Debezium User Guide

Red Hat Integration 2021.Q1

For use with Debezium 1.4

Red Hat Integration Documentation Team

Abstract

This guide describes how to use the connectors provided with Debezium.

Preface

Debezium is a set of distributed services that capture row-level changes in your databases so that your applications can see and respond to those changes. Debezium records all row-level changes committed to each database table. Each application reads the transaction logs of interest to view all operations in the order in which they occurred.

This guide provides details about using the following Debezium connectors:

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. High level overview of Debezium

Debezium is a set of distributed services that capture changes in your databases. Your applications can consume and respond to those changes. Debezium captures each row-level change in each database table in a change event record and streams these records to Kafka topics. Applications read these streams, which provide the change event records in the same order in which they were generated.

More details are in the following sections:

1.1. Debezium Features

Debezium is a set of source connectors for Apache Kafka Connect. Each connector ingests changes from a different database by using that database’s features for change data capture (CDC). Unlike other approaches, such as polling or dual writes, log-based CDC as implemented by Debezium:

  • Ensures that all data changes are captured.
  • Produces change events with a very low delay while avoiding increased CPU usage required for frequent polling. For example, for MySQL or PostgreSQL, the delay is in the millisecond range.
  • Requires no changes to your data model, such as a "Last Updated" column.
  • Can capture deletes.
  • Can capture old record state and additional metadata such as transaction ID and causing query, depending on the database’s capabilities and configuration.

Five Advantages of Log-Based Change Data Capture is a blog post that provides more details.

Debezium connectors capture data changes with a range of related capabilities and options:

  • Snapshots: optionally, an initial snapshot of a database’s current state can be taken if a connector is started and not all logs still exist. Typically, this is the case when the database has been running for some time and has discarded trannsaction logs that are no longer needed for transaction recovery or replication. There are different modes for performing snapshots. See the documentation for the connector that you are using.
  • Filters: you can configure the set of captured schemas, tables and columns with include/exclude list filters.
  • Masking: the values from specific columns can be masked, for example, when they contain sensitive data.
  • Monitoring: most connectors can be monitored by using JMX.
  • Ready-to-use message transformations for:

The documentation for each connector provides details about the connectors features and configuration options.

1.2. Description of Debezium architecture

You deploy Debezium by means of Apache Kafka Connect. Kafka Connect is a framework and runtime for implementing and operating:

  • Source connectors such as Debezium that send records into Kafka
  • Sink connectors that propagate records from Kafka topics to other systems

The following image shows the architecture of a change data capture pipeline based on Debezium:

Debezium Architecture

As shown in the image, the Debezium connectors for MySQL and PostgresSQL are deployed to capture changes to these two types of databases. Each Debezium connector establishes a connection to its source database:

  • The MySQL connector uses a client library for accessing the binlog.
  • The PostgreSQL connector reads from a logical replication stream.

Kafka Connect operates as a separate service besides the Kafka broker.

By default, changes from one database table are written to a Kafka topic whose name corresponds to the table name. If needed, you can adjust the destination topic name by configuring Debezium’s topic routing transformation. For example, you can:

  • Route records to a topic whose name is different from the table’s name
  • Stream change event records for multiple tables into a single topic

After change event records are in Apache Kafka, different connectors in the Kafka Connect eco-system can stream the records to other systems and databases such as Elasticsearch, data warehouses and analytics systems, or caches such as Infinispan. Depending on the chosen sink connector, you might need to configure Debezium’s new record state extraction transformation. This Kafka Connect SMT propagates the after structure from Debezium’s change event to the sink connector. This is in place of the verbose change event record that is propagated by default.

Chapter 2. Debezium connector for MySQL

MySQL has a binary log (binlog) that records all operations in the order in which they are committed to the database. This includes changes to table schemas as well as changes to the data in tables. MySQL uses the binlog for replication and recovery.

The Debezium MySQL connector reads the binlog, produces change events for row-level INSERT, UPDATE, and DELETE operations, and emits the change events to Kafka topics. Client applications read those Kafka topics.

As MySQL is typically set up to purge binlogs after a specified period of time, the MySQL connector performs an initial consistent snapshot of each of your databases. The MySQL connector reads the binlog from the point at which the snapshot was made.

Information and procedures for using a Debezium MySQL connector are organized as follows:

2.1. How Debezium MySQL connectors work

An overview of the MySQL topologies that the connector supports is useful for planning your application. To optimally configure and run a Debezium MySQL connector, it is helpful to understand how the connector tracks the structure of tables, exposes schema changes, performs snapshots, and determines Kafka topic names.

Details are in the following topics:

2.1.1. MySQL topologies supported by Debezium connectors

The Debezium MySQL connector supports the following MySQL topologies:

Standalone
When a single MySQL server is used, the server must have the binlog enabled (and optionally GTIDs enabled) so the Debezium MySQL connector can monitor the server. This is often acceptable, since the binary log can also be used as an incremental backup. In this case, the MySQL connector always connects to and follows this standalone MySQL server instance.
Primary and replica

The Debezium MySQL connector can follow one of the primary servers or one of the replicas (if that replica has its binlog enabled), but the connector sees changes in only the cluster that is visible to that server. Generally, this is not a problem except for the multi-primary topologies.

The connector records its position in the server’s binlog, which is different on each server in the cluster. Therefore, the connector must follow just one MySQL server instance. If that server fails, that server must be restarted or recovered before the connector can continue.

High available clusters
A variety of high availability solutions exist for MySQL, and they make it significantly easier to tolerate and almost immediately recover from problems and failures. Most HA MySQL clusters use GTIDs so that replicas are able to keep track of all changes on any of the primary servers.
Multi-primary

Network Database (NDB) cluster replication uses one or more MySQL replica nodes that each replicate from multiple primary servers. This is a powerful way to aggregate the replication of multiple MySQL clusters. This topology requires the use of GTIDs.

A Debezium MySQL connector can use these multi-primary MySQL replicas as sources, and can fail over to different multi-primary MySQL replicas as long as the new replica is caught up to the old replica. That is, the new replica has all transactions that were seen on the first replica. This works even if the connector is using only a subset of databases and/or tables, as the connector can be configured to include or exclude specific GTID sources when attempting to reconnect to a new multi-primary MySQL replica and find the correct position in the binlog.

Hosted

There is support for the Debezium MySQL connector to use hosted options such as Amazon RDS and Amazon Aurora.

Because these hosted options do not allow a global read lock, table-level locks are used to create the consistent snapshot.

2.1.2. How Debezium MySQL connectors handle database schema changes

When a database client queries a database, the client uses the database’s current schema. However, the database schema can be changed at any time, which means that the connector must be able to identify what the schema was at the time each insert, update, or delete operation was recorded. Also, a connector cannot just use the current schema because the connector might be processing events that are relatively old and may have been recorded before the tables' schemas were changed.

To handle this, MySQL includes in the binlog not only the row-level changes to the data, but also the DDL statements that are applied to the database. As the connector reads the binlog and comes across these DDL statements, it parses them and updates an in-memory representation of each table’s schema. The connector uses this schema representation to identify the structure of the tables at the time of each insert, update, or delete operation and to produce the appropriate change event. In a separate database history Kafka topic, the connector records all DDL statements along with the position in the binlog where each DDL statement appeared.

When the connector restarts after having crashed or been stopped gracefully, the connector starts reading the binlog from a specific position, that is, from a specific point in time. The connector rebuilds the table structures that existed at this point in time by reading the database history Kafka topic and parsing all DDL statements up to the point in the binlog where the connector is starting.

This database history topic is for connector use only. The connector can optionally See emit schema change events to a different topic that is intended for consumer applications.

When the MySQL connector captures changes in a table to which a schema change tool such as gh-ost or pt-online-schema-change is applied there are helper tables created during the migration process. The connector needs to be configured to capture change to these helper tables. If consumers do not need the records generated for helper tables then a single message transform can be applied to filter them out.

See default names for topics that receive Debezium event records.

2.1.3. How Debezium MySQL connectors expose database schema changes

You can configure a Debezium MySQL connector to produce schema change events that include all DDL statements applied to databases in the MySQL server. The connector emits these events to a Kafka topic named serverName where serverName is the name of the connector as specified by the database.server.name connector configuration property.

If you choose to use schema change events, ensure that you consume records from the schema change topic. The database history topic is for connector use only.

Important

A global order for events emitted to the schema change topic is vital. Therefore, you must not partition the database history topic. This means that you must specify a partition count of 1 when creating the database history topic. When relying on auto topic creation, make sure that Kafka’s num.partitions configuration option, which specifies the default number of partitions, is set to 1.

Each record that the connector emits to the schema change topic contains a message key that includes the name of the connected database when the DDL statement was applied, for example:

{
  "schema": {
    "type": "struct",
    "name": "io.debezium.connector.mysql.SchemaChangeKey",
    "optional": false,
    "fields": [
      {
        "field": "databaseName",
        "type": "string",
        "optional": false
      }
    ]
  },
  "payload": {
    "databaseName": "inventory"
  }
}

The schema change event record value contains a structure that includes the DDL statements, the name of the database to which the statements were applied, and the position in the binlog where the statements appeared, for example:

{
  "schema": {
    "type": "struct",
    "name": "io.debezium.connector.mysql.SchemaChangeValue",
    "optional": false,
    "fields": [
      {
        "field": "databaseName",
        "type": "string",
        "optional": false
      },
      {
        "field": "ddl",
        "type": "string",
        "optional": false
      },
      {
        "field": "source",
        "type": "struct",
        "name": "io.debezium.connector.mysql.Source",
        "optional": false,
        "fields": [
          {
            "type": "string",
            "optional": true,
            "field": "version"
          },
          {
            "type": "string",
            "optional": false,
            "field": "name"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "server_id"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "ts_ms"
          },
          {
            "type": "string",
            "optional": true,
            "field": "gtid"
          },
          {
            "type": "string",
            "optional": false,
            "field": "file"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "pos"
          },
          {
            "type": "int32",
            "optional": false,
            "field": "row"
          },
          {
            "type": "boolean",
            "optional": true,
            "default": false,
            "field": "snapshot"
          },
          {
            "type": "int64",
            "optional": true,
            "field": "thread"
          },
          {
            "type": "string",
            "optional": true,
            "field": "db"
          },
          {
            "type": "string",
            "optional": true,
            "field": "table"
          },
          {
            "type": "string",
            "optional": true,
            "field": "query"
          }
        ]
      }
    ]
  },
  "payload": {
    "databaseName": "inventory",
    "ddl": "CREATE TABLE products ( id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255) NOT NULL, description VARCHAR(512), weight FLOAT ); ALTER TABLE products AUTO_INCREMENT = 101;",
    "source" : {
      "version": "1.4.2.Final",
      "name": "mysql-server-1",
      "server_id": 0,
      "ts_ms": 0,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 154,
      "row": 0,
      "snapshot": true,
      "thread": null,
      "db": null,
      "table": null,
      "query": null
    }
  }
}

The ddl field might contain multiple DDL statements. Each statement applies to the database in the databaseName field. The statements appear in the order in which they were applied to the database. The source field is structured exactly as a standard data change event written to table-specific topics. This field is useful to correlate events on different topics.

....
"payload": {
    "databaseName": "inventory",
    "ddl": "CREATE TABLE products ( id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,...)",
    "source" : {
        ...
    }
}
....

A client can submit multiple DDL statements to be applied to multiple databases. If MySQL applies them atomically, the connector takes the DDL statements in order, groups them by database, and creates a schema change event for each group. If MySQL applies them individually, the connector creates a separate schema change event for each statement.

See also: schema history topic.

2.1.4. How Debezium MySQL connectors perform database snapshots

When a Debezium MySQL connector is first started, it performs an initial consistent snapshot of your database. The following flow describes how the connector creates this snapshot. This flow is for the default snapshot mode, which is initial. For information about other snapshot modes, see the MySQL connector snapshot.mode configuration property.

Table 2.1. Workflow for performing an initial snapshot with a global read lock

StepAction

1

Grabs a global read lock that blocks writes by other database clients.

The snapshot itself does not prevent other clients from applying DDL that might interfere with the connector’s attempt to read the binlog position and table schemas. The connector keeps the global read lock while it reads the binlog position, and releases the lock as described in a later step.

2

Starts a transaction with repeatable read semantics to ensure that all subsequent reads within the transaction are done against the consistent snapshot.

3

Reads the current binlog position.

4

Reads the schema of the databases and tables for which the connector is configured to capture changes.

5

Releases the global read lock. Other database clients can now write to the database.

6

If applicable, writes the DDL changes to the schema change topic, including all necessary DROP…​ and CREATE…​ DDL statements.

7

Scans the database tables. For each row, the connector emits CREATE events to the relevant table-specific Kafka topics.

8

Commits the transaction.

9

Records the completed snapshot in the connector offsets.

Connector restarts

If the connector fails, stops, or is rebalanced while performing the initial snapshot, then after the connector restarts, it performs a new snapshot. After that intial snapshot is completed, the Debezium MySQL connector restarts from the same position in the binlog so it does not miss any updates.

If the connector stops for long enough, MySQL could purge old binlog files and the connector’s position would be lost. If the position is lost, the connector reverts to the initial snapshot for its starting position. For more tips on troubleshooting the Debezium MySQL connector, see behavior when things go wrong.

Global read locks not allowed

Some environments do not allow global read locks. If the Debezium MySQL connector detects that global read locks are not permitted, the connector uses table-level locks instead and performs a snapshot with this method. This requires the database user for the Debezium connector to have LOCK TABLES privileges.

Table 2.2. Workflow for performing an initial snapshot with table-level locks

StepAction

1

Obtains table-level locks.

2

Starts a transaction with repeatable read semantics to ensure that all subsequent reads within the transaction are done against the consistent snapshot.

3

Reads and filters the names of the databases and tables.

4

Reads the current binlog position.

5

Reads the schema of the databases and tables for which the connector is configured to capture changes.

6

If applicable, writes the DDL changes to the schema change topic, including all necessary DROP…​ and CREATE…​ DDL statements.

7

Scans the database tables. For each row, the connector emits CREATE events to the relevant table-specific Kafka topics.

8

Commits the transaction.

9

Releases the table-level locks.

10

Records the completed snapshot in the connector offsets.

2.1.5. Default names of Kafka topics that receive Debezium MySQL change event records

The default behavior is that a Debezium MySQL connector writes events for all INSERT, UPDATE, and DELETE operations in one table to one Kafka topic. The Kafka topic naming convention is as follows:

serverName.databaseName.tableName

Suppose that fulfillment is the server name, inventory is the database name, and the database contains tables named orders, customers, and products. The Debezium MySQL connector emits events to three Kafka topics, one for each table in the database:

fulfillment.inventory.orders
fulfillment.inventory.customers
fulfillment.inventory.products

2.2. Descriptions of Debezium MySQL connector data change events

The Debezium MySQL connector generates a data change event for each row-level INSERT, UPDATE, and DELETE operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { 1
   ...
  },
 "payload": { 2
   ...
 },
 "schema": { 3
   ...
 },
 "payload": { 4
   ...
 },
}

Table 2.3. Overview of change event basic content

ItemField nameDescription

1

schema

The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s payload portion. In other words, the first schema field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed.

It is possible to override the table’s primary key by setting the message.key.columns connector configuration property. In this case, the first schema field describes the structure of the key identified by that property.

2

payload

The first payload field is part of the event key. It has the structure described by the previous schema field and it contains the key for the row that was changed.

3

schema

The second schema field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s payload portion. In other words, the second schema describes the structure of the row that was changed. Typically, this schema contains nested schemas.

4

payload

The second payload field is part of the event value. It has the structure described by the previous schema field and it contains the actual data for the row that was changed.

By default, the connector streams change event records to topics with names that are the same as the event’s originating table. See topic names.

Warning

The MySQL connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This means that the logical server name must start with an alphabetic character or an underscore, that is, a-z, A-Z, or _. Each remaining character in the logical server name and each character in the database and table names must be an alphabetic character, a digit, or an underscore, that is, a-z, A-Z, 0-9, or _. If there is an invalid character it is replaced with an underscore character.

This can lead to unexpected conflicts if the logical server name, a database name, or a table name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

More details are in the following topics:

2.2.1. About keys in Debezium MySQL change events

A change event’s key contains the schema for the changed table’s key and the changed row’s actual key. Both the schema and its corresponding payload contain a field for each column in the changed table’s PRIMARY KEY (or unique constraint) at the time the connector created the event.

Consider the following customers table, which is followed by an example of a change event key for this table.

CREATE TABLE customers (
  id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL UNIQUE KEY
) AUTO_INCREMENT=1001;

Every change event that captures a change to the customers table has the same event key schema. For as long as the customers table has the previous definition, every change event that captures a change to the customers table has the following key structure. In JSON, it looks like this:

{
 "schema": { 1
    "type": "struct",
    "name": "mysql-server-1.inventory.customers.Key", 2
    "optional": false, 3
    "fields": [ 4
      {
        "field": "id",
        "type": "int32",
        "optional": false
      }
    ]
  },
 "payload": { 5
    "id": 1001
  }
}

Table 2.4. Description of change event key

ItemField nameDescription

1

schema

The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s payload portion.

2

mysql-server-1.inventory.customers.Key

Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the primary key for the table that was changed. Key schema names have the format connector-name.database-name.table-name.Key. In this example:

  • mysql-server-1 is the name of the connector that generated this event.
  • inventory is the database that contains the table that was changed.
  • customers is the table that was updated.

3

optional

Indicates whether the event key must contain a value in its payload field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a table does not have a primary key.

4

fields

Specifies each field that is expected in the payload, including each field’s name, type, and whether it is required.

5

payload

Contains the key for the row for which this change event was generated. In this example, the key, contains a single id field whose value is 1001.

2.2.2. About values in Debezium MySQL change events

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample table that was used to show an example of a change event key:

CREATE TABLE customers (
  id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL UNIQUE KEY
) AUTO_INCREMENT=1001;

The value portion of a change event for a change to this table is described for:

create events

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers table:

{
  "schema": { 1
    "type": "struct",
    "fields": [
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "mysql-server-1.inventory.customers.Value", 2
        "field": "before"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "mysql-server-1.inventory.customers.Value",
        "field": "after"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "string",
            "optional": false,
            "field": "version"
          },
          {
            "type": "string",
            "optional": false,
            "field": "connector"
          },
          {
            "type": "string",
            "optional": false,
            "field": "name"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "ts_ms"
          },
          {
            "type": "boolean",
            "optional": true,
            "default": false,
            "field": "snapshot"
          },
          {
            "type": "string",
            "optional": false,
            "field": "db"
          },
          {
            "type": "string",
            "optional": true,
            "field": "table"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "server_id"
          },
          {
            "type": "string",
            "optional": true,
            "field": "gtid"
          },
          {
            "type": "string",
            "optional": false,
            "field": "file"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "pos"
          },
          {
            "type": "int32",
            "optional": false,
            "field": "row"
          },
          {
            "type": "int64",
            "optional": true,
            "field": "thread"
          },
          {
            "type": "string",
            "optional": true,
            "field": "query"
          }
        ],
        "optional": false,
        "name": "io.debezium.connector.mysql.Source", 3
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "mysql-server-1.inventory.customers.Envelope" 4
  },
  "payload": { 5
    "op": "c", 6
    "ts_ms": 1465491411815, 7
    "before": null, 8
    "after": { 9
      "id": 1004,
      "first_name": "Anne",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "source": { 10
      "version": "1.4.2.Final",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 0,
      "snapshot": false,
      "db": "inventory",
      "table": "customers",
      "server_id": 0,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 154,
      "row": 0,
      "thread": 7,
      "query": "INSERT INTO customers (first_name, last_name, email) VALUES ('Anne', 'Kretchmar', 'annek@noanswer.org')"
    }
  }
}

Table 2.5. Descriptions of create event value fields

ItemField nameDescription

1

schema

The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular table.

2

name

In the schema section, each name field specifies the schema for a field in the value’s payload.

mysql-server-1.inventory.customers.Value is the schema for the payload’s before and after fields. This schema is specific to the customers table.

Names of schemas for before and after fields are of the form logicalName.tableName.Value, which ensures that the schema name is unique in the database. This means that when using the Avro converter, the resulting Avro schema for each table in each logical source has its own evolution and history.

3

name

io.debezium.connector.mysql.Source is the schema for the payload’s source field. This schema is specific to the MySQL connector. The connector uses it for all events that it generates.

4

name

mysql-server-1.inventory.customers.Envelope is the schema for the overall structure of the payload, where mysql-server-1 is the connector name, inventory is the database, and customers is the table.

5

payload

The value’s actual data. This is the information that the change event is providing.

It may appear that the JSON representations of the events are much larger than the rows they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.

6

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, c indicates that the operation created a row. Valid values are:

  • c = create
  • u = update
  • d = delete
  • r = read (applies to only snapshots)

7

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

8

before

An optional field that specifies the state of the row before the event occurred. When the op field is c for create, as it is in this example, the before field is null since this change event is for new content.

9

after

An optional field that specifies the state of the row after the event occurred. In this example, the after field contains the values of the new row’s id, first_name, last_name, and email columns.

10

source

Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes:

  • Debezium version
  • Connector name
  • binlog name where the event was recorded
  • binlog position
  • Row within the event
  • If the event was part of a snapshot
  • Name of the database and table that contain the new row
  • ID of the MySQL thread that created the event (non-snapshot only)
  • MySQL server ID (if available)
  • Timestamp for when the change was made in the database

If the binlog_rows_query_log_events MySQL configuration option is enabled and the connector configuration include.query property is enabled, the source field also provides the query field, which contains the original SQL statement that caused the change event.

update events

The value of a change event for an update in the sample customers table has the same schema as a create event for that table. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. Here is an example of a change event value in an event that the connector generates for an update in the customers table:

{
  "schema": { ... },
  "payload": {
    "before": { 1
      "id": 1004,
      "first_name": "Anne",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "after": { 2
      "id": 1004,
      "first_name": "Anne Marie",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "source": { 3
      "version": "1.4.2.Final",
      "name": "mysql-server-1",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 1465581029100,
      "snapshot": false,
      "db": "inventory",
      "table": "customers",
      "server_id": 223344,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 484,
      "row": 0,
      "thread": 7,
      "query": "UPDATE customers SET first_name='Anne Marie' WHERE id=1004"
    },
    "op": "u", 4
    "ts_ms": 1465581029523 5
  }
}

Table 2.6. Descriptions of update event value fields

ItemField nameDescription

1

before

An optional field that specifies the state of the row before the event occurred. In an update event value, the before field contains a field for each table column and the value that was in that column before the database commit. In this example, the first_name value is Anne.

2

after

An optional field that specifies the state of the row after the event occurred. You can compare the before and after structures to determine what the update to this row was. In the example, the first_name value is now Anne Marie.

3

source

Mandatory field that describes the source metadata for the event. The source field structure has the same fields as in a create event, but some values are different, for example, the sample update event is from a different position in the binlog. The source metadata includes:

  • Debezium version
  • Connector name
  • binlog name where the event was recorded
  • binlog position
  • Row within the event
  • If the event was part of a snapshot
  • Name of the database and table that contain the updated row
  • ID of the MySQL thread that created the event (non-snapshot only)
  • MySQL server ID (if available)
  • Timestamp for when the change was made in the database

If the binlog_rows_query_log_events MySQL configuration option is enabled and the connector configuration include.query property is enabled, the source field also provides the query field, which contains the original SQL statement that caused the change event.

4

op

Mandatory string that describes the type of operation. In an update event value, the op field value is u, signifying that this row changed because of an update.

5

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

Note

Updating the columns for a row’s primary/unique key changes the value of the row’s key. When a key changes, Debezium outputs three events: a DELETE event and a tombstone event with the old key for the row, followed by an event with the new key for the row. Details are in the next section.

Primary key updates

An UPDATE operation that changes a row’s primary key field(s) is known as a primary key change. For a primary key change, in place of an UPDATE event record, the connector emits a DELETE event record for the old key and a CREATE event record for the new (updated) key. These events have the usual structure and content, and in addition, each one has a message header related to the primary key change:

  • The DELETE event record has __debezium.newkey as a message header. The value of this header is the new primary key for the updated row.
  • The CREATE event record has __debezium.oldkey as a message header. The value of this header is the previous (old) primary key that the updated row had.

delete events

The value in a delete change event has the same schema portion as create and update events for the same table. The payload portion in a delete event for the sample customers table looks like this:

{
  "schema": { ... },
  "payload": {
    "before": { 1
      "id": 1004,
      "first_name": "Anne Marie",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "after": null, 2
    "source": { 3
      "version": "1.4.2.Final",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 1465581902300,
      "snapshot": false,
      "db": "inventory",
      "table": "customers",
      "server_id": 223344,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 805,
      "row": 0,
      "thread": 7,
      "query": "DELETE FROM customers WHERE id=1004"
    },
    "op": "d", 4
    "ts_ms": 1465581902461 5
  }
}

Table 2.7. Descriptions of delete event value fields

ItemField nameDescription

1

before

Optional field that specifies the state of the row before the event occurred. In a delete event value, the before field contains the values that were in the row before it was deleted with the database commit.

2

after

Optional field that specifies the state of the row after the event occurred. In a delete event value, the after field is null, signifying that the row no longer exists.

3

source

Mandatory field that describes the source metadata for the event. In a delete event value, the source field structure is the same as for create and update events for the same table. Many source field values are also the same. In a delete event value, the ts_ms and pos field values, as well as other values, might have changed. But the source field in a delete event value provides the same metadata:

  • Debezium version
  • Connector name
  • binlog name where the event was recorded
  • binlog position
  • Row within the event
  • If the event was part of a snapshot
  • Name of the database and table that contain the updated row
  • ID of the MySQL thread that created the event (non-snapshot only)
  • MySQL server ID (if available)
  • Timestamp for when the change was made in the database

If the binlog_rows_query_log_events MySQL configuration option is enabled and the connector configuration include.query property is enabled, the source field also provides the query field, which contains the original SQL statement that caused the change event.

4

op

Mandatory string that describes the type of operation. The op field value is d, signifying that this row was deleted.

5

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

A delete change event record provides a consumer with the information it needs to process the removal of this row. The old values are included because some consumers might require them in order to properly handle the removal.

MySQL connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

When a row is deleted, the delete event value still works with log compaction, because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that same key, the message value must be null. To make this possible, after Debezium’s MySQL connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value.

2.3. How Debezium MySQL connectors map data types

The Debezium MySQL connector represents changes to rows with events that are structured like the table in which the row exists. The event contains a field for each column value. The MySQL data type of that column dictates how Debezium represents the value in the event.

Columns that store strings are defined in MySQL with a character set and collation. The MySQL connector uses the column’s character set when reading the binary representation of the column values in the binlog events.

The connector can map MySQL data types to both literal and semantic types.

  • Literal type: how the value is represented using Kafka Connect schema types
  • Semantic type: how the Kafka Connect schema captures the meaning of the field (schema name)

Details are in the following sections:

Basic types

The following table shows how the connector maps basic MySQL data types.

Table 2.8. Descriptions of basic type mappings

MySQL typeLiteral typeSemantic type

BOOLEAN, BOOL

BOOLEAN

n/a

BIT(1)

BOOLEAN

n/a

BIT(>1)

BYTES

io.debezium.data.Bits
The length schema parameter contains an integer that represents the number of bits. The byte[] contains the bits in little-endian form and is sized to contain the specified number of bits. For example, where n is bits:
numBytes = n/8 + (n%8== 0 ? 0 : 1)

TINYINT

INT16

n/a

SMALLINT[(M)]

INT16

n/a

MEDIUMINT[(M)]

INT32

n/a

INT, INTEGER[(M)]

INT32

n/a

BIGINT[(M)]

INT64

n/a

REAL[(M,D)]

FLOAT32

n/a

FLOAT[(M,D)]

FLOAT64

n/a

DOUBLE[(M,D)]

FLOAT64

n/a

CHAR(M)]

STRING

n/a

VARCHAR(M)]

STRING

n/a

BINARY(M)]

BYTES or STRING

n/a
Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on the binary.handling.mode connector configuration property setting.

VARBINARY(M)]

BYTES or STRING

n/a
Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on the binary.handling.mode connector configuration property setting.

TINYBLOB

BYTES or STRING

n/a
Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on the binary.handling.mode connector configuration property setting.

TINYTEXT

STRING

n/a

BLOB

BYTES or STRING

n/a
Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on the binary.handling.mode connector configuration property setting.

TEXT

STRING

n/a

MEDIUMBLOB

BYTES or STRING

n/a
Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on the binary.handling.mode connector configuration property setting.

MEDIUMTEXT

STRING

n/a

LONGBLOB

BYTES or STRING

n/a
Either the raw bytes (the default), a base64-encoded String, or a hex-encoded String, based on the binary.handling.mode connector configuration property setting.

LONGTEXT

STRING

n/a

JSON

STRING

io.debezium.data.Json
Contains the string representation of a JSON document, array, or scalar.

ENUM

STRING

io.debezium.data.Enum
The allowed schema parameter contains the comma-separated list of allowed values.

SET

STRING

io.debezium.data.EnumSet
The allowed schema parameter contains the comma-separated list of allowed values.

YEAR[(2|4)]

INT32

io.debezium.time.Year

TIMESTAMP[(M)]

STRING

io.debezium.time.ZonedTimestamp
In ISO 8601 format with microsecond precision. MySQL allows M to be in the range of 0-6.

Temporal types

Excluding the TIMESTAMP data type, MySQL temporal types depend on the value of the time.precision.mode connector configuration property. For TIMESTAMP columns whose default value is specified as CURRENT_TIMESTAMP or NOW, the value 1970-01-01 00:00:00 is used as the default value in the Kafka Connect schema.

MySQL allows zero-values for DATE, `DATETIME, and TIMESTAMP columns because zero-values are sometimes preferred over null values. The MySQL connector represents zero-values as null values when the column definition allows null values, or as the epoch day when the column does not allow null values.

Temporal values without time zones

The DATETIME type represents a local date and time such as "2018-01-13 09:48:27". As you can see, there is no time zone information. Such columns are converted into epoch milliseconds or microseconds based on the column’s precision by using UTC. The TIMESTAMP type represents a timestamp without time zone information. It is converted by MySQL from the server (or session’s) current time zone into UTC when writing and from UTC into the server (or session’s) current time zone when reading back the value. For example:

  • DATETIME with a value of 2018-06-20 06:37:03 becomes 1529476623000.
  • TIMESTAMP with a value of 2018-06-20 06:37:03 becomes 2018-06-20T13:37:03Z.

Such columns are converted into an equivalent io.debezium.time.ZonedTimestamp in UTC based on the server (or session’s) current time zone. The time zone will be queried from the server by default. If this fails, it must be specified explicitly by the database serverTimezone MySQL configuration option. For example, if the database’s time zone (either globally or configured for the connector by means of the serverTimezone option) is "America/Los_Angeles", the TIMESTAMP value "2018-06-20 06:37:03" is represented by a ZonedTimestamp with the value "2018-06-20T13:37:03Z".

The time zone of the JVM running Kafka Connect and Debezium does not affect these conversions.

More details about properties related to termporal values are in the documentation for MySQL connector configuration properties.

time.precision.mode=adaptive_time_microseconds(default)

The MySQL connector determines the literal type and semantic type based on the column’s data type definition so that events represent exactly the values in the database. All time fields are in microseconds. Only positive TIME field values in the range of 00:00:00.000000 to 23:59:59.999999 can be captured correctly.

Table 2.9. Mappings when time.precision.mode=adaptive_time_microseconds

MySQL typeLiteral typeSemantic type

DATE

INT32

io.debezium.time.Date
Represents the number of days since the epoch.

TIME[(M)]

INT64

io.debezium.time.MicroTime
Represents the time value in microseconds and does not include time zone information. MySQL allows M to be in the range of 0-6.

DATETIME, DATETIME(0), DATETIME(1), DATETIME(2), DATETIME(3)

INT64

io.debezium.time.Timestamp
Represents the number of milliseconds past the epoch and does not include time zone information.

DATETIME(4), DATETIME(5), DATETIME(6)

INT64

io.debezium.time.MicroTimestamp
Represents the number of microseconds past the epoch and does not include time zone information.

time.precision.mode=connect

The MySQL connector uses defined Kafka Connect logical types. This approach is less precise than the default approach and the events could be less precise if the database column has a fractional second precision value of greater than 3. Values in only the range of 00:00:00.000 to 23:59:59.999 can be handled. Set time.precision.mode=connect only if you can ensure that the TIME values in your tables never exceed the supported ranges. The connect setting is expected to be removed in a future version of Debezium.

Table 2.10. Mappings when time.precision.mode=connect

MySQL typeLiteral typeSemantic type

DATE

INT32

org.apache.kafka.connect.data.Date
Represents the number of days since the epoch.

TIME[(M)]

INT64

org.apache.kafka.connect.data.Time
Represents the time value in microseconds since midnight and does not include time zone information.

DATETIME[(M)]

INT64

org.apache.kafka.connect.data.Timestamp
Represents the number of milliseconds since the epoch, and does not include time zone information.

Decimal types

Debezium connectors handle decimals according to the setting of the decimal.handling.mode connector configuration property.

decimal.handling.mode=precise

Table 2.11. Mappings when decimal.handing.mode=precise

MySQL typeLiteral typeSemantic type

NUMERIC[(M[,D])]

BYTES

org.apache.kafka.connect.data.Decimal
The scale schema parameter contains an integer that represents how many digits the decimal point shifted.

DECIMAL[(M[,D])]

BYTES

org.apache.kafka.connect.data.Decimal
The scale schema parameter contains an integer that represents how many digits the decimal point shifted.

decimal.handling.mode=double

Table 2.12. Mappings when decimal.handing.mode=double

MySQL typeLiteral typeSemantic type

NUMERIC[(M[,D])]

FLOAT64

n/a

DECIMAL[(M[,D])]

FLOAT64

n/a

decimal.handling.mode=string

Table 2.13. Mappings when decimal.handing.mode=string

MySQL typeLiteral typeSemantic type

NUMERIC[(M[,D])]

STRING

n/a

DECIMAL[(M[,D])]

STRING

n/a

Boolean values

MySQL handles the BOOLEAN value internally in a specific way. The BOOLEAN column is internally mapped to the TINYINT(1) data type. When the table is created during streaming then it uses proper BOOLEAN mapping as Debezium receives the original DDL. During snapshots, Debezium executes SHOW CREATE TABLE to obtain table definitions that return TINYINT(1) for both BOOLEAN and TINYINT(1) columns. Debezium then has no way to obtain the original type mapping and so maps to TINYINT(1).

Following is an example configuration:

converters=boolean
boolean.type=io.debezium.connector.mysql.converters.TinyIntOneToBooleanConverter
boolean.selector=db1.table1.*, db1.table2.column1

Spatial types

Currently, the Debezium MySQL connector supports the following spatial data types.

Table 2.14. Description of spatial type mappings

MySQL typeLiteral typeSemantic type

GEOMETRY,
LINESTRING,
POLYGON,
MULTIPOINT,
MULTILINESTRING,
MULTIPOLYGON,
GEOMETRYCOLLECTION

STRUCT

io.debezium.data.geometry.Geometry
Contains a structure with two fields:

  • srid (INT32: spatial reference system ID that defines the type of geometry object stored in the structure
  • wkb (BYTES): binary representation of the geometry object encoded in the Well-Known-Binary (wkb) format. See the Open Geospatial Consortium for more details.

2.4. Setting up MySQL to run a Debezium connector

Some MySQL setup tasks are required before you can install and run a Debezium connector.

Details are in the following sections:

2.4.1. Creating a MySQL user for a Debezium connector

A Debezium MySQL connector requires a MySQL user account. This MySQL user must have appropriate permissions on all databases for which the Debezium MySQL connector captures changes.

Prerequisites

  • A MySQL server.
  • Basic knowledge of SQL commands.

Procedure

  1. Create the MySQL user:

    mysql> CREATE USER 'user'@'localhost' IDENTIFIED BY 'password';
  2. Grant the required permissions to the user:

    mysql> GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'user' IDENTIFIED BY 'password';

    The table below describes the permissions.

    Important

    If using a hosted option such as Amazon RDS or Amazon Aurora that does not allow a global read lock, table-level locks are used to create the consistent snapshot. In this case, you need to also grant LOCK TABLES permissions to the user that you create. See snapshots for more details.

  3. Finalize the user’s permissions:

    mysql> FLUSH PRIVILEGES;

Table 2.15. Descriptions of user permissions

KeywordDescription

SELECT

Enables the connector to select rows from tables in databases. This is used only when performing a snapshot.

RELOAD

Enables the connector the use of the FLUSH statement to clear or reload internal caches, flush tables, or acquire locks. This is used only when performing a snapshot.

SHOW DATABASES

Enables the connector to see database names by issuing the SHOW DATABASE statement. This is used only when performing a snapshot.

REPLICATION SLAVE

Enables the connector to connect to and read the MySQL server binlog.

REPLICATION CLIENT

Enables the connector the use of the following statements:

  • SHOW MASTER STATUS
  • SHOW SLAVE STATUS
  • SHOW BINARY LOGS

The connector always requires this.

ON

Identifies the database to which the permissions apply.

TO 'user'

Specifies the user to grant the permissions to.

IDENTIFIED BY 'password'

Specifies the user’s MySQL password.

2.4.2. Enabling the MySQL binlog for Debezium

You must enable binary logging for MySQL replication. The binary logs record transaction updates for replication tools to propagate changes.

Prerequisites

  • A MySQL server.
  • Appropriate MySQL user privileges.

Procedure

  1. Check whether the log-bin option is already on:

    mysql> SELECT variable_value as "BINARY LOGGING STATUS (log-bin) ::"
    FROM information_schema.global_variables WHERE variable_name='log_bin';
  2. If it is OFF, configure your MySQL server configuration file with the following properties, which are described in the table below:

    server-id         = 223344
    log_bin           = mysql-bin
    binlog_format     = ROW
    binlog_row_image  = FULL
    expire_logs_days  = 10
  3. Confirm your changes by checking the binlog status once more:

    mysql> SELECT variable_value as "BINARY LOGGING STATUS (log-bin) ::"
    FROM information_schema.global_variables WHERE variable_name='log_bin';

Table 2.16. Descriptions of MySQL binlog configuration properties

PropertyDescription

server-id

The value for the server-id must be unique for each server and replication client in the MySQL cluster. During MySQL connector set up, Debezium assigns a unique server ID to the connector.

log_bin

The value of log_bin is the base name of the sequence of binlog files.

binlog_format

The binlog-format must be set to ROW or row.

binlog_row_image

The binlog_row_image must be set to FULL or full.

expire_logs_days

This is the number of days for automatic binlog file removal. The default is 0, which means no automatic removal. Set the value to match the needs of your environment. See MySQL purges binlog files.

2.4.3. Enabling MySQL Global Transaction Identifiers for Debezium

Global transaction identifiers (GTIDs) uniquely identify transactions that occur on a server within a cluster. Though not required for a Debezium MySQL connector, using GTIDs simplifies replication and enables you to more easily confirm if primary and replica servers are consistent.

GTIDs are available in MySQL 5.6.5 and later. See the MySQL documentation for more details.

Prerequisites

  • A MySQL server.
  • Basic knowledge of SQL commands.
  • Access to the MySQL configuration file.

Procedure

  1. Enable gtid_mode:

    mysql> gtid_mode=ON
  2. Enable enforce_gtid_consistency:

    mysql> enforce_gtid_consistency=ON
  3. Confirm the changes:

    mysql> show global variables like '%GTID%';

Result

+--------------------------+-------+
| Variable_name            | Value |
+--------------------------+-------+
| enforce_gtid_consistency | ON    |
| gtid_mode                | ON    |
+--------------------------+-------+

Table 2.17. Descriptions of GTID options

OptionDescription

gtid_mode

Boolean that specifies whether GTID mode of the MySQL server is enabled or not.

  • ON = enabled
  • OFF = disabled

enforce_gtid_consistency

Boolean that specifies whether the server enforces GTID consistency by allowing the execution of statements that can be logged in a transactionally safe manner. Required when using GTIDs.

  • ON = enabled
  • OFF = disabled

2.4.4. Configuring MySQL session timesouts for Debezium

When an initial consistent snapshot is made for large databases, your established connection could timeout while the tables are being read. You can prevent this behavior by configuring interactive_timeout and wait_timeout in your MySQL configuration file.

Prerequisites

  • A MySQL server.
  • Basic knowledge of SQL commands.
  • Access to the MySQL configuration file.

Procedure

  1. Configure interactive_timeout:

    mysql> interactive_timeout=<duration-in-seconds>
  2. Configure wait_timeout:

    mysql> wait_timeout=<duration-in-seconds>

Table 2.18. Descriptions of MySQL session timeout options

OptionDescription

interactive_timeout

The number of seconds the server waits for activity on an interactive connection before closing it. See MySQL’s documentation for more details.

wait_timeout

The number of seconds the server waits for activity on a non-interactive connection before closing it. See MySQL’s documentation for more details.

2.4.5. Enabling query log events for Debezium MySQL connectors

You might want to see the original SQL statement for each binlog event. Enabling the binlog_rows_query_log_events option in the MySQL configuration file allows you to do this.

This option is available in MySQL 5.6 and later.

Prerequisites

  • A MySQL server.
  • Basic knowlede of SQL commands.
  • Access to the MySQL configuration file.

Procedure

  • Enable binlog_rows_query_log_events:

    mysql> binlog_rows_query_log_events=ON

    binlog_rows_query_log_events is set to a value that enables/disables support for including the original SQL statement in the binlog entry.

    • ON = enabled
    • OFF = disabled

2.5. Deployment of Debezium MySQL connectors

To deploy a Debezium MySQL connector, you add the connector files to Kafka Connect, create a custom container to run the connector, and then add the connector configuration to your container. For details about deploying the Debezium MySQL connector, see the following topics:

2.5.1. Deploying Debezium MySQL connectors

To deploy a Debezium MySQL connector, you must build a custom Kafka Connect container image that contains the Debezium connector archive, and then push this container image to a container registry. You then need to create the following custom resources (CRs):

  • A KafkaConnect CR that defines your Kafka Connect instance. The image property in the CR specifies the name of the container image that you create to run your Debezium connector. You apply this CR to the OpenShift instance where Red Hat AMQ Streams is deployed. AMQ Streams offers operators and images that bring Apache Kafka to OpenShift.
  • A KafkaConnector CR that defines your Debezium MySQL connector. Apply this CR to the same OpenShift instance where you apply the KafkaConnect CR.

Prerequisites

  • MySQL is running and you completed the steps to set up MySQL to work with a Debezium connector.
  • AMQ Streams is deployed on OpenShift and is running Apache Kafka and Kafka Connect. For more information, see Installing Debezium on OpenShift.
  • Podman or Docker is installed.
  • You have an account and permissions to create and manage containers in the container registry (such as quay.io or docker.io) to which you plan to add the container that will run your Debezium connector.

Procedure

  1. Create the Debezium MySQL container for Kafka Connect:

    1. Download the Debezium MySQL connector archive.
    2. Extract the Debezium MySQL connector archive to create a directory structure for the connector plug-in, for example:

      ./my-plugins/
      ├── debezium-connector-mysql
      │   ├── ...
    3. Create a Docker file that uses registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.0 as the base image. For example, from a terminal window, enter the following, replacing my-plugins with the name of your plug-ins directory:

      cat <<EOF >debezium-container-for-mysql.yaml 1
      FROM registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.0
      USER root:root
      COPY ./<my-plugins>/ /opt/kafka/plugins/ 2
      USER 1001
      EOF
      1 1 1 1 1 1
      You can specify any file name that you want.
      2 2 2 2 2 2
      Replace my-plugins with the name of your plug-ins directory.

      The command creates a Docker file with the name debezium-container-for-mysql.yaml in the current directory.

    4. Build the container image from the debezium-container-for-mysql.yaml Docker file that you created in the previous step. From the directory that contains the file, open a terminal window and enter one of the following commands:

      podman build -t debezium-container-for-mysql:latest .
      docker build -t debezium-container-for-mysql:latest .

      The preceding commands build a container image with the name debezium-container-for-mysql.

    5. Push your custom image to a container registry, such as quay.io or an internal container registry. The container registry must be available to the OpenShift instance where you want to deploy the image. Enter one of the following commands:

      podman push <myregistry.io>/debezium-container-for-mysql:latest
      docker push <myregistry.io>/debezium-container-for-mysql:latest
    6. Create a new Debezium MySQL KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies annotations and image properties as shown in the following example:

      apiVersion: kafka.strimzi.io/v1beta1
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
        annotations:
          strimzi.io/use-connector-resources: "true" 1
      spec:
        #...
        image: debezium-container-for-mysql  2
      1
      metadata.annotations indicates to the Cluster Operator that KafkaConnector resources are used to configure connectors in this Kafka Connect cluster.
      2
      spec.image specifies the name of the image that you created to run your Debezium connector. This property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator.
    7. Apply the KafkaConnect CR to the OpenShift Kafka Connect environment by entering the following command:

      oc create -f dbz-connect.yaml

      The command adds a Kafka Connect instance that specifies the name of the image that you created to run your Debezium connector.

  2. Create a KafkaConnector custom resource that configures your Debezium MySQL connector instance.

    You configure a Debezium MySQL connector in a .yaml file that specifies the configuration properties for the connector. The connector configuration might instruct Debezium to produce events for a subset of the schemas and tables, or it might set properties so that Debezium ignores, masks, or truncates values in specified columns that are sensitive, too large, or not needed.

    The following example configures a Debezium connector that connects to a MySQL host, 192.168.99.100, on port 3306, and captures changes to the inventory database. dbserver1 is the server’s logical name.

    MySQL inventory-connector.yaml

      apiVersion: kafka.strimzi.io/v1beta1
      kind: KafkaConnector
      metadata:
        name: inventory-connector  1
        labels:
          strimzi.io/cluster: my-connect-cluster
      spec:
        class: io.debezium.connector.mysql.MySqlConnector
        tasksMax: 1  2
        config:  3
          database.hostname: mysql  4
          database.port: 3306
          database.user: debezium
          database.password: dbz
          database.server.id: 184054  5
          database.server.name: dbserver1 6
          database.include.list: inventory  7
          database.history.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092  8
          database.history.kafka.topic: schema-changes.inventory  9

    Table 2.19. Descriptions of connector configuration settings

    ItemDescription

    1

    The name of the connector.

    2

    Only one task should operate at any one time. Because the MySQL connector reads the MySQL server’s binlog, using a single connector task ensures proper order and event handling. The Kafka Connect service uses connectors to start one or more tasks that do the work, and it automatically distributes the running tasks across the cluster of Kafka Connect services. If any of the services stop or crash, those tasks will be redistributed to running services.

    3

    The connector’s configuration.

    4

    The database host, which is the name of the container running the MySQL server (mysql).

    5

    Unique ID of the connector.

    6

    Logical name of the MySQL server or cluster. This name is used as the prefix for all Kafka topics that receive change event records.

    7

    Changes in only the inventory database are captured.

    8

    The list of Kafka brokers that this connector will use to write and recover DDL statements to the database history topic. Upon restart, the connector recovers the schemas of the database that existed at the point in time in the binlog when the connector should begin reading.

    9

    The name of the database history topic. This topic is for internal use only and should not be used by consumers.

  3. Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in the inventory-connector.yaml file, you would run the following command:

    oc apply -f inventory-connector.yaml

    The preceding command registers inventory-connector and the connector starts to run against the inventory database as defined in the KafkaConnector CR.

  4. Verify that the connector was created and has started:

    1. Display the Kafka Connect log output to verify that the connector was created and has started to capture changes in the specified database:

      oc logs $(oc get pods -o name -l strimzi.io/cluster=my-connect-cluster)
    2. Review the log output to verify that Debezium performs the initial snapshot. The log displays output that is similar to the following messages:

      ... INFO Starting snapshot for ...
      ... INFO Snapshot is using user 'debezium' ...

      If the connector starts correctly without errors, it creates a topic for each table whose changes the connector is capturing. For the example CR, there would be a topic for the table specified in the include.list property. Downstream applications can subscribe to these topics.

    3. Verify that the connector created the topics by running the following command:

      oc get kafkatopics

For the complete list of the configuration properties that you can set for the Debezium MySQL connector, see MySQL connector configuration properties.

Results

When the connector starts, it performs a consistent snapshot of the MySQL databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming change event records to Kafka topics.

2.5.2. Description of Debezium MySQL connector configuration properties

The Debezium MySQL connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:

The following configuration properties are required unless a default value is available.

Table 2.20. Required MySQL connector configuration properties

PropertyDefaultDescription

name

 

Unique name for the connector. Attempting to register again with the same name fails. This property is required by all Kafka Connect connectors.

connector.class

 

The name of the Java class for the connector. Always specify io.debezium.connector.mysql.MySqlConnector for the MySQL connector.

tasks.max

1

The maximum number of tasks that should be created for this connector. The MySQL connector always uses a single task and therefore does not use this value, so the default is always acceptable.

database.hostname

 

IP address or host name of the MySQL database server.

database.port

3306

Integer port number of the MySQL database server.

database.user

 

Name of the MySQL user to use when connecting to the MySQL database server.

database.password

 

Password to use when connecting to the MySQL database server.

database.server.name

 

Logical name that identifies and provides a namespace for the particular MySQL database server/cluster in which Debezium is capturing changes. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names that receive events emitted by this connector. Only alphanumeric characters and underscores are allowed in this name.

database.server.id

random

A numeric ID of this database client, which must be unique across all currently-running database processes in the MySQL cluster. This connector joins the MySQL database cluster as another server (with this unique ID) so it can read the binlog. By default, a random number between 5400 and 6400 is generated, though the recommendation is to explicitly set a value.

database.history.kafka.topic

 

The full name of the Kafka topic where the connector stores the database schema history.

database.history.kafka.bootstrap.servers

 

A list of host/port pairs that the connector uses for establishing an initial connection to the Kafka cluster. This connection is used for retrieving database schema history previously stored by the connector, and for writing each DDL statement read from the source database. Each pair should point to the same Kafka cluster used by the Kafka Connect process.

database.include.list

empty string

An optional, comma-separated list of regular expressions that match the names of the databases for which to capture changes. The connector does not capture changes in any database whose name is not in database.include.list. By default, the connector captures changes in all databases. Do not also set the database.exclude.list connector confiuration property.

database.exclude.list

empty string

An optional, comma-separated list of regular expressions that match the names of databases for which you do not want to capture changes. The connector captures changes in any database whose name is not in the database.exclude.list. Do not also set the database.include.list connector configuration property.

table.include.list

empty string

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers of tables whose changes you want to capture. The connector does not capture changes in any table not included in table.include.list. Each identifier is of the form databaseName.tableName. By default, the connector captures changes in every non-system table in each database whose changes are being captured. Do not also specify the table.exclude.list connector configuration property.

table.exclude.list

empty string

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you do not want to capture. The connector captures changes in any table not included in table.exclude.list. Each identifier is of the form databaseName.tableName. Do not also specify the table.include.list connector configuration property.

column.exclude.list

empty string

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event record values. Fully-qualified names for columns are of the form databaseName.tableName.columnName.

column.include.list

empty string

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns to include in change event record values. Fully-qualified names for columns are of the form databaseName.tableName.columnName.

column.truncate.to._length_.chars

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be truncated in the change event record values if the field values are longer than the specified number of characters. You can configure multiple properties with different lengths in a single configuration. The length must be a positive integer. Fully-qualified names for columns are of the form databaseName.tableName.columnName.

column.mask.with._length_.chars

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be replaced in the change event message values with a field value consisting of the specified number of asterisk (*) characters. You can configure multiple properties with different lengths in a single configuration. Each length must be a positive integer or zero. Fully-qualified names for columns are of the form databaseName.tableName.columnName.

column.mask.hash._hashAlgorithm_.with.salt._salt_

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be pseudonyms in the change event record values. Pseudonyms consist of the hashed value obtained by applying the algorithm hashAlgorithm and salt salt.

Based on the hash function used, referential integrity is kept while data is pseudonymized. Supported hash functions are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation. The hash result is automatically shortened to the length of the column.

You can configure multiple properties with different lengths in a single configuration. Each length must be a positive integer or zero. Fully-qualified names for columns are of the form databaseName.tableName.columnName. For example:

column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = inventory.orders.customerName, inventory.shipment.customerName

CzQMA0cB5K is a randomly selected salt.
Depending on the configured hashAlgorithm, the selected salt, and the actual data set, the resulting masked data set might not be completely anonymized.

column.propagate.source.type

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change event records. These schema parameters:

__Debezium.source.column.type

__Debezium.source.column.length

__Debezium.source.column.scale

are used to propagate the original type name and length for variable-width types, respectively. This is useful to properly size corresponding columns in sink databases. Fully-qualified names for columns are of one of these forms:

databaseName.tableName.columnName

databaseName.schemaName.tableName.columnName

datatype.propagate.source.type

n/a

An optional, comma-separated list of regular expressions that match the database-specific data type name of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change event records. These schema parameters:

__debezium.source.column.type

__debezium.source.column.length

__debezium.source.column.scale

are used to propagate the original type name and length for variable-width types, respectively. This is useful to properly size corresponding columns in sink databases. Fully-qualified data type names are of one of these forms:

databaseName.tableName.typeName

databaseName.schemaName.tableName.typeName

See how MySQL connectors map data types for the list of MySQL-specific data type names.

time.precision.mode

adaptive_time_microseconds

Time, date, and timestamps can be represented with different kinds of precision, including:

adaptive_time_microseconds (the default) captures the date, datetime and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type, with the exception of TIME type fields, which are always captured as microseconds.


connect always represents time and timestamp values using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which use millisecond precision regardless of the database columns' precision.

decimal.handling.mode

precise

Specifies how the connector should handle values for DECIMAL and NUMERIC columns:

precise (the default) represents them precisely using java.math.BigDecimal values represented in change events in a binary form.

double represents them using double values, which may result in a loss of precision but is easier to use.

string encodes values as formatted strings, which is easy to consume but semantic information about the real type is lost.

bigint.unsigned.handling.mode

long

Specifies how BIGINT UNSIGNED columns should be represented in change events. Possible settings are:

long represents values by using Java’s long, which might not offer the precision but which is easy to use in consumers. long is usually the preferred setting.

precise uses java.math.BigDecimal to represent values, which are encoded in the change events by using a binary representation and Kafka Connect’s org.apache.kafka.connect.data.Decimal type. Use this setting when working with values larger than 2^63, because these values cannot be conveyed by using long.

include.schema.changes

true

Boolean value that specifies whether the connector should publish changes in the database schema to a Kafka topic with the same name as the database server ID. Each schema change is recorded by using a key that contains the database name and whose value includes the DDL statement(s). This is independent of how the connector internally records database history.

include.query

false

Boolean value that specifies whether the connector should include the original SQL query that generated the change event.

If you set this option to true then you must also configure MySQL with the binlog_rows_query_log_events option set to ON. When include.query is true, the query is not present for events that the snapshot process generates.

Setting include.query to true might expose tables or fields that are explicitly excluded or masked by including the original SQL statement in the change event. For this reason, the default setting is false.

event.deserialization.failure.handling.mode

fail

Specifies how the connector should react to exceptions during deserialization of binlog events.

fail propagates the exception, which indicates the problematic event and its binlog offset, and causes the connector to stop.

warn logs the problematic event and its binlog offset and then skips the event.

skip passes over the problematic event and does not log anything.

inconsistent.schema.handling.mode

fail

Specifies how the connector should react to binlog events that relate to tables that are not present in internal schema representation. That is, the internal representation is not consistent with the database.

fail throws an exception that indicates the problematic event and its binlog offset, and causes the connector to stop.

warn logs the problematic event and its binlog offset and skips the event.

skip passes over the problematic event and does not log anything.

max.queue.size

8192

Positive integer value that specifies the maximum size of the blocking queue into which change events read from the database log are placed before they are written to Kafka. This queue can provide backpressure to the binlog reader when, for example, writes to Kafka are slow or if Kafka is not available. Events that appear in the queue are not included in the offsets periodically recorded by this connector. Defaults to 8192, and should always be larger than the maximum batch size specified by the max.batch.size property.

max.batch.size

2048

Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector. Defaults to 2048.

max.queue.size.in.bytes

0

Long value for the maximum size in bytes of the blocking queue. The feature is disabled by default, it will be active if it’s set with a positive long value.

poll.interval.ms

1000

Positive integer value that specifies the number of milliseconds the connector should wait for new change events to appear before it starts processing a batch of events. Defaults to 1000 milliseconds, or 1 second.

connect.timeout.ms

30000

A positive integer value that specifies the maximum time in milliseconds this connector should wait after trying to connect to the MySQL database server before timing out. Defaults to 30 seconds.

gtid.source.includes

 

A comma-separated list of regular expressions that match source UUIDs in the GTID set used to find the binlog position in the MySQL server. Only the GTID ranges that have sources that match one of these include patterns are used. Do not also specify a setting for gtid.source.excludes.

gtid.source.excludes

 

A comma-separated list of regular expressions that match source UUIDs in the GTID set used to find the binlog position in the MySQL server. Only the GTID ranges that have sources that do not match any of these exclude patterns are used. Do not also specify a value for gtid.source.includes.

tombstones.on.delete

true

Controls whether a delete event is followed by a tombstone event.

true - a delete operation is represented by a delete event and a subsequent tombstone event.

false - only a delete event is emitted.

After a source record is deleted, emitting a tombstone event (the default behavior) allows Kafka to completely delete all events that pertain to the key of the deleted row.

message.key.columns

n/a

A semicolon separated list of tables with regular expressions that match table column names. The connector maps values in matching columns to key fields in change event records that it sends to Kafka topics. This is useful when a table does not have a primary key, or when you want to order change event records in a Kafka topic according to a field that is not a primary key.

Separate entries with semicolons. Insert a colon between the fully-qualified table name and its regular expression. The format (shown with spaces for clarity only) is:

database-name . table-name : regexp ; …​

For example:

dbA.table_a:regex_1;dbB.table_b:regex_2;dbC.table_c:regex_3

If table_a has an id column, and regex_1 is ^i (matches any column that starts with i), the connector maps the value in the id column of table_a to a key field in change events that the connector sends to Kafka.

binary.handling.mode

bytes

Specifies how binary columns, for example, blob, binary, varbinary, should be represented in change events. Possible settings:

bytes represents binary data as a byte array.

base64 represents binary data as a base64-encoded String.

hex represents binary data as a hex-encoded (base16) String.

Advanced MySQL connector configuration properties

The following table describes advanced MySQL connector properties. The default values for these properties rarely need to be changed. Therefore, you do not need to specify them in the connector configuration.

Table 2.21. Descriptions of MySQL connector advanced configuration properties

PropertyDefaultDescription

connect.keep.alive

true

A Boolean value that specifies whether a separate thread should be used to ensure that the connection to the MySQL server/cluster is kept alive.

table.ignore.builtin

true

A Boolean value that specifies whether built-in system tables should be ignored. This applies regardless of the table include and exclude lists. By default, system tables are excluded from having their changes captured, and no events are generated when changes are made to any system tables.

database.history.kafka.recovery.poll.interval.ms

100

An integer value that specifies the maximum number of milliseconds the connector should wait during startup/recovery while polling for persisted data. The default is 100ms.

database.history.kafka.recovery.attempts

4

The maximum number of times that the connector should try to read persisted history data before the connector recovery fails with an error. The maximum amount of time to wait after receiving no data is recovery.attempts x recovery.poll.interval.ms.

database.history.skip.unparseable.ddl

false

A Boolean value that specifies whether the connector should ignore malformed or unknown database statements or stop processing so a human can fix the issue. The safe default is false. Skipping should be used only with care as it can lead to data loss or mangling when the binlog is being processed.

database.history.store.only.monitored.tables.ddl

false

A Boolean value that specifies whether the connector should record all DDL statements

true records only those DDL statements that are relevant to tables whose changes are being captured by Debezium. Set to true with care because missing data might become necessary if you change which tables have their changes captured.

The safe default is false.

database.ssl.mode

disabled

Specifies whether to use an encrypted connection. Possible settings are:

disabled specifies the use of an unencrypted connection.

preferred establishes an encrypted connection if the server supports secure connections. If the server does not support secure connections, falls back to an unencrypted connection.

required establishes an encrypted connection or fails if one cannot be made for any reason.

verify_ca behaves like required but additionally it verifies the server TLS certificate against the configured Certificate Authority (CA) certificates and fails if the server TLS certificate does not match any valid CA certificates.

verify_identity behaves like verify_ca but additionally verifies that the server certificate matches the host of the remote connection.

binlog.buffer.size

0

The size of a look-ahead buffer used by the binlog reader. The default setting of 0 disables buffering.

Under specific conditions, it is possible that the MySQL binlog contains uncommitted data finished by a ROLLBACK statement. Typical examples are using savepoints or mixing temporary and regular table changes in a single transaction.

When a beginning of a transaction is detected then Debezium tries to roll forward the binlog position and find either COMMIT or ROLLBACK so it can determine whether to stream the changes from the transaction. The size of the binlog buffer defines the maximum number of changes in the transaction that Debezium can buffer while searching for transaction boundaries. If the size of the transaction is larger than the buffer then Debezium must rewind and re-read the events that have not fit into the buffer while streaming.

NOTE: This feature is incubating. Feedback is encouraged. It is expected that this feature is not completely polished.

snapshot.mode

initial

Specifies the criteria for running a snapshot when the connector starts. Possible settings are:

initial - the connector runs a snapshot only when no offsets have been recorded for the logical server name.

when_needed - the connector runs a snapshot upon startup whenever it deems it necessary. That is, when no offsets are available, or when a previously recorded offset specifies a binlog location or GTID that is not available in the server.

never - the connector never uses snapshots. Upon first startup with a logical server name, the connector reads from the beginning of the binlog. Configure this behavior with care. It is valid only when the binlog is guaranteed to contain the entire history of the database.

schema_only - the connector runs a snapshot of the schemas and not the data. This setting is useful when you do not need the topics to contain a consistent snapshot of the data but need them to have only the changes since the connector was started.

schema_only_recovery - this is a recovery setting for a connector that has already been capturing changes. When you restart the connector, this setting enables recovery of a corrupted or lost database history topic. You might set it periodically to "clean up" a database history topic that has been growing unexpectedly. Database history topics require infinite retention.

snapshot.locking.mode

minimal

Controls whether and how long the connector holds the global MySQL read lock, which prevents any updates to the database, while the connector is performing a snapshot. Possible settings are:

minimal - the connector holds the global read lock for only the initial portion of the snapshot during which the connector reads the database schemas and other metadata. The remaining work in a snapshot involves selecting all rows from each table. The connector can do this in a consistent fashion by using a REPEATABLE READ transaction. This is the case even when the global read lock is no longer held and other MySQL clients are updating the database.

minimal_percona - the connector holds the global backup lock for only the initial portion of the snapshot during which the connector reads the database schemas and other metadata. The remaining work in a snapshot involves selecting all rows from each table. The connector can do this in a consistent fashion by using a REPEATABLE READ transaction. This is the case even when the global backup lock is no longer held and other MySQL clients are updating the database. This mode does not flush tables to disk, is not blocked by long-running reads, and is available only in Percona Server.

extended - blocks all writes for the duration of the snapshot. Use this setting if there are clients that are submitting operations that MySQL excludes from REPEATABLE READ semantics.

none - prevents the connector from acquiring any table locks during the snapshot. While this setting is allowed with all snapshot modes, it is safe to use if and only if no schema changes are happening while the snapshot is running. For tables defined with MyISAM engine, the tables would still be locked despite this property being set as MyISAM acquires a table lock. This behavior is unlike InnoDB engine, which acquires row level locks.

snapshot.include.collection.list

All tables specified in table.include.list

An optional, comma-separated list of regular expressions that match names of schemas specified in table.include.list for which you want to take the snapshot.

snapshot.select.statement.overrides

 

Controls which table rows are included in snapshots. This property affects snapshots only. It does not affect events captured from the binlog. Specify a comma-separated list of fully-qualified table names in the form databaseName.tableName.

For each table that you specify, also specify another configuration property: snapshot.select.statement.overrides.DB_NAME.TABLE_NAME. For example, the name of the other configuration property might be: snapshot.select.statement.overrides.customers.orders. Set this property to a SELECT statement that obtains only the rows that you want in the snapshot. When the connector performs a snapshot, it executes this SELECT statement to retrieve data from that table.

A possible use case for setting these properties is large, append-only tables. You can specify a SELECT statement that sets a specific point for where to start a snapshot, or where to resume a snapshot if a previous snapshot was interrupted.

min.row.count.to.stream.results

1000

During a snapshot, the connector queries each table for which the connector is configured to capture changes. The connector uses each query result to produce a read event that contains data for all rows in that table. This property determines whether the MySQL connector puts results for a table into memory, which is fast but requires large amounts of memory, or streams the results, which can be slower but work for very large tables. The setting of this property specifies the minimum number of rows a table must contain before the connector streams results.

To skip all table size checks and always stream all results during a snapshot, set this property to 0.

heartbeat.interval.ms

0

Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default behavior is that the connector does not send heartbeat messages.

Heartbeat messages are useful for monitoring whether the connector is receiving change events from the database. Heartbeat messages might help decrease the number of change events that need to be re-sent when a connector restarts. To send heartbeat messages, set this property to a positive integer, which indicates the number of milliseconds between heartbeat messages.

heartbeat.topics.prefix

__debezium-heartbeat

Controls the name of the topic to which the connector sends heartbeat messages. The topic name has this pattern:

heartbeat.topics.prefix.server.name

For example, if the database server name is fulfillment, the default topic name is __debezium-heartbeat.fulfillment.

database.initial.statements

 

A semicolon separated list of SQL statements to be executed when a JDBC connection, not the connection that is reading the transaction log, to the database is established. To specify a semicolon as a character in a SQL statement and not as a delimiter, use two semicolons, (;;).

The connector might establish JDBC connections at its own discretion, so this property is ony for configuring session parameters. It is not for executing DML statements.

snapshot.delay.ms

 

An interval in milliseconds that the connector should wait before performing a snapshot when the connector starts. If you are starting multiple connectors in a cluster, this property is useful for avoiding snapshot interruptions, which might cause re-balancing of connectors.

snapshot.fetch.size

 

During a snapshot, the connector reads table content in batches of rows. This property specifies the maximum number of rows in a batch.

snapshot.lock.timeout.ms

10000

Positive integer that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If the connector cannot acquire table locks in this time interval, the snapshot fails. See how MySQL connectors perform database snapshots.

enable.time.adjuster

true

Boolean value that indicates whether the connector converts a 2-digit year specification to 4 digits. Set to false when conversion is fully delegated to the database.

MySQL allows users to insert year values with either 2-digits or 4-digits. For 2-digit values, the value gets mapped to a year in the range 1970 - 2069. The default behavior is that the connector does the conversion.

sanitize.field.names

true if connector configuration sets the key.converter or value.converter property to the Avro converter.
false if not.

Indicates whether field names are sanitized to adhere to Avro naming requirements.

skipped.operations

 

Comma-separated list of oplog operations to skip during streaming. Values that you can specify are: c for inserts/create, u for updates, d for deletes. By default, no operations are skipped.

Pass-through configuration properties

The MySQL connector also supports pass-through configuration properties that are used when creating the Kafka producer and consumer. Specifically, all connector configuration properties that begin with the database.history.producer. prefix are used (without the prefix) when creating the Kafka producer that writes to the database history. All properties that begin with the prefix database.history.consumer. are used (without the prefix) when creating the Kafka consumer that reads the database history upon connector start-up.

For example, the following connector configuration properties can be used to secure connections to the Kafka broker:

database.history.producer.security.protocol=SSL
database.history.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.producer.ssl.keystore.password=test1234
database.history.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.producer.ssl.truststore.password=test1234
database.history.producer.ssl.key.password=test1234
database.history.consumer.security.protocol=SSL
database.history.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.consumer.ssl.keystore.password=test1234
database.history.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.consumer.ssl.truststore.password=test1234
database.history.consumer.ssl.key.password=test1234

See the Kafka documentation for more details about pass-through properties.

Pass-through properties for database drivers

In addition to the pass-through properties for the Kafka producer and consumer, there are pass-through properties for database drivers. These properties have the database. prefix. For example, database.tinyInt1isBit=false is passed to the JDBC URL.

2.6. Monitoring Debezium MySQL connector performance

The Debezium MySQL connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect provide.

  • Snapshot metrics provide information about connector operation while performing a snapshot.
  • Binlog metrics provide information about connector operation when the connector is reading the binlog.
  • Schema history metrics provide information about the status of the connector’s schema history.

Debezium monitoring documentation provides details for how to expose these metrics by using JMX.

2.6.1. Monitoring Debezium during snapshots of MySQL databases

The MBean is debezium.mysql:type=connector-metrics,context=snapshot,server=<database.server.name>.

AttributesTypeDescription

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

SnapshotAborted

boolean

Whether the snapshot was aborted.

SnapshotCompleted

boolean

Whether the snapshot completed.

SnapshotDurationInSeconds

long

The total number of seconds that the snapshot has taken so far, even if not complete.

RowsScanned

Map<String, Long>

Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. It will be enabled if max.queue.size.in.bytes is passed with a positive long value.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

The Debezium MySQL connector also provides the HoldingGlobalLock custom snapshot metric. This metric is set to a Boolean value that indicates whether the connector currently holds a global or table write lock.

2.6.2. Monitoring Debezium MySQL connector binlog reading

The MBean is debezium.mysql:type=connector-metrics,context=binlog,server=<database.server.name>.

Transaction-related attributes are available only if binlog event buffering is enabled. See binlog.buffer.size in the advanced connector configuration properties for more details.

AttributesTypeDescription

LastEvent

string

The last streaming event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the streamer and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.

Connected

boolean

Flag that denotes whether the connector is currently connected to the database server.

MilliSecondsBehindSource

long

The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

SourceEventPosition

Map<String, String>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

The Debezium MySQL connector also provides the following custom binlog metrics:

Table 2.22. Descriptions of custom binlog metrics

AttributeTypeDescription

BinlogFilename

string

The name of the binlog file that the connector has most recently read.

BinlogPosition

long

The most recent position (in bytes) within the binlog that the connector has read.

IsGtidModeEnabled

boolean

Flag that denotes whether the connector is currently tracking GTIDs from MySQL server.

GtidSet

string

The string representation of the most recent GTID set processed by the connector when reading the binlog.

NumberOfSkippedEvents

long

The number of events that have been skipped by the MySQL connector. Typically events are skipped due to a malformed or unparseable event from MySQL’s binlog.

NumberOfDisconnects

long

The number of disconnects by the MySQL connector.

NumberOfRolledBackTransactions

long

The number of processed transactions that were rolled back and not streamed.

NumberOfNotWellFormedTransactions

long

The number of transactions that have not conformed to the expected protocol of BEGIN + COMMIT/ROLLBACK. This value should be 0 under normal conditions.

NumberOfLargeTransactions

long

The number of transactions that have not fit into the look-ahead buffer. For optimal performance, this value should be significantly smaller than NumberOfCommittedTransactions and NumberOfRolledBackTransactions.

2.6.3. Monitoring Debezium MySQL connector schema history

The MBean is debezium.mysql:type=connector-metrics,context=schema-history,server=<database.server.name>.

AttributesTypeDescription

Status

string

One of STOPPED, RECOVERING (recovering history from the storage), RUNNING describing the state of the database history.

RecoveryStartTime

long

The time in epoch seconds at what recovery has started.

ChangesRecovered

long

The number of changes that were read during recovery phase.

ChangesApplied

long

the total number of schema changes applied during recovery and runtime.

MilliSecondsSinceLast​RecoveredChange

long

The number of milliseconds that elapsed since the last change was recovered from the history store.

MilliSecondsSinceLast​AppliedChange

long

The number of milliseconds that elapsed since the last change was applied.

LastRecoveredChange

string

The string representation of the last change recovered from the history store.

LastAppliedChange

string

The string representation of the last applied change.

2.7. How Debezium MySQL connectors handle faults and problems

Debezium is a distributed system that captures all changes in multiple upstream databases; it never misses or loses an event. When the system is operating normally or being managed carefully then Debezium provides exactly once delivery of every change event record.

If a fault does happen then the system does not lose any events. However, while it is recovering from the fault, it might repeat some change events. In these abnormal situations, Debezium, like Kafka, provides at least once delivery of change events.

Details are in the following sections:

Configuration and startup errors

In the following situations, the connector fails when trying to start, reports an error or exception in the log, and stops running:

  • The connector’s configuration is invalid.
  • The connector cannot successfully connect to the MySQL server by using the specified connection parameters.
  • The connector is attempting to restart at a position in the binlog for which MySQL no longer has the history available.

In these cases, the error message has details about the problem and possibly a suggested workaround. After you correct the configuration or address the MySQL problem, restart the connector.

MySQL becomes unavailable

If your MySQL server becomes unavailable, the Debezium MySQL connector fails with an error and the connector stops. When the server is available again, restart the connector.

However, if GTIDs are enabled for a highly available MySQL cluster, you can restart the connector immediately. It will connect to a different MySQL server in the cluster, find the location in the server’s binlog that represents the last transaction, and begin reading the new server’s binlog from that specific location.

If GTIDs are not enabled, the connector records the binlog position of only the MySQL server to which it was connected. To restart from the correct binlog position, you must reconnect to that specific server.

Kafka Connect stops gracefully

When Kafka Connect stops gracefully, there is a short delay while the Debezium MySQL connector tasks are stopped and restarted on new Kafka Connect processes.

Kafka Connect process crashes

If Kafka Connect crashes, the process stops and any Debezium MySQL connector tasks terminate without their most recently-processed offsets being recorded. In distributed mode, Kafka Connect restarts the connector tasks on other processes. However, the MySQL connector resumes from the last offset recorded by the earlier processes. This means that the replacement tasks might generate some of the same events processed prior to the crash, creating duplicate events.

Each change event message includes source-specific information that you can use to identify duplicate events, for example:

  • Event origin
  • MySQL server’s event time
  • The binlog file name and position
  • GTIDs (if used)

Kafka becomes unavailable

The Kafka Connect framework records Debezium change events in Kafka by using the Kafka producer API. If the Kafka brokers become unavailable, the Debezium MySQL connector pauses until the connection is reestablished and the connector resumes where it left off.

MySQL purges binlog files

If the Debezium MySQL connector stops for too long, the MySQL server purges older binlog files and the connector’s last position may be lost. When the connector is restarted, the MySQL server no longer has the starting point and the connector performs another initial snapshot. If the snapshot is disabled, the connector fails with an error.

See snapshots for details about how MySQL connectors perform initial snapshots.

Chapter 3. Debezium connector for PostgreSQL

Debezium’s PostgreSQL connector captures row-level changes in the schemas of a PostgreSQL database. PostgreSQL versions 10, 11, and 12 are supported.

The first time it connects to a PostgreSQL server or cluster, the connector takes a consistent snapshot of all schemas. After that snapshot is complete, the connector continuously captures row-level changes that insert, update, and delete database content and that were committed to a PostgreSQL database. The connector generates data change event records and streams them to Kafka topics. For each table, the default behavior is that the connector streams all generated events to a separate Kafka topic for that table. Applications and services consume data change event records from that topic.

Information and procedures for using a Debezium PostgreSQL connector is organized as follows:

3.1. Overview of Debezium PostgreSQL connector

PostgreSQL’s logical decoding feature was introduced in version 9.4. It is a mechanism that allows the extraction of the changes that were committed to the transaction log and the processing of these changes in a user-friendly manner with the help of an output plug-in. The output plug-in enables clients to consume the changes.

The PostgreSQL connector contains two main parts that work together to read and process database changes:

  • pgoutput is the standard logical decoding output plug-in in PostgreSQL 10+. This is the only supported logical decoding output plug-in in this Debezium release. This plug-in is maintained by the PostgreSQL community, and used by PostgreSQL itself for logical replication. This plug-in is always present so no additional libraries need to be installed. The Debezium connector interprets the raw replication event stream directly into change events.
  • Java code (the actual Kafka Connect connector) that reads the changes produced by the logical decoding output plug-in by using PostgreSQL’s streaming replication protocol and the PostgreSQL JDBC driver.

The connector produces a change event for every row-level insert, update, and delete operation that was captured and sends change event records for each table in a separate Kafka topic. Client applications read the Kafka topics that correspond to the database tables of interest, and can react to every row-level event they receive from those topics.

PostgreSQL normally purges write-ahead log (WAL) segments after some period of time. This means that the connector does not have the complete history of all changes that have been made to the database. Therefore, when the PostgreSQL connector first connects to a particular PostgreSQL database, it starts by performing a consistent snapshot of each of the database schemas. After the connector completes the snapshot, it continues streaming changes from the exact point at which the snapshot was made. This way, the connector starts with a consistent view of all of the data, and does not omit any changes that were made while the snapshot was being taken.

The connector is tolerant of failures. As the connector reads changes and produces events, it records the WAL position for each event. If the connector stops for any reason (including communication failures, network problems, or crashes), upon restart the connector continues reading the WAL where it last left off. This includes snapshots. If the connector stops during a snapshot, the connector begins a new snapshot when it restarts.

Important

The connector relies on and reflects the PostgreSQL logical decoding feature, which has the following limitations:

  • Logical decoding does not support DDL changes. This means that the connector is unable to report DDL change events back to consumers.
  • Logical decoding replication slots are supported on only primary servers. When there is a cluster of PostgreSQL servers, the connector can run on only the active primary server. It cannot run on hot or warm standby replicas. If the primary server fails or is demoted, the connector stops. After the primary server has recovered, you can restart the connector. If a different PostgreSQL server has been promoted to primary, adjust the connector configuration before restarting the connector.

Behavior when things go wrong describes what the connector does when there is a problem.

Important

Debezium currently supports databases with UTF-8 character encoding only. With a single byte character encoding, it is not possible to correctly process strings that contain extended ASCII code characters.

3.2. How Debezium PostgreSQL connectors work

To optimally configure and run a Debezium PostgreSQL connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and uses metadata.

Details are in the following topics:

3.2.1. Security for PostgreSQL connector

To use the Debezium connector to stream changes from a PostgreSQL database, the connector must operate with specific privileges in the database. Although one way to grant the necessary privileges is to provide the user with superuser privileges, doing so potentially exposes your PostgreSQL data to unauthorized access. Rather than granting excessive privileges to the Debezium user, it is best to create a dedicated Debezium replication user to which you grant specific privileges.

For more information about configuring privileges for the Debezium PostgreSQL user, see Setting up permissions. For more information about PostgreSQL logical replication security, see the PostgreSQL documentation.

3.2.2. How Debezium PostgreSQL connectors perform database snapshots

Most PostgreSQL servers are configured to not retain the complete history of the database in the WAL segments. This means that the PostgreSQL connector would be unable to see the entire history of the database by reading only the WAL. Consequently, the first time that the connector starts, it performs an initial consistent snapshot of the database. The default behavior for performing a snapshot consists of the following steps. You can change this behavior by setting the snapshot.mode connector configuration property to a value other than initial.

  1. Start a transaction with a SERIALIZABLE, READ ONLY, DEFERRABLE isolation level to ensure that subsequent reads in this transaction are against a single consistent version of the data. Any changes to the data due to subsequent INSERT, UPDATE, and DELETE operations by other clients are not visible to this transaction.
  2. Obtain an ACCESS SHARE MODE lock on each of the tables being tracked to ensure that no structural changes can occur to any of the tables while the snapshot is taking place. These locks do not prevent table INSERT, UPDATE and DELETE operations from taking place during the snapshot.

    This step is omitted when snapshot.mode is set to exported, which allows the connector to perform a lock-free snapshot.

  3. Read the current position in the server’s transaction log.
  4. Scan the database tables and schemas, generate a READ event for each row and write that event to the appropriate table-specific Kafka topic.
  5. Commit the transaction.
  6. Record the successful completion of the snapshot in the connector offsets.

If the connector fails, is rebalanced, or stops after Step 1 begins but before Step 6 completes, upon restart the connector begins a new snapshot. After the connector completes its initial snapshot, the PostgreSQL connector continues streaming from the position that it read in step 3. This ensures that the connector does not miss any updates. If the connector stops again for any reason, upon restart, the connector continues streaming changes from where it previously left off.

Warning

It is strongly recommended that you configure a PostgreSQL connector to set snapshot.mode to exported. The initial, initial only and always modes can lose a few events while a connector switches from performing the snapshot to streaming change event records when a database is under heavy load. This is a known issue and the affected snapshot modes will be reworked to use exported mode internally (DBZ-2337).

Table 3.1. Settings for snapshot.mode connector configuration property

SettingDescription

always

The connector always performs a snapshot when it starts. After the snapshot completes, the connector continues streaming changes from step 3 in the above sequence. This mode is useful in these situations:

  • It is known that some WAL segments have been deleted and are no longer available.
  • After a cluster failure, a new primary has been promoted. The always snapshot mode ensures that the connector does not miss any changes that were made after the new primary had been promoted but before the connector was restarted on the new primary.

never

The connector never performs snapshots. When a connector is configured this way, its behavior when it starts is as follows. If there is a previously stored LSN in the Kafka offsets topic, the connector continues streaming changes from that position. If no LSN has been stored, the connector starts streaming changes from the point in time when the PostgreSQL logical replication slot was created on the server. The never snapshot mode is useful only when you know all data of interest is still reflected in the WAL.

initial only

The connector performs a database snapshot and stops before streaming any change event records. If the connector had started but did not complete a snapshot before stopping, the connector restarts the snapshot process and stops when the snapshot completes.

exported

The connector performs a database snapshot based on the point in time when the replication slot was created. This mode is an excellent way to perform a snapshot in a lock-free way.

3.2.3. How Debezium PostgreSQL connectors stream change event records

The PostgreSQL connector typically spends the vast majority of its time streaming changes from the PostgreSQL server to which it is connected. This mechanism relies on PostgreSQL’s replication protocol. This protocol enables clients to receive changes from the server as they are committed in the server’s transaction log at certain positions, which are referred to as Log Sequence Numbers (LSNs).

Whenever the server commits a transaction, a separate server process invokes a callback function from the logical decoding plug-in. This function processes the changes from the transaction, converts them to a specific format (Protobuf or JSON in the case of Debezium plug-in) and writes them on an output stream, which can then be consumed by clients.

The Debezium PostgreSQL connector acts as a PostgreSQL client. When the connector receives changes it transforms the events into Debezium create, update, or delete events that include the LSN of the event. The PostgreSQL connector forwards these change events in records to the Kafka Connect framework, which is running in the same process. The Kafka Connect process asynchronously writes the change event records in the same order in which they were generated to the appropriate Kafka topic.

Periodically, Kafka Connect records the most recent offset in another Kafka topic. The offset indicates source-specific position information that Debezium includes with each event. For the PostgreSQL connector, the LSN recorded in each change event is the offset.

When Kafka Connect gracefully shuts down, it stops the connectors, flushes all event records to Kafka, and records the last offset received from each connector. When Kafka Connect restarts, it reads the last recorded offset for each connector, and starts each connector at its last recorded offset. When the connector restarts, it sends a request to the PostgreSQL server to send the events starting just after that position.

Note

The PostgreSQL connector retrieves schema information as part of the events sent by the logical decoding plug-in. However, the connector does not retrieve information about which columns compose the primary key. The connector obtains this information from the JDBC metadata (side channel). If the primary key definition of a table changes (by adding, removing or renaming primary key columns), there is a tiny period of time when the primary key information from JDBC is not synchronized with the change event that the logical decoding plug-in generates. During this tiny period, a message could be created with an inconsistent key structure. To prevent this inconsistency, update primary key structures as follows:

  1. Put the database or an application into a read-only mode.
  2. Let Debezium process all remaining events.
  3. Stop Debezium.
  4. Update the primary key definition in the relevant table.
  5. Put the database or the application into read/write mode.
  6. Restart Debezium.

PostgreSQL 10+ logical decoding support (pgoutput)

As of PostgreSQL 10+, there is a logical replication stream mode, called pgoutput that is natively supported by PostgreSQL. This means that a Debezium PostgreSQL connector can consume that replication stream without the need for additional plug-ins. This is particularly valuable for environments where installation of plug-ins is not supported or not allowed.

See Setting up PostgreSQL for more details.

3.2.4. Default names of Kafka topics that receive Debezium PostgreSQL change event records

The PostgreSQL connector writes events for all insert, update, and delete operations on a single table to a single Kafka topic. By default, the Kafka topic name is serverName.schemaName.tableName where:

  • serverName is the logical name of the connector as specified with the database.server.name connector configuration property.
  • schemaName is the name of the database schema where the operation occurred.
  • tableName is the name of the database table in which the operation occurred.

For example, suppose that fulfillment is the logical server name in the configuration for a connector that is capturing changes in a PostgreSQL installation that has a postgres database and an inventory schema that contains four tables: products, products_on_hand, customers, and orders. The connector would stream records to these four Kafka topics:

  • fulfillment.inventory.products
  • fulfillment.inventory.products_on_hand
  • fulfillment.inventory.customers
  • fulfillment.inventory.orders

Now suppose that the tables are not part of a specific schema but were created in the default public PostgreSQL schema. The names of the Kafka topics would be:

  • fulfillment.public.products
  • fulfillment.public.products_on_hand
  • fulfillment.public.customers
  • fulfillment.public.orders

3.2.5. Metadata in Debezium PostgreSQL change event records

In addition to a database change event, each record produced by a PostgreSQL connector contains some metadata. Metadata includes where the event occurred on the server, the name of the source partition and the name of the Kafka topic and partition where the event should go, for example:

"sourcePartition": {
     "server": "fulfillment"
 },
 "sourceOffset": {
     "lsn": "24023128",
     "txId": "555",
     "ts_ms": "1482918357011"
 },
 "kafkaPartition": null
  • sourcePartition always defaults to the setting of the database.server.name connector configuration property.
  • sourceOffset contains information about the location of the server where the event occurred:

    • lsn represents the PostgreSQL Log Sequence Number or offset in the transaction log.
    • txId represents the identifier of the server transaction that caused the event.
    • ts_ms represents the server time at which the transaction was committed in the form of the number of milliseconds since the epoch.
  • kafkaPartition with a setting of null means that the connector does not use a specific Kafka partition. The PostgreSQL connector uses only one Kafka Connect partition and it places the generated events into one Kafka partition.

3.2.6. Debezium PostgreSQL connector-generated events that represent transaction boundaries

Debezium can generate events that represent transaction boundaries and that enrich data change event messages. For every transaction BEGIN and END, Debezium generates an event that contains the following fields:

  • status - BEGIN or END
  • id - string representation of unique transaction identifier
  • event_count (for END events) - total number of events emitted by the transaction
  • data_collections (for END events) - an array of pairs of data_collection and event_count that provides the number of events emitted by changes originating from given data collection

Example

{
  "status": "BEGIN",
  "id": "571",
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "571",
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "s1.a",
      "event_count": 1
    },
    {
      "data_collection": "s2.a",
      "event_count": 1
    }
  ]
}

Transaction events are written to the topic named database.server.name.transaction.

Change data event enrichment

When transaction metadata is enabled the data message Envelope is enriched with a new transaction field. This field provides information about every event in the form of a composite of fields:

  • id - string representation of unique transaction identifier
  • total_order - absolute position of the event among all events generated by the transaction
  • data_collection_order - the per-data collection position of the event among all events that were emitted by the transaction

Following is an example of a message:

{
  "before": null,
  "after": {
    "pk": "2",
    "aa": "1"
  },
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "571",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

3.3. Descriptions of Debezium PostgreSQL connector data change events

The Debezium PostgreSQL connector generates a data change event for each row-level INSERT, UPDATE, and DELETE operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { 1
   ...
  },
 "payload": { 2
   ...
 },
 "schema": { 3
   ...
 },
 "payload": { 4
   ...
 },
}

Table 3.2. Overview of change event basic content

ItemField nameDescription

1

schema

The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s payload portion. In other words, the first schema field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed.

It is possible to override the table’s primary key by setting the message.key.columns connector configuration property. In this case, the first schema field describes the structure of the key identified by that property.

2

payload

The first payload field is part of the event key. It has the structure described by the previous schema field and it contains the key for the row that was changed.

3

schema

The second schema field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s payload portion. In other words, the second schema describes the structure of the row that was changed. Typically, this schema contains nested schemas.

4

payload

The second payload field is part of the event value. It has the structure described by the previous schema field and it contains the actual data for the row that was changed.

By default behavior is that the connector streams change event records to topics with names that are the same as the event’s originating table.

Note

Starting with Kafka 0.10, Kafka can optionally record the event key and value with the timestamp at which the message was created (recorded by the producer) or written to the log by Kafka.

Warning

The PostgreSQL connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This means that the logical server name must start with a Latin letter or an underscore, that is, a-z, A-Z, or _. Each remaining character in the logical server name and each character in the schema and table names must be a Latin letter, a digit, or an underscore, that is, a-z, A-Z, 0-9, or \_. If there is an invalid character it is replaced with an underscore character.

This can lead to unexpected conflicts if the logical server name, a schema name, or a table name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

Details are in the following topics:

3.3.1. About keys in Debezium PostgreSQL change events

For a given table, the change event’s key has a structure that contains a field for each column in the primary key of the table at the time the event was created. Alternatively, if the table has REPLICA IDENTITY set to FULL or USING INDEX there is a field for each unique key constraint.

Consider a customers table defined in the public database schema and the example of a change event key for that table.

Example table

CREATE TABLE customers (
  id SERIAL,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL,
  PRIMARY KEY(id)
);

Example change event key

If the database.server.name connector configuration property has the value PostgreSQL_server, every change event for the customers table while it has this definition has the same key structure, which in JSON looks like this:

{
  "schema": { 1
    "type": "struct",
    "name": "PostgreSQL_server.public.customers.Key", 2
    "optional": false, 3
    "fields": [ 4
          {
              "name": "id",
              "index": "0",
              "schema": {
                  "type": "INT32",
                  "optional": "false"
              }
          }
      ]
  },
  "payload": { 5
      "id": "1"
  },
}

Table 3.3. Description of change event key

ItemField nameDescription

1

schema

The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s payload portion.

2

PostgreSQL_server.inventory.customers.Key

Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the primary key for the table that was changed. Key schema names have the format connector-name.database-name.table-name.Key. In this example:

  • PostgreSQL_server is the name of the connector that generated this event.
  • inventory is the database that contains the table that was changed.
  • customers is the table that was updated.

3

optional

Indicates whether the event key must contain a value in its payload field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a table does not have a primary key.

4

fields

Specifies each field that is expected in the payload, including each field’s name, index, and schema.

5

payload

Contains the key for the row for which this change event was generated. In this example, the key, contains a single id field whose value is 1.

Note

Although the column.exclude.list and column.include.list connector configuration properties allow you to capture only a subset of table columns, all columns in a primary or unique key are always included in the event’s key.

Warning

If the table does not have a primary or unique key, then the change event’s key is null. The rows in a table without a primary or unique key constraint cannot be uniquely identified.

3.3.2. About values in Debezium PostgreSQL change events

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample table that was used to show an example of a change event key:

CREATE TABLE customers (
  id SERIAL,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL,
  PRIMARY KEY(id)
);

The value portion of a change event for a change to this table varies according to the REPLICA IDENTITY setting and the operation that the event is for.

Details follow in these sections:

Replica identity

REPLICA IDENTITY is a PostgreSQL-specific table-level setting that determines the amount of information that is available to the logical decoding plug-in for UPDATE and DELETE events. More specifically, the setting of REPLICA IDENTITY controls what (if any) information is available for the previous values of the table columns involved, whenever an UPDATE or DELETE event occurs.

There are 4 possible values for REPLICA IDENTITY:

  • DEFAULT - The default behavior is that UPDATE and DELETE events contain the previous values for the primary key columns of a table if that table has a primary key. For an UPDATE event, only the primary key columns with changed values are present.

    If a table does not have a primary key, the connector does not emit UPDATE or DELETE events for that table. For a table without a primary key, the connector emits only create events. Typically, a table without a primary key is used for appending messages to the end of the table, which means that UPDATE and DELETE events are not useful.

  • NOTHING - Emitted events for UPDATE and DELETE operations do not contain any information about the previous value of any table column.
  • FULL - Emitted events for UPDATE and DELETE operations contain the previous values of all columns in the table.
  • INDEX index-name - Emitted events for UPDATE and DELETE operations contain the previous values of the columns contained in the specified index. UPDATE events also contain the indexed columns with the updated values.

create events

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers table:

{
    "schema": { 1
        "type": "struct",
        "fields": [
            {
                "type": "struct",
                "fields": [
                    {
                        "type": "int32",
                        "optional": false,
                        "field": "id"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "first_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "last_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "email"
                    }
                ],
                "optional": true,
                "name": "PostgreSQL_server.inventory.customers.Value", 2
                "field": "before"
            },
            {
                "type": "struct",
                "fields": [
                    {
                        "type": "int32",
                        "optional": false,
                        "field": "id"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "first_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "last_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "email"
                    }
                ],
                "optional": true,
                "name": "PostgreSQL_server.inventory.customers.Value",
                "field": "after"
            },
            {
                "type": "struct",
                "fields": [
                    {
                        "type": "string",
                        "optional": false,
                        "field": "version"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "connector"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "name"
                    },
                    {
                        "type": "int64",
                        "optional": false,
                        "field": "ts_ms"
                    },
                    {
                        "type": "boolean",
                        "optional": true,
                        "default": false,
                        "field": "snapshot"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "db"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "schema"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "table"
                    },
                    {
                        "type": "int64",
                        "optional": true,
                        "field": "txId"
                    },
                    {
                        "type": "int64",
                        "optional": true,
                        "field": "lsn"
                    },
                    {
                        "type": "int64",
                        "optional": true,
                        "field": "xmin"
                    }
                ],
                "optional": false,
                "name": "io.debezium.connector.postgresql.Source", 3
                "field": "source"
            },
            {
                "type": "string",
                "optional": false,
                "field": "op"
            },
            {
                "type": "int64",
                "optional": true,
                "field": "ts_ms"
            }
        ],
        "optional": false,
        "name": "PostgreSQL_server.inventory.customers.Envelope" 4
    },
    "payload": { 5
        "before": null, 6
        "after": { 7
            "id": 1,
            "first_name": "Anne",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": { 8
            "version": "1.4.2.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": true,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 555,
            "lsn": 24023128,
            "xmin": null
        },
        "op": "c", 9
        "ts_ms": 1559033904863 10
    }
}

Table 3.4. Descriptions of create event value fields

ItemField nameDescription

1

schema

The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular table.

2

name

In the schema section, each name field specifies the schema for a field in the value’s payload.

PostgreSQL_server.inventory.customers.Value is the schema for the payload’s before and after fields. This schema is specific to the customers table.

Names of schemas for before and after fields are of the form logicalName.tableName.Value, which ensures that the schema name is unique in the database. This means that when using the Avro converter, the resulting Avro schema for each table in each logical source has its own evolution and history.

3

name

io.debezium.connector.postgresql.Source is the schema for the payload’s source field. This schema is specific to the PostgreSQL connector. The connector uses it for all events that it generates.

4

name

PostgreSQL_server.inventory.customers.Envelope is the schema for the overall structure of the payload, where PostgreSQL_server is the connector name, inventory is the database, and customers is the table.

5

payload

The value’s actual data. This is the information that the change event is providing.

It may appear that the JSON representations of the events are much larger than the rows they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.

6

before

An optional field that specifies the state of the row before the event occurred. When the op field is c for create, as it is in this example, the before field is null since this change event is for new content.

Note

Whether or not this field is available is dependent on the REPLICA IDENTITY setting for each table.

7

after

An optional field that specifies the state of the row after the event occurred. In this example, the after field contains the values of the new row’s id, first_name, last_name, and email columns.

8

source

Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Database and table that contains the new row
  • Schema name
  • If the event was part of a snapshot
  • ID of the transaction in which the operation was performed
  • Offset of the operation in the database log
  • Timestamp for when the change was made in the database

9

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, c indicates that the operation created a row. Valid values are:

  • c = create
  • u = update
  • d = delete
  • r = read (applies to only snapshots)

10

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

update events

The value of a change event for an update in the sample customers table has the same schema as a create event for that table. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. Here is an example of a change event value in an event that the connector generates for an update in the customers table:

{
    "schema": { ... },
    "payload": {
        "before": { 1
            "id": 1
        },
        "after": { 2
            "id": 1,
            "first_name": "Anne Marie",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": { 3
            "version": "1.4.2.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": null,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 24023128,
            "xmin": null
        },
        "op": "u", 4
        "ts_ms": 1465584025523  5
    }
}

Table 3.5. Descriptions of update event value fields

ItemField nameDescription

1

before

An optional field that contains values that were in the row before the database commit. In this example, only the primary key column, id, is present because the table’s REPLICA IDENTITY setting is, by default, DEFAULT. + For an update event to contain the previous values of all columns in the row, you would have to change the customers table by running ALTER TABLE customers REPLICA IDENTITY FULL.

2

after

An optional field that specifies the state of the row after the event occurred. In this example, the first_name value is now Anne Marie.

3

source

Mandatory field that describes the source metadata for the event. The source field structure has the same fields as in a create event, but some values are different. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Database and table that contains the new row
  • Schema name
  • If the event was part of a snapshot
  • ID of the transaction in which the operation was performed
  • Offset of the operation in the database log
  • Timestamp for when the change was made in the database

4

op

Mandatory string that describes the type of operation. In an update event value, the op field value is u, signifying that this row changed because of an update.

5

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

Note

Updating the columns for a row’s primary/unique key changes the value of the row’s key. When a key changes, Debezium outputs three events: a DELETE event and a tombstone event with the old key for the row, followed by an event with the new key for the row. Details are in the next section.

Primary key updates

An UPDATE operation that changes a row’s primary key field(s) is known as a primary key change. For a primary key change, in place of sending an UPDATE event record, the connector sends a DELETE event record for the old key and a CREATE event record for the new (updated) key. These events have the usual structure and content, and in addition, each one has a message header related to the primary key change:

  • The DELETE event record has __debezium.newkey as a message header. The value of this header is the new primary key for the updated row.
  • The CREATE event record has __debezium.oldkey as a message header. The value of this header is the previous (old) primary key that the updated row had.

delete events

The value in a delete change event has the same schema portion as create and update events for the same table. The payload portion in a delete event for the sample customers table looks like this:

{
    "schema": { ... },
    "payload": {
        "before": { 1
            "id": 1
        },
        "after": null, 2
        "source": { 3
            "version": "1.4.2.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": null,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 46523128,
            "xmin": null
        },
        "op": "d", 4
        "ts_ms": 1465581902461 5
    }
}

Table 3.6. Descriptions of delete event value fields

ItemField nameDescription

1

before

Optional field that specifies the state of the row before the event occurred. In a delete event value, the before field contains the values that were in the row before it was deleted with the database commit.

In this example, the before field contains only the primary key column because the table’s REPLICA IDENTITY setting is DEFAULT.

2

after

Optional field that specifies the state of the row after the event occurred. In a delete event value, the after field is null, signifying that the row no longer exists.

3

source

Mandatory field that describes the source metadata for the event. In a delete event value, the source field structure is the same as for create and update events for the same table. Many source field values are also the same. In a delete event value, the ts_ms and lsn field values, as well as other values, might have changed. But the source field in a delete event value provides the same metadata:

  • Debezium version
  • Connector type and name
  • Database and table that contains the new row
  • Schema name
  • If the event was part of a snapshot
  • ID of the transaction in which the operation was performed
  • Offset of the operation in the database log
  • Timestamp for when the change was made in the database

4

op

Mandatory string that describes the type of operation. The op field value is d, signifying that this row was deleted.

5

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

A delete change event record provides a consumer with the information it needs to process the removal of this row.

Warning

For a consumer to be able to process a delete event generated for a table that does not have a primary key, set the table’s REPLICA IDENTITY to FULL. When a table does not have a primary key and the table’s REPLICA IDENTITY is set to DEFAULT or NOTHING, a delete event has no before field.

PostgreSQL connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

When a row is deleted, the delete event value still works with log compaction, because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that same key, the message value must be null. To make this possible, the PostgreSQL connector follows a delete event with a special tombstone event that has the same key but a null value.

truncate events

A truncate change event signals that a table has been truncated. The message key is null in this case, the message value looks like this:

{
    "schema": { ... },
    "payload": {
        "source": { 1
            "version": "1.4.2.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": false,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 46523128,
            "xmin": null
        },
        "op": "t", 2
        "ts_ms": 1559033904961 3
    }
}

Table 3.7. Descriptions of truncate event value fields

ItemField nameDescription

1

source

Mandatory field that describes the source metadata for the event. In a truncate event value, the source field structure is the same as for create, update, and delete events for the same table, provides this metadata:

  • Debezium version
  • Connector type and name
  • Database and table that contains the new row
  • Schema name
  • If the event was part of a snapshot (alwas false for delete events)
  • ID of the transaction in which the operation was performed
  • Offset of the operation in the database log
  • Timestamp for when the change was made in the database

2

op

Mandatory string that describes the type of operation. The op field value is t, signifying that this table was truncated.

3

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

In case a single TRUNCATE statement applies to multiple tables, one truncate change event record for each truncated table will be emitted.

Note that since truncate events represent a change made to an entire table and don’t have a message key, unless you’re working with topics with a single partition, there are no ordering guarantees for the change events pertaining to a table (create, update, etc.) and truncate events for that table. For instance a consumer may receive an update event only after a truncate event for that table, when those events are read from different partitions.

3.4. How Debezium PostgreSQL connectors map data types

The PostgreSQL connector represents changes to rows with events that are structured like the table in which the row exists. The event contains a field for each column value. How that value is represented in the event depends on the PostgreSQL data type of the column. The following sections describe how the connector maps PostgreSQL data types to a literal type and a semantic type in event fields.

  • literal type describes how the value is literally represented using Kafka Connect schema types: INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.
  • semantic type describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.

Details are in the following sections:

Basic types

The following table describes how the connector maps basic types.

Table 3.8. Mappings for PostgreSQL basic data types

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

BOOLEAN

BOOLEAN

n/a

BIT(1)

BOOLEAN

n/a

BIT( > 1)

BYTES

io.debezium.data.Bits

The length schema parameter contains an integer that represents the number of bits. The resulting byte[] contains the bits in little-endian form and is sized to contain the specified number of bits. For example, numBytes = n/8 + (n % 8 == 0 ? 0 : 1) where n is the number of bits.

BIT VARYING[(M)]

BYTES

io.debezium.data.Bits

The length schema parameter contains an integer that represents the number of bits (2^31 - 1 in case no length is given for the column). The resulting byte[] contains the bits in little-endian form and is sized based on the content. The specified size (M) is stored in the length parameter of the io.debezium.data.Bits type.

SMALLINT, SMALLSERIAL

INT16

n/a

INTEGER, SERIAL

INT32

n/a

BIGINT, BIGSERIAL

INT64

n/a

REAL

FLOAT32

n/a

DOUBLE PRECISION

FLOAT64

n/a

CHAR[(M)]

STRING

n/a

VARCHAR[(M)]

STRING

n/a

CHARACTER[(M)]

STRING

n/a

CHARACTER VARYING[(M)]

STRING

n/a

TIMESTAMPTZ, TIMESTAMP WITH TIME ZONE

STRING

io.debezium.time.ZonedTimestamp

A string representation of a timestamp with timezone information, where the timezone is GMT.

TIMETZ, TIME WITH TIME ZONE

STRING

io.debezium.time.ZonedTime

A string representation of a time value with timezone information, where the timezone is GMT.

INTERVAL [P]

INT64

io.debezium.time.MicroDuration
(default)

The approximate number of microseconds for a time interval using the 365.25 / 12.0 formula for days per month average.

INTERVAL [P]

STRING

io.debezium.time.Interval
(when interval.handling.mode is set to string)

The string representation of the interval value that follows the pattern P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S, for example, P1Y2M3DT4H5M6.78S.

BYTEA

BYTES or STRING

n/a

Either the raw bytes (the default), a base64-encoded string, or a hex-encoded string, based on the connector’s binary handling mode setting.

JSON, JSONB

STRING

io.debezium.data.Json

Contains the string representation of a JSON document, array, or scalar.

XML

STRING

io.debezium.data.Xml

Contains the string representation of an XML document.

UUID

STRING

io.debezium.data.Uuid

Contains the string representation of a PostgreSQL UUID value.

POINT

STRUCT

io.debezium.data.geometry.Point

Contains a structure with two FLOAT64 fields, (x,y). Each field represents the coordinates of a geometric point.

LTREE

STRING

io.debezium.data.Ltree

Contains the string representation of a PostgreSQL LTREE value.

CITEXT

STRING

n/a

INET

STRING

n/a

INT4RANGE

STRING

n/a

Range of integer.

INT8RANGE

STRING

n/a

Range of bigint.

NUMRANGE

STRING

n/a

Range of numeric.

TSRANGE

STRING

n/a

Contains the string representation of a timestamp range without a time zone.

TSTZRANGE

STRING

n/a

Contains the string representation of a timestamp range with the local system time zone.

DATERANGE

STRING

n/a

Contains the string representation of a date range. It always has an exclusive upper-bound.

ENUM

STRING

io.debezium.data.Enum

Contains the string representation of the PostgreSQL ENUM value. The set of allowed values is maintained in the allowed schema parameter.

Temporal types

Other than PostgreSQL’s TIMESTAMPTZ and TIMETZ data types, which contain time zone information, how temporal types are mapped depends on the value of the time.precision.mode connector configuration property. The following sections describe these mappings:

time.precision.mode=adaptive

When the time.precision.mode property is set to adaptive, the default, the connector determines the literal type and semantic type based on the column’s data type definition. This ensures that events exactly represent the values in the database.

Table 3.9. Mappings when time.precision.mode is adaptive

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

io.debezium.time.Date

Represents the number of days since the epoch.

TIME(1), TIME(2), TIME(3)

INT32

io.debezium.time.Time

Represents the number of milliseconds past midnight, and does not include timezone information.

TIME(4), TIME(5), TIME(6)

INT64

io.debezium.time.MicroTime

Represents the number of microseconds past midnight, and does not include timezone information.

TIMESTAMP(1), TIMESTAMP(2), TIMESTAMP(3)

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information.

TIMESTAMP(4), TIMESTAMP(5), TIMESTAMP(6), TIMESTAMP

INT64

io.debezium.time.MicroTimestamp

Represents the number of microseconds since the epoch, and does not include timezone information.

time.precision.mode=adaptive_time_microseconds

When the time.precision.mode configuration property is set to adaptive_time_microseconds, the connector determines the literal type and semantic type for temporal types based on the column’s data type definition. This ensures that events exactly represent the values in the database, except all TIME fields are captured as microseconds.

Table 3.10. Mappings when time.precision.mode is adaptive_time_microseconds

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

io.debezium.time.Date

Represents the number of days since the epoch.

TIME([P])

INT64

io.debezium.time.MicroTime

Represents the time value in microseconds and does not include timezone information. PostgreSQL allows precision P to be in the range 0-6 to store up to microsecond precision.

TIMESTAMP(1) , TIMESTAMP(2), TIMESTAMP(3)

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

TIMESTAMP(4) , TIMESTAMP(5), TIMESTAMP(6), TIMESTAMP

INT64

io.debezium.time.MicroTimestamp

Represents the number of microseconds past the epoch, and does not include timezone information.

time.precision.mode=connect

When the time.precision.mode configuration property is set to connect, the connector uses Kafka Connect logical types. This may be useful when consumers can handle only the built-in Kafka Connect logical types and are unable to handle variable-precision time values. However, since PostgreSQL supports microsecond precision, the events generated by a connector with the connect time precision mode results in a loss of precision when the database column has a fractional second precision value that is greater than 3.

Table 3.11. Mappings when time.precision.mode is connect

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

org.apache.kafka.connect.data.Date

Represents the number of days since the epoch.

TIME([P])

INT64

org.apache.kafka.connect.data.Time

Represents the number of milliseconds since midnight, and does not include timezone information. PostgreSQL allows P to be in the range 0-6 to store up to microsecond precision, though this mode results in a loss of precision when P is greater than 3.

TIMESTAMP([P])

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information. PostgreSQL allows P to be in the range 0-6 to store up to microsecond precision, though this mode results in a loss of precision when P is greater than 3.

TIMESTAMP type

The TIMESTAMP type represents a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. For example, the TIMESTAMP value "2018-06-20 15:13:16.945104" is represented by an io.debezium.time.MicroTimestamp with the value "1529507596945104" when time.precision.mode is not set to connect.

The timezone of the JVM running Kafka Connect and Debezium does not affect this conversion.

Decimal types

The setting of the PostgreSQL connector configuration property, decimal.handling.mode determines how the connector maps decimal types.

When the decimal.handling.mode property is set to precise, the connector uses the Kafka Connect org.apache.kafka.connect.data.Decimal logical type for all DECIMAL and NUMERIC columns. This is the default mode.

Table 3.12. Mappings when decimal.handling.mode is precise

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

NUMERIC[(M[,D])]

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer representing how many digits the decimal point was shifted.

DECIMAL[(M[,D])]

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer representing how many digits the decimal point was shifted.

There is an exception to this rule. When the NUMERIC or DECIMAL types are used without scale constraints, the values coming from the database have a different (variable) scale for each value. In this case, the connector uses io.debezium.data.VariableScaleDecimal, which contains both the value and the scale of the transferred value.

Table 3.13. Mappings of decimal types when there are no scale constraints

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

NUMERIC

STRUCT

io.debezium.data.VariableScaleDecimal

Contains a structure with two fields: scale of type INT32 that contains the scale of the transferred value and value of type BYTES containing the original value in an unscaled form.

DECIMAL

STRUCT

io.debezium.data.VariableScaleDecimal

Contains a structure with two fields: scale of type INT32 that contains the scale of the transferred value and value of type BYTES containing the original value in an unscaled form.

When the decimal.handling.mode property is set to double, the connector represents all DECIMAL and NUMERIC values as Java double values and encodes them as shown in the following table.

Table 3.14. Mappings when decimal.handling.mode is double

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name)

NUMERIC[(M[,D])]

FLOAT64

 

DECIMAL[(M[,D])]

FLOAT64

 

The last possible setting for the decimal.handling.mode configuration property is string. In this case, the connector represents DECIMAL and NUMERIC values as their formatted string representation, and encodes them as shown in the following table.

Table 3.15. Mappings when decimal.handling.mode is string

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name)

NUMERIC[(M[,D])]

STRING

 

DECIMAL[(M[,D])]

STRING

 

PostgreSQL supports NaN (not a number) as a special value to be stored in DECIMAL/NUMERIC values when the setting of decimal.handling.mode is string or double. In this case, the connector encodes NaN as either Double.NaN or the string constant NAN.

HSTORE type

When the hstore.handling.mode connector configuration property is set to json (the default), the connector represents HSTORE values as string representations of JSON values and encodes them as shown in the following table. When the hstore.handling.mode property is set to map, the connector uses the MAP schema type for HSTORE values.

Table 3.16. Mappings for HSTORE data type

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

HSTORE

STRING

io.debezium.data.Json

Example: output representation using the JSON converter is {\"key\" : \"val\"}

HSTORE

MAP

n/a

Example: output representation using the JSON converter is {"key" : "val"}

Domain types

PostgreSQL supports user-defined types that are based on other underlying types. When such column types are used, Debezium exposes the column’s representation based on the full type hierarchy.

Important

Capturing changes in columns that use PostgreSQL domain types requires special consideration. When a column is defined to contain a domain type that extends one of the default database types and the domain type defines a custom length or scale, the generated schema inherits that defined length or scale.

When a column is defined to contain a domain type that extends another domain type that defines a custom length or scale, the generated schema does not inherit the defined length or scale because that information is not available in the PostgreSQL driver’s column metadata.

Network address types

PostgreSQL has data types that can store IPv4, IPv6, and MAC addresses. It is better to use these types instead of plain text types to store network addresses. Network address types offer input error checking and specialized operators and functions.

Table 3.17. Mappings for network address types

PostgreSQL data typeLiteral type (schema type)Semantic type (schema name) and Notes

INET

STRING

n/a

IPv4 and IPv6 networks

CIDR

STRING

n/a

IPv4 and IPv6 hosts and networks

MACADDR

STRING

n/a

MAC addresses

MACADDR8

STRING

n/a

MAC addresses in EUI-64 format

PostGIS types

The PostgreSQL connector supports all PostGIS data types.

Table 3.18. Mappings of PostGIS data types

PostGIS data typeLiteral type (schema type)Semantic type (schema name) and Notes

GEOMETRY
(planar)

STRUCT

io.debezium.data.geometry.Geometry

Contains a structure with two fields:

  • srid (INT32) - Spatial Reference System Identifier that defines what type of geometry object is stored in the structure.
  • wkb (BYTES) - A binary representation of the geometry object encoded in the Well-Known-Binary format.

For format details, see Open Geospatial Consortium Simple Features Access specification.

GEOGRAPHY
(spherical)

STRUCT

io.debezium.data.geometry.Geography

Contains a structure with two fields:

  • srid (INT32) - Spatial Reference System Identifier that defines what type of geography object is stored in the structure.
  • wkb (BYTES) - A binary representation of the geometry object encoded in the Well-Known-Binary format.

For format details, see Open Geospatial Consortium Simple Features Access specification.

Toasted values

PostgreSQL has a hard limit on the page size. This means that values that are larger than around 8 KBs need to be stored by using link::https://www.postgresql.org/docs/current/storage-toast.html[TOAST storage]. This impacts replication messages that are coming from the database. Values that were stored by using the TOAST mechanism and that have not been changed are not included in the message, unless they are part of the table’s replica identity. There is no safe way for Debezium to read the missing value out-of-bands directly from the database, as this would potentially lead to race conditions. Consequently, Debezium follows these rules to handle toasted values:

  • Tables with REPLICA IDENTITY FULL - TOAST column values are part of the before and after fields in change events just like any other column.
  • Tables with REPLICA IDENTITY DEFAULT - When receiving an UPDATE event from the database, any unchanged TOAST column value that is not part of the replica identity is not contained in the event. Similarly, when receiving a DELETE event, no TOAST columns, if any, are in the before field. As Debezium cannot safely provide the column value in this case, the connector returns a placeholder value as defined by the connector configuration property, toasted.value.placeholder.

3.5. Setting up PostgreSQL to run a Debezium connector

This release of Debezium supports only the native pgoutput logical replication stream. To set up PostgreSQL so that it uses the pgoutput plug-in, you must enable a replication slot, and configure a user with sufficient privileges to perform the replication.

Details are in the following topics:

3.5.1. Configuring a replication slot for the Debezium pgoutput plug-in

PostgreSQL’s logical decoding uses replication slots. To configure a replication slot, specify the following in the postgresql.conf file:

wal_level=logical
max_wal_senders=1
max_replication_slots=1

These settings instruct the PostgreSQL server as follows:

  • wal_level - Use logical decoding with the write-ahead log.
  • max_wal_senders - Use a maximum of one separate process for processing WAL changes.
  • max_replication_slots - Allow a maximum of one replication slot to be created for streaming WAL changes.

Replication slots are guaranteed to retain all WAL entries that are required for Debezium even during Debezium outages. Consequently, it is important to closely monitor replication slots to avoid:

  • Too much disk consumption
  • Any conditions, such as catalog bloat, that can happen if a replication slot stays unused for too long

For more information, see the PostgreSQL documentation for replication slots.

Note

Familiarity with the mechanics and configuration of the PostgreSQL write-ahead log is helpful for using the Debezium PostgreSQL connector.

3.5.2. Setting up PostgreSQL permissions for the Debezium connector

Setting up a PostgreSQL server to run a Debezium connector requires a database user that can perform replications. Replication can be performed only by a database user that has appropriate permissions and only for a configured number of hosts.

Although, by default, superusers have the necessary REPLICATION and LOGIN roles, as mentioned in Security, it is best not to provide the Debezium replication user with elevated privileges. Instead, create a Debezium user that has the the minimum required privileges.

Prerequisites

  • PostgreSQL administrative permissions.

Procedure

  1. To provide a user with replication permissions, define a PostgreSQL role that has at least the REPLICATION and LOGIN permissions, and then grant that role to the user. For example:

    CREATE ROLE <name> REPLICATION LOGIN;

3.5.3. Setting privileges to enable Debezium to create PostgreSQL publications

Debezium streams change events for PostgreSQL source tables from publications that are created for the tables. Publications contain a filtered set of change events that are generated from one or more tables. The data in each publication is filtered based on the publication specification. The specification can be created by the PostgreSQL database administrator or by the Debezium connector. To permit the Debezium PostgreSQL connector to create publications and specify the data to replicate to them, the connector must operate with specific privileges in the database.

There are several options for determining how publications are created. In general, it is best to manually create publications for the tables that you want to capture, before you set up the connector. However, you can configure your environment in a way that permits Debezium to create publications automatically, and to specify the data that is added to them.

Debezium uses include list and exclude list properties to specify how data is inserted in the publication. For more information about the options for enabling Debezium to create publications, see publication.autocreate.mode.

For Debezium to create a PostgreSQL publication, it must run as a user that has the following privileges:

  • Replication privileges in the database to add the table to a publication.
  • CREATE privileges on the database to add publications.
  • SELECT privileges on the tables to copy the initial table data. Table owners automatically have SELECT permission for the table.

To add tables to a publication, the user be an owner of the table. But because the source table already exists, you need a mechanism to share ownership with the original owner. To enable shared ownership, you create a PostgreSQL replication group, and then add the existing table owner and the replication user to the group.

Procedure

  1. Create a replication group.

    CREATE ROLE <replication_group>;
  2. Add the original owner of the table to the group.

    GRANT REPLICATION_GROUP TO <original_owner>;
  3. Add the Debezium replication user to the group.

    GRANT REPLICATION_GROUP TO <replication_user>;
  4. Transfer ownership of the table to <replication_group>.

    ALTER TABLE <table_name> OWNER TO REPLICATION_GROUP;

For Debezium to specify the capture configuration, the value of publication.autocreate.mode must be set to filtered.

3.5.4. Configuring PostgreSQL to allow replication with the Debezium connector host

To enable {prodname] to replicate PostgreSQL data, you must configure the database to permit replication with the host that runs the PostgreSQL connector. To specify the clients that are permitted to replicate with the database, add entries to the PostgreSQL host-based authentication file, pg_hba.conf. For more information about the pg_hba.conf file, see the PostgreSQL documentation.

Procedure

  • Add entries to the pg_hba.conf file to specify the Debezium connector hosts that can replicate with the database host. For example,

    pg_hba.conf file example:

    local   replication     <youruser>                          trust   1
    host    replication     <youruser>  127.0.0.1/32            trust   2
    host    replication     <youruser>  ::1/128                 trust   3

    1 1 1 1 1 1 1 1
    Instructs the server to allow replication for <youruser> locally, that is, on the server machine.
    2 2 2 2 2 2 2 2
    Instructs the server to allow <youruser> on localhost to receive replication changes using IPV4.
    3 3 3 3 3 3 3 3
    Instructs the server to allow <youruser> on localhost to receive replication changes using IPV6.
Note

For more information about network masks, see the PostgreSQL documentation.

3.5.5. Configuring PostgreSQL to manage Debezium WAL disk space consumption

In certain cases, it is possible for PostgreSQL disk space consumed by WAL files to spike or increase out of usual proportions. There are several possible reasons for this situation:

  • The LSN up to which the connector has received data is available in the confirmed_flush_lsn column of the server’s pg_replication_slots view. Data that is older than this LSN is no longer available, and the database is responsible for reclaiming the disk space.

    Also in the pg_replication_slots view, the restart_lsn column contains the LSN of the oldest WAL that the connector might require. If the value for confirmed_flush_lsn is regularly increasing and the value of restart_lsn lags then the database needs to reclaim the space.

    The database typically reclaims disk space in batch blocks. This is expected behavior and no action by a user is necessary.

  • There are many updates in a database that is being tracked but only a tiny number of updates are related to the table(s) and schema(s) for which the connector is capturing changes. This situation can be easily solved with periodic heartbeat events. Set the heartbeat.interval.ms connector configuration property.
  • The PostgreSQL instance contains multiple databases and one of them is a high-traffic database. Debezium captures changes in another database that is low-traffic in comparison to the other database. Debezium then cannot confirm the LSN as replication slots work per-database and Debezium is not invoked. As WAL is shared by all databases, the amount used tends to grow until an event is emitted by the database for which Debezium is capturing changes. To overcome this, it is necessary to:

    • Enable periodic heartbeat record generation with the heartbeat.interval.ms connector configuration property.
    • Regularly emit change events from the database for which Debezium is capturing changes.

    A separate process would then periodically update the table by either inserting a new row or repeatedly updating the same row. PostgreSQL then invokes Debezium, which confirms the latest LSN and allows the database to reclaim the WAL space. This task can be automated by means of the heartbeat.action.query connector configuration property.

3.6. Deployment of Debezium PostgreSQL connectors

To deploy a Debezium PostgreSQL connector, add the connector files to Kafka Connect, create a custom container to run the connector, and add connector configuration to your container. Details are in the following topics:

3.6.1. Deploying Debezium PostgreSQL connectors

To deploy a Debezium PostgreSQL connector, you need to build a custom Kafka Connect container image that contains the Debezium connector archive and push this container image to a container registry. You then need to create two custom resources (CRs):

  • A KafkaConnect CR that configures your Kafka Connector and that specifies the name of the image that you created to run your Debezium connector. You apply this CR to the OpenShift Kafka instance.
  • A KafkaConnector CR that configures your Debezium PostgreSQL connector. You apply this CR to the OpenShift instance where Red Hat AMQ Streams is deployed.

Prerequisites

  • PostgreSQL is running and you performed the steps to set up PostgreSQL to run a Debezium connector.
  • Red Hat AMQ Streams was used to set up and start running Apache Kafka and Kafka Connect on OpenShift. AMQ Streams offers operators and images that bring Kafka to OpenShift.
  • Podman or Docker is installed.
  • You have an account and permissions to create and manage containers in the container registry (such as quay.io or docker.io) to which you plan to add the container that will run your Debezium connector.

Procedure

  1. Create the Debezium PostgreSQL container for Kafka Connect:

    1. Download the Debezium PostgreSQL connector archive.
    2. Extract the Debezium PostgreSQL connector archive to create a directory structure for the connector plug-in, for example:

      ./my-plugins/
      ├── debezium-connector-postgresql
      │   ├── ...
    3. Create a Docker file that uses registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.0 as the base image. For example, from a terminal window, enter the following:

      cat <<EOF >debezium-container-for-postgresql.yaml 1
      FROM {DockerKafkaConnect}
      USER root:root
      COPY ./my-plugins/ /opt/kafka/plugins/ 2
      USER 1001
      EOF
      1
      You can specify any file name that you want.
      2
      Replace my-plugins with the name of your plug-ins directory.

      The command creates a Docker file with the name debezium-container-for-postgresql.yaml in the current directory.

    4. Build the container image from the debezium-container-for-postgresql.yaml Docker file that you created in the previous step. From the directory that contains the file, run the following command:

      podman build -t debezium-container-for-postgresql:latest .
      docker build -t debezium-container-for-postgresql:latest .

      The build command builds a container image with the name debezium-container-for-postgresql.

    5. Push your custom image to a container registry such as quay.io or any internal container registry. Ensure that this registry is reachable from your OpenShift instance. For example:

      podman push debezium-container-for-postgresql:latest
    6. Create a new Debezium PostgreSQL KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies annotations and image properties as shown in the following example:

      apiVersion: kafka.strimzi.io/v1beta1
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
        annotations: strimzi.io/use-connector-resources: "true" 1
      spec:
        image: debezium-container-for-postgresql 2
      1
      metadata.annotations indicates to the Cluster Operator that KafkaConnector resources are used to configure connectors in this Kafka Connect cluster.
      2
      spec.image specifies the name of the image that you created to run your Debezium connector. This property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator.
    7. Apply your KafkaConnect CR to the OpenShift Kafka instance by running the following command:

      oc create -f dbz-connect.yaml

      This updates your Kafka Connect environment in OpenShift to add a Kafka Connector instance that specifies the name of the image that you created to run your Debezium connector.

  2. Create a KafkaConnector custom resource that configures your Debezium PostgreSQL connector instance.

    You configure a Debezium PostgreSQL connector in a .yaml file that sets connector configuration properties. A connector configuration might instruct Debezium to produce events for a subset of the schemas and tables, or it might set properties so that Debezium ignores, masks, or truncates values in specified columns that are sensitive, too large, or not needed. See the complete list of PostgreSQL connector properties that can be specified in these configurations.

    The following example configures a Debezium connector that connects to a PostgreSQL server host, 192.168.99.100, on port 5432. This host has a database named sampledb, a schema named public, and fulfillment is the server’s logical name.

    fulfillment-connector.yaml

    apiVersion: kafka.strimzi.io/v1beta1
      kind: KafkaConnector
      metadata:
        name: fulfillment-connector  1
        labels:
          strimzi.io/cluster: my-connect-cluster
      spec:
        class: io.debezium.connector.postgresql.PostgresConnector
        tasksMax: 1  2
        config:  3
          database.hostname: 192.168.99.100   4
          database.port: 5432
          database.user: debezium
          database.password: dbz
          database.dbname: sampledb
          database.server.name: fulfillment   5
          schema.include.list: public   6
          plugin.name: pgoutput    7

    1
    The name of the connector.
    2
    Only one task should operate at any one time. Because the PostgreSQL connector reads the PostgreSQL server’s binlog, using a single connector task ensures proper order and event handling. The Kafka Connect service uses connectors to start one or more tasks that do the work, and it automatically distributes the running tasks across the cluster of Kafka Connect services. If any of the services stop or crash, those tasks will be redistributed to running services.
    3
    The connector’s configuration.
    4
    The name of the database host that is running the PostgreSQL server. In this example, the database host name is 192.168.99.100.
    5
    A unique server name. The server name is the logical identifier for the PostgreSQL server or cluster of servers. This name is used as the prefix for all Kafka topics that receive change event records.
    6
    The connector captures changes in only the public schema. It is possible to configure the connector to capture changes in only the tables that you choose. See table.include.list connector configuration property.
    7
    The name of the PostgreSQL logical decoding plug-in installed on the PostgreSQL server. While the only supported value for PostgreSQL 10 and later is pgoutput, you must explicitly set plugin.name to pgoutput.
  3. Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in the fulfillment-connector.yaml file, you would run the following command:

    oc apply -f fulfillment-connector.yaml

    This registers fulfillment-connector and the connector starts to run against the sampledb database as defined in the KafkaConnector CR.

  4. Verify that the connector was created and has started:

    1. Display the Kafka Connect log output to verify that the connector was created and has started to capture changes in the specified database:

      oc logs $(oc get pods -o name -l strimzi.io/cluster=my-connect-cluster)
    2. Review the log output to verify that the initial snapshot has been executed. You should see something like this:

      ... INFO Starting snapshot for ...
      ... INFO Snapshot is using user 'debezium' ...

      If the connector starts correctly without errors, it creates a topic for each table whose changes the connector is capturing. For the example CR, there would be a topic for each table in the public schema. Downstream applications can subscribe to these topics.

    3. Verify that the connector created the topics by running the following command:

      oc get kafkatopics

Results

When the connector starts, it performs a consistent snapshot of the PostgreSQL server databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming change event records to Kafka topics.

3.6.2. Description of Debezium PostgreSQL connector configuration properties

The Debezium PostgreSQL connector has many configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:

The following configuration properties are required unless a default value is available.

Table 3.19. Required connector configuration properties

PropertyDefaultDescription

name

 

Unique name for the connector. Attempting to register again with the same name will fail. This property is required by all Kafka Connect connectors.

connector.class

 

The name of the Java class for the connector. Always use a value of io.debezium.connector.postgresql.PostgresConnector for the PostgreSQL connector.

tasks.max

1

The maximum number of tasks that should be created for this connector. The PostgreSQL connector always uses a single task and therefore does not use this value, so the default is always acceptable.

plugin.name

decoderbufs

The name of the PostgreSQL logical decoding plug-in installed on the PostgreSQL server.

The only supported value is pgoutput. You must explicitly set plugin.name to pgoutput.

slot.name

debezium

The name of the PostgreSQL logical decoding slot that was created for streaming changes from a particular plug-in for a particular database/schema. The server uses this slot to stream events to the Debezium connector that you are configuring.

Slot names must conform to PostgreSQL replication slot naming rules, which state: "Each replication slot has a name, which can contain lower-case letters, numbers, and the underscore character."

slot.drop.on.stop

false

Whether or not to delete the logical replication slot when the connector stops in a graceful, expected way. The default behavior is that the replication slot remains configured for the connector when the connector stops. When the connector restarts, having the same replication slot enables the connector to start processing where it left off.

Set to true in only testing or development environments. Dropping the slot allows the database to discard WAL segments. When the connector restarts it performs a new snapshot or it can continue from a persistent offset in the Kafka Connect offsets topic.

publication.name

dbz_publication

The name of the PostgreSQL publication created for streaming changes when using pgoutput.

This publication is created at start-up if it does not already exist and it includes all tables. Debezium then applies its own include/exclude list filtering, if configured, to limit the publication to change events for the specific tables of interest. The connector user must have superuser permissions to create this publication, so it is usually preferable to create the publication before starting the connector for the first time.

If the publication already exists, either for all tables or configured with a subset of tables, Debezium uses the publication as it is defined.

database.hostname

 

IP address or hostname of the PostgreSQL database server.

database.port

5432

Integer port number of the PostgreSQL database server.

database.user

 

Name of the PostgreSQL database user for connecting to the PostgreSQL database server.

database.password

 

Password to use when connecting to the PostgreSQL database server.

database.dbname

 

The name of the PostgreSQL database from which to stream the changes.

database.server.name

 

Logical name that identifies and provides a namespace for the particular PostgreSQL database server or cluster in which Debezium is capturing changes. Only alphanumeric characters and underscores should be used in the database server logical name. The logical name should be unique across all other connectors, since it is used as a topic name prefix for all Kafka topics that receive records from this connector.

schema.include.list

 

An optional, comma-separated list of regular expressions that match names of schemas for which you want to capture changes. Any schema name not included in schema.include.list is excluded from having its changes captured. By default, all non-system schemas have their changes captured. Do not also set the schema.exclude.list property.

schema.exclude.list

 

An optional, comma-separated list of regular expressions that match names of schemas for which you do not want to capture changes. Any schema whose name is not included in schema.exclude.list has its changes captured, with the exception of system schemas. Do not also set the schema.include.list property.

table.include.list

 

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you want to capture. Any table not included in table.include.list does not have its changes captured. Each identifier is of the form schemaName.tableName. By default, the connector captures changes in every non-system table in each schema whose changes are being captured. Do not also set the table.exclude.list property.

table.exclude.list

 

An optional, comma-separated list of regular expressions that match fully-qualified table identifiers for tables whose changes you do not want to capture. Any table not included in table.exclude.list has it changes captured. Each identifier is of the form schemaName.tableName. Do not also set the table.include.list property.

column.include.list

 

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns that should be included in change event record values. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Do not also set the column.exclude.list property.

column.exclude.list

 

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns that should be excluded from change event record values. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Do not also set the column.include.list property.

time.precision.mode

adaptive

Time, date, and timestamps can be represented with different kinds of precision:

adaptive captures the time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type.

adaptive_time_microseconds captures the date, datetime and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type. An exception is TIME type fields, which are always captured as microseconds.

connect always represents time and timestamp values by using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which use millisecond precision regardless of the database columns' precision. See temporal values.

decimal.handling.mode

precise

Specifies how the connector should handle values for DECIMAL and NUMERIC columns:

precise represents values by using java.math.BigDecimal to represent values in binary form in change events.

double represents values by using double values, which might result in a loss of precision but which is easier to use.

string encodes values as formatted strings, which are easy to consume but semantic information about the real type is lost. See Decimal types.

hstore.handling.mode

map

Specifies how the connector should handle values for hstore columns:

map represents values by using MAP.

json represents values by using json string. This setting encodes values as formatted strings such as {"key" : "val"}. See PostgreSQL HSTORE type.

interval.handling.mode

numeric

Specifies how the connector should handle values for interval columns:

numeric represents intervals using approximate number of microseconds.

string represents intervals exactly by using the string pattern representation P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S. For example: P1Y2M3DT4H5M6.78S. See PostgreSQL basic types.

database.sslmode

disable

Whether to use an encrypted connection to the PostgreSQL server. Options include:

disable uses an unencrypted connection.

require uses a secure (encrypted) connection, and fails if one cannot be established.

verify-ca behaves like require but also verifies the server TLS certificate against the configured Certificate Authority (CA) certificates, or fails if no valid matching CA certificates are found.

verify-full behaves like verify-ca but also verifies that the server certificate matches the host to which the connector is trying to connect. See the PostgreSQL documentation for more information.

database.sslcert

 

The path to the file that contains the SSL certificate for the client. See the PostgreSQL documentation for more information.

database.sslkey

 

The path to the file that contains the SSL private key of the client. See the PostgreSQL documentation for more information.

database.sslpassword

 

The password to access the client private key from the file specified by database.sslkey. See the PostgreSQL documentation for more information.

database.sslrootcert

 

The path to the file that contains the root certificate(s) against which the server is validated. See the PostgreSQL documentation for more information.

database.tcpKeepAlive

true

Enable TCP keep-alive probe to verify that the database connection is still alive. See the PostgreSQL documentation for more information.

tombstones.on.delete

true

Controls whether a tombstone event should be generated after a delete event.

true - delete operations are represented by a delete event and a subsequent tombstone event.

false - only a delete event is sent.

After a delete operation, emitting a tombstone event enables Kafka to delete all change event records that have the same key as the deleted row.

column.truncate.to._length_.chars

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form schemaName.tableName.columnName. In change event records, values in these columns are truncated if they are longer than the number of characters specified by length in the property name. You can specify multiple properties with different lengths in a single configuration. Length must be a positive integer, for example, +column.truncate.to.20.chars.

column.mask.with._length_.chars

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form schemaName.tableName.columnName. In change event values, the values in the specified table columns are replaced with length number of asterisk (*) characters. You can specify multiple properties with different lengths in a single configuration. Length must be a positive integer or zero. When you specify zero, the connector replaces a value with an empty string.

column.mask.hash._hashAlgorithm_.with.salt._salt_

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of character-based columns. Fully-qualified names for columns are of the form schemaName.tableName.columnName. In change event values, the values in the specified columns are replaced with pseudonyms.

A pseudonym consists of the hashed value that results from applying the specifed hashAlgorithm and salt. Based on the hash function that is used, referential integrity is kept while column values are replaced with pseudonyms. Supported hash functions are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation.

If necessary, the pseudonym is automatically shortened to the length of the column. You can specify multiple properties with different hash algorithms and salts in a single configuration. In the following example, CzQMA0cB5K is a randomly selected salt.

column.mask.hash.SHA-256.with.salt.CzQMA0cB5K =inventory.orders.customerName,inventory.shipment.customerName

Depending on the hashAlgorithm used, the salt selected, and the actual data set, the resulting masked data set might not be completely masked.

column.propagate.source.type

n/a

An optional, comma-separated list of regular expressions that match the fully-qualified names of columns. Fully-qualified names for columns are of the form databaseName.tableName.columnName, or databaseName.schemaName.tableName.columnName.

For each specified column, the connector adds the column’s original type and original length as parameters to the corresponding field schemas in the emitted change records. The following added schema parameters propagate the original type name and also the original length for variable-width types:

__debezium.source.column.type + __debezium.source.column.length + __debezium.source.column.scale

This property is useful for properly sizing corresponding columns in sink databases.

datatype.propagate.source.type

n/a

An optional, comma-separated list of regular expressions that match the database-specific data type name for some columns. Fully-qualified data type names are of the form databaseName.tableName.typeName, or databaseName.schemaName.tableName.typeName.

For these data types, the connector adds parameters to the corresponding field schemas in emitted change records. The added parameters specify the original type and length of the column:

__debezium.source.column.type + __debezium.source.column.length + __debezium.source.column.scale

These parameters propagate a column’s original type name and length, for variable-width types, respectively. This property is useful for properly sizing corresponding columns in sink databases.

See the list of PostgreSQL-specific data type names.

message.key.columns

empty string

A semicolon separated list of tables with regular expressions that match table column names. The connector maps values in matching columns to key fields in change event records that it sends to Kafka topics. This is useful when a table does not have a primary key, or when you want to order change event records in a Kafka topic according to a field that is not a primary key.

Separate entries with semicolons. Insert a colon between the fully-qualified table name and its regular expression. The format is:

schema-name.table-name:_regexp_;…​

For example,

schemaA.table_a:regex_1;schemaB.table_b:regex_2;schemaC.table_c:regex_3

If table_a has a an id column, and regex_1 is ^i (matches any column that starts with i), the connector maps the value in table_a's id column to a key field in change events that the connector sends to Kafka.

publication.autocreate.mode

all_tables

Applies only when streaming changes by using the pgoutput plug-in. The setting determines how creation of a publication should work. Possible settings are:

all_tables - If a publication exists, the connector uses it. If a publication does not exist, the connector creates a publication for all tables in the database for which the connector is capturing changes. This requires that the database user that has permission to perform replications also has permission to create a publication. This is granted with CREATE PUBLICATION <publication_name> FOR ALL TABLES;.

disabled - The connector does not attempt to create a publication. A database administrator or the user configured to perform replications must have created the publication before running the connector. If the connector cannot find the publication, the connector throws an exception and stops.

filtered - If a publication exists, the connector uses it. If no publication exists, the connector creates a new publication for tables that match the current filter configuration as specified by the database.exclude.list, schema.include.list, schema.exclude.list, and table.include.list connector configuration properties. For example: CREATE PUBLICATION <publication_name> FOR TABLE <tbl1, tbl2, tbl3>.

binary.handling.mode

bytes

Specifies how binary (bytea) columns should be represented in change events:

bytes represents binary data as byte array.

base64 represents binary data as base64-encoded strings.

hex represents binary data as hex-encoded (base16) strings.

truncate.handling.mode

bytes

Specifies how whether TRUNCATE events should be propagated or not (only available when using the pgoutput plug-in with Postgres 11 or later):

skip causes those event to be omitted (the default).

include causes hos events to be included.

+ Please see truncate events for the structure of truncate events and their ordering semantics.

The following advanced configuration properties have defaults that work in most situations and therefore rarely need to be specified in the connector’s configuration.

Table 3.20. Advanced connector configuration properties

PropertyDefaultDescription

snapshot.mode

initial

Specifies the criteria for performing a snapshot when the connector starts:

initial - The connector performs a snapshot only when no offsets have been recorded for the logical server name.

always - The connector performs a snapshot each time the connector starts.

never - The connector never performs snapshots. When a connector is configured this way, its behavior when it starts is as follows. If there is a previously stored LSN in the Kafka offsets topic, the connector continues streaming changes from that position. If no LSN has been stored, the connector starts streaming changes from the point in time when the PostgreSQL logical replication slot was created on the server. The never snapshot mode is useful only when you know all data of interest is still reflected in the WAL.

initial_only - The connector performs an initial snapshot and then stops, without processing any subsequent changes.

exported - The connector performs a snapshot based on the point in time when the replication slot was created. This is an excellent way to perform the snapshot in a lock-free way.


Thereference table for snapshot mode settings has more details.

snapshot.include.collection.list

All tables specified in table.include.list

An optional, comma-separated list of regular expressions that match names of schemas specified in table.include.list for which you want to take the snapshot when the snapshot.mode is not never

snapshot.lock.timeout.ms

10000

Positive integer value that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If the connector cannot acquire table locks in this time interval, the snapshot fails. How the connector performs snapshots provides details.

snapshot.select.statement.overrides

 

Controls which table rows are included in snapshots. This property affects snapshots only. It does not affect events that are generated by the logical decoding plug-in. Specify a comma-separated list of fully-qualified table names in the form databaseName.tableName.

For each table that you specify, also specify another configuration property: snapshot.select.statement.overrides.DB_NAME.TABLE_NAME, for example: snapshot.select.statement.overrides.customers.orders. Set this property to a SELECT statement that obtains only the rows that you want in the snapshot. When the connector performs a snapshot, it executes this SELECT statement to retrieve data from that table.

A possible use case for setting these properties is large, append-only tables. You can specify a SELECT statement that sets a specific point for where to start a snapshot, or where to resume a snapshot if a previous snapshot was interrupted.

event.processing.failure.handling.mode

fail

Specifies how the connector should react to exceptions during processing of events:

fail propagates the exception, indicates the offset of the problematic event, and causes the connector to stop.

warn logs the offset of the problematic event, skips that event, and continues processing.

skip skips the problematic event and continues processing.

max.queue.size

20240

Positive integer value for the maximum size of the blocking queue. The connector places change events received from streaming replication in the blocking queue before writing them to Kafka. This queue can provide backpressure when, for example, writing records to Kafka is slower that it should be or Kafka is not available.

max.batch.size

10240

Positive integer value that specifies the maximum size of each batch of events that the connector processes.

max.queue.size.in.bytes

0

Long value for the maximum size in bytes of the blocking queue. The feature is disabled by default, it will be active if it’s set with a positive long value.

poll.interval.ms

1000

Positive integer value that specifies the number of milliseconds the connector should wait for new change events to appear before it starts processing a batch of events. Defaults to 1000 milliseconds, or 1 second.

include.unknown.datatypes

false

Specifies connector behavior when the connector encounters a field whose data type is unknown. The default behavior is that the connector omits the field from the change event and logs a warning.

Set this property to true if you want the change event to contain an opaque binary representation of the field. This lets consumers decode the field. You can control the exact representation by setting the binary handling mode property.

Note

Consumers risk backward compatibility issues when include.unknown.datatypes is set to true. Not only may the database-specific binary representation change between releases, but if the data type is eventually supported by Debezium, the data type will be sent downstream in a logical type, which would require adjustments by consumers. In general, when encountering unsupported data types, create a feature request so that support can be added.

database.initial.statements

 

A semicolon separated list of SQL statements that the connector executes when it establishes a JDBC connection to the database. To use a semicolon as a character and not as a delimiter, specify two consecutive semicolons, ;;.

The connector may establish JDBC connections at its own discretion. Consequently, this property is useful for configuration of session parameters only, and not for executing DML statements.

The connector does not execute these statements when it creates a connection for reading the transaction log.

heartbeat.interval.ms

0

Controls how frequently the connector sends heartbeat messages to a Kafka topic. The default behavior is that the connector does not send heartbeat messages.

Heartbeat messages are useful for monitoring whether the connector is receiving change events from the database. Heartbeat messages might help decrease the number of change events that need to be re-sent when a connector restarts. To send heartbeat messages, set this property to a positive integer, which indicates the number of milliseconds between heartbeat messages.

Heartbeat messages are needed when there are many updates in a database that is being tracked but only a tiny number of updates are related to the table(s) and schema(s) for which the connector is capturing changes. In this situation, the connector reads from the database transaction log as usual but rarely emits change records to Kafka. This means that no offset updates are committed to Kafka and the connector does not have an opportunity to send the latest retrieved LSN to the database. The database retains WAL files that contain events that have already been processed by the connector. Sending heartbeat messages enables the connector to send the latest retrieved LSN to the database, which allows the database to reclaim disk space being used by no longer needed WAL files.

heartbeat.topics.prefix

__debezium-heartbeat

Controls the name of the topic to which the connector sends heartbeat messages. The topic name has this pattern:

<heartbeat.topics.prefix>.<server.name>

For example, if the database server name is fulfillment, the default topic name is __debezium-heartbeat.fulfillment.

heartbeat.action.query

 

Specifies a query that the connector executes on the source database when the connector sends a heartbeat message.

This is useful for resolving the situation described in WAL disk space consumption, where capturing changes from a low-traffic database on the same host as a high-traffic database prevents Debezium from processing WAL records and thus acknowledging WAL positions with the database. To address this situation, create a heartbeat table in the low-traffic database, and set this property to a statement that inserts records into that table, for example:

INSERT INTO test_heartbeat_table (text) VALUES ('test_heartbeat')

This allows the connector to receive changes from the low-traffic database and acknowledge their LSNs, which prevents unbounded WAL growth on the database host.

schema.refresh.mode

columns_diff

Specify the conditions that trigger a refresh of the in-memory schema for a table.

columns_diff is the safest mode. It ensures that the in-memory schema stays in sync with the database table’s schema at all times.

columns_diff_exclude_unchanged_toast instructs the connector to refresh the in-memory schema cache if there is a discrepancy with the schema derived from the incoming message, unless unchanged TOASTable data fully accounts for the discrepancy.

This setting can significantly improve connector performance if there are frequently-updated tables that have TOASTed data that are rarely part of updates. However, it is possible for the in-memory schema to become outdated if TOASTable columns are dropped from the table.

snapshot.delay.ms

 

An interval in milliseconds that the connector should wait before performing a snapshot when the connector starts. If you are starting multiple connectors in a cluster, this property is useful for avoiding snapshot interruptions, which might cause re-balancing of connectors.

snapshot.fetch.size

10240

During a snapshot, the connector reads table content in batches of rows. This property specifies the maximum number of rows in a batch.

slot.stream.params

 

Semicolon separated list of parameters to pass to the configured logical decoding plug-in. For example, add-tables=public.table,public.table2;include-lsn=true.

sanitize.field.names

true if connector configuration sets the key.converter or value.converter property to the Avro converter.

false if not.

Indicates whether field names are sanitized to adhere to Avro naming requirements.

slot.max.retries

6

If connecting to a replication slot fails, this is the maximum number of consecutive attempts to connect.

slot.retry.delay.ms

10000 (10 seconds)

The number of milliseconds to wait between retry attempts when the connector fails to connect to a replication slot.

toasted.value.placeholder

__debezium_unavailable_value

Specifies the constant that the connector provides to indicate that the original value is a toasted value that is not provided by the database. If the setting of toasted.value.placeholder starts with the hex: prefix it is expected that the rest of the string represents hexadecimally encoded octets. See toasted values for additional details.

provide.transaction.metadata

false

Determines whether the connector generates events with transaction boundaries and enriches change event envelopes with transaction metadata. Specify true if you want the connector to do this. See Transaction metadata for details.

retriable.restart.connector.wait.ms

10000 (10 seconds)

The number of milliseconds to wait before restarting a connector after a retriable error occurs.

Pass-through connector configuration properties

The connector also supports pass-through configuration properties that are used when creating the Kafka producer and consumer.

Be sure to consult the Kafka documentation for all of the configuration properties for Kafka producers and consumers. The PostgreSQL connector does use the new consumer configuration properties.

3.7. Monitoring Debezium PostgreSQL connector performance

The Debezium PostgreSQL connector provides two types of metrics that are in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect provide.

  • Snapshot metrics provide information about connector operation while performing a snapshot.
  • Streaming metrics provide information about connector operation when the connector is capturing changes and streaming change event records.

Debezium monitoring documentation provides details for how to expose these metrics by using JMX.

3.7.1. Monitoring Debezium during snapshots of PostgreSQL databases

The MBean is debezium.postgres:type=connector-metrics,context=snapshot,server=<database.server.name>.

AttributesTypeDescription

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

SnapshotAborted

boolean

Whether the snapshot was aborted.

SnapshotCompleted

boolean

Whether the snapshot completed.

SnapshotDurationInSeconds

long

The total number of seconds that the snapshot has taken so far, even if not complete.

RowsScanned

Map<String, Long>

Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. It will be enabled if max.queue.size.in.bytes is passed with a positive long value.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

3.7.2. Monitoring Debezium PostgreSQL connector record streaming

The MBean is debezium.postgres:type=connector-metrics,context=streaming,server=<database.server.name>.

AttributesTypeDescription

LastEvent

string

The last streaming event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the streamer and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.

Connected

boolean

Flag that denotes whether the connector is currently connected to the database server.

MilliSecondsBehindSource

long

The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

SourceEventPosition

Map<String, String>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

3.8. How Debezium PostgreSQL connectors handle faults and problems

Debezium is a distributed system that captures all changes in multiple upstream databases; it never misses or loses an event. When the system is operating normally or being managed carefully then Debezium provides exactly once delivery of every change event record.

If a fault does happen then the system does not lose any events. However, while it is recovering from the fault, it might repeat some change events. In these abnormal situations, Debezium, like Kafka, provides at least once delivery of change events.

Details are in the following sections:

Configuration and startup errors

In the following situations, the connector fails when trying to start, reports an error/exception in the log, and stops running:

  • The connector’s configuration is invalid.
  • The connector cannot successfully connect to PostgreSQL by using the specified connection parameters.
  • The connector is restarting from a previously-recorded position in the PostgreSQL WAL (by using the LSN) and PostgreSQL no longer has that history available.

In these cases, the error message has details about the problem and possibly a suggested workaround. After you correct the configuration or address the PostgreSQL problem, restart the connector.

PostgreSQL becomes unavailable

When the connector is running, the PostgreSQL server that it is connected to could become unavailable for any number of reasons. If this happens, the connector fails with an error and stops. When the server is available again, restart the connector.

The PostgreSQL connector externally stores the last processed offset in the form of a PostgreSQL LSN. After a connector restarts and connects to a server instance, the connector communicates with the server to continue streaming from that particular offset. This offset is available as long as the Debezium replication slot remains intact. Never drop a replication slot on the primary server or you will lose data. See the next section for failure cases in which a slot has been removed.

Cluster failures

As of release 12, PostgreSQL allows logical replication slots only on primary servers. This means that you can point a Debezium PostgreSQL connector to only the active primary server of a database cluster. Also, replication slots themselves are not propagated to replicas. If the primary server goes down, a new primary must be promoted.

The new primary must have a replication slot that is configured for use by the pgoutput plug-in and the database in which you want to capture changes. Only then can you point the connector to the new server and restart the connector.

There are important caveats when failovers occur and you should pause Debezium until you can verify that you have an intact replication slot that has not lost data. After a failover:

  • There must be a process that re-creates the Debezium replication slot before allowing the application to write to the new primary. This is crucial. Without this process, your application can miss change events.
  • You might need to verify that Debezium was able to read all changes in the slot before the old primary failed.

One reliable method of recovering and verifying whether any changes were lost is to recover a backup of the failed primary to the point immediately before it failed. While this can be administratively difficult, it allows you to inspect the replication slot for any unconsumed changes.

Kafka Connect process stops gracefully

Suppose that Kafka Connect is being run in distributed mode and a Kafka Connect process is stopped gracefully. Prior to shutting down that process, Kafka Connect migrates the process’s connector tasks to another Kafka Connect process in that group. The new connector tasks start processing exactly where the prior tasks stopped. There is a short delay in processing while the connector tasks are stopped gracefully and restarted on the new processes.

Kafka Connect process crashes

If the Kafka Connector process stops unexpectedly, any connector tasks it was running terminate without recording their most recently processed offsets. When Kafka Connect is being run in distributed mode, Kafka Connect restarts those connector tasks on other processes. However, PostgreSQL connectors resume from the last offset that was recorded by the earlier processes. This means that the new replacement tasks might generate some of the same change events that were processed just prior to the crash. The number of duplicate events depends on the offset flush period and the volume of data changes just before the crash.

Because there is a chance that some events might be duplicated during a recovery from failure, consumers should always anticipate some duplicate events. Debezium changes are idempotent, so a sequence of events always results in the same state.

In each change event record, Debezium connectors insert source-specific information about the origin of the event, including the PostgreSQL server’s time of the event, the ID of the server transaction, and the position in the write-ahead log where the transaction changes were written. Consumers can keep track of this information, especially the LSN, to determine whether an event is a duplicate.

Kafka becomes unavailable

As the connector generates change events, the Kafka Connect framework records those events in Kafka by using the Kafka producer API. Periodically, at a frequency that you specify in the Kafka Connect configuration, Kafka Connect records the latest offset that appears in those change events. If the Kafka brokers become unavailable, the Kafka Connect process that is running the connectors repeatedly tries to reconnect to the Kafka brokers. In other words, the connector tasks pause until a connection can be re-established, at which point the connectors resume exactly where they left off.

Connector is stopped for a duration

If the connector is gracefully stopped, the database can continue to be used. Any changes are recorded in the PostgreSQL WAL. When the connector restarts, it resumes streaming changes where it left off. That is, it generates change event records for all database changes that were made while the connector was stopped.

A properly configured Kafka cluster is able to handle massive throughput. Kafka Connect is written according to Kafka best practices, and given enough resources a Kafka Connect connector can also handle very large numbers of database change events. Because of this, after being stopped for a while, when a Debezium connector restarts, it is very likely to catch up with the database changes that were made while it was stopped. How quickly this happens depends on the capabilities and performance of Kafka and the volume of changes being made to the data in PostgreSQL.

Chapter 4. Debezium connector for MongoDB

Debezium’s MongoDB connector tracks a MongoDB replica set or a MongoDB sharded cluster for document changes in databases and collections, recording those changes as events in Kafka topics. The connector automatically handles the addition or removal of shards in a sharded cluster, changes in membership of each replica set, elections within each replica set, and awaiting the resolution of communications problems.

4.1. Overview

MongoDB’s replication mechanism provides redundancy and high availability, and is the preferred way to run MongoDB in production. MongoDB connector captures the changes in a replica set or sharded cluster.

A MongoDB replica set consists of a set of servers that all have copies of the same data, and replication ensures that all changes made by clients to documents on the replica set’s primary are correctly applied to the other replica set’s servers, called secondaries. MongoDB replication works by having the primary record the changes in its oplog (or operation log), and then each of the secondaries reads the primary’s oplog and applies in order all of the operations to their own documents. When a new server is added to a replica set, that server first performs an snapshot of all of the databases and collections on the primary, and then reads the primary’s oplog to apply all changes that might have been made since it began the snapshot. This new server becomes a secondary (and able to handle queries) when it catches up to the tail of the primary’s oplog.

The MongoDB connector uses this same replication mechanism, though it does not actually become a member of the replica set. Just like MongoDB secondaries, however, the connector always reads the oplog of the replica set’s primary. And, when the connector sees a replica set for the first time, it looks at the oplog to get the last recorded transaction and then performs a snapshot of the primary’s databases and collections. When all the data is copied, the connector then starts streaming changes from the position it read earlier from the oplog. Operations in the MongoDB oplog are idempotent, so no matter how many times the operations are applied, they result in the same end state.

As the MongoDB connector processes changes, it periodically records the position in the oplog where the event originated. When the MongoDB connector stops, it records the last oplog position that it processed, so that upon restart it simply begins streaming from that position. In other words, the connector can be stopped, upgraded or maintained, and restarted some time later, and it will pick up exactly where it left off without losing a single event. Of course, MongoDB’s oplogs are usually capped at a maximum size, which means that the connector should not be stopped for too long, or else some of the operations in the oplog might be purged before the connector has a chance to read them. In this case, upon restart the connector will detect the missing oplog operations, perform a snapshot, and then proceed with streaming the changes.

The MongoDB connector is also quite tolerant of changes in membership and leadership of the replica sets, of additions or removals of shards within a sharded cluster, and network problems that might cause communication failures. The connector always uses the replica set’s primary node to stream changes, so when the replica set undergoes an election and a different node becomes primary, the connector will immediately stop streaming changes, connect to the new primary, and start streaming changes using the new primary node. Likewise, if connector experiences any problems communicating with the replica set primary, it will try to reconnect (using exponential backoff so as to not overwhelm the network or replica set) and continue streaming changes from where it last left off. In this way the connector is able to dynamically adjust to changes in replica set membership and to automatically handle communication failures.

4.2. Setting up MongoDB

The MongoDB connector uses MongoDB’s oplog to capture the changes, so the connector works only with MongoDB replica sets or with sharded clusters where each shard is a separate replica set. See the MongoDB documentation for setting up a replica set or sharded cluster. Also, be sure to understand how to enable access control and authentication with replica sets.

You must also have a MongoDB user that has the appropriate roles to read the admin database where the oplog can be read. Additionally, the user must also be able to read the config database in the configuration server of a sharded cluster and must have listDatabases privilege action.

4.3. Supported MongoDB topologies

The MongoDB connector can be used with a variety of MongoDB topologies.

4.3.1. MongoDB replica set

The MongoDB connector can capture changes from a single MongoDB replica set. Production replica sets require a minimum of at least three members.

To use the MongoDB connector with a replica set, provide the addresses of one or more replica set servers as seed addresses through the connector’s mongodb.hosts property. The connector will use these seeds to connect to the replica set, and then once connected will get from the replica set the complete set of members and which member is primary. The connector will start a task to connect to the primary and capture the changes from the primary’s oplog. When the replica set elects a new primary, the task will automatically switch over to the new primary.

Note

When MongoDB is fronted by a proxy (such as with Docker on OS X or Windows), then when a client connects to the replica set and discovers the members, the MongoDB client will exclude the proxy as a valid member and will attempt and fail to connect directly to the members rather than go through the proxy.

In such a case, set the connector’s optional mongodb.members.auto.discover configuration property to false to instruct the connector to forgo membership discovery and instead simply use the first seed address (specified via the mongodb.hosts property) as the primary node. This may work, but still make cause issues when election occurs.

4.3.2. MongoDB sharded cluster

A MongoDB sharded cluster consists of:

  • One or more shards, each deployed as a replica set;
  • A separate replica set that acts as the cluster’s configuration server
  • One or more routers (also called mongos) to which clients connect and that routes requests to the appropriate shards

To use the MongoDB connector with a sharded cluster, configure the connector with the host addresses of the configuration server replica set. When the connector connects to this replica set, it discovers that it is acting as the configuration server for a sharded cluster, discovers the information about each replica set used as a shard in the cluster, and will then start up a separate task to capture the changes from each replica set. If new shards are added to the cluster or existing shards removed, the connector will automatically adjust its tasks accordingly.

4.3.3. MongoDB standalone server

The MongoDB connector is not capable of monitoring the changes of a standalone MongoDB server, since standalone servers do not have an oplog. The connector will work if the standalone server is converted to a replica set with one member.

Note

MongoDB does not recommend running a standalone server in production.

4.4. How the MongoDB connector works

When a MongoDB connector is configured and deployed, it starts by connecting to the MongoDB servers at the seed addresses, and determines the details about each of the available replica sets. Since each replica set has its own independent oplog, the connector will try to use a separate task for each replica set. The connector can limit the maximum number of tasks it will use, and if not enough tasks are available the connector will assign multiple replica sets to each task, although the task will still use a separate thread for each replica set.

Note

When running the connector against a sharded cluster, use a value of tasks.max that is greater than the number of replica sets. This will allow the connector to create one task for each replica set, and will let Kafka Connect coordinate, distribute, and manage the tasks across all of the available worker processes.

4.4.1. Logical connector name

The connector configuration property mongodb.name serves as a logical name for the MongoDB replica set or sharded cluster. The connector uses the logical name in a number of ways: as the prefix for all topic names, and as a unique identifier when recording the oplog position of each replica set.

You should give each MongoDB connector a unique logical name that meaningfully describes the source MongoDB system. We recommend logical names begin with an alphabetic or underscore character, and remaining characters that are alphanumeric or underscore.

4.4.2. Performing a snapshot

When a task starts up using a replica set, it uses the connector’s logical name and the replica set name to find an offset that describes the position where the connector previously stopped reading changes. If an offset can be found and it still exists in the oplog, then the task immediately proceeds with streaming changes, starting at the recorded offset position.

However, if no offset is found or if the oplog no longer contains that position, the task must first obtain the current state of the replica set contents by performing a snapshot. This process starts by recording the current position of the oplog and recording that as the offset (along with a flag that denotes a snapshot has been started). The task will then proceed to copy each collection, spawning as many threads as possible (up to the value of the snapshot.max.threads configuration property) to perform this work in parallel. The connector will record a separate read event for each document it sees, and that read event will contain the object’s identifier, the complete state of the object, and source information about the MongoDB replica set where the object was found. The source information will also include a flag that denotes the event was produced during a snapshot.

This snapshot will continue until it has copied all collections that match the connector’s filters. If the connector is stopped before the tasks' snapshots are completed, upon restart the connector begins the snapshot again.

Note

Try to avoid task reassignment and reconfiguration while the connector is performing a snapshot of any replica sets. The connector does log messages with the progress of the snapshot. For utmost control, run a separate cluster of Kafka Connect for each connector.

4.4.3. Streaming changes

Once the connector task for a replica set has an offset, it uses the offset to determine the position in the oplog where it should start streaming changes. The task will then connect to the replica set’s primary node and start streaming changes from that position, processing all of the create, insert, and delete operations and converting them into Debezium change events. Each change event includes the position in the oplog where the operation was found, and the connector periodically records this as its most recent offset. The interval at which the offset is recorded is governed by offset.flush.interval.ms, which is a Kafka Connect worker configuration property.

When the connector is stopped gracefully, the last offset processed is recorded so that, upon restart, the connector will continue exactly where it left off. If the connector’s tasks terminate unexpectedly, however, then the tasks may have processed and generated events after it last records the offset but before the last offset is recorded; upon restart, the connector begins at the last recorded offset, possibly generating some the same events that were previously generated just prior to the crash.

Note

When everything is operating nominally, Kafka consumers will actually see every message exactly once. However, when things go wrong Kafka can only guarantee consumers will see every message at least once. Therefore, your consumers need to anticipate seeing messages more than once.

As mentioned above, the connector tasks always use the replica set’s primary node to stream changes from the oplog, ensuring that the connector sees the most up-to-date operations as possible and can capture the changes with lower latency than if secondaries were to be used instead. When the replica set elects a new primary, the connector immediately stops streaming changes, connects to the new primary, and starts streaming changes from the new primary node at the same position. Likewise, if the connector experiences any problems communicating with the replica set members, it trys to reconnect, by using exponential backoff so as to not overwhelm the replica set, and once connected it continues streaming changes from where it last left off. In this way, the connector is able to dynamically adjust to changes in replica set membership and automatically handle communication failures.

To summarize, the MongoDB connector continues running in most situations. Communication problems might cause the connector to wait until the problems are resolved.

4.4.4. Topics names

The MongoDB connector writes events for all insert, update, and delete operations to documents in each collection to a single Kafka topic. The name of the Kafka topics always takes the form logicalName.databaseName.collectionName, where logicalName is the logical name of the connector as specified with the mongodb.name configuration property, databaseName is the name of the database where the operation occurred, and collectionName is the name of the MongoDB collection in which the affected document existed.

For example, consider a MongoDB replica set with an inventory database that contains four collections: products, products_on_hand, customers, and orders. If the connector monitoring this database were given a logical name of fulfillment, then the connector would produce events on these four Kafka topics:

  • fulfillment.inventory.products
  • fulfillment.inventory.products_on_hand
  • fulfillment.inventory.customers
  • fulfillment.inventory.orders

Notice that the topic names do not incorporate the replica set name or shard name. As a result, all changes to a sharded collection (where each shard contains a subset of the collection’s documents) all go to the same Kafka topic.

You can set up Kafka to auto-create the topics as they are needed. If not, then you must use Kafka administration tools to create the topics before starting the connector.

4.4.5. Partitions

The MongoDB connector does not make any explicit determination of the topic partitions for events. Instead, it allows Kafka to determine the partition based on the key. You can change Kafka’s partitioning logic by defining in the Kafka Connect worker configuration the name of the Partitioner implementation.

Kafka maintains total order only for events written to a single topic partition. Partitioning the events by key does mean that all events with the same key always go to the same partition. This ensures that all events for a specific document are always totally ordered.

4.4.6. Data change events

The Debezium MongoDB connector generates a data change event for each document-level operation that inserts, updates, or deletes data. Each event contains a key and a value. The structure of the key and the value depends on the collection that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { 1
   ...
  },
 "payload": { 2
   ...
 },
 "schema": { 3
   ...
 },
 "payload": { 4
   ...
 },
}

Table 4.1. Overview of change event basic content

ItemField nameDescription

1

schema

The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s payload portion. In other words, the first schema field describes the structure of the key for the document that was changed.

2

payload

The first payload field is part of the event key. It has the structure described by the previous schema field and it contains the key for the document that was changed.

3

schema

The second schema field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s payload portion. In other words, the second schema describes the structure of the document that was changed. Typically, this schema contains nested schemas.

4

payload

The second payload field is part of the event value. It has the structure described by the previous schema field and it contains the actual data for the document that was changed.

By default, the connector streams change event records to topics with names that are the same as the event’s originating collection. See topic names.

Warning

The MongoDB connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This means that the logical server name must start with a Latin letter or an underscore, that is, a-z, A-Z, or _. Each remaining character in the logical server name and each character in the database and collection names must be a Latin letter, a digit, or an underscore, that is, a-z, A-Z, 0-9, or \_. If there is an invalid character it is replaced with an underscore character.

This can lead to unexpected conflicts if the logical server name, a database name, or a collection name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

4.4.6.1. Change event keys

A change event’s key contains the schema for the changed document’s key and the changed document’s actual key. For a given collection, both the schema and its corresponding payload contain a single id field. The value of this field is the document’s identifier represented as a string that is derived from MongoDB extended JSON serialization strict mode.

Consider a connector with a logical name of fulfillment, a replica set containing an inventory database, and a customers collection that contains documents such as the following.

Example document

{
  "_id": 1004,
  "first_name": "Anne",
  "last_name": "Kretchmar",
  "email": "annek@noanswer.org"
}

Example change event key

Every change event that captures a change to the customers collection has the same event key schema. For as long as the customers collection has the previous definition, every change event that captures a change to the customers collection has the following key structure. In JSON, it looks like this:

{
  "schema": { 1
    "type": "struct",
    "name": "fulfillment.inventory.customers.Key", 2
    "optional": false, 3
    "fields": [ 4
      {
        "field": "id",
        "type": "string",
        "optional": false
      }
    ]
  },
  "payload": { 5
    "id": "1004"
  }
}

Table 4.2. Description of change event key

ItemField nameDescription

1

schema

The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s payload portion.

2

fulfillment.inventory.customers.Key

Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the key for the document that was changed. Key schema names have the format connector-name.database-name.collection-name.Key. In this example:

  • fulfillment is the name of the connector that generated this event.
  • inventory is the database that contains the collection that was changed.
  • customers is the collection that contains the document that was updated.

3

optional

Indicates whether the event key must contain a value in its payload field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a document does not have a key.

4

fields

Specifies each field that is expected in the payload, including each field’s name, type, and whether it is required.

5

payload

Contains the key for the document for which this change event was generated. In this example, the key contains a single id field of type string whose value is 1004.

This example uses a document with an integer identifier, but any valid MongoDB document identifier works the same way, including a document identifier. For a document identifier, an event key’s payload.id value is a string that represents the updated document’s original _id field as a MongoDB extended JSON serialization that uses strict mode. The following table provides examples of how different types of _id fields are represented.

Table 4.3. Examples of representing document _id fields in event key payloads

TypeMongoDB _id ValueKey’s payload

Integer

1234

{ "id" : "1234" }

Float

12.34

{ "id" : "12.34" }

String

"1234"

{ "id" : "\"1234\"" }

Document

{ "hi" : "kafka", "nums" : [10.0, 100.0, 1000.0] }

{ "id" : "{\"hi\" : \"kafka\", \"nums\" : [10.0, 100.0, 1000.0]}" }

ObjectId

ObjectId("596e275826f08b2730779e1f")

{ "id" : "{\"$oid\" : \"596e275826f08b2730779e1f\"}" }

Binary

BinData("a2Fma2E=",0)

{ "id" : "{\"$binary\" : \"a2Fma2E=\", \"$type\" : \"00\"}" }

4.4.6.2. Change event values

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample document that was used to show an example of a change event key:

Example document

{
  "_id": 1004,
  "first_name": "Anne",
  "last_name": "Kretchmar",
  "email": "annek@noanswer.org"
}

The value portion of a change event for a change to this document is described for each event type:

4.4.6.3. create events

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers collection:

{
    "schema": { 1
      "type": "struct",
      "fields": [
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json", 2
          "version": 1,
          "field": "after"
        },
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json",
          "version": 1,
          "field": "patch"
        },
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json",
          "version": 1,
          "field": "filter"
        },
        {
          "type": "struct",
          "fields": [
            {
              "type": "string",
              "optional": false,
              "field": "version"
            },
            {
              "type": "string",
              "optional": false,
              "field": "connector"
            },
            {
              "type": "string",
              "optional": false,
              "field": "name"
            },
            {
              "type": "int64",
              "optional": false,
              "field": "ts_ms"
            },
            {
              "type": "boolean",
              "optional": true,
              "default": false,
              "field": "snapshot"
            },
            {
              "type": "string",
              "optional": false,
              "field": "db"
            },
            {
              "type": "string",
              "optional": false,
              "field": "rs"
            },
            {
              "type": "string",
              "optional": false,
              "field": "collection"
            },
            {
              "type": "int32",
              "optional": false,
              "field": "ord"
            },
            {
              "type": "int64",
              "optional": true,
              "field": "h"
            }
          ],
          "optional": false,
          "name": "io.debezium.connector.mongo.Source", 3
          "field": "source"
        },
        {
          "type": "string",
          "optional": true,
          "field": "op"
        },
        {
          "type": "int64",
          "optional": true,
          "field": "ts_ms"
        }
      ],
      "optional": false,
      "name": "dbserver1.inventory.customers.Envelope" 4
      },
    "payload": { 5
      "after": "{\"_id\" : {\"$numberLong\" : \"1004\"},\"first_name\" : \"Anne\",\"last_name\" : \"Kretchmar\",\"email\" : \"annek@noanswer.org\"}", 6
      "patch": null,
      "source": { 7
        "version": "1.4.2.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": false,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 31,
        "h": 1546547425148721999
      },
      "op": "c", 8
      "ts_ms": 1558965515240 9
    }
  }

Table 4.4. Descriptions of create event value fields

ItemField nameDescription

1

schema

The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular collection.

2

name

In the schema section, each name field specifies the schema for a field in the value’s payload.

io.debezium.data.Json is the schema for the payload’s after, patch, and filter fields. This schema is specific to the customers collection. A create event is the only kind of event that contains an after field. An update event contains a filter field and a patch field. A delete event contains a filter field, but not an after field nor a patch field.

3

name

io.debezium.connector.mongo.Source is the schema for the payload’s source field. This schema is specific to the MongoDB connector. The connector uses it for all events that it generates.

4

name

dbserver1.inventory.customers.Envelope is the schema for the overall structure of the payload, where dbserver1 is the connector name, inventory is the database, and customers is the collection. This schema is specific to the collection.

5

payload

The value’s actual data. This is the information that the change event is providing.

It may appear that the JSON representations of the events are much larger than the documents they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.

6

after

An optional field that specifies the state of the document after the event occurred. In this example, the after field contains the values of the new document’s _id, first_name, last_name, and email fields. The after value is always a string. By convention, it contains a JSON representation of the document. MongoDB’s oplog entries contain the full state of a document only for _create_ events; in other words, a create event is the only kind of event that contains an after field.

7

source

Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes:

  • Debezium version.
  • Name of the connector that generated the event.
  • Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes.
  • Names of the collection and database that contain the new document.
  • If the event was part of a snapshot.
  • Timestamp for when the change was made in the database and ordinal of the event within the timestamp.
  • Unique identifier of the MongoDB operation, which depends on the version of MongoDB. It is either the h field in the oplog event, or a field named stxnid, which represents the lsid and txnNumber fields from the oplog event.

8

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, c indicates that the operation created a document. Valid values are:

  • c = create
  • u = update
  • d = delete
  • r = read (applies to only snapshots)

9

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

4.4.6.4. update events

The value of a change event for an update in the sample customers collection has the same schema as a create event for that collection. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. An update event does not have an after value. Instead, it has these two fields:

  • patch is a string field that contains the JSON representation of the idempotent update operation
  • filter is a string field that contains the JSON representation of the selection criteria for the update. The filter string can include multiple shard key fields for sharded collections.

Here is an example of a change event value in an event that the connector generates for an update in the customers collection:

{
    "schema": { ... },
    "payload": {
      "op": "u", 1
      "ts_ms": 1465491461815, 2
      "patch": "{\"$set\":{\"first_name\":\"Anne Marie\"}}", 3
      "filter": "{\"_id\" : {\"$numberLong\" : \"1004\"}}", 4
      "source": { 5
        "version": "1.4.2.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }

Table 4.5. Descriptions of update event value fields

ItemField nameDescription

1

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, u indicates that the operation updated a document.

2

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

3

patch

Contains the JSON string representation of the actual MongoDB idempotent change to the document. In this example, the update changed the first_name field to a new value.

An update event value does not contain an after field.

4

filter

Contains the JSON string representation of the MongoDB selection criteria that was used to identify the document to be updated.

5

source

Mandatory field that describes the source metadata for the event. This field contains the same information as a create event for the same collection, but the values are different since this event is from a different position in the oplog. The source metadata includes:

  • Debezium version.
  • Name of the connector that generated the event.
  • Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes.
  • Names of the collection and database that contain the updated document.
  • If the event was part of a snapshot.
  • Timestamp for when the change was made in the database and ordinal of the event within the timestamp.
  • Unique identifier of the MongoDB operation, which depends on the version of MongoDB. It is either the h field in the oplog event, or a field named stxnid, which represents the lsid and txnNumber fields from the oplog event.
Warning

In a Debezium change event, MongoDB provides the content of the patch field. The format of this field depends on the version of the MongoDB database. Consequently, be prepared for potential changes to the format when you upgrade to a newer MongoDB database version. Examples in this document were obtained from MongoDB 3.4, In your application, event formats might be different.

Note

In MongoDB’s oplog, update events do not contain the before or after states of the changed document. Consequently, it is not possible for a Debezium connector to provide this information. However, a Debezium connector provides a document’s starting state in create and read events. Downstream consumers of the stream can reconstruct document state by keeping the latest state for each document and comparing the state in a new event with the saved state. Debezium connector’s are not able to keep this state.

4.4.6.5. delete events

The value in a delete change event has the same schema portion as create and update events for the same collection. The payload portion in a delete event contains values that are different from create and update events for the same collection. In particular, a delete event contains neither an after value nor a patch value. Here is an example of a delete event for a document in the customers collection:

{
    "schema": { ... },
    "payload": {
      "op": "d", 1
      "ts_ms": 1465495462115, 2
      "filter": "{\"_id\" : {\"$numberLong\" : \"1004\"}}", 3
      "source": { 4
        "version": "1.4.2.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }

Table 4.6. Descriptions of delete event value fields

ItemField nameDescription

1

op

Mandatory string that describes the type of operation. The op field value is d, signifying that this document was deleted.

2

ts_ms

Optional field that displays the time at which the connector processed the event. The time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

3

filter

Contains the JSON string representation of the MongoDB selection criteria that was used to identify the document to be deleted.

4

source

Mandatory field that describes the source metadata for the event. This field contains the same information as a create or update event for the same collection, but the values are different since this event is from a different position in the oplog. The source metadata includes:

  • Debezium version.
  • Name of the connector that generated the event.
  • Logical name of the MongoDB replica set, which forms a namespace for generated events and is used in Kafka topic names to which the connector writes.
  • Names of the collection and database that contained the deleted document.
  • If the event was part of a snapshot.
  • Timestamp for when the change was made in the database and ordinal of the event within the timestamp.
  • Unique identifier of the MongoDB operation, which depends on the version of MongoDB. It is either the h field in the oplog event, or a field named stxnid, which represents the lsid and txnNumber fields from the oplog event.

MongoDB connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

All MongoDB connector events for a uniquely identified document have exactly the same key. When a document is deleted, the delete event value still works with log compaction because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that key, the message value must be null. To make this possible, after Debezium’s MongoDB connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value. A tombstone event informs Kafka that all messages with that same key can be removed.

4.4.7. Transaction Metadata

Debezium can generate events that represents transaction metadata boundaries and enrich data messages.

4.4.7.1. Transaction boundaries

Debezium generates events for every transaction BEGIN and END. Every event contains

  • status - BEGIN or END
  • id - string representation of unique transaction identifier
  • event_count (for END events) - total number of events emmitted by the transaction
  • data_collections (for END events) - an array of pairs of data_collection and event_count that provides number of events emitted by changes originating from given data collection

Following is an example of what a message looks like:

{
  "status": "BEGIN",
  "id": "1462833718356672513",
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "1462833718356672513",
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "rs0.testDB.tablea",
      "event_count": 1
    },
    {
      "data_collection": "rs0.testDB.tableb",
      "event_count": 1
    }
  ]
}

The transaction events are written to the topic named <database.server.name>.transaction.

4.4.7.2. Data events enrichment

When transaction metadata is enabled the data message Envelope is enriched with a new transaction field. This field provides information about every event in the form of a composite of fields:

  • id - string representation of unique transaction identifier
  • total_order - the absolute position of the event among all events generated by the transaction
  • data_collection_order - the per-data collection position of the event among all events that were emitted by the transaction

Following is an example of what a message looks like:

{
  "before": null,
  "after": {
    "pk": "2",
    "aa": "1"
  },
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "1462833718356672513",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

4.5. Deploying the MongoDB connector

To deploy a Debezium MongoDB connector, install the Debezium MongoDB connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

To install the MongoDB connector, follow the procedures in Installing Debezium on OpenShift. The main steps are:

  1. Use Red Hat AMQ Streams to set up Apache Kafka and Kafka Connect on OpenShift. AMQ Streams offers operators and images that bring Kafka to OpenShift.
  2. Download the Debezium MongoDB connector.
  3. Extract the connector files into your Kafka Connect environment.
  4. Add the connector plug-in’s parent directory to your Kafka Connect plugin.path, for example:

    plugin.path=/kafka/connect

    The above example assumes that you extracted the Debezium MongoDB connector to the /kafka/connect/Debezium-connector-mongodb path.

  5. Restart your Kafka Connect process to ensure that the new JAR files are picked up.

You also need to set up MongoDB to run a Debezium connector.

Additional resources

For more information about the deployment process, and deploying connectors with AMQ Streams, see the Debezium installation guides.

4.5.1. Example configuration

To use the connector to produce change events for a particular MongoDB replica set or sharded cluster, create a configuration file in JSON. When the connector starts, it will perform a snapshot of the collections in your MongoDB replica sets and start reading the replica sets' oplogs, producing events for every inserted, updated, and deleted document. Optionally filter out collections that are not needed.

Following is an example of the configuration for a MongoDB connector that monitors a MongoDB replica set rs0 at port 27017 on 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium MongoDB connector in a .yaml file using the configuration properties available for the connector.

apiVersion: kafka.strimzi.io/v1beta1
  kind: KafkaConnector
  metadata:
    name: inventory-connector 1
    labels: strimzi.io/cluster: my-connect-cluster
  spec:
    class: io.debezium.connector.mongodb.MongoDbConnector 2
    config:
     mongodb.hosts: rs0/192.168.99.100:27017 3
     mongodb.name: fulfillment 4
     collection.include.list: inventory[.]* 5
1 1 1 1 1 1
The name of our connector when we register it with a Kafka Connect service.
2 2 2 2 2 2
The name of the MongoDB connector class.
3 3 3 3 3 3
The host addresses to use to connect to the MongoDB replica set.
4 4 4 4 4 4
The logical name of the MongoDB replica set, which forms a namespace for generated events and is used in all the names of the Kafka topics to which the connector writes, the Kafka Connect schema names, and the namespaces of the corresponding Avro schema when the Avro converter is used.
5 5 5 5
A list of regular expressions that match the collection namespaces (for example, <dbName>.<collectionName>) of all collections to be monitored. This is optional.

See the complete list of connector properties that can be specified in these configurations.

This configuration can be sent via POST to a running Kafka Connect service, which will then record the configuration and start up the one connector task that will connect to the MongoDB replica set or sharded cluster, assign tasks for each replica set, perform a snapshot if necessary, read the oplog, and record events to Kafka topics.

4.5.2. Adding connector configuration

You can use a provided Debezium container to deploy a Debezium MongoDB connector. In this procedure, you build a custom Kafka Connect container image for Debezium, configure the Debezium connector as needed, and then add your connector configuration to your Kafka Connect environment.

Prerequisites

  • Podman or Docker is installed and you have sufficient rights to create and manage containers.
  • You installed the Debezium MongoDB connector archive.

Procedure

  1. Extract the Debezium MongoDB connector archive to create a directory structure for the connector plug-in, for example:

    tree ./my-plugins/
    ./my-plugins/
    ├── debezium-connector-mongodb
    │   ├── ...
  2. Create and publish a custom image for running your Debezium connector:

    1. Create a new Dockerfile by using registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.0 as the base image. In the following example, you would replace my-plugins with the name of your plug-ins directory:

      FROM registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.0
      USER root:root
      COPY ./my-plugins/ /opt/kafka/plugins/
      USER 1001

      Before Kafka Connect starts running the connector, Kafka Connect loads any third-party plug-ins that are in the /opt/kafka/plugins directory.

    2. Build the container image. For example, if you saved the Dockerfile that you created in the previous step as debezium-container-for-mongodb, and if the Dockerfile is in the current directory, then you would run the following command:

      podman build -t debezium-container-for-mongodb:latest .

    3. Push your custom image to your container registry, for example:

      podman push debezium-container-for-mongodb:latest

    4. Point to the new container image. Do one of the following:

      • Edit the spec.image property of the KafkaConnector custom resource. If set, this property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator. For example:

        apiVersion: kafka.strimzi.io/v1beta1
        kind: KafkaConnector
        metadata:
          name: my-connect-cluster
        spec:
          #...
          image: debezium-container-for-mongodb
      • In the install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml file, edit the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable to point to the new container image and reinstall the Cluster Operator. If you edit this file you must apply it to your OpenShift cluster.
  3. Create a KafkaConnector custom resource that defines your Debezium MongoDB connector instance. See the connector configuration example.
  4. Apply the connector instance, for example:

    oc apply -f inventory-connector.yaml

    This registers inventory-connector and the connector starts to run against the inventory database.

  5. Verify that the connector was created and has started to capture changes in the specified database. You can verify the connector instance by watching the Kafka Connect log output as, for example, inventory-connector starts.

    1. Display the Kafka Connect log output:

      oc logs $(oc get pods -o name -l strimzi.io/name=my-connect-cluster-connect)
    2. Review the log output to verify that the initial snapshot has been executed. You should see something like the following lines:

      ... INFO Starting snapshot for ...
      ... INFO Snapshot is using user 'debezium' ...

Results

When the connector starts, it performs a consistent snapshot of the MongoDB databases that the connector is configured for. The connector then starts generating data change events for document-level operations and streaming change event records to Kafka topics.

4.5.3. Monitoring

The Debezium MongoDB connector has two metric types in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect have.

Please refer to the monitoring documentation for details of how to expose these metrics via JMX.

4.5.3.1. Snapshot Metrics

The MBean is debezium.mongodb:type=connector-metrics,context=snapshot,server=<mongodb.name>.

AttributesTypeDescription

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

SnapshotAborted

boolean

Whether the snapshot was aborted.

SnapshotCompleted

boolean

Whether the snapshot completed.

SnapshotDurationInSeconds

long

The total number of seconds that the snapshot has taken so far, even if not complete.

RowsScanned

Map<String, Long>

Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes. It will be enabled if max.queue.size.in.bytes is passed with a positive long value.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

The Debezium MongoDB connector also provides the following custom snapshot metrics:

AttributeTypeDescription

NumberOfDisconnects

long

Number of database disconnects.

4.5.3.2. Streaming Metrics

The MBean is debezium.sql_server:type=connector-metrics,context=streaming,server=<mongodb.name>.

AttributesTypeDescription

LastEvent

string

The last streaming event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the streamer and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.

Connected

boolean

Flag that denotes whether the connector is currently connected to the database server.

MilliSecondsBehindSource

long

The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incoporate any differences between the clocks on the machines where the database server and the connector are running.

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

SourceEventPosition

Map<String, String>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

MaxQueueSizeInBytes

long

The maximum buffer of the queue in bytes.

CurrentQueueSizeInBytes

long

The current data of records in the queue in bytes.

The Debezium MongoDB connector also provides the following custom streaming metrics:

AttributeTypeDescription

NumberOfDisconnects

long

Number of database disconnects.

NumberOfPrimaryElections

long

Number of primary node elections.

4.5.4. Connector properties

The following configuration properties are required unless a default value is available.

PropertyDefaultDescription

name

 

Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)

connector.class

 

The name of the Java class for the connector. Always use a value of io.debezium.connector.mongodb.MongoDbConnector for the MongoDB connector.

mongodb.hosts

 

The comma-separated list of hostname and port pairs (in the form 'host' or 'host:port') of the MongoDB servers in the replica set. The list can contain a single hostname and port pair. If mongodb.members.auto.discover is set to false, then the host and port pair should be prefixed with the replica set name (e.g., rs0/localhost:27017).

mongodb.name

 

A unique name that identifies the connector and/or MongoDB replica set or sharded cluster that this connector monitors. Each server should be monitored by at most one Debezium connector, since this server name prefixes all persisted Kafka topics emanating from the MongoDB replica set or cluster. Only alphanumeric characters and underscores should be used.

mongodb.user

 

Name of the database user to be used when connecting to MongoDB. This is required only when MongoDB is configured to use authentication.

mongodb.password

 

Password to be used when connecting to MongoDB. This is required only when MongoDB is configured to use authentication.

mongodb.authsource

admin

Database (authentication source) containing MongoDB credentials. This is required only when MongoDB is configured to use authentication with another authentication database than admin.

mongodb.ssl.enabled

false

Connector will use SSL to connect to MongoDB instances.

mongodb.ssl.invalid.hostname.allowed

false

When SSL is enabled this setting controls whether strict hostname checking is disabled during connection phase. If true the connection will not prevent man-in-the-middle attacks.

database.include.list

empty string

An optional comma-separated list of regular expressions that match database names to be monitored; any database name not included in database.include.list is excluded from monitoring. By default all databases are monitored. Must not be used with database.exclude.list.

database.exclude.list

empty string

An optional comma-separated list of regular expressions that match database names to be excluded from monitoring; any database name not included in database.exclude.list is monitored. Must not be used with database.include.list.

collection.include.list

empty string

An optional comma-separated list of regular expressions that match fully-qualified namespaces for MongoDB collections to be monitored; any collection not included in collection.include.list is excluded from monitoring. Each identifier is of the form databaseName.collectionName. By default the connector will monitor all collections except those in the local and admin databases. Must not be used with collection.exclude.list.

collection.exclude.list

empty string

An optional comma-separated list of regular expressions that match fully-qualified namespaces for MongoDB collections to be excluded from monitoring; any collection not included in collection.exclude.list is monitored. Each identifier is of the form databaseName.collectionName. Must not be used with collection.include.list.

snapshot.mode

initial

Specifies the criteria for running a snapshot upon startup of the connector. The default is initial, and specifies the connector reads a snapshot when either no offset is found or if the oplog no longer contains the previous offset. The never option specifies that the connector should never use snapshots, instead the connector should proceed to tail the log.

snapshot.include.collection.list

All collections specified in collection.include.list

An optional, comma-separated list of regular expressions that match names of schemas specified in collection.include.list for which you want to take the snapshot.

field.exclude.list

empty string

An optional comma-separated list of the fully-qualified names of fields that should be excluded from change event message values. Fully-qualified names for fields are of the form databaseName.collectionName.fieldName.nestedFieldName, where databaseName and collectionName may contain the wildcard (*) which matches any characters.

field.renames

empty string

An optional comma-separated list of the fully-qualified replacements of fields that should be used to rename fields in change event message values. Fully-qualified replacements for fields are of the form databaseName.collectionName.fieldName.nestedFieldName:newNestedFieldName, where databaseName and collectionName may contain the wildcard (*) which matches any characters, the colon character (:) is used to determine rename mapping of field. The next field replacement is applied to the result of the previous field replacement in the list, so keep this in mind when renaming multiple fields that are in the same path.

tasks.max

1

The maximum number of tasks that should be created for this connector. The MongoDB connector will attempt to use a separate task for each replica set, so the default is acceptable when using the connector with a single MongoDB replica set. When using the connector with a MongoDB sharded cluster, we recommend specifying a value that is equal to or more than the number of shards in the cluster, so that the work for each replica set can be distributed by Kafka Connect.

snapshot.max.threads

1

Positive integer value that specifies the maximum number of threads used to perform an intial sync of the collections in a replica set. Defaults to 1.

tombstones.on.delete

true

Controls whether a tombstone event should be generated after a delete event.
When true the delete operations are represented by a delete event and a subsequent tombstone event. When false only a delete event is sent.
Emitting the tombstone event (the default behavior) allows Kafka to completely delete all events pertaining to the given key once the source record got deleted.

snapshot.delay.ms

 

An interval in milliseconds that the connector should wait before taking a snapshot after starting up;
Can be used to avoid snapshot interruptions when starting multiple connectors in a cluster, which may cause re-balancing of connectors.

snapshot.fetch.size

0

Specifies the maximum number of documents that should be read in one go from each collection while taking a snapshot. The connector will read the collection contents in multiple batches of this size.
Defaults to 0, which indicates that the server chooses an appropriate fetch size.

The following advanced configuration properties have good defaults that will work in most situations and therefore rarely need to be specified in the connector’s configuration.

PropertyDefaultDescription

max.queue.size

8192

Positive integer value that specifies the maximum size of the blocking queue into which change events read from the database log are placed before they are written to Kafka. This queue can provide backpressure to the oplog reader when, for example, writes to Kafka are slower or if Kafka is not available. Events that appear in the queue are not included in the offsets periodically recorded by this connector. Defaults to 8192, and should always be larger than the maximum batch size specified in the max.batch.size property.

max.batch.size

2048

Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector. Defaults to 2048.

max.queue.size.in.bytes

0

Long value for the maximum size in bytes of the blocking queue. The feature is disabled by default, it will be active if it’s set with a positive long value.

poll.interval.ms

1000

Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 1000 milliseconds, or 1 second.

connect.backoff.initial.delay.ms

1000

Positive integer value that specifies the initial delay when trying to reconnect to a primary after the first failed connection attempt or when no primary is available. Defaults to 1 second (1000 ms).

connect.backoff.max.delay.ms

1000

Positive integer value that specifies the maximum delay when trying to reconnect to a primary after repeated failed connection attempts or when no primary is available. Defaults to 120 seconds (120,000 ms).

connect.max.attempts

16

Positive integer value that specifies the maximum number of failed connection attempts to a replica set primary before an exception occurs and task is aborted. Defaults to 16, which with the defaults for connect.backoff.initial.delay.ms and connect.backoff.max.delay.ms results in just over 20 minutes of attempts before failing.

mongodb.members.auto.discover

true

Boolean value that specifies whether the addresses in 'mongodb.hosts' are seeds that should be used to discover all members of the cluster or replica set (true), or whether the address(es) in mongodb.hosts should be used as is (false). The default is true and should be used in all cases except where MongoDB is fronted by a proxy.

heartbeat.interval.ms

0

Controls how frequently heartbeat messages are sent.
This property contains an interval in milliseconds that defines how frequently the connector sends messages into a heartbeat topic. This can be used to monitor whether the connector is still receiving change events from the database. You also should leverage heartbeat messages in cases where only records in non-captured collections are changed for a longer period of time. In such situation the connector would proceed to read the oplog from the database but never emit any change messages into Kafka, which in turn means that no offset updates are committed to Kafka. This will cause the oplog files to be rotated out but connector will not notice it so on restart some events are no longer available which leads to the need of re-execution of the initial snapshot.

Set this parameter to 0 to not send heartbeat messages at all.
Disabled by default.

heartbeat.topics.prefix

__debezium-heartbeat

Controls the naming of the topic to which heartbeat messages are sent.
The topic is named according to the pattern <heartbeat.topics.prefix>.<server.name>.

sanitize.field.names

true when connector configuration explicitly specifies the key.converter or value.converter parameters to use Avro, otherwise defaults to false.

Whether field names are sanitized to adhere to Avro naming requirements.

skipped.operations

 

comma-separated list of oplog operations that will be skipped during streaming. The operations include: c for inserts/create, u for updates, and d for deletes. By default, no operations are skipped.

snapshot.collection.filter.overrides

 

Controls which collection items are included in snapshot. This property affects snapshots only. Specify a comma-separated list of collection names in the form databaseName.collectionName.

For each collection that you specify, also specify another configuration property: snapshot.collection.filter.overrides.databaseName.collectionName. For example, the name of the other configuration property might be: snapshot.collection.filter.overrides.customers.orders. Set this property to a valid filter expression that retrieves only the items that you want in the snapshot. When the connector performs a snapshot, it retrieves only the items that matches the filter expression.

provide.transaction.metadata

false

When set to true Debezium generates events with transaction boundaries and enriches data events envelope with transaction metadata.

See Transaction Metadata for additional details.

retriable.restart.connector.wait.ms

10000 (10 seconds)

The number of milliseconds to wait before restarting a connector after a retriable error occurs.

mongodb.poll.interval.ms

30000

The interval in which the connector polls for new, removed, or changed replica sets.

mongodb.connect.timeout.ms

10000 (10 seconds)

The number of milliseconds the driver will wait before a new connection attempt is aborted.

mongodb.socket.timeout.ms

0

The number of milliseconds before a send/receive on the socket can take before a timeout occurs. A value of 0 disables this behavior.

mongodb.server.selection.timeout.ms

30000 (30 seconds)

The number of milliseconds the driver will wait to select a server before it times out and throws an error.

4.6. MongoDB connector common issues

Debezium is a distributed system that captures all changes in multiple upstream databases, and will never miss or lose an event. Of course, when the system is operating nominally or being administered carefully, then Debezium provides exactly once delivery of every change event. However, if a fault does happen then the system will still not lose any events, although while it is recovering from the fault it may repeat some change events. Thus, in these abnormal situations Debezium (like Kafka) provides at least once delivery of change events.

The rest of this section describes how Debezium handles various kinds of faults and problems.

4.6.1. Configuration and startup errors

The connector will fail upon startup, report an error/exception in the log, and stop running when the connector’s configuration is invalid, or when the connector repeatedly fails to connect to MongoDB using the specified connectivity parameters. Reconnection is done using exponential backoff, and the maximum number of attempts is configurable.

In these cases, the error will have more details about the problem and possibly a suggested work around. The connector can be restarted when the configuration has been corrected or the MongoDB problem has been addressed.

4.6.2. MongoDB becomes unavailable

Once the connector is running, if the primary node of any of the MongoDB replica sets become unavailable or unreachable, the connector will repeatedly attempt to reconnect to the primary node, using exponential backoff to prevent saturating the network or servers. If the primary remains unavailable after the configurable number of connection attempts, the connector will fail.

The attempts to reconnect are controlled by three properties:

  • connect.backoff.initial.delay.ms - The delay before attempting to reconnect for the first time, with a default of 1 second (1000 milliseconds).
  • connect.backoff.max.delay.ms - The maximum delay before attempting to reconnect, with a default of 120 seconds (120,000 milliseconds).
  • connect.max.attempts - The maximum number of attempts before an error is produced, with a default of 16.

Each delay is double that of the prior delay, up to the maximum delay. Given the default values, the following table shows the delay for each failed connection attempt and the total accumulated time before failure.

Reconnection attempt numberDelay before attempt, in secondsTotal delay before attempt, in minutes and seconds

1

1

00:01

2

2

00:03

3

4

00:07

4

8

00:15

5

16

00:31

6

32

01:03

7

64

02:07

8

120

04:07

9

120

06:07

10

120

08:07

11

120

10:07

12

120

12:07

13

120

14:07

14

120

16:07

15

120

18:07

16

120

20:07

4.6.3. Kafka Connect process stops gracefully

If Kafka Connect is being run in distributed mode, and a Kafka Connect process is stopped gracefully, then prior to shutdown of that processes Kafka Connect will migrate all of the process' connector tasks to another Kafka Connect process in that group, and the new connector tasks will pick up exactly where the prior tasks left off. There is a short delay in processing while the connector tasks are stopped gracefully and restarted on the new processes.

If the group contains only one process and that process is stopped gracefully, then Kafka Connect will stop the connector and record the last offset for each replica set. Upon restart, the replica set tasks will continue exactly where they left off.

4.6.4. Kafka Connect process crashes

If the Kafka Connector process stops unexpectedly, then any connector tasks it was running will terminate without recording their most recently-processed offsets. When Kafka Connect is being run in distributed mode, it will restart those connector tasks on other processes. However, the MongoDB connectors will resume from the last offset recorded by the earlier processes, which means that the new replacement tasks may generate some of the same change events that were processed just prior to the crash. The number of duplicate events depends on the offset flush period and the volume of data changes just before the crash.

Note

Because there is a chance that some events may be duplicated during a recovery from failure, consumers should always anticipate some events may be duplicated. Debezium changes are idempotent, so a sequence of events always results in the same state.

Debezium also includes with each change event message the source-specific information about the origin of the event, including the MongoDB event’s unique transaction identifier (h) and timestamp (sec and ord). Consumers can keep track of other of these values to know whether it has already seen a particular event.

4.6.5. Kafka becomes unavailable

As the connector generates change events, the Kafka Connect framework records those events in Kafka using the Kafka producer API. Kafka Connect will also periodically record the latest offset that appears in those change events, at a frequency that you have specified in the Kafka Connect worker configuration. If the Kafka brokers become unavailable, the Kafka Connect worker process running the connectors will simply repeatedly attempt to reconnect to the Kafka brokers. In other words, the connector tasks will simply pause until a connection can be reestablished, at which point the connectors will resume exactly where they left off.

4.6.6. Connector is stopped for a duration

If the connector is gracefully stopped, the replica sets can continue to be used and any new changes are recorded in MongoDB’s oplog. When the connector is restarted, it will resume streaming changes for each replica set where it last left off, recording change events for all of the changes that were made while the connector was stopped. If the connector is stopped long enough such that MongoDB purges from its oplog some operations that the connector has not read, then upon startup the connector will perform a snapshot.

A properly configured Kafka cluster is capable of massive throughput. Kafka Connect is written with Kafka best practices, and given enough resources will also be able to handle very large numbers of database change events. Because of this, when a connector has been restarted after a while, it is very likely to catch up with the database, though how quickly will depend upon the capabilities and performance of Kafka and the volume of changes being made to the data in MongoDB.

Note

If the connector remains stopped for long enough, MongoDB might purge older oplog files and the connector’s last position may be lost. In this case, when the connector configured with initial snapshot mode (the default) is finally restarted, the MongoDB server will no longer have the starting point and the connector will fail with an error.

4.6.7. MongoDB loses writes

It is possible for MongoDB to lose commits in specific failure situations. For example, if the primary applies a change and records it in its oplog before it then crashes unexpectedly, the secondary nodes may not have had a chance to read those changes from the primary’s oplog before the primary crashed. If one such secondary is then elected as primary, its oplog is missing the last changes that the old primary had recorded and no longer has those changes.

In these cases where MongoDB loses changes recorded in a primary’s oplog, it is possible that the MongoDB connector may or may not capture these lost changes. At this time, there is no way to prevent this side effect of MongoDB.

Chapter 5. Debezium connector for SQL Server

The Debezium SQL Server connector captures row-level changes that occur in the schemas of a SQL Server database.

For details about the Debezium SQL Server connector and its use, see following topics:

The first time that the Debezium SQL Server connector connects to a SQL Server database or cluster, it takes a consistent snapshot of the schemas in the database. After the initial snapshot is complete, the connector continuously captures row-level changes for INSERT, UPDATE, or DELETE operations that are committed to the SQL Server databases that are enabled for CDC. The connector produces events for each data change operation, and streams them to Kafka topics. The connector streams all of the events for a table to a dedicated Kafka topic. Applications and services can then consume data change event records from that topic.

5.1. Overview of Debezium SQL Server connector

The Debezium SQL Server connector is based on the change data capture feature that is available in SQL Server 2016 Service Pack 1 (SP1) and later Standard edition or Enterprise edition. The SQL Server capture process monitors designated databases and tables, and stores the changes into specifically created change tables that have stored procedure facades.

To enable the Debezium SQL Server connector to capture change event records for database operations, you must first enable change data capture on the SQL Server database. CDC must be enabled on both the database and on each table that you want to capture. After you set up CDC on the source database, the connector can capture row-level INSERT, UPDATE, and DELETE operations that occur in the database. The connector writes event records for each source table to a Kafka topic especially dedicated to that table. One topic exists for each captured table. Client applications read the Kafka topics for the database tables that they follow, and can respond to the row-level events they they consume from those topics.

The first time that the connector connects to a SQL Server database or cluster, it takes a consistent snapshot of the schemas for all tables for which it is configured to capture changes, and streams this state to Kafka. After the snapshot is complete, the connector continuously captures subsequent row-level changes that occur. By first establishing a consistent view of all of the data, the connector can continue reading without having lost any of the changes that were made while the snapshot was taking place.

The Debezium SQL Server connector is tolerant of failures. As the connector reads changes and produces events, it periodically records the position of events in the database log (LSN / Log Sequence Number). If the connector stops for any reason (including communication failures, network problems, or crashes), after a restart the connector resumes reading the SQL Server CDC tables from the last point that it read.

Note

Offsets are committed periodically. They are not committed at the time that a change event occurs. As a result, following an outage, duplicate events might be generated.

Fault tolerance also applies to snapshots. That is, if the connector stops during a snapshot, the connector begins a new snapshot when it restarts.

5.2. How Debezium SQL Server connectors work

To optimally configure and run a Debezium SQL Server connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and uses metadata.

For details about how the connector works, see the following sections:

5.2.1. How Debezium SQL Server connectors perform database snapshots

SQL Server CDC is not designed to store a complete history of database changes. For the Debezium SQL Server connector to establish a baseline for the current state of the database, it uses a process called snapshotting.

You can configure how the connector creates snapshots. By default, the connector’s snapshot mode is set to initial. Based on this initial snapshot mode, the first time that the connector starts, it performs an initial consistent snapshot of the database. This initial snapshot captures the structure and data for any tables that match the criteria defined by the include and exclude properties that are configured for the connector (for example, table.include.list, column.include.list, table.exclude.list, and so forth).

When the connector creates a snapshot, it completes the following tasks:

  1. Determines the tables to be captured.
  2. Obtains a lock on the SQL Server tables for which CDC is enabled to prevent structural changes from occurring during creation of the snapshot. The level of the lock is determined by snapshot.isolation.mode configuration option.
  3. Reads the maximum log sequence number (LSN) position in the server’s transaction log.
  4. Captures the structure of all relevant tables.
  5. Releases the locks obtained in Step 2, if necessary. In most cases, locks are held for only a short period of time.
  6. Scans the SQL Server source tables and schemas to be captured based on the LSN position that was read in Step 3, generates a READ event for each row in the table, and writes the events to the Kafka topic for the table.
  7. Records the successful completion of the snapshot in the connector offsets.

The resulting initial snapshot captures the current state of each row in the tables that are enabled for CDC. From this baseline state, the connector captures subsequent changes as they occur.

5.2.2. How Debezium SQL Server connectors read change data tables

When the connector first starts, it takes a structural snapshot of the structure of the captured tables and persists this information to its internal database history topic. The connector then identifies a change table for each source table, and completes the following steps.

  1. For each change table, the connector read all of the changes that were created between the last stored maximum LSN and the current maximum LSN.
  2. The connector sorts the changes that it reads in ascending order, based on the values of their commit LSN and change LSN. This sorting order ensures that the changes are replayed by Debezium in the same order in which they occurred in the database.
  3. The connector passes the commit and change LSNs as offsets to Kafka Connect.
  4. The connector stores the maximum LSN and restarts the process from Step 1.

After a restart, the connector resumes processing from the last offset (commit and change LSNs) that it read.

The connector is able to detect whether CDC is enabled or disabled for included source tables and adjust its behavior.

5.2.3. Default names of Kafka topics that receive Debezium SQL Server change event records

The SQL Server connector writes events for all INSERT, UPDATE, and DELETE operations for a specific table to a single Kafka topic. By default, the Kafka topic name takes the form serverName.schemaName.tableName. The following list provides definitions for the components of the default name:

serverName
The logical name of the connector, as specified by the database.server.name configuration property.
schemaName
The name of the database schema in which the change event occurred.
tableName
The name of the database table in which the change event occurred.

For example, suppose that fulfillment is the logical server name in the configuration for a connector that is capturing changes in a SQL Server installation. The server has an inventory database with the schema name dbo, and the database contains tables with the names products, products_on_hand, customers, and orders. The connector would stream records to the following Kafka topics:

  • fulfillment.dbo.products
  • fulfillment.dbo.products_on_hand
  • fulfillment.dbo.customers
  • fulfillment.dbo.orders

If the default topic name do not meet your requirements, you can configure custom topic names. To configure custom topic names, you specify regular expressions in the logical topic routing SMT. For more information about using the logical topic routing SMT to customize topic naming, see Routing Debezium event records to topics that you specify.

5.2.4. How the Debezium SQL Server connector uses the schema change topic

For each table for which CDC is enabled, the Debezium SQL Server connector stores a history of schema changes in a database history topic. This topic reflects an internal connector state and you should not use it directly. Application that require notifications about schema changes, should obtain the information from the public schema change topic. The connector writes all of these events to a Kafka topic named <serverName>, where serverName is the name of the connector that is specified in the database.server.name configuration property.

Warning

The format of the messages that a connector emits to its schema change topic is in an incubating state and can change without notice.

Debezium emits a message to the schema change topic when the following events occur:

  • You enable CDC for a table.
  • You disable CDC for a table.
  • You alter the structure of a table for which CDC is enabled by following the schema evolution procedure.

A message to the schema change topic contains a logical representation of the table schema, for example:

{
  "schema": {
  ...
  },
  "payload": {
    "source": {
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1588252618953,
      "snapshot": "true",
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": null,
      "commit_lsn": "00000025:00000d98:00a2",
      "event_serial_no": null
    },
    "databaseName": "testDB", 1
    "schemaName": "dbo",
    "ddl": null, 2
    "tableChanges": [ 3
      {
        "type": "CREATE", 4
        "id": "\"testDB\".\"dbo\".\"customers\"", 5
        "table": { 6
          "defaultCharsetName": null,
          "primaryKeyColumnNames": [ 7
            "id"
          ],
          "columns": [ 8
            {
              "name": "id",
              "jdbcType": 4,
              "nativeType": null,
              "typeName": "int identity",
              "typeExpression": "int identity",
              "charsetName": null,
              "length": 10,
              "scale": 0,
              "position": 1,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "first_name",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 2,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "last_name",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 3,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            },
            {
              "name": "email",
              "jdbcType": 12,
              "nativeType": null,
              "typeName": "varchar",
              "typeExpression": "varchar",
              "charsetName": null,
              "length": 255,
              "scale": null,
              "position": 4,
              "optional": false,
              "autoIncremented": false,
              "generated": false
            }
          ]
        }
      }
    ]
  }
}

Table 5.1. Descriptions of fields in messages emitted to the schema change topic

ItemField nameDescription

1

databaseName
schemaName

Identifies the database and the schema that contain the change.

2

ddl

Always null for the SQL Server connector. For other connectors, this field contains the DDL responsible for the schema change. This DDL is not available to SQL Server connectors.

3

tableChanges

An array of one or more items that contain the schema changes generated by a DDL command.

4

type

Describes the kind of change. The value is one of the following:

  • CREATE - table created
  • ALTER - table modified
  • DROP - table deleted

5

id

Full identifier of the table that was created, altered, or dropped.

6

table

Represents table metadata after the applied change.

7

primaryKeyColumnNames

List of columns that compose the table’s primary key.

8

columns

Metadata for each column in the changed table.

In messages that the connector sends to the schema change topic, the key is the name of the database that contains the schema change. In the following example, the payload field contains the key:

{
  "schema": {
    "type": "struct",
    "fields": [
      {
        "type": "string",
        "optional": false,
        "field": "databaseName"
      }
    ],
    "optional": false,
    "name": "io.debezium.connector.sqlserver.SchemaChangeKey"
  },
  "payload": {
    "databaseName": "testDB"
  }
}

5.2.5. Descriptions of Debezium SQL Server connector data change events

The Debezium SQL Server connector generates a data change event for each row-level INSERT, UPDATE, and DELETE operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { 1
   ...
  },
 "payload": { 2
   ...
 },
 "schema": { 3
   ...
 },
 "payload": { 4
   ...
 },
}

Table 5.2. Overview of change event basic content

ItemField nameDescription

1

schema

The first schema field is part of the event key. It specifies a Kafka Connect schema that describes what is in the event key’s payload portion. In other words, the first schema field describes the structure of the primary key, or the unique key if the table does not have a primary key, for the table that was changed.

It is possible to override the table’s primary key by setting the message.key.columns connector configuration property. In this case, the first schema field describes the structure of the key identified by that property.

2

payload

The first payload field is part of the event key. It has the structure described by the previous schema field and it contains the key for the row that was changed.

3

schema

The second schema field is part of the event value. It specifies the Kafka Connect schema that describes what is in the event value’s payload portion. In other words, the second schema describes the structure of the row that was changed. Typically, this schema contains nested schemas.

4

payload

The second payload field is part of the event value. It has the structure described by the previous schema field and it contains the actual data for the row that was changed.

By default, the connector streams change event records to topics with names that are the same as the event’s originating table. See topic names.

Warning

The SQL Server connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This means that the logical server name must start with a Latin letter or an underscore, that is, a-z, A-Z, or _. Each remaining character in the logical server name and each character in the database and table names must be a Latin letter, a digit, or an underscore, that is, a-z, A-Z, 0-9, or \_. If there is an invalid character it is replaced with an underscore character.

This can lead to unexpected conflicts if the logical server name, a database name, or a table name contains invalid characters, and the only characters that distinguish names from one another are invalid and thus replaced with underscores.

For details about change events, see the following topics:

5.2.5.1. About keys in Debezium SQL Server change events

A change event’s key contains the schema for the changed table’s key and the changed row’s actual key. Both the schema and its corresponding payload contain a field for each column in the changed table’s primary key (or unique key constraint) at the time the connector created the event.

Consider the following customers table, which is followed by an example of a change event key for this table.

Example table

CREATE TABLE customers (
  id INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL UNIQUE
);

Example change event key

Every change event that captures a change to the customers table has the same event key schema. For as long as the customers table has the previous definition, every change event that captures a change to the customers table has the following key structure, which in JSON, looks like this:

{
    "schema": { 1
        "type": "struct",
        "fields": [ 2
            {
                "type": "int32",
                "optional": false,
                "field": "id"
            }
        ],
        "optional": false, 3
        "name": "server1.dbo.customers.Key" 4
    },
    "payload": { 5
        "id": 1004
    }
}

Table 5.3. Description of change event key

ItemField nameDescription

1

schema

The schema portion of the key specifies a Kafka Connect schema that describes what is in the key’s payload portion.

2

fields

Specifies each field that is expected in the payload, including each field’s name, type, and whether it is required. In this example, there is one required field named id of type int32.

3

optional

Indicates whether the event key must contain a value in its payload field. In this example, a value in the key’s payload is required. A value in the key’s payload field is optional when a table does not have a primary key.

4

server1.dbo.customers.Key

Name of the schema that defines the structure of the key’s payload. This schema describes the structure of the primary key for the table that was changed. Key schema names have the format connector-name.database-schema-name.table-name.Key. In this example:

  • server1 is the name of the connector that generated this event.
  • dbo is the database schema for the table that was changed.
  • customers is the table that was updated.

5

payload

Contains the key for the row for which this change event was generated. In this example, the key, contains a single id field whose value is 1004.

5.2.5.2. About values in Debezium SQL Server change events

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample table that was used to show an example of a change event key:

CREATE TABLE customers (
  id INTEGER IDENTITY(1001,1) NOT NULL PRIMARY KEY,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL UNIQUE
);

The value portion of a change event for a change to this table is described for each event type.

create events

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers table:

{
  "schema": { 1
    "type": "struct",
    "fields": [
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "server1.dbo.customers.Value", 2
        "field": "before"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "int32",
            "optional": false,
            "field": "id"
          },
          {
            "type": "string",
            "optional": false,
            "field": "first_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "last_name"
          },
          {
            "type": "string",
            "optional": false,
            "field": "email"
          }
        ],
        "optional": true,
        "name": "server1.dbo.customers.Value",
        "field": "after"
      },
      {
        "type": "struct",
        "fields": [
          {
            "type": "string",
            "optional": false,
            "field": "version"
          },
          {
            "type": "string",
            "optional": false,
            "field": "connector"
          },
          {
            "type": "string",
            "optional": false,
            "field": "name"
          },
          {
            "type": "int64",
            "optional": false,
            "field": "ts_ms"
          },
          {
            "type": "boolean",
            "optional": true,
            "default": false,
            "field": "snapshot"
          },
          {
            "type": "string",
            "optional": false,
            "field": "db"
          },
          {
            "type": "string",
            "optional": false,
            "field": "schema"
          },
          {
            "type": "string",
            "optional": false,
            "field": "table"
          },
          {
            "type": "string",
            "optional": true,
            "field": "change_lsn"
          },
          {
            "type": "string",
            "optional": true,
            "field": "commit_lsn"
          },
          {
            "type": "int64",
            "optional": true,
            "field": "event_serial_no"
          }
        ],
        "optional": false,
        "name": "io.debezium.connector.sqlserver.Source", 3
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "server1.dbo.customers.Envelope" 4
  },
  "payload": { 5
    "before": null, 6
    "after": { 7
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "source": { 8
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559729468470,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000758:0003",
      "commit_lsn": "00000027:00000758:0005",
      "event_serial_no": "1"
    },
    "op": "c", 9
    "ts_ms": 1559729471739 10
  }
}

Table 5.4. Descriptions of create event value fields

ItemField nameDescription

1

schema

The value’s schema, which describes the structure of the value’s payload. A change event’s value schema is the same in every change event that the connector generates for a particular table.

2

name

In the schema section, each name field specifies the schema for a field in the value’s payload.

server1.dbo.customers.Value is the schema for the payload’s before and after fields. This schema is specific to the customers table.

Names of schemas for before and after fields are of the form logicalName.database-schemaName.tableName.Value, which ensures that the schema name is unique in the database. This means that when using the Avro converter, the resulting Avro schema for each table in each logical source has its own evolution and history.

3

name

io.debezium.connector.sqlserver.Source is the schema for the payload’s source field. This schema is specific to the SQL Server connector. The connector uses it for all events that it generates.

4

name

server1.dbo.customers.Envelope is the schema for the overall structure of the payload, where server1 is the connector name, dbo is the database schema name, and customers is the table.

5

payload

The value’s actual data. This is the information that the change event is providing.

It may appear that the JSON representations of the events are much larger than the rows they describe. This is because the JSON representation must include the schema and the payload portions of the message. However, by using the Avro converter, you can significantly decrease the size of the messages that the connector streams to Kafka topics.

6

before

An optional field that specifies the state of the row before the event occurred. When the op field is c for create, as it is in this example, the before field is null since this change event is for new content.

7

after

An optional field that specifies the state of the row after the event occurred. In this example, the after field contains the values of the new row’s id, first_name, last_name, and email columns.

8

source

Mandatory field that describes the source metadata for the event. This field contains information that you can use to compare this event with other events, with regard to the origin of the events, the order in which the events occurred, and whether events were part of the same transaction. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Database and schema names
  • Timestamp for when the change was made in the database
  • If the event was part of a snapshot
  • Name of the table that contains the new row
  • Server log offsets

9

op

Mandatory string that describes the type of operation that caused the connector to generate the event. In this example, c indicates that the operation created a row. Valid values are:

  • c = create
  • u = update
  • d = delete
  • r = read (applies to only snapshots)

10

ts_ms

Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time when a change was committed in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

update events

The value of a change event for an update in the sample customers table has the same schema as a create event for that table. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. Here is an example of a change event value in an event that the connector generates for an update in the customers table:

{
  "schema": { ... },
  "payload": {
    "before": { 1
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "after": { 2
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "source": { 3
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559729995937,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000ac0:0002",
      "commit_lsn": "00000027:00000ac0:0007",
      "event_serial_no": "2"
    },
    "op": "u", 4
    "ts_ms": 1559729998706  5
  }
}

Table 5.5. Descriptions of update event value fields

ItemField nameDescription

1

before

An optional field that specifies the state of the row before the event occurred. In an update event value, the before field contains a field for each table column and the value that was in that column before the database commit. In this example, the email value is john.doe@example.org.

2

after

An optional field that specifies the state of the row after the event occurred. You can compare the before and after structures to determine what the update to this row was. In the example, the email value is now noreply@example.org.

3

source

Mandatory field that describes the source metadata for the event. The source field structure has the same fields as in a create event, but some values are different, for example, the sample update event has a different offset. The source metadata includes:

  • Debezium version
  • Connector type and name
  • Database and schema names
  • Timestamp for when the change was made in the database
  • If the event was part of a snapshot
  • Name of the table that contains the new row
  • Server log offsets

The event_serial_no field differentiates events that have the same commit and change LSN. Typical situations for when this field has a value other than 1:

  • update events have the value set to 2 because the update generates two events in the CDC change table of SQL Server (see the source documentation for details). The first event contains the old values and the second contains contains new values. The connector uses values in the first event to create the second event. The connector drops the first event.
  • When a primary key is updated SQL Server emits two evemts. A delete event for the removal of the record with the old primary key value and a create event for the addition of the record with the new primary key. Both operations share the same commit and change LSN and their event numbers are 1 and 2, respectively.

4

op

Mandatory string that describes the type of operation. In an update event value, the op field value is u, signifying that this row changed because of an update.

5

ts_ms

Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time when the change was committed to the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

Note

Updating the columns for a row’s primary/unique key changes the value of the row’s key. When a key changes, Debezium outputs three events: a delete event and a tombstone event with the old key for the row, followed by a create event with the new key for the row.

delete events

The value in a delete change event has the same schema portion as create and update events for the same table. The payload portion in a delete event for the sample customers table looks like this:

{
  "schema": { ... },
  },
  "payload": {
    "before": { <>
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "after": null, 1
    "source": { 2
      "version": "1.4.2.Final",
      "connector": "sqlserver",
      "name": "server1",
      "ts_ms": 1559730445243,
      "snapshot": false,
      "db": "testDB",
      "schema": "dbo",
      "table": "customers",
      "change_lsn": "00000027:00000db0:0005",
      "commit_lsn": "00000027:00000db0:0007",
      "event_serial_no": "1"
    },
    "op": "d", 3
    "ts_ms": 1559730450205 4
  }
}

Table 5.6. Descriptions of delete event value fields

ItemField nameDescription

1

before

Optional field that specifies the state of the row before the event occurred. In a delete event value, the before field contains the values that were in the row before it was deleted with the database commit.

2

after

Optional field that specifies the state of the row after the event occurred. In a delete event value, the after field is null, signifying that the row no longer exists.

3

source

Mandatory field that describes the source metadata for the event. In a delete event value, the source field structure is the same as for create and update events for the same table. Many source field values are also the same. In a delete event value, the ts_ms and pos field values, as well as other values, might have changed. But the source field in a delete event value provides the same metadata:

  • Debezium version
  • Connector type and name
  • Database and schema names
  • Timestamp for when the change was made in the database
  • If the event was part of a snapshot
  • Name of the table that contains the new row
  • Server log offsets

4

op

Mandatory string that describes the type of operation. The op field value is d, signifying that this row was deleted.

5

ts_ms

Optional field that displays the time at which the connector processed the event. In the event message envelope, the time is based on the system clock in the JVM running the Kafka Connect task.

In the source object, ts_ms indicates the time that the change was made in the database. By comparing the value for payload.source.ts_ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium.

SQL Server connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

When a row is deleted, the delete event value still works with log compaction, because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that same key, the message value must be null. To make this possible, after Debezium’s SQL Server connector emits a delete event, the connector emits a special tombstone event that has the same key but a null value.

5.2.6. Debezium SQL Server connector-generated events that represent transaction boundaries

Debezium can generate events that represent transaction boundaries and that enrich data change event messages.

Database transactions are represented by a statement block that is enclosed between the BEGIN and END keywords. Debezium generates transaction boundary events for the BEGIN and END delimiters in every transaction. Transaction boundary events contain the following fields:

status
BEGIN or END
id
String representation of unique transaction identifier.
event_count (for END events)
Total number of events emitted by the transaction.
data_collections (for END events)
An array of pairs of data_collection and event_count that provides the number of events emitted by changes originating from given data collection.

The following example shows a typical transaction boundary message:

Example: SQL Server connector transaction boundary event

{
  "status": "BEGIN",
  "id": "00000025:00000d08:0025",
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "00000025:00000d08:0025",
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "testDB.dbo.tablea",
      "event_count": 1
    },
    {
      "data_collection": "testDB.dbo.tableb",
      "event_count": 1
    }
  ]
}

The transaction events are written to the topic named <database.server.name>.transaction.

5.2.6.1. Change data event enrichment

When transaction metadata is enabled, the data message Envelope is enriched with a new transaction field. This field provides information about every event in the form of a composite of fields:

id
String representation of unique transaction identifier
total_order
The absolute position of the event among all events generated by the transaction
data_collection_order
The per-data collection position of the event among all events that were emitted by the transaction

The following example shows what a typical message looks like:

{
  "before": null,
  "after": {
    "pk": "2",
    "aa": "1"
  },
  "source": {
...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "00000025:00000d08:0025",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

5.2.7. How Debezium SQL Server connectors map data types

The Debezium SQL Server connector represents changes to table row data by producing events that are structured like the table in which the row exists. Each event contains fields to represent the column values for the row. The way in which an event represents the column values for an operation depends on the SQL data type of the column. In the event, the connector maps the fields for each SQL Server data type to both a literal type and a semantic type.

The connector can map SQL Server data types to both literal and semantic types.

Literal type
Describes how the value is literally represented by using Kafka Connect schema types, namely INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.
Semantic type
Describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.

For more information about data type mappings, see the following sections:

Basic types

The following table shows how the connector maps basic SQL Server data types.

Table 5.7. Data type mappings used by the SQL Server connector

SQL Server data typeLiteral type (schema type)Semantic type (schema name) and Notes

BIT

BOOLEAN

n/a

TINYINT

INT16

n/a

SMALLINT

INT16

n/a

INT

INT32

n/a

BIGINT

INT64

n/a

REAL

FLOAT32

n/a

FLOAT[(N)]

FLOAT64

n/a

CHAR[(N)]

STRING

n/a

VARCHAR[(N)]

STRING

n/a

TEXT

STRING

n/a

NCHAR[(N)]

STRING

n/a

NVARCHAR[(N)]

STRING

n/a

NTEXT

STRING

n/a

XML

STRING

io.debezium.data.Xml

Contains the string representation of an XML document

DATETIMEOFFSET[(P)]

STRING

io.debezium.time.ZonedTimestamp

A string representation of a timestamp with timezone information, where the timezone is GMT

Other data type mappings are described in the following sections.

If present, a column’s default value is propagated to the corresponding field’s Kafka Connect schema. Change messages will contain the field’s default value (unless an explicit column value had been given), so there should rarely be the need to obtain the default value from the schema.

Temporal values

Other than SQL Server’s DATETIMEOFFSET data type (which contain time zone information), the other temporal types depend on the value of the time.precision.mode configuration property. When the time.precision.mode configuration property is set to adaptive (the default), then the connector will determine the literal type and semantic type for the temporal types based on the column’s data type definition so that events exactly represent the values in the database:

SQL Server data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

io.debezium.time.Date

Represents the number of days since the epoch.

TIME(0), TIME(1), TIME(2), TIME(3)

INT32

io.debezium.time.Time

Represents the number of milliseconds past midnight, and does not include timezone information.

TIME(4), TIME(5), TIME(6)

INT64

io.debezium.time.MicroTime

Represents the number of microseconds past midnight, and does not include timezone information.

TIME(7)

INT64

io.debezium.time.NanoTime

Represents the number of nanoseconds past midnight, and does not include timezone information.

DATETIME

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

SMALLDATETIME

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

DATETIME2(0), DATETIME2(1), DATETIME2(2), DATETIME2(3)

INT64

io.debezium.time.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

DATETIME2(4), DATETIME2(5), DATETIME2(6)

INT64

io.debezium.time.MicroTimestamp

Represents the number of microseconds past the epoch, and does not include timezone information.

DATETIME2(7)

INT64

io.debezium.time.NanoTimestamp

Represents the number of nanoseconds past the epoch, and does not include timezone information.

When the time.precision.mode configuration property is set to connect, then the connector will use the predefined Kafka Connect logical types. This may be useful when consumers only know about the built-in Kafka Connect logical types and are unable to handle variable-precision time values. On the other hand, since SQL Server supports tenth of microsecond precision, the events generated by a connector with the connect time precision mode will result in a loss of precision when the database column has a fractional second precision value greater than 3:

SQL Server data typeLiteral type (schema type)Semantic type (schema name) and Notes

DATE

INT32

org.apache.kafka.connect.data.Date

Represents the number of days since the epoch.

TIME([P])

INT64

org.apache.kafka.connect.data.Time

Represents the number of milliseconds since midnight, and does not include timezone information. SQL Server allows P to be in the range 0-7 to store up to tenth of a microsecond precision, though this mode results in a loss of precision when P > 3.

DATETIME

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information.

SMALLDATETIME

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds past the epoch, and does not include timezone information.

DATETIME2

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since the epoch, and does not include timezone information. SQL Server allows P to be in the range 0-7 to store up to tenth of a microsecond precision, though this mode results in a loss of precision when P > 3.

Timestamp values

The DATETIME, SMALLDATETIME and DATETIME2 types represent a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. So for instance the DATETIME2 value "2018-06-20 15:13:16.945104" is represented by a io.debezium.time.MicroTimestamp with the value "1529507596945104".

Note that the timezone of the JVM running Kafka Connect and Debezium does not affect this conversion.

Table 5.8. Decimal values

SQL Server data typeLiteral type (schema type)Semantic type (schema name)

NUMERIC[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal

DECIMAL[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal

SMALLMONEY

BYTES

org.apache.kafka.connect.data.Decimal

MONEY

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer that represents how many digits the decimal point was shifted. The connect.decimal.precision schema parameter contains an integer that represents the precision of the given decimal value.

5.3. Setting up SQL Server to run a Debezium connector

For Debezium to capture change events from SQL Server tables, a SQL Server administrator with the necessary privileges must first run a query to enable CDC on the database. The administrator must then enable CDC for each table that you want Debezium to capture.

For details about setting up SQL Server for use with the Debezium connector, see the following sections:

After CDC is applied, it captures all of the INSERT, UPDATE, and DELETE operations that are committed to the tables for which CDD is enabled. The Debezium connector can then capture these events and emit them to Kafka topics.

5.3.1. Enabling CDC on the SQL Server database

Before you can enable CDC for a table, you must enable it for the SQL Server database. A SQL Server administrator enables CDC by running a system stored procedure. System stored procedures can be run by using SQL Server Management Studio, or by using Transact-SQL.

Prerequisites

  • You are a member of the sysadmin fixed server role for the SQL Server.
  • You are a db_owner of the database.
  • The SQL Server Agent is running.
Note

The SQL Server CDC feature processes changes that occur in user-created tables only. You cannot enable CDC on the SQL Server master database.

Procedure

  1. From the View menu in SQL Server Management Studio, click Template Explorer.
  2. In the Template Browser, expand SQL Server Templates.
  3. Expand Change Data Capture > Configuration and then click Enable Database for CDC.
  4. In the template, replace the database name in the USE statement with the name of the database that you want to enable for CDC.
  5. Run the stored procedure sys.sp_cdc_enable_db to enable the database for CDC.

    After the database is enabled for CDC, a schema with the name cdc is created, along with a CDC user, metadata tables, and other system objects.

    The following example shows how to enable CDC for the database MyDB:

    Example: Enabling a SQL Server database for the CDC template

    USE MyDB
    GO
    EXEC sys.sp_cdc_enable_db
    GO

5.3.2. Enabling CDC on a SQL Server table

A SQL Server administrator must enable change data capture on the source tables that you want to Debezium to capture. The database must already be enabled for CDC. To enable CDC on a table, a SQL Server administrator runs the stored procedure sys.sp_cdc_enable_table for the table. The stored procedures can be run by using SQL Server Management Studio, or by using Transact-SQL. SQL Server CDC must be enabled for every table that you want to capture.

Prerequisites

  • CDC is enabled on the SQL Server database.
  • The SQL Server Agent is running.
  • You are a member of the db_owner fixed database role for the database.

Procedure

  1. From the View menu in SQL Server Management Studio, click Template Explorer.
  2. In the Template Browser, expand SQL Server Templates.
  3. Expand Change Data Capture > Configuration, and then click Enable Table Specifying Filegroup Option.
  4. In the template, replace the table name in the USE statement with the name of the table that you want to capture.
  5. Run the stored procedure sys.sp_cdc_enable_table.

    The following example shows how to enable CDC for the table MyTable:

    Example: Enabling CDC for a SQL Server table

    USE MyDB
    GO
    
    EXEC sys.sp_cdc_enable_table
    @source_schema = N'dbo',
    @source_name   = N'MyTable', //<.>
    @role_name     = N'MyRole',  //<.>
    @filegroup_name = N'MyDB_CT',//<.>
    @supports_net_changes = 0
    GO

    <.> Specifies the name of the table that you want to capture. <.> Specifies a role MyRole to which you can add users to whom you want to grant SELECT permission on the captured columns of the source table. Users in the sysadmin or db_owner role also have access to the specified change tables. Set the value of @role_name to NULL, to allow only members in the sysadmin or db_owner to have full access to captured information. <.> Specifies the filegroup where SQL Server places the change table for the captured table. The named filegroup must already exist. It is best not to locate change tables in the same filegroup that you use for source tables.

5.3.3. Verifying that the user has access to the CDC table

A SQL Server administrator can run a system stored procedure to query a database or table to retrieve its CDC configuration information. The stored procedures can be run by using SQL Server Management Studio, or by using Transact-SQL.

Prerequisites

  • You have SELECT permission on all of the captured columns of the capture instance. Members of the db_owner database role can view information for all of the defined capture instances.
  • You have membership in any gating roles that are defined for the table information that the query includes.

Procedure

  1. From the View menu in SQL Server Management Studio, click Object Explorer.
  2. From the Object Explorer, expand Databases, and then expand your database object, for example, MyDB.
  3. Expand Programmability > Stored Procedures > System Stored Procedures.
  4. Run the sys.sp_cdc_help_change_data_capture stored procedure to query the table.

    Queries should not return empty results.

    The following example runs the stored precedure sys.sp_cdc_help_change_data_capture on the database MyDB:

    Example: Querying a table for CDC configuration information

    USE MyDB;
    GO
    EXEC sys.sp_cdc_help_change_data_capture
    GO

    The query returns configuration information for each table in the database that is enabled for CDC and that contains change data that the caller is authorized to access. If the result is empty, verify that the user has privileges to access both the capture instance and the CDC tables.

5.3.4. SQL Server on Azure

The Debezium SQL Server connector has not been tested with SQL Server on Azure.

5.3.5. Effect of SQL Server capture job agent configuration on server load and latency

When a database administrator enables change data capture for a source table, the capture job agent begins to run. The agent reads new change event records from the transaction log and replicates the event records to a change data table. Between the time that a change is committed in the source table, and the time that the change appears in the corresponding change table, there is always a small latency interval. This latency interval represents a gap between when changes occur in the source table and when they become available for Debezium to stream to Apache Kafka.

Ideally, for applications that must respond quickly to changes in data, you want to maintain close synchronization between the source and change tables. You might imagine that running the capture agent to continuously process change events as rapidly as possible might result in increased throughput and reduced latency — populating change tables with new event records as soon as possible after the events occur, in near real time. However, this is not necessarily the case. There is a performance penalty to pay in the pursuit of more immediate synchronization. Each time that the capture job agent queries the database for new event records, it increases the CPU load on the database host. The additional load on the server can have a negative effect on overall database performance, and potentially reduce transaction efficiency, especially during times of peak database use.

It’s important to monitor database metrics so that you know if the database reaches the point where the server can no longer support the capture agent’s level of activity. If you notice performance problems, there are SQL Server capture agent settings that you can modify to help balance the overall CPU load on the database host with a tolerable degree of latency.

5.3.6. SQL Server capture job agent configuration parameters

On SQL Server, parameters that control the behavior of the capture job agent are defined in the SQL Server table msdb.dbo.cdc_jobs. If you experience performance issues while running the capture job agent, adjust capture jobs settings to reduce CPU load by running the sys.sp_cdc_change_job stored procedure and supplying new values.

Note

Specific guidance about how to configure SQL Server capture job agent parameters is beyond the scope of this documentation.

The following parameters are the most significant for modifying capture agent behavior for use with the Debezium SQL Server connector:

pollinginterval
  • Specifies the number of seconds that the capture agent waits between log scan cycles.
  • A higher value reduces the load on the database host and increases latency.
  • A value of 0 specifies no wait between scans.
  • The default value is 5.
maxtrans
  • Specifies the maximum number of transactions to process during each log scan cycle. After the capture job processes the specified number of transactions, it pauses for the length of time that the pollinginterval specifies before the next scan begins.
  • A lower value reduces the load on the database host and increases latency.
  • The default value is 500.
maxscans
  • Specifies a limit on the number of scan cycles that the capture job can attempt in capturing the full contents of the database transaction log. If the continuous parameter is set to 1, the job pauses for the length of time that the pollinginterval specifies before it resumes scanning.
  • A lower values reduces the load on the database host and increases latency.
  • The default value is 10.

Additional resources

  • For more information about capture agent parameters, see the SQL Server documentation.

5.4. Deployment of Debezium SQL Server connectors

To deploy a Debezium SQL Server connector, you add the connector files to Kafka Connect, create a custom container to run the connector, and then add connector configuration to your container. For details about deploying the Debezium SQL Server connector, see the following topics:

5.4.1. Deploying Debezium SQL Server connectors

To deploy a Debezium SQL Server connector, you must build a custom Kafka Connect container image that contains the Debezium connector archive, and then push this container image to a container registry. You then need to create the following custom resources (CRs):

  • A KafkaConnect CR that defines your Kafka Connect instance. The image property in the CR specifies the name of the container image that you create to run your Debezium connector. You apply this CR to the OpenShift instance where Red Hat AMQ Streams is deployed. AMQ Streams offers operators and images that bring Apache Kafka to OpenShift.
  • A KafkaConnector CR that defines your Debezium SQL Server connector. Apply this CR to the same OpenShift instance where you apply the KafkaConnect CR.

Prerequisites

  • SQL Server is running and you completed the steps to set up SQL Server to work with a Debezium connector.
  • AMQ Streams is deployed on OpenShift and is running Apache Kafka and Kafka Connect. For more information, see Installing Debezium on OpenShift
  • Podman or Docker is installed.
  • You have an account and permissions to create and manage containers in the container registry (such as quay.io or docker.io) to which you plan to add the container that will run your Debezium connector.

Procedure

  1. Create the Debezium SQL Server container for Kafka Connect:

    1. Download the Debezium SQL Server connector archive.
    2. Extract the Debezium SQL Server connector archive to create a directory structure for the connector plug-in, for example:

      ./my-plugins/
      ├── debezium-connector-sqlserver
      │   ├── ...
    3. Create a Docker file that uses registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.0 as the base image. For example, from a terminal window, enter the following, replacing my-plugins with the name of your plug-ins directory:

      cat <<EOF >debezium-container-for-sqlserver.yaml 1
      FROM registry.redhat.io/amq7/amq-streams-kafka-26-rhel7:1.6.0
      USER root:root
      COPY ./<my-plugins>/ /opt/kafka/plugins/ 2
      USER 1001
      EOF
      1 1 1 1 1 1
      You can specify any file name that you want.
      2 2 2 2 2 1 2
      Replace my-plugins with the name of your plug-ins directory.

      The command creates a Docker file with the name debezium-container-for-sqlserver.yaml in the current directory.

    4. Build the container image from the debezium-container-for-sqlserver.yaml Docker file that you created in the previous step. From the directory that contains the file, open a terminal window and enter one of the following commands:

      podman build -t debezium-container-for-sqlserver:latest .
      docker build -t debezium-container-for-sqlserver:latest .

      The preceding commands build a container image with the name debezium-container-for-sqlserver.

    5. Push your custom image to a container registry, such as quay.io or an internal container registry. The container registry must be available to the OpenShift instance where you want to deploy the image. Enter one of the following commands:

      podman push <myregistry.io>/debezium-container-for-sqlserver:latest
      docker push <myregistry.io>/debezium-container-for-sqlserver:latest
    6. Create a new Debezium SQL Server KafkaConnect custom resource (CR). For example, create a KafkaConnect CR with the name dbz-connect.yaml that specifies annotations and image properties as shown in the following example:

      apiVersion: kafka.strimzi.io/v1beta1
      kind: KafkaConnect
      metadata:
        name: my-connect-cluster
        annotations:
          strimzi.io/use-connector-resources: "true" 1
      spec:
        #...
        image: debezium-container-for-sqlserver  2
      1
      metadata.annotations indicates to the Cluster Operator that KafkaConnector resources are used to configure connectors in this Kafka Connect cluster.
      2
      spec.image specifies the name of the image that you created to run your Debezium connector. This property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable in the Cluster Operator
    7. Apply the KafkaConnect CR to the OpenShift Kafka Connect environment by entering the following command:

      oc create -f dbz-connect.yaml

      The command adds a Kafka Connect instance that specifies the name of the image that you created to run your Debezium connector.

  2. Create a KafkaConnector custom resource that configures your Debezium SQL Server connector instance.

    You configure a Debezium SQL Server connector in a .yaml file that specifies the configuration properties for the connector. The connector configuration might instruct Debezium to produce events for a subset of the schemas and tables, or it might set properties so that Debezium ignores, masks, or truncates values in specified columns that are sensitive, too large, or not needed.

    The following example configures a Debezium connector that connects to a SQL server host, 192.168.99.100, on port 1433. This host has a database named testDB, a table with the name customers, and fulfillment is the server’s logical name.

    SQL Server fulfillment-connector.yaml

    apiVersion: kafka.strimzi.io/v1beta1
    kind: KafkaConnector
    metadata:
      name: fulfillment-connector 1
      labels:
        strimzi.io/cluster: my-connect-cluster
      annotations:
        strimzi.io/use-connector-resources: 'true'
    spec:
      class: io.debezium.connector.sqlserver.SqlServerConnector 2
      config:
        database.hostname: 192.168.99.100 3
        database.port: 1433 4
        database.user: debezium 5
        database.password: dbz 6
        database.dbname: testDB 7
        database.server.name: fullfullment 8
        database.include.list: dbo.customers 9
        database.history.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092 10
        database.history.kafka.topic: dbhistory.fullfillment 11

    Table 5.9. Descriptions of connector configuration settings

    ItemDescription

    1

    The name of our connector when we register it with a Kafka Connect service.

    2

    The name of this SQL Server connector class.

    3

    The address of the SQL Server instance.

    4

    The port number of the SQL Server instance.

    5

    The name of the SQL Server user.

    6

    The password for the SQL Server user.

    7

    The name of the database to capture changes from.

    8

    The logical name of the SQL Server instance/cluster, which forms a namespace and is used in all the names of the Kafka topics to which the connector writes, the Kafka Connect schema names, and the namespaces of the corresponding Avro schema when the Avro converter is used.

    9

    A list of all tables whose changes Debezium should capture.

    10

    The list of Kafka brokers that this connector will use to write and recover DDL statements to the database history topic.

    11

    The name of the database history topic where the connector will write and recover DDL statements. This topic is for internal use only and should not be used by consumers.

  3. Create your connector instance with Kafka Connect. For example, if you saved your KafkaConnector resource in the fulfillment-connector.yaml file, you would run the following command:

    oc apply -f fulfillment-connector.yaml

    The preceding command registers fulfillment-connector and the connector starts to run against the testDB database as defined in the KafkaConnector CR.

  4. Verify that the connector was created and has started:

    1. Display the Kafka Connect log output to verify that the connector was created and has started to capture changes in the specified database:

      oc logs $(oc get pods -o name -l strimzi.io/cluster=my-connect-cluster)
    2. Review the log output to verify that Debezium performs the initial snapshot. The log displays output that is similar to the following messages:

      ... INFO Starting snapshot for ...
      ... INFO Snapshot is using user 'debezium' ...

      If the connector starts correctly without errors, it creates a topic for each table whose changes the connector is capturing. For the example CR, there would be a topic for the table specified in the include.list property. Downstream applications can subscribe to these topics.

    3. Verify that the connector created the topics by running the following command:

      oc get kafkatopics

For the complete list of the configuration properties that you can set for the Debezium SQL Server connector, see SQL Server connector properties.

Results

When the connector starts, it performs a consistent snapshot of the SQL Server databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming the change event records to Kafka topics.

5.4.2. Descriptions of Debezium SQL Server connector configuration properties

The Debezium SQL Server connector has numerous configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values.

The following configuration properties are required unless a default value is available.

PropertyDefaultDescription

name

 

Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)

connector.class

 

The name of the Java class for the connector. Always use a value of io.debezium.connector.sqlserver.SqlServerConnector for the SQL Server connector.

tasks.max

1

The maximum number of tasks that should be created for this connector. The SQL Server connector always uses a single task and therefore does not use this value, so the default is always acceptable.

database.hostname

 

IP address or hostname of the SQL Server database server.

database.port

1433

Integer port number of the SQL Server database server.

database.user

 

Username to use when connecting to the SQL Server database server.

database.password

 

Password to use when connecting to the SQL Server database server.

database.dbname

 

The name of the SQL Server database from which to stream the changes

database.server.name

 

Logical name that identifies and provides a namespace for the SQL Server database server that you want Debezium to capture. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names emanating from this connector. Only alphanumeric characters and underscores should be used.

database.history.kafka.topic

 

The full name of the Kafka topic where the connector will store the database schema history.

database.history.kafka.bootstrap.servers

 

A list of host and port pairs that the connector will use for establishing an initial connection to the Kafka cluster. This connection is used for retrieving database schema history previously stored by the connector, and for writing each DDL statement read from the source database. This should point to the same Kafka cluster used by the Kafka Connect process.

table.include.list

 

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables that you want Debezium to capture; any table that is not included in table.include.list is excluded from capture. Each identifier is of the form schemaName.tableName. By default, the connector captures all non-system tables for the designated schemas. Must not be used with table.exclude.list.

table.exclude.list

 

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for the tables that you want to exclude from being captured; Debezium captures all tables that are not included in table.exclude.list. Each identifier is of the form schemaName.tableName. Must not be used with table.include.list.

column.include.list

empty string

An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be included in the change event message values. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Note that primary key columns are always included in the event’s key, even if not included in the value. Do not also set the column.exclude.list property.

column.exclude.list

empty string

An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be excluded from change event message values. Fully-qualified names for columns are of the form schemaName.tableName.columnName. Note that primary key columns are always included in the event’s key, also if excluded from the value. Do not also set the column.include.list property.

column.mask.hash._hashAlgorithm_.with.salt._salt_

n/a

An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be pseudonyms in the change event message values with a field value consisting of the hashed value using the algorithm hashAlgorithm and salt salt. Based on the used hash function referential integrity is kept while data is pseudonymized. Supported hash functions are described in the MessageDigest section of the Java Cryptography Architecture Standard Algorithm Name Documentation. The hash is automatically shortened to the length of the column.

Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form schemaName.tableName.columnName.

Example:

column.mask.hash.SHA-256.with.salt.CzQMA0cB5K = dbo.orders.customerName, dbo.shipment.customerName

where CzQMA0cB5K is a randomly selected salt.

Note: Depending on the hashAlgorithm used, the salt selected and the actual data set, the resulting masked data set may not be completely anonymized.

time.precision.mode

adaptive

Time, date, and timestamps can be represented with different kinds of precision, including: adaptive (the default) captures the time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type; or connect always represents time and timestamp values using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which uses millisecond precision regardless of the database columns' precision. See temporal values.

include.schema.changes

true

Boolean value that specifies whether the connector should publish changes in the database schema to a Kafka topic with the same name as the database server ID. Each schema change is recorded with a key that contains the database name and a value that is a JSON structure that describes the schema update. This is independent of how the connector internally records database history. The default is true.

tombstones.on.delete

true

Controls whether a tombstone event should be generated after a delete event.
When true the delete operations are represented by a delete event and a subsequent tombstone event. When false only a delete event is sent.
Emitting the tombstone event (the default behavior) allows Kafka to completely delete all events pertaining to the given key once the source record got deleted.

column.truncate.to._length_.chars

n/a

An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be truncated in the change event message values if the field values are longer than the specified number of characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer. Fully-qualified names for columns are of the form schemaName.tableName.columnName.

column.mask.with._length_.chars

n/a

An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be replaced in the change event message values with a field value consisting of the specified number of asterisk (*) characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form schemaName.tableName.columnName.

column.propagate.source.type

n/a

An optional comma-separated list of regular expressions that match the fully-qualified names of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters __debezium.source.column.type, __debezium.source.column.length and __debezium.source.column.scale is used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified names for columns are of the form schemaName.tableName.columnName.

datatype.propagate.source.type+

n/a

An optional comma-separated list of regular expressions that match the database-specific data type name of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters __debezium.source.column.type, __debezium.source.column.length and __debezium.source.column.scale will be used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified data type names are of the form schemaName.tableName.typeName. See SQL Server data types for the list of SQL Server-specific data type names.

message.key.columns

n/a

A semi-colon list of regular expressions that match fully-qualified tables and columns to map a primary key.
Each item (regular expression) must match the fully-qualified <fully-qualified table>:<a comma-separated list of columns> representing the custom key.
Fully-qualified tables could be defined as schemaName.tableName.

binary.handling.mode

bytes

Specifies how binary (binary, varbinary) columns should be represented in change events, including: bytes represents binary data as byte array (default), base64 represents binary data as base64-encoded String, hex represents binary data as hex-encoded (base16) String

The following advanced configuration properties have good defaults that will work in most situations and therefore rarely need to be specified in the connector’s configuration.

PropertyDefaultDescription

snapshot.mode

initial

A mode for taking an initial snapshot of the structure and optionally data of captured tables. Once the snapshot is complete, the connector will continue reading change events from the database’s redo logs. The following values are supported:

  • initial: Takes a snapshot of structure and data of captured tables; useful if topics should be populated with a complete representation of the data from the captured tables.
  • initial_only: Takes a snapshot of structure and data like initial but instead does not transition into streaming changes once the snapshot has completed.
  • schema_only: Takes a snapshot of the structure of captured tables only; useful if only changes happening from now onwards should be propagated to topics.

snapshot.include.collection.list

All tables specified in table.include.list

An optional, comma-separated list of regular expressions that match names of schemas specified in table.include.list for which you want to take the snapshot.

snapshot.isolation.mode

repeatable_read

Mode to control which transaction isolation level is used and how long the connector locks tables that are designated for capture. The following values are supported:

  • read_uncommitted
  • read_committed
  • repeatable_read
  • snapshot
  • exclusive (exclusive mode uses repeatable read isolation level, however, it takes the exclusive lock on all tables to be read).

The snapshot, read_committed and read_uncommitted modes do not prevent other transactions from updating table rows during initial snapshot. The exclusive and repeatable_read modes do prevent concurrent updates.

Mode choice also affects data consistency. Only exclusive and snapshot modes guarantee full consistency, that is, initial snapshot and streaming logs constitute a linear history. In case of repeatable_read and read_committed modes, it might happen that, for instance, a record added appears twice - once in initial snapshot and once in streaming phase. Nonetheless, that consistency level should do for data mirroring. For read_uncommitted there are no data consistency guarantees at all (some data might be lost or corrupted).

source.timestamp.mode

commit

String that represents the criteria of the attached timestamp within the source record (ts_ms).

  • commit (default) sets the source timestamp to the time when the record was committed to the database.
  • processing sets the source timestamp to the time when Debezium accesses the record in the change table. Use the processing option if you want {prodname] to set the top level ts_ms value, or if you want to avoid the additional cost of Debezium querying the database to extract the LSN timestamps.

event.processing.failure.handling.mode

fail

Specifies how the connector should react to exceptions during processing of events. fail will propagate the exception (indicating the offset of the problematic event), causing the connector to stop.
warn will cause the problematic event to be skipped and the offset of the problematic event to be logged.
skip will cause the problematic event to be skipped.

poll.interval.ms

1000

Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 1000 milliseconds, or 1 second.

max.queue.size

8192

Positive integer value that specifies the maximum size of the blocking queue into which change events read from the database log are placed before they are written to Kafka. This queue can provide backpressure to the CDC table reader when, for example, writes to Kafka are slower or if Kafka is not available. Events that appear in the queue are not included in the offsets periodically recorded by this connector. Defaults to 8192, and should always be larger than the maximum batch size specified in the max.batch.size property.

max.batch.size

2048

Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector. Defaults to 2048.

heartbeat.interval.ms

0

Controls how frequently heartbeat messages are sent.
This property contains an interval in milliseconds that defines how frequently the connector sends messages to a heartbeat topic. The property can be used to confirm whether the connector is still receiving change events from the database. You also should leverage heartbeat messages in cases where only records in non-captured tables are changed for a longer period of time. In such situation the connector would proceed to read the log from the database but never emit any change messages into Kafka, which in turn means that no offset updates are committed to Kafka. This may result in more change events to be re-sent after a connector restart. Set this parameter to 0 to not send heartbeat messages at all.
Disabled by default.

heartbeat.topics.prefix

__debezium-heartbeat

Controls the naming of the topic to which heartbeat messages are sent.
The topic is named according to the pattern <heartbeat.topics.prefix>.<server.name>.

snapshot.delay.ms

 

An interval in milli-seconds that the connector should wait before taking a snapshot after starting up;
Can be used to avoid snapshot interruptions when starting multiple connectors in a cluster, which may cause re-balancing of connectors.

snapshot.fetch.size

2000

Specifies the maximum number of rows that should be read in one go from each table while taking a snapshot. The connector will read the table contents in multiple batches of this size. Defaults to 2000.

query.fetch.size

 

Specifies the number of rows that will be fetched for each database round-trip of a given query. Defaults to the JDBC driver’s default fetch size.

snapshot.lock.timeout.ms

10000

An integer value that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If table locks cannot be acquired in this time interval, the snapshot will fail (also see snapshots).
When set to 0 the connector will fail immediately when it cannot obtain the lock. Value -1 indicates infinite waiting.

snapshot.select.statement.overrides

 

Controls which rows from tables are included in snapshot.
This property contains a comma-separated list of fully-qualified tables (SCHEMA_NAME.TABLE_NAME). Select statements for the individual tables are specified in further configuration properties, one for each table, identified by the id snapshot.select.statement.overrides.[SCHEMA_NAME].[TABLE_NAME]. The value of those properties is the SELECT statement to use when retrieving data from the specific table during snapshotting. A possible use case for large append-only tables is setting a specific point where to start (resume) snapshotting, in case a previous snapshotting was interrupted.
Note: This setting has impact on snapshots only. Events captured during log reading are not affected by it.

sanitize.field.names

true when connector configuration explicitly specifies the key.converter or value.converter parameters to use Avro, otherwise defaults to false.

Whether field names are sanitized to adhere to Avro naming requirements.

database.server.timezone

 

Timezone of the server.

This property defines the timezone of the transaction timestamp (ts_ms) that is retrieved from the server (which is actually not zoned). By default, the value is unset. Set a value for the property only when running on SQL Server 2014 or older, and the database server and the JVM running the Debezium connector use different timezones.

When unset, default behavior is to use the timezone of the VM running the Debezium connector. In this case, when running on on SQL Server 2014 or older and using different timezones on server and the connector, incorrect ts_ms values may be produced.
Possible values include "Z", "UTC", offset values like "+02:00", short zone ids like "CET", and long zone ids like "Europe/Paris".

provide.transaction.metadata

false

When set to true Debezium generates events with transaction boundaries and enriches data events envelope with transaction metadata.

See Transaction Metadata for additional details.

retriable.restart.connector.wait.ms

10000 (10 seconds)

The number of milli-seconds to wait before restarting a connector after a retriable error occurs.

The connector also supports pass-through configuration properties that are used when creating the Kafka producer and consumer. Specifically, all connector configuration properties that begin with the database.history.producer. prefix are used (without the prefix) when creating the Kafka producer that writes to the database history, and all those that begin with the prefix database.history.consumer. are used (without the prefix) when creating the Kafka consumer that reads the database history upon connector startup.

For example, the following connector configuration properties can be used to secure connections to the Kafka broker:

In addition to the pass-through to the Kafka producer and consumer, the properties starting with database., e.g. database.applicationName=debezium are passed to the JDBC URL.

database.history.producer.security.protocol=SSL
database.history.producer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.producer.ssl.keystore.password=test1234
database.history.producer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.producer.ssl.truststore.password=test1234
database.history.producer.ssl.key.password=test1234
database.history.consumer.security.protocol=SSL
database.history.consumer.ssl.keystore.location=/var/private/ssl/kafka.server.keystore.jks
database.history.consumer.ssl.keystore.password=test1234
database.history.consumer.ssl.truststore.location=/var/private/ssl/kafka.server.truststore.jks
database.history.consumer.ssl.truststore.password=test1234
database.history.consumer.ssl.key.password=test1234

Be sure to consult the Kafka documentation for all of the configuration properties for Kafka producers and consumers. (The SQL Server connector does use the new consumer.)

5.5. Refreshing capture tables after a schema change

When change data capture is enabled for a SQL Server table, as changes occur in the table, event records are persisted to a capture table on the server. If you introduce a change in the structure of the source table change, for example, by adding a new column, that change is not dynamically reflected in the change table. For as long as the capture table continues to use the outdated schema, the Debezium connector is unable to emit data change events for the table correctly. You must intervene to refresh the capture table to enable the connector to resume processing change events.

Because of the way that CDC is implemented in SQL Server, you cannot use Debezium to update capture tables. To refresh capture tables, one must be a SQL Server database operator with elevated privileges. As a Debezium user, you must coordinate tasks with the SQL Server database operator to complete the schema refresh and restore streaming to Kafka topics.

You can use one of the following methods to update capture tables after a schema change:

There are advantages and disadvantages to using each type of procedure.

Warning

Whether you use the online or offline update method, you must complete the entire schema update process before you apply subsequent schema updates on the same source table. The best practice is to execute all DDLs in a single batch so the procedure can be run only once.

Note

Some schema changes are not supported on source tables that have CDC enabled. For example, if CDC is enabled on a table, SQL Server does not allow you to change the schema of the table if you renamed one of its columns or changed the column type.

Note

After you change a column in a source table from NULL to NOT NULL or vice versa, the SQL Server connector cannot correctly capture the changed information until after you create a new capture instance. If you do not create a new capture table after a change to the column designation, change event records that the connector emits do not correctly indicate whether the column is optional. That is, columns that were previously defined as optional (or NULL) continue to be, despite now being defined as NOT NULL. Similarly, columns that had been defined as required (NOT NULL), retain that designation, although they are now defined as NULL.

5.5.1. Running an offline update after a schema change

Offline schema updates provide the safest method for updating capture tables. However, offline updates might not be feasible for use with applications that require high-availability.

Prerequisites

  • An update was committed to the schema of a SQL Server table that has CDC enabled.
  • You are a SQL Server database operator with elevated privileges.

Procedure

  1. Suspend the application that updates the database.
  2. Wait for the Debezium connector to stream all unstreamed change event records.
  3. Stop the Debezium connector.
  4. Apply all changes to the source table schema.
  5. Create a new capture table for the update source table using sys.sp_cdc_enable_table procedure with a unique value for parameter @capture_instance.
  6. Resume the application that you suspended in Step 1.
  7. Start the Debezium connector.
  8. After the Debezium connector starts streaming from the new capture table, drop the old capture table by running the stored procedure sys.sp_cdc_disable_table with the parameter @capture_instance set to the old capture instance name.

5.5.2. Running an online update after a schema change

The procedure for completing an online schema updates is simpler than the procedure for running an offline schema update, and you can complete it without requiring any downtime in application and data processing. However, with online schema updates, a potential processing gap can occur after you update the schema in the source database, but before you create the new capture instance. During that interval, change events continue to be captured by the old instance of the change table, Q and the change data that is saved to the old table retains the structure of the earlier schema. So, for example, if you added a new column to a source table, change events that are produced before the new capture table is ready, do not contain a field for the new column. If your application does not tolerate such a transition period, it is best to use the offline schema update procedure.

Prerequisites

  • An update was committed to the schema of a SQL Server table that has CDC enabled.
  • You are a SQL Server database operator with elevated privileges.

Procedure

  1. Apply all changes to the source table schema.
  2. Create a new capture table for the update source table by running the sys.sp_cdc_enable_table stored procedure with a unique value for the parameter @capture_instance.
  3. When Debezium starts streaming from the new capture table, you can drop the old capture table by running the sys.sp_cdc_disable_table stored procedure with the parameter @capture_instance set to the old capture instance name.

Example: Running an online schema update after a database schema change

The following example shows how to complete an online schema update in the change table after the column phone_number is added to the customers source table.

  1. Modify the schema of the customers source table by running the following query to add the phone_number field:

    ALTER TABLE customers ADD phone_number VARCHAR(32);
  2. Create the new capture instance by running the sys.sp_cdc_enable_table stored procedure.

    EXEC sys.sp_cdc_enable_table @source_schema = 'dbo', @source_name = 'customers', @role_name = NULL, @supports_net_changes = 0, @capture_instance = 'dbo_customers_v2';
    GO
  3. Insert new data into the customers table by running the following query:

    INSERT INTO customers(first_name,last_name,email,phone_number) VALUES ('John','Doe','john.doe@example.com', '+1-555-123456');
    GO

    The Kafka Connect log reports on configuration updates through entries similar to the following message:

    connect_1    | 2019-01-17 10:11:14,924 INFO   ||  Multiple capture instances present for the same table: Capture instance "dbo_customers" [sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_CT, startLsn=00000024:00000d98:0036, changeTableObjectId=1525580473, stopLsn=00000025:00000ef8:0048] and Capture instance "dbo_customers_v2" [sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_v2_CT, startLsn=00000025:00000ef8:0048, changeTableObjectId=1749581271, stopLsn=NULL]   [io.debezium.connector.sqlserver.SqlServerStreamingChangeEventSource]
    connect_1    | 2019-01-17 10:11:14,924 INFO   ||  Schema will be changed for ChangeTable [captureInstance=dbo_customers_v2, sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_v2_CT, startLsn=00000025:00000ef8:0048, changeTableObjectId=1749581271, stopLsn=NULL]   [io.debezium.connector.sqlserver.SqlServerStreamingChangeEventSource]
    ...
    connect_1    | 2019-01-17 10:11:33,719 INFO   ||  Migrating schema to ChangeTable [captureInstance=dbo_customers_v2, sourceTableId=testDB.dbo.customers, changeTableId=testDB.cdc.dbo_customers_v2_CT, startLsn=00000025:00000ef8:0048, changeTableObjectId=1749581271, stopLsn=NULL]   [io.debezium.connector.sqlserver.SqlServerStreamingChangeEventSource]

    Eventually, the phone_number field is added to the schema and its value appears in messages written to the Kafka topic.

    ...
         {
            "type": "string",
            "optional": true,
            "field": "phone_number"
         }
    ...
        "after": {
          "id": 1005,
          "first_name": "John",
          "last_name": "Doe",
          "email": "john.doe@example.com",
          "phone_number": "+1-555-123456"
        },
  4. Drop the old capture instance by running the sys.sp_cdc_disable_table stored procedure.

    EXEC sys.sp_cdc_disable_table @source_schema = 'dbo', @source_name = 'dbo_customers', @capture_instance = 'dbo_customers';
    GO

5.6. Monitoring Debezium SQL Server connector performance

The Debezium SQL Server connector provides three types of metrics that are in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect provide. The connector provides the following metrics:

For information about how to expose the preceding metrics through JMX, see the Debezium monitoring documentation.

5.6.1. Debezium SQL Server connector snapshot metrics

The MBean is debezium.sql_server:type=connector-metrics,context=snapshot,server=<database.server.name>.

AttributesTypeDescription

LastEvent

string

The last snapshot event that the connector has read.

MilliSecondsSinceLastEvent

long

The number of milliseconds since the connector has read and processed the most recent event.

TotalNumberOfEventsSeen

long

The total number of events that this connector has seen since last started or reset.

NumberOfEventsFiltered

long

The number of events that have been filtered by include/exclude list filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapacity

int

The length the queue used to pass events between the snapshotter and the main Kafka Connect loop.

QueueRemainingCapacity

int

The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.

TotalTableCount

int

The total number of tables that are being included in the snapshot.

RemainingTableCount

int

The number of tables that the snapshot has yet to copy.

SnapshotRunning

boolean

Whether the snapshot was started.

SnapshotAborted

boolean

Whether the snapshot was aborted.

SnapshotCompleted

boolean

Whether the snapshot completed.

SnapshotDurationInSeconds

long

The total number of seconds that the snapshot has taken so far, even if not complete.

RowsScanned

Map<String, Long>

Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.