AND AUTOMATION category.
  3. Select the desired AMQ Streams product. The Software Downloads page opens.
  4. Click the Download link for your component.

Installing packages with DNF

To install a package and all the package dependencies, use:

dnf install <package_name>

To install a previously-downloaded package from a local directory, use:

dnf install <path_to_download_package>

Revised on 2023-10-16 10:49:24 UTC

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