Debezium User Guide

Red Hat Integration 2020-Q2

For use with Debezium 1.1

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 Debezium connectors:

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, ingesting changes from different databases using change data capture (CDC). Unlike other approaches such as polling or dual writes, log-based CDC as implemented by Debezium:

  • makes sure that all data changes are captured
  • produces change events with a very low delay (e.g. ms range for MySQL or Postgres) while avoiding increased CPU usage of frequent polling
  • requires no changes to your data model (such as "Last Updated" column)
  • can capture deletes
  • can capture old record state and further metadata such as transaction id and causing query (depending on the database’s capabilities and configuration)

To learn more about the advantages of log-based CDC, refer to this blog post.

The actual change data capture feature of Debezium is amended 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 gets started up and not all logs still exist (typically the case when the database has been running for some time and has discarded any transaction logs not needed any longer for transaction recovery or replication); different modes exist for snapshotting, refer to the docs of the specific connector you’re using to learn more
  • Filters: the set of captured schemas, tables and columns can be configured via whitelist/blacklist filters
  • Masking: the values from specific columns can be masked, e.g. for sensitive data
  • Monitoring: most connectors can be monitored using JMX
  • Different ready-to-use message transformations: e.g. for message routing, extraction of new record state (relational connectors, MongoDB) and routing of events from a transactional outbox table

Refer to the connector documentation for a list of all supported databases and detailed information about the features and configuration options of each connector.

1.2. 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 and the data within tables. MySQL uses the binlog for replication and recovery.

The MySQL connector reads the binlog and produces change events for row-level INSERT, UPDATE, and DELETE operations and records the change events in a Kafka topic. 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 and initial consistent snapshot of each of your databases. The MySQL connector reads the binlog from the point at which the snapshot was made.

2.1. Overview of how the MySQL connector works

The Debezium MySQL connector tracks the structure of the tables, performs snapshots, transforms binlog events into Debezium change events and records where those events are recorded in Kafka.

2.1.1. How the MySQL connector uses database schemas

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 the row-level changes to the data and 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 and to produce the appropriate change event. In a separate database history Kafka topic, the connector also 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 generate schema change events on a different topic that is intended for consumer applications. This is described in how the MySQL connector handles schema change topics.

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 then helper tables created during the migration process need to be included among whitelisted tables.

If downstream systems do not need the messages generated by the temporary table then a simple message transform can be written and applied to filter them out.

For information about topic naming conventions, see MySQL connector and Kafka topics.

2.1.2. How the MySQL connector performs database snapshots

When your Debezium MySQL connector is first started, it performs an initial consistent snapshot of your database. The following flow describes how this snapshot is completed.

Note

This is the default snapshot mode which is set as initial in the snapshot.mode property. For other snapshots modes, please check out the MySQL connector configuration properties.

The connector…​
StepAction

1

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

Note

The snapshot itself does not prevent other clients from applying DDL which might interfere with the connector’s attempt to read the binlog position and table schemas. The global read lock is kept while the binlog position is read before released 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 allowed by the connector’s configuration.

5

Releases the global read lock. This now allows other database clients to write to the database.

6

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

Note

This happens if applicable.

7

Scans the database tables and generates CREATE events on the relevant table-specific Kafka topics for each row.

8

Commits the transaction.

9

Records the completed snapshot in the connector offsets.

2.1.2.1. What happens if the connector fails?

If the connector fails, stops, or is rebalanced while making the initial snapshot, the connector creates a new snapshot once restarted. Once that intial snapshot is completed, the Debezium MySQL connector restarts from the same position in the binlog so it does not miss any updates.

Note

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 MySQL connector common issues.

2.1.2.2. What if Global Read Locks are not allowed?

Some environments do not allow a global read lock. 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.

Important

The user must have LOCK_TABLES privileges.

The connector…​
StepAction

1

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

2

Reads and filters the names of the databases and tables.

3

Reads the current binlog position.

4

Reads the schema of the databases and tables allowed by the connector’s configuration.

5

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

Note

This happens if applicable.

6

Scans the database tables and generates CREATE events on the relevant table-specific Kafka topics for each row.

7

Commits the transaction.

8

Releases the table-level locks.

9

Records the completed snapshot in the connector offsets.

2.1.3. How the MySQL connector handles schema change topics

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

Important

If you choose to use schema change events, use the schema change topic and do not consume the database history topic.

Note

It is vital that there is a global order of the events in the database schema history. Therefore, the database history topic must not be partitioned. This means that a partition count of 1 must be specified when creating this topic. When relying on auto topic creation, make sure that Kafka’s num.partitions configuration option (the default number of partitions) is set to 1.

2.1.3.1. Schema change topic structure

Each message that is written to the schema change topic contains a message key which includes the name of the connected database used when applying DDL statements: 

{
  "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 message value contains a structure that includes the DDL statements, the database to which the statements were applied, and the position in the binlog where the statements appeared: 

{
  "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_sec"
          },
          {
            "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": "0.10.0.Beta4",
      "name": "mysql-server-1",
      "server_id": 0,
      "ts_sec": 0,
      "gtid": null,
      "file": "mysql-bin.000003",
      "pos": 154,
      "row": 0,
      "snapshot": true,
      "thread": null,
      "db": null,
      "table": null,
      "query": null
    }
  }
}
2.1.3.1.1. Important tips regarding schema change topics

The ddl field may contain multiple DDL statements. Every statement applies to the database in the databaseName field and appears in the same order as they were applied in 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 topic. 

....
    "payload": {
        "databaseName": "inventory",
        "ddl": "CREATE TABLE products ( id INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,...
        "source" : {
            ....
        }
    }
....
What if a client submits DDL statements 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.

Additional resources

2.1.4. MySQL connector events

All data change events produced by the Debezium MySQL connector contain a key and a value. The change event key and the change event value each contain a schema and a payload where the schema describes the structure of the payload and the payload contains the data.

Warning

The MySQL connector ensures that all Kafka Connect schema names adhere to the Avro schema name format. This is important as any character that is not a latin letter or underscore is replaced by an underscore which can lead to unexpected conflicts in schema names when the logical server names, database names, and table names container other characters that are replaced with these underscores.

2.1.4.1. Change event key

For any given table, the change event’s key has a structure that contains a field for each column in the PRIMARY KEY (or unique constraint) at the time the event was created. Let us look at an example table and then how the schema and payload would appear for the table.

example 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;

example change event key

{
 "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
  }
}

1
The schema describes what is in the payload.
2
The mysql-server-1.inventory.customers.Key is the name of the schema which defines the structure where mysql-server-1 is the connector name, inventory is the database, and customers is the table.
3
Denotes that the payload is not optional.
4
Specifies the type of fields expected in the payload.
5
The payload itself, which in this case only contains a single id field.

This key describes the row in the inventory.customers table which is out from the connector entitled mysql-server-1 whose id primary key column has a value of 1001.

2.1.4.2. Change event value

The change event value contains a schema and a payload section. There are three types of change event values which have an envelope structure. The fields in this structure are explained below and marked on each of the change event value examples.

ItemField nameDescription

1

name

mysql-server-1.inventory.customers.Key is the name of the schema which defines the structure where mysql-server-1 is the connector name, inventory is the database and customers is the table

2

op

A mandatory string that describes the type of operation.

values

  • c = create
  • u = update
  • d = delete
  • r = read (initial snapshot only)

3

before

An optional field that specifies the state of the row before the event occurred.

4

after

An optional field that specifies the state of the row after the event occurred.

5

source

A mandatory field that describes the source metadata for the event including:

  • the Debezium version
  • the connector name
  • the binlog name where the event was recorded
  • the binlog position
  • the row within the event
  • if the event was part of a snapshot
  • the name of the affected database and table
  • the id of the MySQL thread creating the event (non-snapshot only)
  • the MySQL server ID (if available)
  • timestamp
Note

If the binlog_rows_query_log_events option is enabled and the connector has the include.query option enabled, a query field is displayed which contains the original SQL statement that generated the event.

6

ts_ms

An optional field that displays the time at which the connector processed the event.

Note

The time is based on the system clock in the JVM running the Kafka Connect task.

Let us look at an example table and then how the schema and payload would appear for the table.

example 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;

2.1.4.2.1. Create change event value

This example shows a create event for 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",
        "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.product.connector.mysql.Source",
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "mysql-server-1.inventory.customers.Envelope"
  },
  "payload": { 2
    "op": "c",
    "ts_ms": 1465491411815,
    "before": null,
    "after": {
      "id": 1004,
      "first_name": "Anne",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "source": {
      "version": "1.1.2.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')"
    }
  }
}
1
The schema portion of this event’s value shows the schema for the envelope, the schema for the source structure (which is specific to the MySQL connector and reused across all events), and the table-specific schemas for the before and after fields.
2
The payload portion of this event’s value shows the information in the event, namely that it is describing that the row was created (because op=c), and that the after field value contains the values of the new inserted row’s id, first_name, last_name, and email columns.
2.1.4.2.2. Update change event value

The value of an update change event on the customers table has the exact same schema as a create event. The payload is structured the same, but holds different values. Here is an example (formatted for readability): 

{
  "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": "1.1.2.Final",
      "name": "mysql-server-1",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 1465581,
      "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
  }
}

Comparing this to the value in the insert event, you can see a couple of differences in the payload section:

1
The before field now has the state of the row with the values before the database commit.
2
The after field now has the updated state of the row, and the first_name value is now Anne Marie. You can compare the before and after structures to determine what actually changed in this row because of the commit.
3
The source field structure has the same fields as before, but the values are different (this event is from a different position in the binlog). The source structure shows information about MySQL’s record of this change (providing traceability). It also has information you can use to compare to other events in this and other topics to know whether this event occurred before, after, or as part of the same MySQL commit as other events.
4
The op field value is now u, signifying that this row changed because of an update.
5
The ts_ms field shows the timestamp when Debezium processed this event.
Note

When the columns for a row’s primary or unique key are updated, the value of the row’s key is changed and Debezium outputs three events: a DELETE event and tombstone event with the old key for the row, followed by an INSERT event with the new key for the row.

2.1.4.2.3. Delete change event value

The value of a delete change event on the customers table has the exact same schema as create and update events. The payload is structured the same, but holds different values. Here is an example (formatted for readability): 

{
  "schema": { ... },
  "payload": {
    "before": { 1
      "id": 1004,
      "first_name": "Anne Marie",
      "last_name": "Kretchmar",
      "email": "annek@noanswer.org"
    },
    "after": null, 2
    "source": { 3
      "version": "1.1.2.Final",
      "connector": "mysql",
      "name": "mysql-server-1",
      "ts_ms": 1465581,
      "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
  }
}

Comparing the payload portion to the payloads in the create and update events, you can see some differences:

1
The before field now has the state of the row that was deleted with the database commit.
2
The after field is null, signifying that the row no longer exists.
3
The source field structure has many of the same values as before, except the ts_sec and pos fields have changed (and the file might have changed in other scenarios).
4
The op field value is now d, signifying that this row was deleted.
5
The ts_ms shows the timestamp when Debezium processed this event.

This event provides a consumer with the information that 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.

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

When a row is deleted, the delete event value listed above still works with log compaction, because Kafka can still remove all earlier messages with that same key. If the message value is null, Kafka knows that it can remove all messages with that same key. To make this possible, Debezium’s MySQL connector always follows a delete event with a special tombstone event that has the same key but a null value.

2.1.5. How the MySQL connector maps 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 the value is represented 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 following table shows how the connector maps the 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)
MySQL typeLiteral typeSemantic type

BOOLEAN, BOOL

BOOLEAN

n/a

BIT(1)

BOOLEAN

n/a

BIT(>1)

BYTES

io.debezium.data.Bits

Note

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.

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

n/a

VARBINARY(M)]

BYTES

n/a

TINYBLOB

BYTES

n/a

TINYTEXT

STRING

n/a

BLOB

BYTES

n/a

TEXT

STRING

n/a

MEDIUMBLOB

BYTES

n/a

MEDIUMTEXT

STRING

n/a

LONGBLOB

BYTES

n/a

LONGTEXT

STRING

n/a

JSON

STRING

io.debezium.data.Json

Note

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

ENUM

STRING

io.debezium.data.Enum

Note

The allowed schema parameter contains the comma-separated list of allowed values.

SET

STRING

io.debezium.data.EnumSet

Note

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

Note

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

2.1.5.1. Temporal values

Excluding the TIMESTAMP data type, MySQL temporal types depend on the value of the time.precision.mode 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 milli-seconds or micro-seconds based on the column’s precision by using UTC. The TIMESTAMP type represents a timestamp without time zone information and is converted by MySQL from the server (or session’s) current time zone into UTC when writing and vice versa 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 connector configuration property. For example, if the database’s time zone (either globally or configured for the connector by means of the database.serverTimezone property) 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".

Note that 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.

MySQL typeLiteral typeSemantic type

DATE

INT32

io.debezium.time.Date

Note

Represents the number of days since epoch.

TIME[(M)]

INT64

io.debezium.time.MicroTime

Note

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

Note

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

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

INT64

io.debezium.time.MicroTimestamp

Note

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

time.precision.mode=connect

The MySQL connector uses the predefined 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. Only values in 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.

MySQL typeLiteral typeSemantic type

DATE

INT32

org.apache.kafka.connect.data.Date

Note

Represents the number of days since epoch.

TIME[(M)]

INT64

org.apache.kafka.connect.data.Time

Note

Represents the time value in microseconds since midnight and does not include time zone information.

DATETIME[(M)]

INT64

org.apache.kafka.connect.data.Timestamp

Note

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

== Decimal values

Decimals are handled via the decimal.handling.mode property.

Tip
decimal.handling.mode=precise
MySQL typeLiteral typeSemantic type

NUMERIC[(M[,D])]

BYTES

org.apache.kafka.connect.data.Decimal

Note

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

Note

The scale schema parameter contains an integer that represents how many digits the decimal point shifted.

decimal.handling.mode=double
MySQL typeLiteral typeSemantic type

NUMERIC[(M[,D])]

FLOAT64

n/a

DECIMAL[(M[,D])]

FLOAT64

n/a

decimal.handling.mode=string
MySQL typeLiteral typeSemantic type

NUMERIC[(M[,D])]

STRING

n/a

DECIMAL[(M[,D])]

STRING

n/a

2.1.5.2. Boolean values

MySQL handles the BOOLEAN value internally in a specific way. The BOOLEAN column is internally mapped to TINYINT(1) datatype. When the table is created during streaming then it uses proper BOOLEAN mapping as Debezium receives the original DDL. During snapshot Debezium executes SHOW CREATE TABLE to obtain table definition which returns TINYINT(1) for both BOOLEAN and TINYINT(1) columns.

Debezium then has no way how to obtain the original type mapping and will map to TINYINT(1).

An example configuration is 

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

2.1.5.3. Spatial data types

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

MySQL typeLiteral typeSemantic type

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

STRUCT

io.debezium.data.geometry.Geometry

Note

Contains a structure with two fields:

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

2.1.6. The MySQL connector and Kafka topics

The Debezium MySQL connector writes events for all INSERT, UPDATE, and DELETE operations from a single table to a single Kafka topic. The Kafka topic naming convention is as follows:

format

serverName.databaseName.tableName

Example 2.1. example

Let us say that fulfillment is the server name and inventory is the database which contains three tables of orders, customers, and products. The Debezium MySQL connector produces events on three Kafka topics, one for each table in the database: 

fulfillment.inventory.orders

fulfillment.inventory.customers

fulfillment.inventory.products

2.1.7. MySQL supported topologies

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.
Master and slave

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

The connector records its position in the server’s binlog, which is different on each server in the cluster. Therefore, the connector will need to follow just one MySQL server instance. If that server fails, it 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 far easier to tolerate and almost immediately recover from problems and failures. Most HA MySQL clusters use GTIDs so that slaves are able to keep track of all changes on any of the master.
Multi-master

A multi-master MySQL topology uses one or more MySQL slaves that each replicate from multiple masters. This is a powerful way to aggregate the replication of multiple MySQL clusters, and requires using GTIDs.

The Debezium MySQL connector can use these multi-master MySQL slaves as sources, and can fail over to different multi-master MySQL slaves as long as thew new slave is caught up to the old slave (e.g., the new slave has all of the transactions that were last seen on the first slave). This works even if the connector is only using 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-master MySQL slave 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.

Important

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

2.2. Setting up MySQL server

2.2.1. Creating a MySQL user for Debezium

You have to define a MySQL user with appropriate permissions on all databases that the Debezium MySQL connector monitors.

Prerequisites

  • You must have a MySQL server.
  • You must know basic SQL commands.

Procedure

  1. Create the MySQL user: 
mysql> CREATE USER 'user'@'localhost' IDENTIFIED BY 'password';
  1. Grant the required permissions to the user: 
mysql> GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'user' IDENTIFIED BY 'password';
Tip

See permissions explained for notes on each permission.

Important

If using a hosted option such as Amazon RDS or Amazon Aurora that do 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 Overview of how the MySQL connector works for more details.

  1. Finalize the user’s permissions: 
mysql> FLUSH PRIVILEGES;

2.2.1.1. Permissions explained

Permission/itemDescription

SELECT

enables the connector to select rows from tables in databases

Note

This is only used 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.

Note

This is only used when performing a snapshot.

SHOW DATABASES

enables the connector to see database names by issuing the SHOW DATABASE statement.

Note

This is only used 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 following statements:

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

This is always required for the connector.

ON

Identifies the database to which the permission apply.

TO 'user'

Specifies the user to which the permissions are granted.

IDENTIFIED BY 'password'

Specifies the password for the user.

2.2.2. 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

  • You must have a MySQL server.
  • You should have appropriate MySQL user privileges.

Procedure

  1. Check if the log-bin option is already on or not. 
mysql> SELECT variable_value as "BINARY LOGGING STATUS (log-bin) ::"
FROM information_schema.global_variables WHERE variable_name='log_bin';
  1. If OFF, configure your MySQL server configuration file with the following:
Tip

See Binlog config properties for notes on each property. 

server-id         = 223344 1
log_bin           = mysql-bin 2
binlog_format     = ROW 3
binlog_row_image  = FULL 4
expire_logs_days  = 10 5
  1. 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';

2.2.2.1. Binlog configuration properties

NumberPropertyDescription

1

server-id

The value for the server-id must be unique for each server and replication client within the MySQL cluster. When the MySQL connector is setup, we assign the connector a unique server ID.

2

log_bin

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

3

binlog_format

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

4

binlog_row_image

The binlog_row_image must be set to FULL or full.

5

expire_logs_days

This is the number of days for automatic binlog file removal. The default is 0 which means no automatic removal.

Tip

Set the value to match the needs of your environment.

2.2.3. 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 the Debezium MySQL connector, using GTIDs simplifies replication and allows you to more easily confirm if master and slave servers are consistent.

Note

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

Prerequisites

  • You must have a MySQL server.
  • You must know basic SQL commands.
  • You must have access to the MySQL configuration file.

Procedure

  1. Enable gtid_mode: 
mysql> gtid_mode=ON
  1. Enable enforce_gtid_consistency: 
mysql> enforce_gtid_consistency=ON
  1. Confirm the changes: 
mysql> show global variables like '%GTID%';

response

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

2.2.3.1. Options explained

Permission/itemDescription

gtid_mode

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

  • ON = enabled
  • OFF = disabled

enforce_gtid_consistency

Boolean which instructs the server whether or not to enforce 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

2.2.4. Setting up session timeouts 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

  • You must have a MySQL server.
  • You must know basic SQL commands.
  • You must have access to the MySQL configuration file.

Procedure

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

2.2.4.1. Options explained

Permission/itemDescription

interactive_timeout

The number of seconds the server waits for activity on an interactive connection before closing it.

Tip

See MySQL’s documentation for more details.

wait_timeout

The number of seconds the server waits for activity on a noninteractive connection before closing it.

Tip

See MySQL’s documentation for more details.

2.2.5. Enabling query log events for Debezium

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.

Note

This option is only available from MySQL 5.6 and later.

Prerequisites

  • You must have a MySQL server.
  • You must know basic SQL commands.
  • You must have access to the MySQL configuration file.

Procedure

  1. Enable binlog_rows_query_log_events: 
mysql> binlog_rows_query_log_events=ON

2.2.5.1. Options explained

Permission/itemDescription

binlog_rows_query_log_events`

Boolean which enables/disables support for including the original SQL statement in the binlog entry.

  • ON = enabled
  • OFF = disabled

2.3. Deploying the MySQL connector

2.3.1. Installing the MySQL connector

Installing the Debezium MySQL connector is a simple process whereby you only need to download the JAR, extract it to your Kafka Connect environment, and ensure the plug-in’s parent directory is specified in your Kafka Connect environment.

Prerequisites

Procedure

  1. Download the Debezium MySQL connector.
  2. Extract the files into your Kafka Connect environment.
  3. Add the plug-in’s parent directory to your Kafka Connect plugin.path: 
plugin.path=/kafka/connect
Note

The above example assumes you have extracted the Debezium MySQL connector to the /kafka/connect/debezium-connector-mysql path.

  1. Restart your Kafka Connect process. This ensures the new JARs are picked up.

2.3.2. Configuring the MySQL connector

Typically, you configure the Debezium MySQL connector in a .yaml file using the configuration properties available for the connector.

Prerequisites

Procedure

  1. Set the "name" of the connector in the .yaml file.
  2. Set the configuration properties that you require for your Debezium MySQL connector.
Tip

For a complete list of configuration properties, see MySQL connector configuration properties.

MySQL connector example configuration

  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.whitelist: inventory  7
      database.history.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092  8
      database.history.kafka.topic: schema-changes.inventory  9

1 1
The name of the connector.
2 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 3
The connector’s configuration.
4 4
The database host, which is the name of the container running the MySQL server (mysql).
5 5 6
A unique server ID and name. The server name is the logical identifier for the MySQL server or cluster of servers. This name will be used as the prefix for all Kafka topics.
7
Only changes in the inventory database will be detected.
8 9
The connector will store the history of the database schemas in Kafka using this broker (the same broker to which you are sending events) and topic name. Upon restart, the connector will recover the schemas of the database that existed at the point in time in the binlog when the connector should begin reading.

2.3.3. MySQL connector configuration properties

The configuration properties listed here are required to run the Debezium MySQL connector. There are also advanced MySQL connector properties whose default value rarely needs to be changed and therefore, they do not need to be specified in the connector configuration.

Tip

The Debezium MySQL connector supports pass-through configuration when creating the Kafka producer and consumer. See information about pass-through properties at the end of this section, and also see the Kafka documentation for more details about pass-through 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.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 hostname of the MySQL database server.

database.port

3306

Integer port number of the MySQL database server.

database.user

 

Name of the MySQL database 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 being monitored. 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.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 is generated between 5400 and 6400, though we recommend setting an explicit value.

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/port pairs that the connector will use for establishing an initial connection to the Kafka cluster. This connection will be 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.

database.whitelist

empty string

An optional comma-separated list of regular expressions that match database names to be monitored; any database name not included in the whitelist will be excluded from monitoring. By default all databases will be monitored. May not be used with database.blacklist.

database.blacklist

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 the blacklist will be monitored. May not be used with database.whitelist.

table.whitelist

empty string

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored; any table not included in the whitelist will be excluded from monitoring. Each identifier is of the form databaseName.tableName. By default the connector will monitor every non-system table in each monitored database. May not be used with table.blacklist.

table.blacklist

empty string

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring; any table not included in the blacklist will be monitored. Each identifier is of the form databaseName.tableName. May not be used with table.whitelist.

column.blacklist

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 databaseName.tableName.columnName, or databaseName.schemaName.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 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 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. 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 databaseName.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 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 names for columns are of the form databaseName.tableName.columnName, or 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 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 databaseName.tableName.typeName, or databaseName.schemaName.tableName.typeName. See how the MySQL connector maps 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; 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.

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; or double represents them using double values, which may result in a loss of precision but will be far easier to use. string option encodes values as formatted string which is easy to consume but a semantic information about the real type is lost.

bigint.unsigned.handling.mode

long

Specifies how BIGINT UNSIGNED columns should be represented in change events, including: precise uses java.math.BigDecimal to represent values, which are encoded in the change events using a binary representation and Kafka Connect’s org.apache.kafka.connect.data.Decimal type; long (the default) represents values using Java’s long, which may not offer the precision but will be far easier to use in consumers. long is usually the preferable setting. Only when working with values larger than 2^63, the precise setting should be used as those values cannot be conveyed 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 will be recorded 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. The default is true.

include.query

false

Boolean value that specifies whether the connector should include the original SQL query that generated the change event.
Note: This option requires MySQL be configured with the binlog_rows_query_log_events option set to ON. Query will not be present for events generated from the snapshot process.
WARNING: Enabling this option may expose tables or fields explicitly blacklisted or masked by including the original SQL statement in the change event. For this reason this option is defaulted to 'false'.

event.processing​.failure.handling.mode

fail

Specifies how the connector should react to exceptions during deserialization of binlog events. fail will propagate the exception (indicating the problematic event and its binlog offset), causing the connector to stop.
warn will cause the problematic event to be skipped and the problematic event and its binlog offset to be logged.
skip will cause problematic event will be skipped.

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 (i.e. internal representation is not consistent with database) fail will throw an exception (indicating the problematic event and its binlog offset), causing the connector to stop.
warn will cause the problematic event to be skipped and the problematic event and its binlog offset to be logged.
skip will cause the problematic event to be skipped.

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 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.

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.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 matching one of these include patterns will be used. May not be used with 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 matching none of these exclude patterns will be used. May not be used with gtid.source.includes.

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.

message.key.columns

empty string

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 table>:<a comma-separated list of columns> representing the custom key.
Fully-qualified tables could be defined as databaseName.tableName.

2.3.3.1. Advanced MySQL connector properties

The following table describes advanced MySQL connector properties.

PropertyDefaultDescription

connect.keep.alive

true

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

table.ignore.builtin

true

Boolean value that specifies whether built-in system tables should be ignored. This applies regardless of the table whitelist or blacklists. By default system tables are excluded from monitoring, and no events are generated when changes are made to any of the 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 attempt 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

Boolean value that specifies if connector should ignore malformed or unknown database statements or stop processing and let operator to 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 binlog is processed.

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

false

Boolean value that specifies if connector should should record all DDL statements or (when true) only those that are relevant to tables that are monitored by Debezium (via filter configuration). The safe default is false. This feature should be used only with care as the missing data might be necessary when the filters are changed.

database.ssl.mode

disabled

Specifies whether to use an encrypted connection. The default is disabled, and specifies to use an unencrypted connection.

The preferred option establishes an encrypted connection if the server supports secure connections but falls back to an unencrypted connection otherwise.

The required option establishes an encrypted connection but will fail if one cannot be made for any reason.

The verify_ca option behaves like required but additionally it verifies the server TLS certificate against the configured Certificate Authority (CA) certificates and will fail if it doesn’t match any valid CA certificates.

The verify_identity option 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.
Under specific conditions it is possible that 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 decide whether the changes from the transaction will be streamed or not. The size of the buffer defines the maximum number of changes in the transaction that Debezium can buffer while searching for transaction boundaries. If the size of transaction is larger than the buffer then Debezium needs to rewind and re-read the events that has not fit into the buffer while streaming. Value 0 disables buffering.
Disabled by default.
Note: This feature should be considered an incubating one. We need a feedback from customers but it is expected that it is not completely polished.

snapshot.mode

initial

Specifies the criteria for running a snapshot upon startup of the connector. The default is initial, and specifies the connector can run a snapshot only when no offsets have been recorded for the logical server name. The when_needed option specifies that the connector run a snapshot upon startup whenever it deems it necessary (when no offsets are available, or when a previously recorded offset specifies a binlog location or GTID that is not available in the server). The never option specifies that the connect should never use snapshots and that upon first startup with a logical server name the connector should read from the beginning of the binlog; this should be used with care, as it is only valid when the binlog is guaranteed to contain the entire history of the database. If you don’t need the topics to contain a consistent snapshot of the data but only need them to have the changes since the connector was started, you can use the schema_only option, where the connector only snapshots the schemas (not the data).

schema_only_recovery is a recovery option for an existing connector to recover a corrupted or lost database history topic, or to periodically "clean up" a database history topic (which requires infinite retention) that may be growing unexpectedly.

snapshot.locking.mode

minimal

Controls if and how long the connector holds onto the global MySQL read lock (preventing any updates to the database) while it is performing a snapshot. There are three possible values minimal, extended, and none.

minimal The connector holds the global read lock for just the initial portion of the snapshot while the connector reads the database schemas and other metadata. The remaining work in a snapshot involves selecting all rows from each table, and this can be done in a consistent fashion using the REPEATABLE READ transaction even when the global read lock is no longer held and while other MySQL clients are updating the database.

extended In some cases where clients are submitting operations that MySQL excludes from REPEATABLE READ semantics, it may be desirable to block all writes for the entire duration of the snapshot. For these such cases, use this option.

none Will prevent the connector from acquiring any table locks during the snapshot process. This value can be used with all snapshot modes but it is safe to use if and only if no schema changes are happening while the snapshot is taken. Note that 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.select.statement.overrides

 

Controls which rows from tables will be included in snapshot.
This property contains a comma-separated list of fully-qualified tables (DB_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.[DB_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 from binlog are not affected by it at all.

min.row.count.to.stream.results

1000

During a snapshot operation, the connector will query each included table to produce a read event for all rows in that table. This parameter determines whether the MySQL connection will pull all results for a table into memory (which is fast but requires large amounts of memory), or whether the results will instead be streamed (can be slower, but will work for very large tables). The value specifies the minimum number of rows a table must contain before the connector will stream results, and defaults to 1,000. Set this parameter to '0' to skip all table size checks and always stream all results during a snapshot.

heartbeat.interval.ms

0

Controls how frequently the heartbeat messages are sent.
This property contains an interval in milli-seconds that defines how frequently the connector sends heartbeat messages into a heartbeat topic. 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>.

database.initial.statements

 

A semicolon separated list of SQL statements to be executed when a JDBC connection (not the transaction log reading connection) to the database is established. Use doubled semicolon (';;') to use a semicolon as a character and not as a delimiter.
Note: The connector may establish JDBC connections at its own discretion, so this should typically be used for configuration of session parameters only, but not for executing DML statements.

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

 

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.

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 table locks cannot be acquired in this time interval, the snapshot will fail. See How the MySQL connector performs database snapshots.

enable.time.adjuster

 

MySQL allows user to insert year value as either 2-digit or 4-digit. In case of two digits the value is automatically mapped to 1970 - 2069 range. This is usually done by database.
Set to true (the default) when Debezium should do the conversion.
Set to false when conversion is fully delegated to the database.

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 will be sanitized to adhere to Avro naming requirements.

2.3.3.2. 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

2.3.3.3. 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.

2.3.4. MySQL connector monitoring metrics

The Debezium MySQL connector has three metric types in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect have.

Refer to the monitoring documentation for details of how to expose these metrics via JMX.

2.3.4.1. Snapshot metrics

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

AttributeTypeDescription

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.

HoldingGlobalLock

boolean

Whether the connector currently holds a global or table write lock.

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.

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 whitelist or blacklist filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapcity

int

The length of the queue used to pass events between snapshot reader and the main Kafka Connect loop.

QueueRemainingCapcity

int

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

2.3.4.2. Binlog metrics

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

Note

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

AttributeTypeDescription

Connected

boolean

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

BinlogFilename

string

The name of the binlog filename 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 seen by the connector when reading the binlog.

LastEvent

string

The last binlog event that the connector has read.

SecondsSinceLastEvent (obsolete)

long

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

SecondsBehindMaster (obsolete)

long

The number of seconds between the last event’s MySQL timestamp and the connector processing it. The values will incorporate any differences between the clocks on the machines where the MySQL server and the MySQL connector are running.

MilliSecondsBehindSource

long

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

TotalNumberOfEventsSeen

long

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

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.

NumberOfEventsFiltered

long

The number of events that have been filtered by whitelist or blacklist filtering rules configured on the connector.

NumberOfDisconnects

long

The number of disconnects by the MySQL connector.

SourceEventPosition

map<string, string>

The coordinates of the last received event.

LastTransactionId

string

Transaction identifier of the last processed transaction.

LastEvent

string

The last binlog event that the connector has read.

MilliSecondsSinceLastEvent

long

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

MonitoredTables

string[]

The list of tables that are monitored by Debezium.

QueueTotalCapcity

int

The length of the queue used to pass events between binlog reader and the main Kafka Connect loop.

QueueRemainingCapcity

int

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

NumberOfCommittedTransactions

long

The number of processed transactions that were committed.

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 expected protocol BEGIN + COMMIT/ROLLBACK. Should be 0 under normal conditions.

NumberOfLargeTransactions

long

The number of transactions that have not fitted into the look-ahead buffer. Should be significantly smaller than NumberOfCommittedTransactions and NumberOfRolledBackTransactions for optimal performance.

2.3.4.3. Schema history metrics

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

AttributeTypeDescription

Status

string

One of STOPPED, RECOVERING (recovering history from the storage), RUNNING describing 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 applie during recovery and runtime.

MilliSecondsSinceLastRecoveredChange

long

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

MilliSecondsSinceLastAppliedChange

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.

2.4. MySQL connector common issues

2.4.1. Configuration and startup errors

The Debezium MySQL connector fails, reports an error, and stops running when the following startup errors occur:

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

If you receive any of these errors, you receive more details in the error message. The error message also contains workarounds where possible.

2.4.2. MySQL is unavailable

If your MySQL server becomes unavailable, the Debezium MySQL connector fails with an error and the connector stops. You simply need to restart the connector when the server is available.

2.4.2.1. Using GTIDs

If you have GTIDs enabled and a highly available MySQL cluster, restart the connector immediately as the connector will simply 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.

2.4.2.2. Not Using GTIDs

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

2.4.3. Kafka Connect stops

There are three scenarios that cause some issues when Kafka Connect stops:

2.4.3.1. Kafka Connect stops gracefully

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

2.4.3.2. 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 may generate some of the same events processed prior to the crash, creating duplicate events.

Tip

Each change event message includes source-specific information about:

  • the event origin
  • the MySQL server’s event time
  • the binlog filename and position
  • GTIDs (if used)

2.4.3.3. Kafka becomes unavailable

The Kafka Connect framework records Debezium change events in Kafka 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 last left off.

2.4.4. 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.

Tip

See How the MySQL connector performs database snapshots for more information on initial snapshots.

Chapter 3. Debezium Connector for PostgreSQL

Debezium’s PostgreSQL Connector can monitor and record row-level changes in the schemas of a PostgreSQL database.

The first time it connects to a PostgreSQL server/cluster, it reads a consistent snapshot of all of the schemas. When that snapshot is complete, the connector continuously streams the changes that were committed to PostgreSQL 9.6 or later and generates corresponding insert, update and delete events. All of the events for each table are recorded in a separate Kafka topic, where they can be easily consumed by applications and services.

3.1. Overview

PostgreSQL’s logical decoding feature was first introduced in version 9.4 and is a mechanism which allows the extraction of the changes which were committed to the transaction log and the processing of these changes in a user-friendly manner via the help of an output plug-in. This output plug-in must be installed prior to running the PostgreSQL server and enabled together with a replication slot in order for clients to be able to consume the changes.

PostgreSQL connector contains two different parts which work together in order to be able to read and process server changes:

  • A logical decoding output plug-in, which has to be installed and configured in the PostgreSQL server.
  • Java code (the actual Kafka Connect connector) which reads the changes produced by the plug-in, using PostgreSQL’s streaming replication protocol, via the PostgreSQL JDBC driver

The connector then produces a change event for every row-level insert, update, and delete operation that was received, recording all the change events for each table in a separate Kafka topic. Your client applications read the Kafka topics that correspond to the database tables they’re interested in following, and react to every row-level event it sees in those topics.

PostgreSQL normally purges 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, we start with a consistent view of all of the data, yet continue reading without having lost any of the changes made while the snapshot was taking place.

The connector is also tolerant of failures. As the connector reads changes and produces events, it records the position in the write-ahead log with each event. If the connector stops for any reason (including communication failures, network problems, or crashes), upon restart it simply continues reading the WAL where it last left off. This includes snapshots: if the snapshot was not completed when the connector is stopped, upon restart it will begin a new snapshot.

3.1.1. Logical decoding output plug-in

The pgoutput logical decoder is the only supported logical decoder in the Tecnhology Preview release of Debezium.

pgoutput, the standard logical decoding plug-in in PostgreSQL 10+, is maintained by the Postgres community, and is also used by Postgres for logical replication. The pgoutput plug-in is always present, meaning that no additional libraries must be installed, and the connector will interpret the raw replication event stream into change events directly.

Important

The connector’s functionality relies on PostgreSQL’s logical decoding feature. Please be aware of the following limitations which are also reflected by the connector:

  1. Logical Decoding does not support DDL changes: this means that the connector is unable to report DDL change events back to consumers.
  2. Logical Decoding replication slots are only supported on primary servers: this means that when there is a cluster of PostgreSQL servers, the connector can only run on the active primary server. It cannot run on hot or warm standby replicas. If the primary server fails or is demoted, the connector will stop. Once the primary has recovered the connector can simply be restarted. If a different PostgreSQL server has been promoted to primary, the connector configuration must be adjusted before the connector is restarted. Make sure you read more about how the connector behaves when things go wrong.
Important

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

3.2. Setting up PostgreSQL

This release of Debezium only supports the native pgoutput logical replication stream. To set up PostgreSQL using pgoutput, you will need to enable a replication slot, and configure a user with sufficient privileges to perform the replication.

3.2.1. Configuring the replication slot

PostgreSQL’s logical decoding uses replication slots.

First, you configure the replication slot:

postgresql.conf

wal_level=logical
max_wal_senders=1
max_replication_slots=1

  • wal_level tells the server to use logical decoding with the write-ahead log
  • max_wal_senders tells the server to use a maximum of 1 separate processes for processing WAL changes
  • max_replication_slots tells the server to allow a maximum of 1 replication slots to be created for streaming WAL changes

Replication slots are guaranteed to retain all WAL required for Debezium even during Debezium outages. It is important for this reason to closely monitor replication slots to avoid too much disk consumption and other conditions that can happen such as catalog bloat if a replication slot stays unused for too long. For more information, refer to the the Postgres documentation.

Note

We recommend reading and understanding the WAL configuration documentation regarding the mechanics and configuration of the PostgreSQL write-ahead log.

3.2.2. Setting up Permissions

Next, configure a database user who can perform replications.

Replication can only be performed by a database user that has appropriate permissions and only for a configured number of hosts.

In order to give a user replication permissions, define a PostgreSQL role that has at least the REPLICATION and LOGIN permissions. For example: 

CREATE ROLE name REPLICATION LOGIN;
Note

Superusers have by default both of the above roles.

Finally, configure the PostgreSQL server to allow replication to take place between the server machine and the host on which the PostgreSQL connector is running:

pg_hba.conf

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

1
Tells the server to allow replication for <youruser> locally (i.e. on the server machine)
2
Tells the server to allow <youruser> on localhost to receive replication changes using IPV4
3
Tells the server to allow <youruser> on localhost to receive replication changes using IPV6
Note

See the PostgreSQL documentation for more information on network masks.

3.2.3. WAL Disk Space Consumption

In certain cases, it is possible that PostgreSQL disk space consumed by WAL files either experiences spikes or increases out of usual proportions. There are three potential reasons that explain the situation:

  • Debezium regularly confirms LSN of processed events to the database. This is visible as confirmed_flush_lsn in the pg_replication_slots slots table. The database is responsible for reclaiming the disk space and the WAL size can be calculated from restart_lsn of the same table. So if the confirmed_flush_lsn is regularly increasing and restart_lsn lags then the database does need to reclaim the space. Disk space is usually reclaimed in batch blocks so this is expected behavior and no action on a user’s side is necessary.
  • There are many updates in a monitored database but only a minuscule amount relates to the monitored table(s) and/or schema(s). This situation can be easily solved by enabling periodic heartbeat events using heartbeat.interval.ms configuration option.
  • The PostgreSQL instance contains multiple databases where one of them is a high-traffic database. Debezium monitors another database that is low-traffic in comparison to the other one. Debezium then cannot confirm the LSN as replication slots work per-database and Debezium is not invoked. As WAL is shared by all databases it tends to grow until an event is emitted by the database monitored by Debezium.

To overcome the third cause it is necessary to

  • enable periodic heartbeat record generation using the heartbeat.interval.ms configuration option
  • regularly emit change events from the database tracked by Debezium

A separate process would then periodically update the table (either inserting a new event or updating the same row all over). PostgreSQL then will invoke Debezium which will confirm the latest LSN and allow the database to reclaim the WAL space. This task can be automated by means of the heartbeat.action.query connector option (see below).

Tip

For users on AWS RDS with Postgres, a similar situation to the third cause may occur on an idle environment, since AWS RDS makes writes to its own system tables not visible to the useres on a frequent basis (5 minutes). Again regularly emitting events will solve the problem.

3.2.4. How the PostgreSQL connector works

3.2.4.1. Snapshots

Most PostgreSQL servers are configured to not retain the complete history of the database in the WAL segments, so the PostgreSQL connector would be unable to see the entire history of the database by simply reading the WAL. So, by default the connector will upon first startup perform an initial consistent snapshot of the database. Each snapshot consists of the following steps (when using the builtin snapshot modes, custom snapshot modes may override this):

  1. Start a transaction with a SERIALIZABLE, READ ONLY, DEFERRABLE isolation level to ensure that all subsequent reads within this transaction are done against a single consistent version of the data. Any changes to the data due to subsequent INSERT, UPDATE, and DELETE operations by other clients will not be visible to this transaction.
  2. Obtain a ACCESS SHARE MODE lock on each of the monitored tables to ensure that no structural changes can occur to any of the tables while the snapshot is taking place. Note that these locks do not prevent table INSERTS, UPDATES and DELETES from taking place during the operation. This step is omitted when using the exported snapshot mode to allow for a lock-free snapshots.
  3. Read the current position in the server’s transaction log.
  4. Scan all of the database tables and schemas, and 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 will begin a new snapshot. Once the connector does complete its initial snapshot, the PostgreSQL connector then continues streaming from the position read during step 3, ensuring that it does not miss any updates. If the connector stops again for any reason, upon restart it will simply continue streaming changes from where it previously left off.

A second snapshot mode allows the connector to perform snapshots always. This behavior tells the connector to always perform a snapshot when it starts up, and after the snapshot completes to continue streaming changes from step 3 in the above sequence. This mode can be used in cases when it is known that some WAL segments have been deleted and are no longer available, or in case of a cluster failure after a new primary has been promoted so that the connector does not miss any potential changes that could have taken place after the new primary had been promoted but before the connector was restarted on the new primary.

The third snapshot mode instructs the connector to never performs snapshots. When a new connector is configured this way, if will either continue streaming changes from a previous stored offset or it will start from the point in time when the PostgreSQL logical replication slot was first created on the server. Note that this mode is useful only when you know all data of interest is still reflected in the WAL.

The fourth snapshot mode, initial only, will perform a database snapshot and then stop before streaming any other changes. If the connector had started but did not complete a snapshot before stopping, the connector will restart the snapshot process and stop once the snapshot completes.

The fifth snapshot mode, exported, will perform 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.

3.2.4.2. Streaming Changes

The PostgreSQL connector will typically spend 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 where the client can receive changes from the server as they are committed in the server’s transaction log at certain positions (also known as Log Sequence Numbers or in short 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 PostgreSQL connector acts as a PostgreSQL client, and when it receives these changes it transforms the events into Debezium create, update, or delete events that include the LSN position of the event. The PostgreSQL connector forwards these change events to the Kafka Connect framework (running in the same process), which then asynchronously writes them in the same order to the appropriate Kafka topic. Kafka Connect uses the term offset for the source-specific position information that Debezium includes with each event, and Kafka Connect periodically records the most recent offset in another Kafka topic.

When Kafka Connect gracefully shuts down, it stops the connectors, flushes all events to Kafka, and records the last offset received from each connector. Upon restart, Kafka Connect reads the last recorded offset for each connector, and starts the connector from that point. The PostgreSQL connector uses the LSN recorded in each change event as the offset, so that upon restart the connector requests the PostgreSQL server send it the events starting just after that position.

Note

The PostgreSQL connector retrieves the schema information as part of the events sent by the logical decoder plug-in. The only exception is the information about which columns compose the primary key, as this information is obtained from the JDBC metadata (side channel). If the primary key definition of a table changes (by adding, removing or renaming PK columns), then there exists a slight risk of an unfortunate timing when the primary key information from JDBC will not be synchronized with the change data in the logical decoding event and a small amount of messages will be created with an inconsistent key structure. If this happens then a restart of the connector and a reprocessing of the messages will fix the issue. To prevent the issue completely it is recommended to synchronize updates to the primary key structure with Debezium roughly using following sequence of operations:

  • Put the database or an application into a read-only mode
  • Let Debezium process all remaining events
  • Stop Debezium
  • Update the primary key definition
  • Put the database or the application into read/write state and start Debezium again

3.2.4.3. PostgreSQL 10+ Logical Decoding Support (pgoutput)

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

See Setting up PostgreSQL for more details.

3.2.4.4. Topics Names

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 configuration property, schemaName is the name of the database schema where the operation occurred, and tableName is the name of the database table on which the operation occurred.

For example, consider a PostgreSQL installation with a postgres database and an inventory schema that contains four tables: products, products_on_hand, customers, and orders. If the connector monitoring this database were given a logical server 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

If on the other hand the tables were not part of a specific schema but rather created in the default public PostgreSQL schema, then the name of the Kafka topics would be:

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

3.2.4.5. Meta Information

Each record produced by the PostgreSQL connector has, in addition to the database event, some meta-information about 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 be placed: 

"sourcePartition": {
     "server": "fulfillment"
 },
 "sourceOffset": {
     "lsn": "24023128",
     "txId": "555",
     "ts_ms": "1482918357011"
 },
 "kafkaPartition": null

The PostgreSQL connector uses only 1 Kafka Connect partition and it places the generated events into 1 Kafka partition. Therefore, the name of the sourcePartition will always default to the name of the database.server.name configuration property, while the kafkaPartition has the value null which means that the connector does not use a specific Kafka partition.

The sourceOffset portion of the message 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 which caused the event
  • ts_ms represents the number of microseconds since Unix Epoch as the server time at which the transaction was committed

3.2.4.6. Events

All data change events produced by the PostgreSQL connector have a key and a value, although the structure of the key and value depend on the table from which the change events originated (see Topic names).

Note

Starting with Kafka 0.10, Kafka can optionally record with the message key and value 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 are valid Avro schema names. This means that the logical server name must start with Latin letters or an underscore (e.g., [a-z,A-Z,_]), and the remaining characters in the logical server name and all characters in the schema and table names must be Latin letters, digits, or an underscore (e.g., [a-z,A-Z,0-9,\_]). If not, then all invalid characters will automatically be replaced with an underscore character.

This can lead to unexpected conflicts when the logical server name, schema names, and table names contain other characters, and the only distinguishing characters between table full names are invalid and thus replaced with underscores.

Debezium and Kafka Connect are designed around continuous streams of event messages, and the structure of these events may change over time. This could be difficult for consumers to deal with, so to make it easy Kafka Connect makes each event self-contained. Every message key and value has two parts: a schema and payload. The schema describes the structure of the payload, while the payload contains the actual data.

3.2.4.6.1. Change Event’s Key

For a given table, the change event’s key will have a structure that contains a field for each column in the primary key (or unique key constraint with REPLICA IDENTITY set to FULL or USING INDEX on the table) of the table at the time the event was created.

Consider a customers table defined in the public database schema: 

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)
);

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

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

The schema portion of the key contains a Kafka Connect schema describing what is in the key portion. In this case, it means that the payload value is not optional, is a structure defined by a schema named PostgreSQL_server.public.customers.Key, and has one required field named id of type int32. If you look at the value of the key’s payload field, you see that it is indeed a structure (which in JSON is just an object) with a single id field, whose value is 1.

Therefore, we interpret this key as describing the row in the public.customers table (output from the connector named PostgreSQL_server) whose id primary key column had a value of 1.

Note

Although the column.blacklist configuration property allows 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 will be null. This makes sense since the rows in a table without a primary or unique key constraint cannot be uniquely identified.

3.2.4.6.2. Change Event’s Value

The value of the change event message is a bit more complicated. Like the message key, it has a schema section and payload section. The payload section of every change event value produced by the PostgreSQL connector has an envelope structure with the following fields:

  • op is a mandatory field that contains a string value describing the type of operation. Values for the PostgreSQL connector are c for create (or insert), u for update, d for delete, and r for read (in the case of a snapshot).
  • before is an optional field that if present contains the state of the row before the event occurred. The structure will be described by the PostgreSQL_server.public.customers.Value Kafka Connect schema, which the PostgreSQL_server connector uses for all rows in the public.customers table.
Warning

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

  • after is an optional field that if present contains the state of the row after the event occurred. The structure is described by the same PostgreSQL_server.public.customers.Value Kafka Connect schema used in before.
  • source is a mandatory field that contains a structure describing the source metadata for the event, which in the case of PostgreSQL contains several fields: the Debezium version, the connector name, the name of the affected database, schema and table, whether the event is part of an ongoing snapshot or not and the same fields from the record’s meta information section
  • ts_ms is optional and if present contains the time (using the system clock in the JVM running the Kafka Connect task) at which the connector processed the event.

And of course, the schema portion of the event message’s value contains a schema that describes this envelope structure and the nested fields within it.

3.2.4.6.3. Replica Identity

REPLICA IDENTITY is a PostgreSQL specific table-level setting which determines the amount of information that is available to logical decoding in case of UPDATE and DELETE events. More specifically, this controls what (if any) information is available regarding the previous values of the table columns involved, whenever one of the aforementioned events occur.

There are 4 possible values for REPLICA IDENTITY:

  • DEFAULT - UPDATE and DELETE events will only contain the previous values for the primary key columns of a table, in case of UPDATE only the primary columns with changed values are present
  • NOTHING - UPDATE and DELETE events will not contain any information about the previous value on any of the table columns
  • FULL - UPDATE and DELETE events will contain the previous values of all the table’s columns
  • INDEX index name - UPDATE and DELETE events will contain the previous values of the columns contained in the index definition named index name, in case of UPDATE only the indexed columns with changed values are present
3.2.4.6.4. Create Events

Let’s look at what a create event value might look like for our customers table: 

{
    "schema": {
        "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",
                "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",
                "field": "source"
            },
            {
                "type": "string",
                "optional": false,
                "field": "op"
            },
            {
                "type": "int64",
                "optional": true,
                "field": "ts_ms"
            }
        ],
        "optional": false,
        "name": "PostgreSQL_server.inventory.customers.Envelope"
    },
    "payload": {
        "before": null,
        "after": {
            "id": 1,
            "first_name": "Anne",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": {
            "version": "1.1.2.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",
        "ts_ms": 1559033904863
    }
}

If we look at the schema portion of this event’s value, we can see the schema for the envelope, the schema for the source structure (which is specific to the PostgreSQL connector and reused across all events), and the table-specific schemas for the before and after fields.

Note

The names of the schemas for the before and after fields are of the form logicalName.schemaName.tableName.Value, and thus are entirely independent from all other schemas for all other tables.

This means that when using the Avro Converter, the resulting Avro schemas for each table in each logical source have their own evolution and history.

If we look at the payload portion of this event’s value, we can see the information in the event, namely that it is describing that the row was created (since op=c), and that the after field value contains the values of the new inserted row’s' id, first_name, last_name, and email columns.

Note

It may appear that the JSON representations of the events are much larger than the rows they describe. This is true, because the JSON representation must include the schema and the payload portions of the message.

It is possible and even recommended to use the Avro Converter to dramatically decrease the size of the actual messages written to the Kafka topics.

3.2.4.6.5. Update Events

The value of an update change event on this table will actually have the exact same schema, and its payload will be structured the same but will hold different values. Here’s an example: 

{
    "schema": { ... },
    "payload": {
        "before": {
            "id": 1
        },
        "after": {
            "id": 1,
            "first_name": "Anne Marie",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": {
            "version": "1.1.2.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",
        "ts_ms": 1465584025523
    }
}

When we compare this to the value in the insert event, we see a couple of differences in the payload section:

  • The op field value is now u, signifying that this row changed because of an update
  • The before field now has the state of the row with the values before the database commit, but only for the primary key column id. This is because the REPLICA IDENTITY which is by default DEFAULT.
Note

Should we want to see the previous values of all the columns for the row, we would have to change the customers table first by running ALTER TABLE customers REPLICA IDENTITY FULL

  • The after field now has the updated state of the row, and here was can see that the first_name value is now Anne Marie.
  • The source field structure has the same fields as before, but the values are different since this event is from a different position in the WAL.
  • The ts_ms shows the timestamp that Debezium processed this event.

There are several things we can learn by just looking at this payload section. We can compare the before and after structures to determine what actually changed in this row because of the commit. The source structure tells us information about PostgreSQL’s record of this change (providing traceability), but more importantly this has information we can compare to other events in this and other topics to know whether this event occurred before, after, or as part of the same PostgreSQL commit as other events.

Note

When the columns for a row’s primary/unique key are updated, the value of the row’s key has changed so Debezium will output three events: a DELETE event and tombstone event with the old key for the row, followed by an INSERT event with the new key for the row.

3.2.4.6.6. Delete Events

So far we’ve seen samples of create and update events. Now, let’s look at the value of a delete event for the same table. Once again, the schema portion of the value will be exactly the same as with the create and update events: 

{
    "schema": { ... },
    "payload": {
        "before": {
            "id": 1
        },
        "after": null,
        "source": {
            "version": "1.1.2.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",
        "ts_ms": 1465581902461
    }
}

If we look at the payload portion, we see a number of differences compared with the create or update event payloads:

  • The op field value is now d, signifying that this row was deleted
  • The before field now has the state of the row that was deleted with the database commit. Again this only contains the primary key column due to the REPLICA IDENTITY setting
  • The after field is null, signifying that the row no longer exists
  • The source field structure has many of the same values as before, except the ts_ms, lsn and txId fields have changed
  • The ts_ms shows the timestamp that Debezium processed this event.

This event gives a consumer all kinds of information that it can use to process the removal of this row.

Warning

Please pay attention to the tables without PK, any delete messages from such table with REPLICA IDENTITY DEFAULT will have no before part (because they have no PK which is the only field for the default identity level) and therefore will be skipped as totally empty. To be able to process messages from tables without PK set REPLICA IDENTITY to FULL level.

The PostgreSQL connector’s events are designed to work with Kafka log compaction, which allows for the removal of some older messages as long as at least the most recent message for every key is kept. This allows Kafka to reclaim storage space while ensuring the topic contains a complete dataset and can be used for reloading key-based state.

When a row is deleted, the delete event value listed above still works with log compaction, since Kafka can still remove all earlier messages with that same key. But only if the message value is null will Kafka know that it can remove all messages with that same key. To make this possible, the PostgreSQL connector always follows the delete event with a special tombstone event that has the same key but null value.

3.2.5. Transaction Metadata

Debezium can generate events that represents transaction metadata boundaries and enrich data messages.

3.2.5.1. 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": "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
    }
  ]
}

The transaction events are written to the topic named <database.server.name>.transaction.

3.2.5.2. 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": "571",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

3.2.5.3. Data Types

As described above, the PostgreSQL connector represents the changes to rows with events that are structured like the table in which the row exist. The event contains a field for each column value, and how that value is represented in the event depends on the PostgreSQL data type of the column. This section describes this mapping.

The following table describes how the connector maps each of the PostgreSQL data types to a literal type and semantic type within the events' fields.

Here, the literal type describes how the value is literally represented using Kafka Connect schema types, namely INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.

The 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.

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

BOOLEAN

BOOLEAN

n/a

 

BIT(1)

BOOLEAN

n/a

 

BIT( > 1), BIT VARYING[(M)]

BYTES

io.debezium.data.Bits

The length schema parameter contains an integer representing the number of bits. The resulting byte[] will contain the bits in little-endian form and will be sized to contain at least the specified number of bits (e.g., numBytes = n/8 + (n%8== 0 ? 0 : 1) where n is the number of bits).

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 pattern P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S, e.g. P1Y2M3DT4H5M6.78S

BYTEA

BYTES

n/a

 

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 2 FLOAT64 fields - (x,y) - each representing 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 timestamp range without time zone.

TSTZRANGE

STRING

n/a

Contains the string representation of a timestamp range with (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 are maintained in the schema parameter named allowed.

Other data type mappings are described in the following sections.

3.2.5.3.1. Temporal Values

Other than PostgreSQL’s TIMESTAMPTZ and TIMETZ data types (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:

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

DATE

INT32

io.debezium.time.Date

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

When the time.precision.mode configuration property is set to adaptive_time_microseconds, 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, except that all TIME fields will be captured as microseconds:

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

DATE

INT32

io.debezium.time.Date

Represents the number of days since 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 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 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 PostgreSQL supports 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:

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

DATE

INT32

org.apache.kafka.connect.data.Date

Represents the number of days since 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 > 3.

TIMESTAMP([P])

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since 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 > 3.

3.2.5.3.2. TIMESTAMP values

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

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

3.2.5.3.3. Decimal Values

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

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

NUMERIC[(M[,D])]

BYTES

org.apache.kafka.connect.data.Decimal

The scaled 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 scaled 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 any scale constraints then it means that the values coming from the database have a different (variable) scale for each value. In this case a type io.debezium.data.VariableScaleDecimal is used and it contains both value and scale of the transferred value.

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

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.

However, when decimal.handling.mode configuration property is set to double, then the connector will represent all DECIMAL and NUMERIC values as Java double values and encodes them as follows:

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

NUMERIC[(M[,D])]

FLOAT64

  

DECIMAL[(M[,D])]

FLOAT64

  

The last option for decimal.handling.mode configuration property is string. In this case the connector will represent all DECIMAL and NUMERIC values as their formatted string representation and encodes them as follows:

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

NUMERIC[(M[,D])]

STRING

  

DECIMAL[(M[,D])]

STRING

  

PostgreSQL supports NaN (not a number) special value to be stored in the DECIMAL/NUMERIC values. Only string and double modes are able to handle such values encoding them as either Double.NaN or string constant NAN.

3.2.5.3.4. HStore Values

When hstore.handling.mode configuration property is set to json (the default), the connector will represent all HSTORE values as string-ified JSON values and encode them as follows:

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

HSTORE

STRING

io.debezium.data.Json

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

When hstore.handling.mode configuration property is set to map, then the connector will use the MAP schema type for all HSTORE columns.

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

HSTORE

MAP

 

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

3.2.5.4. PostgreSQL Domain Types

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

Important

Special consideration should be taken when monitoring columns that use domain types.

When a column is defined using a domain type that extends one of the default database types and the domain type defines a custom length/scale, the generated schema will inherit that defined length/scale.

When a column is defined using a domain type that extends another domain type that defines a custom length/scale, the generated schema will not inherit the defined length/scale because the PostgreSQL driver’s column metadata implementation.

3.2.5.4.1. Network Address Types

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

PostgreSQL Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

INET

STRING

 

IPv4 and IPv6 networks

CIDR

STRING

 

IPv4 and IPv6 hosts and networks

MACADDR

STRING

 

MAC addresses

MACADDR8

STRING

 

MAC addresses in EUI-64 format

3.2.5.4.2. PostGIS Types

The PostgreSQL connector also has full support for all of the PostGIS data types

PostGIS Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

GEOMETRY
(planar)

STRUCT

io.debezium.data.geometry.Geometry

Contains a structure with 2 fields

  • srid (INT32) - Spatial Reference System Identifier defining 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. Please see Open Geospatial Consortium Simple Features Access specification for the format details.

GEOGRAPHY
(spherical)

STRUCT

io.debezium.data.geometry.Geography

Contains a structure with 2 fields

  • srid (INT32) - Spatial Reference System Identifier defining 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. Please see Open Geospatial Consortium Simple Features Access specification for the format details.
3.2.5.4.3. Toasted values

PostgreSQL has a hard limit on the page size. This means that values larger than ca. 8 KB need to be stored using TOAST storage. This impacts replication messages coming from database, as the values that were stored using the TOAST mechanism and 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 database, as this would lead into race conditions potentially. Debezium thus follows these rules to handle the toasted values:

  • tables with REPLICA IDENTITY FULL: TOAST column values are part of the before and after blocks of change events as any other column
  • tables with REPLICA IDENTITY DEFAULT: when receiving an UPDATE event from the database, any unchanged TOAST column value which is not part of the replica identity will not be part of that event; similarly, when receiving a DELETE event, any such TOAST column will not be part of the before block. As Debezium cannot safely provide the column value in this case, it returns a placeholder value defined in configuration option toasted.value.placeholder.
Important

There is a specific problem related to Amazon RDS instances. wal2json plug-in has evolved over the time and there were releases that provided out-of-band toasted values. Amazon supports different versions of the plug-in for different PostgreSQL versions. Please consult Amazon’s documentation to obtain version to version mapping. For consistent toasted values handling we recommend to

  • use pgoutput plug-in for PostgreSQL 10+ instances
  • set include-unchanged-toast=0 for older versions of the wal2json plug-in by using the slot.stream.params configuration option

3.3. Deploying the PostgreSQL Connector

Installing the PostgreSQL connector is a simple process whereby you only need to download the JAR, extract it to your Kafka Connect environment, and ensure the plug-in’s parent directory is specified in your Kafka Connect environment.

Prerequisites

Procedure

  1. Download the Debezium 1.1.3 PostgreSQL connector.
  2. Extract the files into your Kafka Connect environment.

  3. Add the plug-in’s parent directory to your Kafka Connect plugin.path: 

    plugin.path=/kafka/connect
Note

The above example assumes you have extracted the Debezium PostgreSQL connector to the /kafka/connect/Debezium-connector-postgresql path.

  1. Restart your Kafka Connect process. This ensures the new JARs are picked up.

Additional resources

For more information on the deployment process, and deploying connectors with AMQ Streams, refer to the Debezium installation guides.

3.3.1. Example Configuration

To use the connector to produce change events for a particular PostgreSQL server or cluster:

  1. Install the logical decoding plug-in
  2. Configure the PostgreSQL server to support logical replication
  3. Create a configuration file for the PostgreSQL connector.

When the connector starts, it will grab a consistent snapshot of the databases in your PostgreSQL server and start streaming changes, producing events for every inserted, updated, and deleted row. You can also choose to produce events for a subset of the schemas and tables. Optionally ignore, mask, or truncate columns that are sensitive, too large, or not needed.

Following is an example of the configuration for a PostgreSQL connector that monitors a PostgreSQL server at port 5432 on 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium PostgreSQL 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.postgresql.PostgresConnector
    tasksMax: 1  2
    config:  3
      database.hostname: postgresqldb   4
      database.port: 5432
      database.user: debezium
      database.password: dbz
      database.dbname: postgres
      database.server.name: fullfillment   5
      database.whitelist: public.inventory   6
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 database host, which is the name of the container running the PostgreSQL server (postgresqldb).
5
A unique server name. The server name is the logical identifier for the PostgreSQL server or cluster of servers. This name will be used as the prefix for all Kafka topics.
6
Only changes in the public.inventory database will be detected.

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 PostgreSQL database and record events to Kafka topics.

3.3.2. Monitoring

The Debezium PostgreSQL connector has two metric types in addition to the built-in support for JMX metrics that Zookeeper, Kafka, and Kafka Connect have.

  • snapshot metrics; for monitoring the connector when performing snapshots
  • streaming metrics; for monitoring the connector when processing change events via logical decoding

Please refer to the monitoring documentation for details of how to expose these metrics via JMX.

3.3.2.1. Snapshot Metrics

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

Attribute Name

Type

Description

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 whitelist or blacklist filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapcity

int

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

QueueRemainingCapcity

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.

3.3.2.2. Streaming Metrics

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

Attribute Name

Type

Description

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 whitelist or blacklist filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapcity

int

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

QueueRemainingCapcity

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 incorporate 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.

3.3.3. Connector Properties

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

Property

Default

Description

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 Postgres logical decoding plug-in installed on the server. The only supported value is pgoutput.

When the processed transactions are very large it is possible that the JSON batch event with all changes in the transaction will not fit into the hard-coded memory buffer of size 1 GB. In such cases it is possible to switch to so-called streaming mode when every change in transactions is sent as a separate message from PostgreSQL into Debezium.

slot.name

debezium

The name of the Postgres logical decoding slot created for streaming changes from a plug-in and database instance. Values must conform to Postgres 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 drop the logical replication slot when the connector finishes orderly. Should only be set to true in testing or development environments. Dropping the slot allows WAL segments to be discarded by the database, so it may happen that after a restart the connector cannot resume from the WAL position where it left off before.

publication.name

dbz_publication

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

This publication is created at start-up if it does not already exist to include all tables. Debezium will then use its own white-/blacklist filtering capabilities to limit change events to the specific tables of interest if configured. Note the connector user must have superuser permissions in order to create this publication, so it is usually preferable to create the publication upfront.

If the publication already exists (either for all tables or configured with a subset of tables), Debezium will instead use the publication as 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 to use when 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/cluster being monitored. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names coming from this connector. Only alphanumeric characters and underscores should be used.

schema.whitelist

 

An optional comma-separated list of regular expressions that match schema names to be monitored; any schema name not included in the whitelist will be excluded from monitoring. By default all non-system schemas will be monitored. May not be used with schema.blacklist.

schema.blacklist

 

An optional comma-separated list of regular expressions that match schema names to be excluded from monitoring; any schema name not included in the blacklist will be monitored, with the exception of system schemas. May not be used with schema.whitelist.

table.whitelist

 

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored; any table not included in the whitelist will be excluded from monitoring. Each identifier is of the form schemaName.tableName. By default the connector will monitor every non-system table in each monitored schema. May not be used with table.blacklist.

table.blacklist

 

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring; any table not included in the blacklist will be monitored. Each identifier is of the form schemaName.tableName. May not be used with table.whitelist.

column.blacklist

 

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.

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; 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, with the exception of TIME type fields, which are always captured as microseconds; 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.

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; or double represents them using double values, which may result in a loss of precision but will be far easier to use. string option encodes values as formatted string which is easy to consume but a semantic information about the real type is lost. See Section 3.2.5.3.3, “Decimal Values”.

hstore.handling.mode

map

Specifies how the connector should handle values for hstore columns: map (the default) represents using MAP; or json represents them using json string.json option encodes values as formatted string such as {"key" : "val"}. See Section 3.2.5.3.4, “HStore Values”.

interval.handling.mode

numeric

Specifies how the connector should handle values for interval columns: numeric (the default) represents interval using approximate number of microseconds; string represents them exactly, using the string pattern representation P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S, e.g. P1Y2M3DT4H5M6.78S. See Section 3.2.5.3, “Data Types”.

database.sslmode

disable

Whether to use an encrypted connection to the PostgreSQL server. Options include: disable (the default) to use an unencrypted connection ; require to use a secure (encrypted) connection, and fail if one cannot be established; verify-ca like require but additionally verify the server TLS certificate against the configured Certificate Authority (CA) certificates, or fail if no valid matching CA certificates are found; verify-full like verify-ca but additionally verify that the server certificate matches the host to which the connection is attempted. See the PostgreSQL documentation for more information.

database.sslcert

 

The path to the file containing the SSL Certificate for the client. See the PostgreSQL documentation for more information.

database.sslkey

 

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

database.tcpKeepAlive

 

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

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 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 names for columns are of the form databaseName.tableName.columnName, or 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 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 databaseName.tableName.typeName, or databaseName.schemaName.tableName.typeName. See the list of PostgreSQL-specific data type names.

message.key.columns

empty string

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.

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.

Property

Default

Description

snapshot.mode

initial

Specifies the criteria for running a snapshot upon startup of the connector. The default is initial, and specifies the connector can run a snapshot only when no offsets have been recorded for the logical server name. The always option specifies that the connector run a snapshot each time on startup. The never option specifies that the connect should never use snapshots and that upon first startup with a logical server name the connector should read from either from where it last left off (last LSN position) or start from the beginning from the point of the view of the logical replication slot. The initial_only option specifies that the connector should only take an initial snapshot and then stop, without processing any subsequent changes. The exported option specifies that the database snapshot will be based on the point in time when the replication slot was created and is an excellent way to perform the snapshot in a lock-free way.

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 table locks cannot be acquired in this time interval, the snapshot will fail. See snapshots

snapshot.select.statement.overrides

 

Controls which rows from tables will be included in snapshot.
This property contains a comma-separated list of fully-qualified tables (DB_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.[DB_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 generated by logical decoder are not affected by it at all.

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.

max.queue.size

20240

Positive integer value that specifies the maximum size of the blocking queue into which change events received via streaming replication are placed before they are written to Kafka. This queue can provide backpressure when, for example, writes to Kafka are slower or if Kafka is not available.

max.batch.size

10240

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

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.

include.unknown.datatypes

false

When Debezium meets a field whose data type is unknown, then by default the field is omitted from the change event and a warning is logged. In some cases it may be preferable though to include the field and send it downstream to clients in the opaque binary representation so the clients will decode it themselves. Set to false to filter unknown data out of events and true to keep them in binary format.

Note

The clients risk backward compatibility issues. Not only may the database specific binary representation change between releases, but also when the datatype is supported by Debezium eventually, it will be sent downstream in a logical type, requiring adjustments by consumers. In general, when encountering unsupported data types, please file a feature request so that support can be added.

database.initial.statements

 

A semicolon separated list of SQL statements to be executed when a JDBC connection (not the transaction log reading connection) to the database is established. Use doubled semicolon (';;') to use a semicolon as a character and not as a delimiter.

Note

The connector may establish JDBC connections at its own discretion, so this should typically be used for configuration of session parameters only, but not for executing DML statements.

heartbeat.interval.ms

0

Controls how frequently heartbeat messages are sent.
This property contains an interval in milli-seconds 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 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 will be committed to Kafka. This will cause the WAL files to be retained by the database longer than needed (as the connector actually has processed them already but never got a chance to flush the latest retrieved LSN to the database) and also 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>.

heartbeat.action.query

 

If specified, this query will be executed upon every heartbeat against the source database.

This can be used to overcome 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 any WAL records and thus acknowledging WAL positions with the database.

Inserting records into some heartbeat table (which must have been created upfront) will allow the connector to receive changes from the low-traffic database and acknowledge their LSNs, preventing an unbounded WAL growth on the database host.

Example: INSERT INTO test_heartbeat_table (text) VALUES ('test_heartbeat')

schema.refresh.mode

columns_diff

Specify the conditions that trigger a refresh of the in-memory schema for a table.

columns_diff (the default) is the safest mode, ensuring 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 between it and the schema derived from the incoming message, unless unchanged TOASTable data fully accounts for the discrepancy.

This setting can improve connector performance significantly if there are frequently-updated tables that have TOASTed data that are rarely part of these 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 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

10240

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 10240.

slot.stream.params

 

Optional list of parameters to be passed to the configured logical decoding plug-in. For example, add-tables=public.table,public.table2;include-lsn=true.

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 will be sanitized to adhere to Avro naming requirements. See Avro naming for more details.

slot.max.retries

6

How many times to retry connecting to a replication slot when an attempt fails.

slot.retry.delay.ms

10000 (10 seconds)

The number of milli-seconds to wait between retry attempts when the connector fails to connect to a replication slot.

toasted.value.placeholder

__debezium_unavailable_value

Specify the constant that will be provided by Debezium to indicate that the original value is a toasted value not provided by the database. If starts with hex: prefix it is expected that the rest of the string repesents hexadecimally encoded octets. See Toasted Values for additional details.

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.

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.)

3.4. PostgreSQL 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.

3.4.1. 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, when the connector cannot successfully connect to PostgreSQL using the specified connectivity parameters, or when the connector is restarting from a previously-recorded position in the PostgreSQL WAL (via the LSN value) and PostgreSQL no longer has that history available.

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 PostgreSQL problem has been addressed.

3.4.2. PostgreSQL Becomes Unavailable

Once the connector is running, if the PostgreSQL server it has been connected to becomes unavailable for any reason, the connector will fail with an error and the connector will stop. Simply restart the connector when the server is available.

The PostgreSQL connector stores externally the last processed offset (in the form of a PostgreSQL log sequence number value). Once a connector is restarted and connects to a server instance, it will ask the server to continue streaming from that particular offset. This offset will always remain available so long as the Debezium replication slot remains intact. Never drop a replication slot on the primary or you will lose data. See the next section for failure cases when a slot has been removed.

3.4.3. Cluster Failures

As of 12, PostgreSQL allows logical replication slots only on primary servers, which means that a PostgreSQL connector can only be pointed to the active primary of a database cluster. Also replication slots themselves are not propagated to replicas. If the primary node goes down, only after a new primary has been promoted (with the logical decoding plug-in installed) and a replication slot has been created there, the connector can be restarted and pointed to the new server.

There are some really important caveats to failovers, and you should pause Debezium until you can verify that you have a replication slot intact which has not lost data. After a failover, you will miss change events unless your administration of failovers includes a process to recreate the Debezium replication slot before the application is allowed to write to the new primary. You also may need to verify in a failover situation that Debezium was able to read all changes in the slot before the old primary failed.

One reliable method of recovering and verifying any lost changes (yet administratively difficult) is to recover a backup of your failed primary to the point immediately before it failed, which would allow you to inspect the replication slot for any unconsumed changes. In any case, it is crucial that you recreate the replication slot on the new primary prior to allowing writes to it.

3.4.4. 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 will be a short delay in processing while the connector tasks are stopped gracefully and restarted on the new processes.

3.4.5. Kafka Connect Process Crashes

If the Kafka Connector process stops unexpectedly, then any connector tasks it was running will obviously 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 PostgreSQL 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 will depend 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 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 position) to know whether they have already seen a particular event.

3.4.6. 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 you’ve 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 re-established, at which point the connectors will resume exactly where they left off.

3.4.7. Connector Is Stopped for a Duration

If the connector is gracefully stopped, the database can continue to be used and any new changes will be recorded in the PostgreSQL WAL. When the connector is restarted, it will resume streaming changes where it last left off, recording change events for all of the changes that were made while the connector was stopped.

A properly configured Kafka cluster is able to handle 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 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.

Additional resources

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 initial.sync.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. Events

All data change events produced by the MongoDB connector have a key and a value.

Debezium and Kafka Connect are designed around continuous streams of event messages, and the structure of these events could potentially change over time if the source of those events changed in structure or if the connector is improved or changed. This could be difficult for consumers to deal with, so to make it very easy Kafka Connect makes each event self-contained. Every message key and value has two parts: a schema and payload. The schema describes the structure of the payload, while the payload contains the actual data.

4.4.6.1. Change event’s key

For a given collection, the change event’s key contains a single id field. Its value is the document’s identifier represented as string which is derived from the MongoDB extended JSON serialization in strict mode. Consider a connector with a logical name of fulfillment, a replica set containing an inventory database with a customers collection containing documents such as: 

{
  "_id": 1004,
  "first_name": "Anne",
  "last_name": "Kretchmar",
  "email": "annek@noanswer.org"
}

Every change event for the customers collection will feature the same key structure, which in JSON looks like this: 

{
  "schema": {
    "type": "struct",
    "name": "fulfillment.inventory.customers.Key"
    "optional": false,
    "fields": [
      {
        "field": "id",
        "type": "string",
        "optional": false
      }
    ]
  },
  "payload": {
    "id": "1004"
  }
}

The schema portion of the key contains a Kafka Connect schema describing what is in the payload portion. In this case, it means that the payload value is not optional, is a structure defined by a schema named fulfillment.inventory.customers.Key, and has one required field named id of type string. If you look at the value of the key’s payload field, you can see that it is indeed a structure (which in JSON is just an object) with a single id field, whose value is a string containing the integer 1004.

This example used a document with an integer identifier, but any valid MongoDB document identifier (including documents) will work. The value of the id field in the payload will simply be a string representing a MongoDB extended JSON serialization (strict mode) of the original document’s _id field. Find below a few examples showing how _id fields of different types will get encoded as the event key’s payload:

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’s value

The value of the change event message is a bit more complicated. Like the key message, it has a schema section and payload section. The payload section of every change event value produced by the MongoDB connector has an envelope structure with the following fields:

  • op is a mandatory field that contains a string value describing the type of operation. Values for the MongoDB connector are c for create (or insert), u for update, d for delete, and r for read (in the case of a snapshot).
  • after is an optional field that if present contains the state of the document after the event occurred. MongoDB’s oplog entries only contain the full state of a document for create events, so these are the only events that contain an after field.
  • source is a mandatory field that contains a structure describing the source metadata for the event, which in the case of MongoDB contains several fields: the Debezium version, the logical name, the replica set’s name, the namespace of the collection, the MongoDB timestamp (and ordinal of the event within the timestamp) at which the event occurred, the identifier of the MongoDB operation (e.g., the h field in the oplog event), and the initial sync flag if the event resulted during a snapshot.
  • ts_ms is optional and if present contains the time (using the system clock in the JVM running the Kafka Connect task) at which the connector processed the event.

And of course, the schema portion of the event message’s value contains a schema that describes this envelope structure and the nested fields within it.

Let’s look at what a create/read event value might look like for our customers collection: 

{
    "schema": {
      "type": "struct",
      "fields": [
        {
          "type": "string",
          "optional": true,
          "name": "io.debezium.data.Json",
          "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",
          "field": "source"
        },
        {
          "type": "string",
          "optional": true,
          "field": "op"
        },
        {
          "type": "int64",
          "optional": true,
          "field": "ts_ms"
        }
      ],
      "optional": false,
      "name": "dbserver1.inventory.customers.Envelope"
      },
    "payload": {
      "after": "{\"_id\" : {\"$numberLong\" : \"1004\"},\"first_name\" : \"Anne\",\"last_name\" : \"Kretchmar\",\"email\" : \"annek@noanswer.org\"}",
      "patch": null,
      "source": {
        "version": "1.1.2.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 31,
        "h": 1546547425148721999
      },
      "op": "r",
      "ts_ms": 1558965515240
    }
  }

If we look at the schema portion of this event’s value, we can see the schema for the envelope is specific to the collection, and the schema for the source structure (which is specific to the MongoDB connector and reused across all events). Also note that the after value is always a string, and that by convention it will contain a JSON representation of the document.

If we look at the payload portion of this event’s value, we can see the information in the event, namely that it is describing that the document was read as part of an snapshot (since op=r and snapshot=true), and that the after field value contains the JSON string representation of the document.

Note

It may appear that the JSON representations of the events are much larger than the rows they describe. This is true, because the JSON representation must include the schema and the payload portions of the message.

The value of an update change event on this collection will actually have the exact same schema, and its payload is structured the same but will hold different values. Specifically, an update event will not have an after value and will instead have a patch string containing the JSON representation of the idempotent update operation and a filter string containing the JSON representation of the selection criteria for the update. The filter string can include multiple shard key fields for sharded collections. Here’s an example: 

{
    "schema": { ... },
    "payload": {
      "op": "u",
      "ts_ms": 1465491461815,
      "patch": "{\"$set\":{\"first_name\":\"Anne Marie\"}}",
      "filter": "{\"_id\" : {\"$numberLong\" : \"1004\"}}",
      "source": {
        "version": "1.1.2.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }

When we compare this to the value in the insert event, we see a couple of differences in the payload section:

  • The op field value is now u, signifying that this document changed because of an update
  • The patch field appears and has the stringified JSON representation of the actual MongoDB idempotent change to the document, which in this example involves setting the first_name field to a new value
  • The filter field appears and has the stringified JSON representation of the MongoDB selection criteria used for the update
  • The after field no longer appears
  • The source field structure has the same fields as before, but the values are different since this event is from a different position in the oplog
  • The ts_ms shows the timestamp that Debezium processed this event
Warning

The content of the patch field is provided by MongoDB itself and its exact format depends on the specific database version. You should therefore be prepared for potential changes to the format when upgrading the MongoDB instance to a new version.

All examples in this document were obtained from MongoDB 3.4 and might differ if you use a different one.

Note

Update events in MongoDB’s oplog do not have the before or after states of the changed document, so there’s no way for the connector to provide this information. However, because create or read events do contain the starting state, downstream consumers of the stream can actually fully-reconstruct the state by keeping the latest state for each document and applying each event to that state. Debezium connector’s are not able to keep such state, so it is not able to do this.

So far, you have seen samples of create/read and update events. The following sample shows the value of a delete event for the same collection. The value of a delete event on this collection has the exact same schema, and its payload is structured the same but it holds different values. In particular, a delete event does not have an after value nor a patch value: 

{
    "schema": { ... },
    "payload": {
      "op": "d",
      "ts_ms": 1465495462115,
      "filter": "{\"_id\" : {\"$numberLong\" : \"1004\"}}",
      "source": {
        "version": "1.1.2.Final",
        "connector": "mongodb",
        "name": "fulfillment",
        "ts_ms": 1558965508000,
        "snapshot": true,
        "db": "inventory",
        "rs": "rs0",
        "collection": "customers",
        "ord": 6,
        "h": 1546547425148721999
      }
    }
  }

When we compare this to the value in the other events, we see a couple of differences in the payload section:

  • The op field value is now d, signifying that this document was deleted
  • The patch field does not appear
  • The after field does not appear
  • The filter field appears and has the stringified JSON representation of the MongoDB selection criteria used for the delete
  • The source field structure has the same fields as before, but the values are different since this event is from a different position in the oplog
  • The ts_ms shows the timestamp that Debezium processed this event

The MongoDB connector provides one other kind of event. Each delete event is followed by a tombstone event that has the same key as the delete event but a null value. This provides Kafka with the information needed to run its log compaction mechanism to remove all messages with that key.

Note

All MongoDB connector events are designed to work with Kafka log compaction, which allows for the removal of older messages as long as at least the most recent message for every key is kept. This is how Kafka can reclaim storage space while ensuring that the topic contains a complete dataset and can be used for reloading key-based state.

All MongoDB connector events for a uniquely identified document have exactly the same key, signaling to Kafka that only the latest event be kept. 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 tranaction 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": "00000025:00000d08:0025",
  "event_count": null,
  "data_collections": null
}

{
  "status": "END",
  "id": "00000025:00000d08:0025",
  "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": "00000025:00000d08:0025",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

4.5. Deploying the MongoDB connector

Installing the MongoDB connector is a simple process whereby you only need to download the JAR, extract it to your Kafka Connect environment, and ensure the plug-in’s parent directory is specified in your Kafka Connect environment.

Prerequisites

Procedure

  1. Download the Debezium MongoDB connector.
  2. Extract the files into your Kafka Connect environment.

  3. Add the plug-in’s parent directory to your Kafka Connect plugin.path: 

    plugin.path=/kafka/connect
Note

The above example assumes you have extracted the Debezium MongoDB connector to the /kafka/connect/Debezium-connector-mongodb path.

  1. Restart your Kafka Connect process. This ensures the new JARs are picked up.

Additional resources

For more information on the deployment process, and deploying connectors with AMQ Streams, refer to 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 row. 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.whitelist: inventory[.]* 5
1
The name of our connector when we register it with a Kafka Connect service.
2
The name of the MongoDB connector class.
3
The host addresses to use to connect to the MongoDB replica set.
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 Connector is used.
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. 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.

4.5.2.1. Snapshot Metrics

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

Attribute Name

Type

Description

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 whitelist or blacklist filtering rules configured on the connector.

MonitoredTables

string[]

The list of collections that are monitored by the connector.

QueueTotalCapcity

int

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

QueueRemainingCapcity

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 collections that are being included in the snapshot.

RemainingTableCount

int

The number of collections 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 documents exported for each collection in the snapshot. Collections are incrementally added to the Map during processing. Updates every 10,000 documents scanned and upon completing a collection.

The Debezium MongoDB connector also provides the following custom snapshot metrics:

AttributeTypeDescription

NumberOfDisconnects

long

Number of database disconnects.

4.5.2.2. Streaming Metrics

The MBean is debezium.sql_server:type=connector-metrics,context=streaming,server=<mongodb.name>.

Attribute Name

Type

Description

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 whitelist or blacklist filtering rules configured on the connector.

MonitoredTables

string[]

The list of collections that are monitored by the connector.

QueueTotalCapcity

int

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

QueueRemainingCapcity

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 mongodb.

MilliSecondsBehindSource

long

The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incorporate 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.

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.

4.5.3. 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.whitelist

empty string

An optional comma-separated list of regular expressions that match database names to be monitored; any database name not included in the whitelist is excluded from monitoring. By default all databases is monitored. May not be used with database.blacklist.

database.blacklist

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 the blacklist is monitored. May not be used with database.whitelist.

collection.whitelist

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 the whitelist 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. May not be used with collection.blacklist.

collection.blacklist

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 the blacklist is monitored. Each identifier is of the form databaseName.collectionName. May not be used with collection.whitelist.

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.

field.blacklist

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.

initial.sync.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 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

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.

Property

Default

Description

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.

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 milli-seconds 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: i for inserts, u for updates, and d for deletes. By default, no operations are skipped.

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.

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 number

Delay before attempt, in seconds

Total 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

Debezium’s SQL Server Connector can monitor and record the row-level changes in the schemas of a SQL Server database.

The first time it connects to a SQL Server database/cluster, it reads a consistent snapshot of all of the schemas. When that snapshot is complete, the connector continuously streams the changes that were committed to SQL Server and generates corresponding insert, update and delete events. All of the events for each table are recorded in a separate Kafka topic, where they can be easily consumed by applications and services.

5.1. Overview

The functionality of the connector is based upon change data capture feature provided by SQL Server Standard (since SQL Server 2016 SP1) or Enterprise edition. Using this mechanism a SQL Server capture process monitors all databases and tables the user is interested in and stores the changes into specifically created CDC tables that have stored procedure facade.

The database operator must enable CDC for the table(s) that should be captured by the connector. The connector then produces a change event for every row-level insert, update, and delete operation that was published via the CDC API, recording all the change events for each table in a separate Kafka topic. The client applications read the Kafka topics that correspond to the database tables they’re interested in following, and react to every row-level event it sees in those topics.

The database operator normally enables CDC in the mid-life of a database an/or table. This means that the connector does not have the complete history of all changes that have been made to the database. Therefore, when the SQL Server connector first connects to a particular SQL Server 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, we start with a consistent view of all of the data, yet continue reading without having lost any of the changes made while the snapshot was taking place.

The connector is also tolerant of failures. As the connector reads changes and produces events, it records the position in the database log (LSN / Log Sequence Number), that is associated with CDC record, with each event. If the connector stops for any reason (including communication failures, network problems, or crashes), upon restart it simply continues reading the CDC tables where it last left off. This includes snapshots: if the snapshot was not completed when the connector is stopped, upon restart it begins a new snapshot.

5.2. Setting up SQL Server

Before using the SQL Server connector to monitor the changes committed on SQL Server, first enable CDC on a monitored database. Please bear in mind that CDC cannot be enabled for the master database. 

-- ====
-- Enable Database for CDC template
-- ====
USE MyDB
GO
EXEC sys.sp_cdc_enable_db
GO

Then enable CDC for each table that you plan to monitor. 

-- ====
-- Enable a Table Specifying Filegroup Option Template
-- ====
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

Verify that the user have access to the CDC table. 

-- ====
-- Verify the user of the connector have access, this query should not have empty result
-- ====

EXEC sys.sp_cdc_help_change_data_capture
GO

If the result is empty then please make sure that the user has privileges to access both the capture instance and CDC tables.

5.2.1. SQL Server on Azure

The SQL Server plug-in has not been tested with SQL Server on Azure. We welcome any feedback from a user to try the plug-in with database in managed environments.

5.3. How the SQL Server connector works

5.3.1. Snapshots

SQL Server CDC is not designed to store the complete history of database changes. It is thus necessary that Debezium establishes the baseline of current database content and streams it to the Kafka. This is achieved via a process called snapshotting.

By default (snapshotting mode initial) the connector will upon the first startup perform an initial consistent snapshot of the database (meaning the structure and data within any tables to be captured as per the connector’s filter configuration).

Each snapshot consists of the following steps:

  1. Determine the tables to be captured
  2. Obtain a lock on each of the monitored tables to ensure that no structural changes can occur to any of the tables. The level of the lock is determined by snapshot.isolation.mode configuration option.
  3. Read the maximum LSN ("log sequence number") position in the server’s transaction log.
  4. Capture the structure of all relevant tables.
  5. Optionally release the locks obtained in step 2, i.e. the locks are held usually only for a short period of time.
  6. Scan all of the relevant database tables and schemas as valid at the LSN position read in step 3, and generate a READ event for each row and write that event to the appropriate table-specific Kafka topic.
  7. Record the successful completion of the snapshot in the connector offsets.

5.3.2. Reading the change data tables

Upon first start-up, the connector takes a structural snapshot of the structure of the captured tables and persists this information in its internal database history topic. Then the connector identifies a change table for each of the source tables and executes the main loop

  1. For each change table read all changes that were created between last stored maximum LSN and current maximum LSN
  2. Order the read changes incrementally according to commit LSN and change LSN. This ensures that the changes are replayed by Debezium in the same order as were made to the database.
  3. Pass commit and change LSNs as offsets to Kafka Connect.
  4. Store the maximum LSN and repeat the loop.

After a restart, the connector will resume from the offset (commit and change LSNs) where it left off before.

The connector is able to detect whether CDC is enabled or disabled for whitelisted source tables and adjust its behavior.

5.3.3. Topic names

The SQL Server connector writes events for all insert, update, and delete operations on a single table to a single Kafka topic. The name of the Kafka topics always takes the form serverName.schemaName.tableName, where serverName is the logical name of the connector as specified with the database.server.name configuration property, schemaName is the name of the schema where the operation occurred, and tableName is the name of the database table on which the operation occurred.

For example, consider a SQL Server installation with an inventory database that contains four tables: products, products_on_hand, customers, and orders in schema dbo. If the connector monitoring this database were given a logical server name of fulfillment, then the connector would produce events on these four Kafka topics:

  • fulfillment.dbo.products
  • fulfillment.dbo.products_on_hand
  • fulfillment.dbo.customers
  • fulfillment.dbo.orders

5.3.4. Events

All data change events produced by the SQL Server connector have a key and a value, although the structure of the key and value depend on the table from which the change events originated (see Topic names).

Warning

The SQL Server connector ensures that all Kafka Connect schema names are valid Avro schema names. This means that the logical server name must start with Latin letters or an underscore (e.g., [a-z,A-Z,_]), and the remaining characters in the logical server name and all characters in the schema and table names must be Latin letters, digits, or an underscore (e.g., [a-z,A-Z,0-9,\_]). If not, then all invalid characters will automatically be replaced with an underscore character.

This can lead to unexpected conflicts when the logical server name, schema names, and table names contain other characters, and the only distinguishing characters between table full names are invalid and thus replaced with underscores.

Debezium and Kafka Connect are designed around continuous streams of event messages, and the structure of these events may change over time. This could be difficult for consumers to deal with, so to make it easy Kafka Connect makes each event self-contained. Every message key and value has two parts: a schema and payload. The schema describes the structure of the payload, while the payload contains the actual data.

5.3.4.1. Change Event Keys

For a given table, the change event’s key will have a structure that contains a field for each column in the primary key (or unique key constraint) of the table at the time the event was created.

Consider a customers table defined in the inventory database’s schema dbo: 

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
);

If the database.server.name configuration property has the value server1, every change event for the customers table while it has this definition will feature the same key structure, which in JSON looks like this: 

{
    "schema": {
        "type": "struct",
        "fields": [
            {
                "type": "int32",
                "optional": false,
                "field": "id"
            }
        ],
        "optional": false,
        "name": "server1.dbo.customers.Key"
    },
    "payload": {
        "id": 1004
    }
}

The schema portion of the key contains a Kafka Connect schema describing what is in the key portion. In this case, it means that the payload value is not optional, is a structure defined by a schema named server1.dbo.customers.Key, and has one required field named id of type int32. If you look at the value of the key’s payload field, you can see that it is indeed a structure (which in JSON is just an object) with a single id field, whose value is 1004.

Therefore, you can interpret this key as describing the row in the dbo.customers table (output from the connector named server1) whose id primary key column had a value of 1004.

5.3.4.2. Change Event Values

Like the message key, the value of a change event message has a schema section and payload section. The payload section of every change event value produced by the SQL Server connector has an envelope structure with the following fields:

  • op is a mandatory field that contains a string value describing the type of operation. Values for the SQL Server connector are c for create (or insert), u for update, d for delete, and r for read (in the case of a snapshot).
  • before is an optional field that if present contains the state of the row before the event occurred. The structure is described by the server1.dbo.customers.Value Kafka Connect schema, which the server1 connector uses for all rows in the dbo.customers table.
  • after is an optional field that if present contains the state of the row after the event occurred. The structure is described by the same server1.dbo.customers.Value Kafka Connect schema used in before.

  • source is a mandatory field that contains a structure describing the source metadata for the event, which in the case of SQL Server contains these fields: the Debezium version, the connector name, whether the event is part of an ongoing snapshot or not, the commit LSN (not while snapshotting), the LSN of the change, database, schema and table where the change happened, and a timestamp representing the point in time when the record was changed in the source database (during snapshotting, this is the point in time of snapshotting).

    Also a field event_serial_no is present during streaming. This is used to differentiate among events that have the same commit and change LSN. There are mostly two situations when you can see it present with value different from 1:

    • update events will have the value set to 2, this is because the update generates two events in the CDC change table of SQL Server (source documentation). The first one contains the old values and the second one contains new values. So the first one is dropped and the values from it are used with the second one to create the Debezium change event.
    • when a primary key is updated, then SQL Server emits two records - delete to remove the record with the old primary key value and insert to create the record with the new primary key. Both operations share the same commit and change LSN and their event numbers are 1 and 2.
  • ts_ms is optional and if present contains the time (using the system clock in the JVM running the Kafka Connect task) at which the connector processed the event.

And of course, the schema portion of the event message’s value contains a schema that describes this envelope structure and the nested fields within it.

5.3.4.2.1. Create events

Let’s look at what a create event value might look like for our customers table: 

{
  "schema": {
    "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",
        "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",
        "field": "source"
      },
      {
        "type": "string",
        "optional": false,
        "field": "op"
      },
      {
        "type": "int64",
        "optional": true,
        "field": "ts_ms"
      }
    ],
    "optional": false,
    "name": "server1.dbo.customers.Envelope"
  },
  "payload": {
    "before": null,
    "after": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "source": {
      "version": "1.1.2.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",
    "ts_ms": 1559729471739
  }
}

If we look at the schema portion of this event’s value, we can see the schema for the envelope, the schema for the source structure (which is specific to the SQL Server connector and reused across all events), and the table-specific schemas for the before and after fields.

Note

The names of the schemas for the before and after fields are of the form logicalName.schemaName.tableName.Value, and thus are entirely independent from all other schemas for all other tables. This means that when using the Avro Converter, the resulting Avro schemas for each table in each logical source have their own evolution and history.

If we look at the payload portion of this event’s value, we can see the information in the event, namely that it is describing that the row was created (since op=c), and that the after field value contains the values of the new inserted row’s' id, first_name, last_name, and email columns.

Note

It may appear that the JSON representations of the events are much larger than the rows they describe. This is true, because the JSON representation must include the schema and the payload portions of the message. It is possible and even recommended to use the to dramatically decrease the size of the actual messages written to the Kafka topics.

5.3.4.2.2. Update events

The value of an update change event on this table will actually have the exact same schema, and its payload is structured the same but will hold different values. Here’s an example: 

{
  "schema": { ... },
  "payload": {
    "before": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "john.doe@example.org"
    },
    "after": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "source": {
      "version": "1.1.2.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",
    "ts_ms": 1559729998706
  }
}

When we compare this to the value in the insert event, we see a couple of differences in the payload section:

  • The op field value is now u, signifying that this row changed because of an update
  • The before field now has the state of the row with the values before the database commit
  • The after field now has the updated state of the row, and here was can see that the email value is now noreply@example.org.
  • The source field structure has the same fields as before, but the values are different since this event is from a different position in the transaction log.
  • The event_serial_no field has value 2. That is due to the update event composed of two events behind the scenes and we are exposing only the second one. If you are interested in details please check the source documentation and refer to the field $operation.
  • The ts_ms shows the timestamp that Debezium processed this event.

There are several things we can learn by just looking at this payload section. We can compare the before and after structures to determine what actually changed in this row because of the commit. The source structure tells us information about SQL Server’s record of this change (providing traceability), but more importantly this has information we can compare to other events in this and other topics to know whether this event occurred before, after, or as part of the same SQL Server commit as other events.

Note

When the columns for a row’s primary/unique key are updated, the value of the row’s key has changed so Debezium will output three events: a DELETE event and a tombstone event with the old key for the row, followed by an INSERT event with the new key for the row.

5.3.4.2.3. Delete events

So far, you have seen samples of create and update events. The following sample shows the value of a delete event for the same table. Once again, the schema portion of the value is exactly the same as with the create and update events: 

{
  "schema": { ... },
  },
  "payload": {
    "before": {
      "id": 1005,
      "first_name": "john",
      "last_name": "doe",
      "email": "noreply@example.org"
    },
    "after": null,
    "source": {
      "version": "1.1.2.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",
    "ts_ms": 1559730450205
  }
}

If we look at the payload portion, we see a number of differences compared with the create or update event payloads:

  • The op field value is now d, signifying that this row was deleted
  • The before field now has the state of the row that was deleted with the database commit.
  • The after field is null, signifying that the row no longer exists
  • The source field structure has many of the same values as before, except the ts_ms, commit_lsn and change_lsn fields have changed
  • The ts_ms shows the timestamp that Debezium processed this event.

This event gives a consumer all kinds of information that it can use to process the removal of this row.

The SQL Server connector’s events are designed to work with Kafka log compaction, which allows for the removal of some older messages as long as at least the most recent message for every key is kept. This allows Kafka to reclaim storage space while ensuring the topic contains a complete dataset and can be used for reloading key-based state.

When a row is deleted, the delete event value listed above still works with log compaction, since Kafka can still remove all earlier messages with that same key. But only if the message value is null will Kafka know that it can remove all messages with that same key. To make this possible, the SQL Server connector always follows the delete event with a special tombstone event that has the same key but null value.

5.3.5. Transaction Metadata

Debezium can generate events that represents tranaction metadata boundaries and enrich data messages.

5.3.5.1. 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": "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.

5.3.5.2. 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": "00000025:00000d08:0025",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

5.3.6. Database schema evolution

Debezium is able to capture schema changes over time. Due to the way CDC is implemented in SQL Server, it is necessary to work in co-operation with a database operator in order to ensure the connector continues to produce data change events when the schema is updated.

As was already mentioned before, Debezium uses SQL Server’s change data capture functionality. This means that SQL Server creates a capture table that contains all changes executed on the source table. Unfortunately, the capture table is static and needs to be updated when the source table structure changes. This update is not done by the connector itself but must be executed by an operator with elevated privileges.

There are generally two procedures how to execute the schema change:

  • cold - this is executed when Debezium is stopped
  • hot - executed while Debezium is running

Both approaches have their own advantages and disadvantages.

Warning

In both cases, it is critically important to execute the procedure completely before a new schema update on the same source table is made. It is thus recommended to execute all DDLs in a single batch so the procedure is done only once.

Note

Not all schema changes are supported when CDC is enabled for a source table. One such exception identified is renaming a column or changing its type, SQL Server will not allow executing the operation.

Note

Although not required by SQL Server’s CDC mechanism itself, a new capture instance must be created when altering a column from NULL to NOT NULL or vice versa. This is required so that the SQL Server connector can pick up that changed information. Otherwise, emitted change events will have the optional value for the corresponding field (true or false) set to match the original value.

5.3.6.1. Cold schema update

This is the safest procedure but might not be feasible for applications with high-availability requirements. The operator should follow this sequence of steps

  1. Suspend the application that generates the database records
  2. Wait for Debezium to stream all unstreamed changes
  3. Stop the 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
  7. Start the connector
  8. When Debezium starts streaming from the new capture table it is possible to drop the old one using sys.sp_cdc_disable_table stored procedure with parameter @capture_instance set to the old capture instance name

5.3.6.2. Hot schema update

The hot schema update does not require any downtime in application and data processing. The procedure itself is also much simpler than in case of cold schema update

  1. Apply all changes to the source table schema
  2. 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
  3. When Debezium starts streaming from the new capture table it is possible to drop the old one using sys.sp_cdc_disable_table stored procedure with parameter @capture_instance set to the old capture instance name

The hot schema update has one drawback. There is a period of time between the database schema update and creating the new capture instance. All changes that will arrive during this period are captured by the old instance with the old structure. For instance this means that in case of a newly added column any change event produced during this time will not yet contain a field for that new column. If your application does not tolerate such a transition period we recommend to follow the cold schema update.

5.3.6.3. Example

In this example, a column phone_number is added to the customers table. 

# Start the database shell
docker-compose -f docker-compose-sqlserver.yaml exec sqlserver bash -c '/opt/mssql-tools/bin/sqlcmd -U sa -P $SA_PASSWORD -d testDB'
-- Modify the source table schema
ALTER TABLE customers ADD phone_number VARCHAR(32);

-- Create the new capture instance
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

-- Insert new data
INSERT INTO customers(first_name,last_name,email,phone_number) VALUES ('John','Doe','john.doe@example.com', '+1-555-123456');
GO

Kafka Connect log will contain messages like these: 

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, there is a new field in the schema and value of the 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"
    },
-- Drop the old capture instance
EXEC sys.sp_cdc_disable_table @source_schema = 'dbo', @source_name = 'dbo_customers', @capture_instance = 'dbo_customers';
GO

5.3.7. Data types

As described above, the SQL Server connector represents the changes to rows with events that are structured like the table in which the row exist. The event contains a field for each column value, and how that value is represented in the event depends on the SQL data type of the column. This section describes this mapping.

The following table describes how the connector maps each of the SQL Server data types to a literal type and semantic type within the events' fields. Here, the literal type describes how the value is literally represented using Kafka Connect schema types, namely INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT. The 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.

SQL Server Data Type

Literal type (schema type)

Semantic type (schema name)

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 a 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.

5.3.7.1. 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 Type

Literal type (schema type)

Semantic type (schema name)

Notes

DATE

INT32

io.debezium.time.Date

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

SMALLDATETIME

INT64

io.debezium.time.Timestamp

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

DATETIME2(4), DATETIME2(5), DATETIME2(6)

INT64

io.debezium.time.MicroTimestamp

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

DATETIME2(7)

INT64

io.debezium.time.NanoTimestamp

Represents the number of nanoseconds past 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 Type

Literal type (schema type)

Semantic type (schema name)

Notes

DATE

INT32

org.apache.kafka.connect.data.Date

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

SMALLDATETIME

INT64

org.apache.kafka.connect.data.Timestamp

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

DATETIME2

INT64

org.apache.kafka.connect.data.Timestamp

Represents the number of milliseconds since epoch, and does not include timezone information. SQL Server allows P to be in the range 0-7 to store up to tenth of microsecond precision, though this mode results in a loss of precision when P > 3.

5.3.7.1.1. 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.

5.3.7.2. Decimal values

SQL Server Data Type

Literal type (schema type)

Semantic type (schema name)

Notes

NUMERIC[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer representing how many digits the decimal point was shifted. The connect.decimal.precision schema parameter contains an integer representing the precision of the given decimal value.

DECIMAL[(P[,S])]

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer representing how many digits the decimal point was shifted. The connect.decimal.precision schema parameter contains an integer representing the precision of the given decimal value.

SMALLMONEY

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer representing how many digits the decimal point was shifted. The connect.decimal.precision schema parameter contains an integer representing the precision of the given decimal value.

MONEY

BYTES

org.apache.kafka.connect.data.Decimal

The scale schema parameter contains an integer representing how many digits the decimal point was shifted. The connect.decimal.precision schema parameter contains an integer representing the precision of the given decimal value.

5.4. Deploying the SQL Server connector

Installing the SQL Server connector is a simple process whereby you only need to download the JAR, extract it to your Kafka Connect environment, and ensure the plug-in’s parent directory is specified in your Kafka Connect environment.

Prerequisites

Procedure

  1. Download the Debezium SQL Server connector.
  2. Extract the files into your Kafka Connect environment.

  3. Add the plug-in’s parent directory to your Kafka Connect plugin.path: 

    plugin.path=/kafka/connect
Note

The above example assumes you have extracted the Debezium SQL Server connector to the /kafka/connect/debezium-connector-sqlserver path.

  1. Restart your Kafka Connect process. This ensures the new JARs are picked up.

Additional resources

For more information on the deployment process, and deploying connectors with AMQ Streams, refer to the Debezium installation guides.

5.4.1. Example configuration

To use the connector to produce change events for a particular SQL Server database or cluster:

  1. Enable the CDC on SQL Server to publish the CDC events in the database.
  2. Create a configuration file for the SQL Server connector.

When the connector starts, it will grab a consistent snapshot of the schemas in your SQL Server database and start streaming changes, producing events for every inserted, updated, and deleted row. You can also choose to produce events for a subset of the schemas and tables. Optionally ignore, mask, or truncate columns that are sensitive, too large, or not needed.

Following is an example of the configuration for a connector instance that monitors a SQL Server server at port 1433 on 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium SQL Server 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.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.whitelist: dbo.customers   9
      database.history.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092  10
      database.history.kafka.topic: dbhistory.fullfillment  11
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 Connector 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.

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 SQL Server database, read the transaction log, and record events to Kafka topics.

5.4.2. Monitoring

The Debezium SQL Server connector has three 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.

5.4.2.1. Snapshot Metrics

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

Attribute Name

Type

Description

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 whitelist or blacklist filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapcity

int

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

QueueRemainingCapcity

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.

5.4.2.2. Streaming Metrics

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

Attribute Name

Type

Description

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 whitelist or blacklist filtering rules configured on the connector.

MonitoredTables

string[]

The list of tables that are monitored by the connector.

QueueTotalCapcity

int

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

QueueRemainingCapcity

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 incorporate 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.

5.4.2.3. Schema History Metrics

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

Attribute Name

Type

Description

Status

string

One of STOPPED, RECOVERING (recovering history from the storage), RUNNING describing 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 applie during recovery and runtime.

MilliSecondsSinceLastRecoveredChange

long

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

MilliSecondsSinceLastAppliedChange

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.

5.4.3. Connector properties

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

Property

Default

Description

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 particular SQL Server database server being monitored. 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/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.whitelist

 

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored; any table not included in the whitelist is excluded from monitoring. Each identifier is of the form schemaName.tableName. By default the connector will monitor every non-system table in each monitored schema. May not be used with table.blacklist.

table.blacklist

 

An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring; any table not included in the blacklist is monitored. Each identifier is of the form schemaName.tableName. May not be used with table.whitelist.

column.blacklist

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 blacklisted from the value.

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.

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

empty string

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.

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.

Property

Default

Description

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.

Supported values are:
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.
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.isolation.mode

repeatable_read

Mode to control which transaction isolation level is used and how long the connector locks the monitored tables. There are five possible values: read_uncommitted, read_committed, repeatable_read, snapshot, and exclusive ( in fact, exclusive mode uses repeatable read isolation level, however, it takes the exclusive lock on all tables to be read).

It is worth documenting that snapshot, read_committed and read_uncommitted modes do not prevent other transactions from updating table rows during initial snapshot, while exclusive and repeatable_read do.

Another aspect is 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).

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 milli-seconds 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 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.

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 is used to define the timezone of the transaction timestamp (ts_ms) retrieved from the server (which is actually not zoned). Default value is unset. Should only be specified when running on SQL Server 2014 or older and using different timezones for the database server and the JVM running the Debezium connector.
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.

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.)

Chapter 6. Monitoring Debezium

You can use the JMX metrics provided by Zookeeper and Kafka to monitor Debezium. To use these metrics, you must enable them when you start the Zookeeper, Kafka, and Kafka Connect services. Enabling JMX involves setting the correct environment variables.

Note

If you are running multiple services on the same machine, be sure to use distinct JMX ports for each service.

6.1. Monitoring Debezium on RHEL

6.1.1. Zookeeper JMX environment variables

Zookeeper has built-in support for JMX. When running Zookeeper using a local installation, the zkServer.sh script recognizes the following environment variables:

JMXPORT
Enables JMX and specifies the port number that will be used for JMX. The value is used to specify the JVM parameter -Dcom.sun.management.jmxremote.port=$JMXPORT.
JMXAUTH
Whether JMX clients must use password authentication when connecting. Must be either true or false. The default is false. The value is used to specify the JVM parameter -Dcom.sun.management.jmxremote.authenticate=$JMXAUTH.
JMXSSL
Whether JMX clients connect using SSL/TLS. Must be either true or false. The default is false. The value is used to specify the JVM parameter -Dcom.sun.management.jmxremote.ssl=$JMXSSL.
JMXLOG4J
Whether the Log4J JMX MBeans should be disabled. Must be either true (default) or false. The default is true. The value is used to specify the JVM parameter -Dzookeeper.jmx.log4j.disable=$JMXLOG4J.

6.1.2. Kafka JMX environment variables

When running Kafka using a local installation, the kafka-server-start.sh script recognizes the following environment variables:

JMX_PORT
Enables JMX and specifies the port number that will be used for JMX. The value is used to specify the JVM parameter -Dcom.sun.management.jmxremote.port=$JMX_PORT.
KAFKA_JMX_OPTS

The JMX options, which are passed directly to the JVM during startup. The default options are:

  • -Dcom.sun.management.jmxremote
  • -Dcom.sun.management.jmxremote.authenticate=false
  • -Dcom.sun.management.jmxremote.ssl=false

6.1.3. Kafka Connect JMX environment variables

When running Kafka using a local installation, the connect-distributed.sh script recognizes the following environment variables:

JMX_PORT
Enables JMX and specifies the port number that will be used for JMX. The value is used to specify the JVM parameter -Dcom.sun.management.jmxremote.port=$JMX_PORT.
KAFKA_JMX_OPTS

The JMX options, which are passed directly to the JVM during startup. The default options are:

  • -Dcom.sun.management.jmxremote
  • -Dcom.sun.management.jmxremote.authenticate=false
  • -Dcom.sun.management.jmxremote.ssl=false

6.2. Monitoring Debezium on OpenShift

If you are using Debezium on OpenShift, you can obtain JMX metrics by opening a JMX port on 9999. For more information, see JMX Options.

In addition, you can use Prometheus and Grafana to monitor the JMX metrics. For more information, see Introducing Metrics.

Chapter 7. Debezium logging

Debezium has extensive logging built into its connectors, and you can change the logging configuration to control which of these log statements appear in the logs and where those logs are sent. Debezium (as well as Kafka, Kafka Connect, and Zookeeper) use the Log4j logging framework for Java.

By default, the connectors produce a fair amount of useful information when they start up, but then produce very few logs when the connector is keeping up with the source databases. This is often sufficient when the connector is operating normally, but may not be enough when the connector is behaving unexpectedly. In such cases, you can change the logging level so that the connector generates much more verbose log messages describing what the connector is doing and what it is not doing.

7.1. Logging concepts

Before configuring logging, you should understand what Log4J loggers, log levels, and appenders are.

Loggers

Each log message produced by the application is sent to a specific logger (for example, io.debezium.connector.mysql). Loggers are arranged in hierarchies. For example, the io.debezium.connector.mysql logger is the child of the io.debezium.connector logger, which is the child of the io.debezium logger. At the top of the hierarchy, the root logger defines the default logger configuration for all of the loggers beneath it.

Log levels

Every log message produced by the application will also have a specific log level:

  1. ERROR - errors, exceptions, and other significant problems
  2. WARN - potential problems and issues
  3. INFO - status and general activity (usually low-volume)
  4. DEBUG - more detailed activity that would be useful in diagnosing unexpected behavior
  5. TRACE - very verbose and detailed activity (usually very high-volume)

Appenders

An appender is essentially a destination where log messages will be written. Each appender controls the format of its log messages, giving you even more control over what the log messages look like.

To configure logging, you specify the desired level for each logger and the appender(s) where those log messages should be written. Since loggers are hierarchical, the configuration for the root logger serves as a default for all of the loggers below it, although you can override any child (or descendant) logger.

7.2. Understanding the default logging configuration

If you are running Debezium connectors in a Kafka Connect process, then Kafka Connect will use the Log4j configuration file (for example, /opt/kafka/config/connect-log4j.properties) in the Kafka installation. By default, this file contains the following configuration:

connect-log4j.properties

log4j.rootLogger=INFO, stdout  1

log4j.appender.stdout=org.apache.log4j.ConsoleAppender  2
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout  3
log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c)%n  4
...

1
The root logger, which defines the default logger configuration. By default, loggers will include INFO, WARN, and ERROR messages. These log messages will be written to the stdout appender.
2
The stdout appender will write log messages to the console (as opposed to a file).
3
The stdout appender will use a pattern matching algorithm to format the log messages.
4
The pattern for the stdout appender (see the Log4j documentation for details).

Unless you configure other loggers, all of the loggers used by Debezium will inherit the rootLogger configuration.

7.3. Configuring logging

By default, Debezium connectors write all INFO, WARN, and ERROR messages to the console. However, you can change this configuration in the following ways:

Note

This section only covers a couple methods you can use to configure Debezium logging with Log4j. For more information about using Log4j, search for tutorials to set up and use appenders to send log messages to specific destinations.

7.3.1. Changing the logging level

The default Debezium logging level provides sufficient information to show whether a connector is healthy or not. However, if a connector is not healthy, you can change its logging level to troubleshoot the issue.

In general, Debezium connectors send their log messages to loggers with names that match the fully-qualified name of the Java class that is generating the log message. Debezium uses packages to organize code with similar or related functions. This means that you can control all of the log messages for a specific class or for all of the classes within or under a specific package.

Procedure

  1. Open the log4j.properties file.

  2. Configure a logger for the connector.

    This example configures loggers for the MySQL connector and the database history implementation used by the connector, and sets them to log DEBUG level messages:

    log4j.properties

    ...
log4j.logger.io.debezium.connector.mysql=DEBUG, stdout  1
log4j.logger.io.debezium.relational.history=DEBUG, stdout  2

log4j.additivity.io.debezium.connector.mysql=false  3
log4j.additivity.io.debezium.relational.history=false  4
...

    1
    Configures the logger named io.debezium.connector.mysql to send DEBUG, INFO, WARN, and ERROR messages to the stdout appender.
    2
    Configures the logger named io.debezium.relational.history to send DEBUG, INFO, WARN, and ERROR messages to the stdout appender.
    3 4
    Turns off additivity, which means that the log messages will not be sent to appenders of parent loggers (this can prevent seeing duplicate log messages when using multiple appenders).

  3. If necessary, change the logging level for a specific subset of the classes within the connector.

    Increasing the logging level for the entire connector increases the log verbosity, which can make it difficult to understand what is happening. In these cases, you can change the logging level just for the subset of classes that are related to the issue that you are troubleshooting.

    1. Set the connector’s logging level to either DEBUG or TRACE.

    2. Review the connector’s log messages.

      Find the log messages that are related to the issue that you are troubleshooting. The end of each log message shows the name of the Java class that produced the message.

    3. Set the connector’s logging level back to INFO.

    4. Configure a logger for each Java class that you identified.

      For example, consider a scenario in which you are unsure why the MySQL connector is skipping some events when it is processing the binlog. Rather than turn on DEBUG or TRACE logging for the entire connector, you can keep the connector’s logging level at INFO and then configure DEBUG or TRACE on just the class that is reading the binlog:

      log4j.properties

      ...
log4j.logger.io.debezium.connector.mysql=INFO, stdout
log4j.logger.io.debezium.connector.mysql.BinlogReader=DEBUG, stdout
log4j.logger.io.debezium.relational.history=INFO, stdout

log4j.additivity.io.debezium.connector.mysql=false
log4j.additivity.io.debezium.relational.history=false
log4j.additivity.io.debezium.connector.mysql.BinlogReader=false
...

7.3.2. Adding mapped diagnostic contexts

Most Debezium connectors (and the Kafka Connect workers) use multiple threads to perform different activities. This can make it difficult to look at a log file and find only those log messages for a particular logical activity. To make the log messages easier to find, Debezium provides several mapped diagnostic contexts (MDC) that provide additional information for each thread.

Debezium provides the following MDC properties:

dbz.connectorType
A short alias for the type of connector. For example, MySql, Mongo, Postgres, and so on. All threads associated with the same type of connector use the same value, so you can use this to find all log messages produced by a given type of connector.
dbz.connectorName
The name of the connector or database server as defined in the connector’s configuration. For example products, serverA, and so on. All threads associated with a specific connector instance use the same value, so you can find all of the log messages produced by a specific connector instance.
dbz.connectorContext
A short name for an activity running as a separate thread running within the connector’s task. For example, main, binlog, snapshot, and so on. In some cases, when a connector assigns threads to specific resources (such as a table or collection), the name of that resource could be used instead. Each thread associated with a connector would use a distinct value, so you can find all of the log messages associated with this particular activity.

To enable MDC for a connector, you configure an appender in the log4j.properties file.

Procedure

  1. Open the log4j.properties file.

  2. Configure an appender to use any of the supported Debezium MDC properties.

    In this example, the stdout appender is configured to use these MDC properties:

    log4j.properties

    ...
log4j.appender.stdout.layout.ConversionPattern=%d{ISO8601} %-5p  %X{dbz.connectorType}|%X{dbz.connectorName}|%X{dbz.connectorContext}  %m   [%c]%n
...

    This will produce log messages similar to these: 

    ...
2017-02-07 20:49:37,692 INFO   MySQL|dbserver1|snapshot  Starting snapshot for jdbc:mysql://mysql:3306/?useInformationSchema=true&nullCatalogMeansCurrent=false&useSSL=false&useUnicode=true&characterEncoding=UTF-8&characterSetResults=UTF-8&zeroDateTimeBehavior=convertToNull with user 'debezium'   [io.debezium.connector.mysql.SnapshotReader]
2017-02-07 20:49:37,696 INFO   MySQL|dbserver1|snapshot  Snapshot is using user 'debezium' with these MySQL grants:   [io.debezium.connector.mysql.SnapshotReader]
2017-02-07 20:49:37,697 INFO   MySQL|dbserver1|snapshot  	GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'debezium'@'%'   [io.debezium.connector.mysql.SnapshotReader]
...

    Each line in the log includes the connector type (for example, MySQL), the name of the connector (for example, dbserver1), and the activity of the thread (for example, snapshot).

7.4. Debezium logging on OpenShift

If you are using Debezium on OpenShift, you can use the Kafka Connect loggers to configure the Debezium loggers and logging levels. For more information, see Kafka Connect loggers.

Chapter 8. Configuring Debezium connectors for your application

When default Debezium connector behavior is not right for your application, you can use the following Debezium features to configure the behavior you need.

  • The ByLogicalTableRouter SMT re-routes data change event records to topics that you specify.
  • The ExtractNewRecordState SMT flattens the complex structure of a data change event record into the simplified format that might be required by some Kafka consumers.
  • Configuring Avro serialization for PostgreSQL, MongoDB, or SQL Server connectors makes it easier for change event record consumers to adapt to a changing record schema.
  • The CloudEventsConverter enables a Debezium connector to emit change event records that conform to the CloudEvents specification.

8.1. Routing change event records to topics that you specify

Each Kafka record that contains a data change event has a default destination topic. If you need to, you can re-route records to topics that you specify before the records reach the Kafka Connect converter. To do this, Debezium provides the ByLogicalTableRouter single message transformation (SMT). Configure this transformation in the Debezium connector’s Kafka Connect configuration. Configuration options enable you to specify the following:

  • An expression for identifying the records to re-route
  • An expression that resolves to the destination topic
  • How to ensure a unique key among the records being re-routed to the destination topic

It is up to you to ensure that the transformation configuration provides the behavior that you want. Debezium does not validate the behavior that results from your configuration of the transformation.

The ByLogicalTableRouter transformation is a Kafka Connect SMT.

The following topics provide details:

8.1.1. Use case for routing records to topics that you specify

The default behavior is that a Debezium connector sends each change event record to a topic whose name is formed from the name of the database and the name of the table in which the change was made. In other words, a topic receives records for one physical table. When you want a topic to receive records for more than one physical table, you must configure the Debezium connector to re-route the records to that topic.

Logical tables

A logical table is a common use case for routing records for multiple physical tables to one topic. In a logical table, there are multiple physical tables that all have the same schema. For example, sharded tables have the same schema. A logical table might consist of two or more sharded tables: db_shard1.my_table and db_shard2.my_table. The tables are in different shards and are physically distinct but together they form a logical table. You can re-route change event records for tables in any of the shards to the same topic.

Partitioned PostgreSQL tables

When the Debezium PostgreSQL connector captures changes in a partitioned table, the default behavior is that change event records are routed to a different topic for each partition. To emit records from all partitions to one topic, configure the ByLogicalTableRouter SMT. Because each key in a partitioned table is guaranteed to be unique, configure key.enforce.uniqueness=false so that the SMT does not add a key field to ensure unique keys. The addition of a key field is default behavior.

8.1.2. Example of routing records for multiple tables to one topic

To route change event records for multiple physical tables to the same topic, configure the ByLogicalTableRouter transformation in the Kafka Connect configuration for the Debezium connector. Configuration of the ByLogicalTableRouter SMT requires you to specify regular expressions that determine:

  • The tables for which to route records. These tables must all have the same schema.
  • The destination topic name.

For example, configuration in a .properties file looks like this: 

transforms=Reroute
transforms.Reroute.type=io.debezium.transforms.ByLogicalTableRouter
transforms.Reroute.topic.regex=(.*)customers_shard(.*)
transforms.Reroute.topic.replacement=$1customers_all_shards
topic.regex

Specifies a regular expression that the transformation applies to each change event record to determine if it should be routed to a particular topic.

In the example, the regular expression, (.)customers_shard(.) matches records for changes to tables whose names include the customers_shard string. This would re-route records for tables with the following names:

myserver.mydb.customers_shard1
myserver.mydb.customers_shard2
myserver.mydb.customers_shard3

topic.replacement
Specifies a regular expression that represents the destination topic name. The transformation routes each matching record to the topic identified by this expression. In this example, records for the three sharded tables listed above would be routed to the myserver.mydb.customers_all_shards topic.

8.1.3. Ensuring unique keys across records routed to the same topic

A Debezium change event key uses the table columns that make up the table’s primary key. To route records for multiple physical tables to one topic, the event key must be unique across all of those tables. However, it is possible for each physical table to have a primary key that is unique within only that table. For example, a row in the myserver.mydb.customers_shard1 table might have the same key value as a row in the myserver.mydb.customers_shard2 table.

To ensure that each event key is unique across the tables whose change event records go to the same topic, the ByLogicalTableRouter transformation inserts a field into change event keys. By default, the name of the inserted field is __dbz__physicalTableIdentifier. The value of the inserted field is the default destination topic name.

If you want to, you can configure the ByLogicalTableRouter transformation to insert a different field into the key. To do this, specify the key.field.name option and set it to a field name that does not clash with existing primary key field names. For example: 

transforms=Reroute
transforms.Reroute.type=io.debezium.transforms.ByLogicalTableRouter
transforms.Reroute.topic.regex=(.*)customers_shard(.*)
transforms.Reroute.topic.replacement=$1customers_all_shards
transforms.Reroute.key.field.name=shard_id

This example adds the shard_id field to the key structure in routed records.

If you want to adjust the value of the key’s new field, configure both of these options:

key.field.regex
Specifies a regular expression that the transformation applies to the default destination topic name to capture one or more groups of characters.
key.field.replacement
Specifies a regular expression for determining the value of the inserted key field in terms of those captured groups.

For example: 

transforms.Reroute.key.field.regex=(.*)customers_shard(.*)
transforms.Reroute.key.field.replacement=$2

With this configuration, suppose that the default destination topic names are:

myserver.mydb.customers_shard1
myserver.mydb.customers_shard2
myserver.mydb.customers_shard3

The transformation uses the values in the second captured group, the shard numbers, as the value of the key’s new field. In this example, the inserted key field’s values would be 1, 2, or 3.

If your tables contain globally unique keys and you do not need to change the key structure, you can set the key.enforce.uniqueness property to false: 

...
transforms.Reroute.key.enforce.uniqueness=false
...

8.1.4. Options for configuring ByLogicalTableRouter transformation

Property

Default

Description

topic.regex

 

Specifies a regular expression that the transformation applies to each change event record to determine if it should be routed to a particular topic.

topic.replacement

 

Specifies a regular expression that represents the destination topic name. The transformation routes each matching record to the topic identified by this expression. This expression can refer to groups captured by the regular expression that you specify for topic.regex. To refer to a group, specify $1, $2, and so on.

key.enforce.uniqueness

true

Indicates whether to add a field to the record’s change event key. Adding a key field ensures that each event key is unique across the tables whose change event records go to the same topic. This helps to prevent collisions of change events for records that have the same key but that originate from different source tables.

Specify false if you do not want the transformation to add a key field. For example, if you are routing records from a partitioned PostgreSQL table to one topic, you can configure key.enforce.uniqueness=false because unique keys are guaranteed in partitioned PostgreSQL tables.

key.field.name

__dbz__physicalTableIdentifier

Name of a field to be added to the change event key. The value of this field identifies the original table name. For the SMT to add this field, key.enforce.uniqueness must be true, which is the default.

key.field.regex

 

Specifies a regular expression that the transformation applies to the default destination topic name to capture one or more groups of characters. For the SMT to apply this expression, key.enforce.uniqueness must be true, which is the default.

key.field.replacement

 

Specifies a regular expression for determining the value of the inserted key field in terms of the groups captured by the expression specified for key.field.regex. For the SMT to apply this expression, key.enforce.uniqueness must be true, which is the default.

8.2. Extracting source record after state from Debezium change events

A Debezium data change event has a complex structure that provides a wealth of information. Kafka records that convey Debezium change events contain all of this information. However, parts of a Kafka ecosystem might expect Kafka records that provide a flat structure of field names and values. To provide this kind of record, Debezium provides the ExtractNewRecordState single message transformation (SMT). Configure this transformation when consumers need Kafka records that have a format that is simpler than Kafka records that contain Debezium change events.

The ExtractNewRecordState transformation is a Kafka Connect SMT.

The transformation is available to only SQL database connectors.

The following topics provide details:

8.2.1. Description of Debezium change event structure

Debezium generates data change events that have a complex structure. Each event consists of three parts:

  • Metadata, which includes but is not limited to:

    • The operation that made the change
    • Source information such as the names of the database and table where the change was made
    • Time stamp for when the change was made
    • Optional transaction information
  • Row data before the change
  • Row data after the change

For example, the structure of an UPDATE change event looks like this: 

{
	"op": "u",
	"source": {
		...
	},
	"ts_ms" : "...",
	"before" : {
		"field1" : "oldvalue1",
		"field2" : "oldvalue2"
	},
	"after" : {
		"field1" : "newvalue1",
		"field2" : "newvalue2"
	}
}

This complex format provides the most information about changes happening in the system. However, other connectors or other parts of the Kafka ecosystem usually expect the data in a simple format like this: 

{
	"field1" : "newvalue1",
	"field2" : "newvalue2"
}

To provide the needed Kafka record format for consumers, configure the ExtractNewRecordState SMT.

8.2.2. Behavior of Debezium ExtractNewRecordState transformation

The ExtractNewRecordState SMT extracts the after field from a Debezium change event in a Kafka record. The SMT replaces the original change event with only its after field to create a simple Kafka record.

You can configure the ExtractNewRecordState SMT for a Debezium connector or for a sink connector that consumes messages emitted by a Debezium connector. The advantage of configuring ExtractNewRecordState for a sink connector is that records stored in Apache Kafka contain whole Debezium change events. The decision to apply the SMT to a source or sink connector depends on your particular use case.

You can configure the transformation to do any of the following:

  • Add metadata from the change event to the simplified Kafka record. The default behavior is that the SMT does not add metadata.
  • Keep Kafka records that contain change events for DELETE operations in the stream. The default behavior is that the SMT drops Kafka records for DELETE operation change events because most consumers cannot yet handle them.

A database DELETE operation causes Debezium to generate two Kafka records:

  • A record that contains "op": "d", the before row data, and some other fields.
  • A tombstone record that has the same key as the deleted row and a value of null. This record is a marker for Apache Kafka. It indicates that log compaction can remove all records that have this key.

Instead of dropping the record that contains the before row data, you can configure the ExtractNewRecordData SMT to do one of the following:

  • Keep the record in the stream and edit it to have only the "value": "null" field.
  • Keep the record in the stream and edit it to have a value field that contains the key/value pairs that were in the before field with an added "__deleted": "true" entry.

Similary, instead of dropping the tombstone record, you can configure the ExtractNewRecordData SMT to keep the tombstone record in the stream.

8.2.3. Configuration of ExtractNewRecordState transformation

Configure the Debezium ExtractNewRecordState SMT in a Kafka Connect source or sink connector by adding the SMT configuration details to your connector’s configuration. To obtain the default behavior, in a .properties file, you would specify something like the following: 

transforms=unwrap,...
transforms.unwrap.type=io.debezium.transforms.ExtractNewRecordState

As for any Kafka Connect connector configuration, you can set transforms= to multiple, comma-separated, SMT aliases in the order in which you want Kafka Connect to apply the SMTs.

The following .properties example sets several ExtractNewRecordState options: 

transforms=unwrap,...
transforms.unwrap.type=io.debezium.transforms.ExtractNewRecordState
transforms.unwrap.drop.tombstones=false
transforms.unwrap.delete.handling.mode=rewrite
transforms.unwrap.add.fields=table,lsn
drop.tombstones=false
Keeps tombstone records for DELETE operations in the event stream.
delete.handling.mode=rewrite

For DELETE operations, edits the Kafka record by flattening the value field that was in the change event. The value field directly contains the key/value pairs that were in the before field. The SMT adds __deleted and sets it to true, for example: 

"value": {
  "pk": 2,
  "cola": null,
  "__deleted": "true"
}
add.fields=table,lsn
Adds change event metadata for the table and lsn fields to the simplified Kafka record.

8.2.4. Example of adding metadata to the Kafka record

The ExtractNewRecordState SMT can add original, change event metadata to the simplified Kafka record. For example, you might want the simplified record’s header or value to contain any of the following:

  • The type of operation that made the change
  • The name of the database or table that was changed
  • Connector-specific fields such as the Postgres LSN field

To add metadata to the simplified Kafka record’s header, specify the add.header option. To add metadata to the simplified Kafka record’s value, specify the add.fields option. Each of these options takes a comma separated list of change event field names. Do not specify spaces. When there are duplicate field names, to add metadata for one of those fields, specify the struct as well as the field. For example: 

transforms=unwrap,...
transforms.unwrap.type=io.debezium.transforms.ExtractNewRecordState
transforms.unwrap.add.fields=op,table,lsn,source.ts_ms
transforms.unwrap.add.headers=db
transforms.unwrap.delete.handling.mode=rewrite

With that configuration, a simplified Kafka record would contain something like the following: 

{
 ...
	"__op" : "c",
	"__table": "MY_TABLE",
	"__lsn": "123456789",
	"__source_ts_ms" : "123456789",
 ...
}

Also, simplified Kafka records would have a __db header.

In the simplified Kafka record, the SMT prefixes the metadata field names with a double underscore. When you specify a struct, the SMT also inserts an underscore between the struct name and the field name.

To add metadata to a simplified Kafka record that is for a DELETE operation, you must also configure delete.handling.mode=rewrite.

8.2.5. Options for configuring ExtractNewRecordState transformation

The following table describes the options that you can specify for the ExtractNewRecordState SMT.

Property

Default

Description

drop.tombstones

true

Debezium generates a tombstone record for each DELETE operation. The default behavior is that ExtractNewRecordState removes tombstone records from the stream. To keep tombstone records in the stream, specify drop.tombstones=false.

delete.handling.mode

drop

Debezium generates a change event record for each DELETE operation. The default behavior is that ExtractNewRecordState removes these records from the stream. To keep Kafka records for DELETE operations in the stream, set delete.handling.mode to none or rewrite.

Specify none to keep the change event record in the stream. The record contains only "value": "null".

Specify rewrite to keep the change event record in the stream and edit the record to have a value field that contains the key/value pairs that were in the before field and also add __deleted: true to the value. This is another way to indicate that the record has been deleted.

When you specify rewrite, the updated simplified records for DELETE operations might be all you need to track deleted records. You can consider accepting the default behavior of dropping the tombstone records that the Debezium connector creates.

route.by.field

 

To use row data to determine the topic to route the record to, set this option to an after field attribute. The SMT routes the record to the topic whose name matches the value of the specified after field attribute. For a DELETE operation, set this option to a before field attribute.

For example, configuration of route.by.field=destination routes records to the topic whose name is the value of after.destination. The default behavior is that a Debezium connector sends each change event record to a topic whose name is formed from the name of the database and the name of the table in which the change was made.

If you are configuring the ExtractNewRecordState SMT on a sink connector, setting this option might be useful when the destination topic name dictates the name of the database table that will be updated with the simplified change event record. If the topic name is not correct for your use case, you can configure route.by.field to re-route the event.

add.fields

 

Set this option to a comma-separated list, with no spaces, of metadata fields to add to the simplified Kafka record’s value. When there are duplicate field names, to add metadata for one of those fields, specify the struct as well as the field, for example source.ts_ms.

When the SMT adds metadata fields to the simplified record’s value, it prefixes each metadata field name with a double underscore. For a struct specification, the SMT also inserts an underscore between the struct name and the field name.

If you specify a field that is not in the change event record, the SMT still adds the field to the record’s value.

add.headers

 

Set this option to a comma-separated list, with no spaces, of metadata fields to add to the header of the simplified Kafka record. When there are duplicate field names, to add metadata for one of those fields, specify the struct as well as the field, for example source.ts_ms.

When the SMT adds metadata fields to the simplified record’s header, it prefixes each metadata field name with a double underscore. For a struct specification, the SMT also inserts an underscore between the struct name and the field name.

If you specify a field that is not in the change event record, the SMT does not add the field to the header.

8.3. Avro Serialization

Important

Using Avro to serialize record keys and values is a Technology Preview feature. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.

A Debezium connector works in the Kafka Connect framework to capture each row-level change in a database by generating a change event record. For each change event record, the Debezium connector does the following:

  1. Applies configured transformations
  2. Serializes the record key and value into a binary form by using the configured Kafka Connect converters
  3. Writes the record to the correct Kafka topic

You can specify converters for each individual Debezium connector instance. Kafka Connect provides a JSON converter that serializes the record keys and values into JSON documents. The default behavior is that the JSON converter includes the record’s message schema, which makes each record very verbose. The Getting Started with Debezium guide shows what the records look like when both payload and schemas are included. If you want records to be serialized with JSON, consider setting the following connector configuration properties to false:

  • key.converter.schemas.enable
  • value.converter.schemas.enable

Setting these properties to false excludes the verbose schema information from each record.

Alternatively, you can serialize the record keys and values by using Apache Avro. The Avro binary format is compact and efficient. Avro schemas make it possible to ensure that each record has the correct structure. Avro’s schema evolution mechanism enables schemas to evolve. This is essential for Debezium connectors, which dynamically generate each record’s schema to match the structure of the database table that was changed. Over time, change event records written to the same Kafka topic might have different versions of the same schema. Avro serialization makes it easier for change event record consumers to adapt to a changing record schema.

To use Apache Avro serialization, you must deploy a schema registry that manages Avro message schemas and their versions. For information about setting up this registry, see the documentation for Red Hat Integration - Service Registry.

8.3.1. About the Red Hat Integration - Service Registry

Red Hat Integration - Service Registry provides several components that work with Avro:

  • An Avro converter that you can specify in Debezium connector configurations. This converter maps Kafka Connect schemas to Avro schemas. The converter then uses the Avro schemas to serialize the record keys and values into Avro’s compact binary form.

  • An API and schema registry that tracks:

    • Avro schemas that are used in Kafka topics
    • Where the Avro converter sends the generated Avro schemas

    Since the Avro schemas are stored in this registry, each record needs to contain only a tiny schema identifier. This makes each record even smaller. For an I/O bound system like Kafka, this means more total throughput for producers and consumers.

  • Avro Serdes (serializers and deserializers) for Kafka producers and consumers. Kafka consumer applications that you write to consume change event records can use Avro Serdes to deserialize the change event records.

To use the Service Registry with Debezium, add Service Registry converters and their dependencies to the Kafka Connect container image that you are using for running a Debezium connector.

Note

The Service Registry project also provides a JSON converter. This converter combines the advantage of less verbose messages with human-readable JSON. Messages do not contain the schema information themselves, but only a schema ID.

8.3.2. Deployment overview

To deploy a Debezium connector that uses Avro serialization, there are three main tasks:

  1. Deploy a Red Hat Integration - Service Registry instance by following the instructions in Getting Started with Service Registry.
  2. Install the Avro converter by downloading the Debezium Service Registry Kafka Connect zip file and extracting it into the Debezium connector’s directory.

  3. Configure a Debezium connector instance to use Avro serialization by setting configuration properties as follows: 

    key.converter=io.apicurio.registry.utils.converter.AvroConverter
key.converter.apicurio.registry.url=http://apicurio:8080/api
key.converter.apicurio.registry.global-id=io.apicurio.registry.utils.serde.strategy.AutoRegisterIdStrategy
value.converter=io.apicurio.registry.utils.converter.AvroConverter
value.converter.apicurio.registry.url=http://apicurio:8080/api
value.converter.apicurio.registry.global-id=io.apicurio.registry.utils.serde.strategy.AutoRegisterIdStrategy

Internally, Kafka Connect always uses JSON key/value converters for storing configuration and offsets.

8.3.3. Deploying with Debezium containers

In your environment, you might want to use a provided Debezium container to deploy Debezium connectors that use Avro serializaion. Follow the procedure here to do that. In this procedure, you build a custom Kafka Connect container image for Debezium, and you configure the Debezium connector to use the Avro converter.

Prerequisites

  • You have cluster administrator access to an OpenShift cluster.
  • You downloaded the Debezium connector plug-in(s) that you want to deploy with Avro serialization.

Procedure

  1. Deploy an instance of Service Registry. See Getting Started with Service Registry, Installing Service Registry from the OpenShift OperatorHub, which provides instructions for:

    • Installing AMQ Streams
    • Setting up AMQ Streams storage
    • Installing Service Registry

  2. Extract the Debezium connector archive(s) to create a directory structure for the connector plug-in(s). If you downloaded and extracted the archive for each Debezium connector, the structure looks like this: 

    tree ./my-plugins/
./my-plugins/
├── debezium-connector-mongodb
|   ├── ...
├── debezium-connector-mysql
│   ├── ...
├── debezium-connector-postgres
│   ├── ...
└── debezium-connector-sqlserver
    ├── ...

  3. Add the Avro converter to the directory that contains the Debezium connector that you want to configure to use Avro serialization:

    1. Go to the Red Hat Integration download site and download the Service Registry Kafka Connect zip file.
    2. Extract the archive into the desired Debezium connector directory.

    To configure more than one type of Debezium connector to use Avro serialization, extract the archive into the directory for each relevant connector type. While this duplicates the files, it removes the possibility of conflicting dependencies.

  4. Create and publish a custom image for running Debezium connectors that are configured to use the Avro converter:

    1. Create a new Dockerfile by using registry.redhat.io/amq7/amq-streams-kafka-25:1.5.0 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-25:1.5.0
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 docker container image. For example, if you saved the docker file that you created in the previous step as debezium-container-with-avro, then you would run the following command:

      docker build -t debezium-container-with-avro:latest

    3. Push your custom image to your container registry, for example:

      docker push debezium-container-with-avro:latest

    4. Point to the new container image. Do one of the following:

      • Edit the KafkaConnect.spec.image property of the KafkaConnect 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: KafkaConnect
metadata:
  name: my-connect-cluster
spec:
  #...
  image: debezium-container-with-avro
      • 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 will need to apply it to your OpenShift cluster.

  5. Deploy each Debezium connector that is configured to use the Avro converter. For each Debezium connector:

    1. Create a Debezium connector instance. The following inventory-connector.yaml file example creates a KafkaConnector custom resource that defines a MySQL connector instance that is configured to use the Avro converter: 

      apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnector
metadata:
  name: inventory-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  class: io.debezium.connector.mysql.MySqlConnector
  tasksMax: 1
  config:
    database.hostname: mysql
    database.port: 3306
    database.user: debezium
    database.password: dbz
    database.server.id: 184054
    database.server.name: dbserver1
    database.whitelist: inventory
    database.history.kafka.bootstrap.servers: my-cluster-kafka-bootstrap:9092
    database.history.kafka.topic: schema-changes.inventory
    key.converter: io.apicurio.registry.utils.converter.AvroConverter
    key.converter.apicurio.registry.url: http://apicurio:8080/api
    key.converter.apicurio.registry.global-id: io.apicurio.registry.utils.serde.strategy.AutoRegisterIdStrategy
    value.converter: io.apicurio.registry.utils.converter.AvroConverter
    value.converter.apicurio.registry.url: http://apicurio:8080/api
    value.converter.apicurio.registry.global-id: io.apicurio.registry.utils.serde.strategy.AutoRegisterIdStrategy

    2. 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.

  6. Verify that the connector was created and has started to track 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: 

      ...
2020-02-21 17:57:30,801 INFO Starting snapshot for jdbc:mysql://mysql:3306/?useInformationSchema=true&nullCatalogMeansCurrent=false&useSSL=false&useUnicode=true&characterEncoding=UTF-8&characterSetResults=UTF-8&zeroDateTimeBehavior=CONVERT_TO_NULL&connectTimeout=30000 with user 'debezium' with locking mode 'minimal' (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
2020-02-21 17:57:30,805 INFO Snapshot is using user 'debezium' with these MySQL grants: (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
...

      Taking the snapshot involves a number of steps: 

      ...
2020-02-21 17:57:30,822 INFO Step 0: disabling autocommit, enabling repeatable read transactions, and setting lock wait timeout to 10 (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
2020-02-21 17:57:30,836 INFO Step 1: flush and obtain global read lock to prevent writes to database (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
2020-02-21 17:57:30,839 INFO Step 2: start transaction with consistent snapshot (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
2020-02-21 17:57:30,840 INFO Step 3: read binlog position of MySQL master (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
2020-02-21 17:57:30,843 INFO 	 using binlog 'mysql-bin.000003' at position '154' and gtid '' (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
...
2020-02-21 17:57:34,423 INFO Step 9: committing transaction (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
2020-02-21 17:57:34,424 INFO Completed snapshot in 00:00:03.632 (io.debezium.connector.mysql.SnapshotReader) [debezium-mysqlconnector-dbserver1-snapshot]
...

      After completing the snapshot, Debezium begins tracking changes in, for example, the inventory database’s binlog for change events: 

      ...
2020-02-21 17:57:35,584 INFO Transitioning from the snapshot reader to the binlog reader (io.debezium.connector.mysql.ChainedReader) [task-thread-inventory-connector-0]
2020-02-21 17:57:35,613 INFO Creating thread debezium-mysqlconnector-dbserver1-binlog-client (io.debezium.util.Threads) [task-thread-inventory-connector-0]
2020-02-21 17:57:35,630 INFO Creating thread debezium-mysqlconnector-dbserver1-binlog-client (io.debezium.util.Threads) [blc-mysql:3306]
Feb 21, 2020 5:57:35 PM com.github.shyiko.mysql.binlog.BinaryLogClient connect
INFO: Connected to mysql:3306 at mysql-bin.000003/154 (sid:184054, cid:5)
2020-02-21 17:57:35,775 INFO Connected to MySQL binlog at mysql:3306, starting at binlog file 'mysql-bin.000003', pos=154, skipping 0 events plus 0 rows (io.debezium.connector.mysql.BinlogReader) [blc-mysql:3306]
...

8.3.4. Naming

As stated in the Avro documentation, names must adhere to the following rules:

  • Start with [A-Za-z_]
  • Subsequently contains only [A-Za-z0-9_] characters

Debezium uses the column’s name as the basis for the corresponding Avro field. This can lead to problems during serialization if the column name does not also adhere to the Avro naming rules. Each Debezium connector provides a configuration property, sanitize.field.names that you can set to true if you have columns that do not adhere to Avro rules for names. Setting sanitize.field.names to true allows serialization of non-conformant fields without having to actually modify your schema.

8.4. Exporting CloudEvents

CloudEvents is a specification for describing event data in a common way. Its aim is to provide interoperability across services, platforms and systems. Debezium enables you to configure a MongoDB, MySQL, PostgreSQL, or SQL Server connector to emit change event records that conform to the CloudEvents specification.

Important

Emitting change event records in CloudEvents format is a Technology Preview feature. Technology Preview features are not supported with Red Hat production service-level agreements (SLAs) and might not be functionally complete; therefore, Red Hat does not recommend implementing any Technology Preview features in production environments. This Technology Preview feature provides early access to upcoming product innovations, enabling you to test functionality and provide feedback during the development process. For more information about support scope, see Technology Preview Features Support Scope.

The CloudEvents specification defines:

  • A set of standardized event attributes
  • Rules for defining custom attributes
  • Encoding rules for mapping event formats to serialized representations such as JSON
  • Protocol bindings for transport layers such as Apache Kafka, HTTP or AMQP

To configure a Debezium connector to emit change event records that conform to the CloudEvents specification, Debezium provides the io.debezium.converters.CloudEventsConverter, which is a Kafka Connect message converter.

Currently, only structured mapping mode is supported. The CloudEvents change event envelope must be JSON and the data format must be JSON. It is expected that a future Debezium release will support binary mapping mode.

8.4.1. Example event format

The following example shows what a CloudEvents change event record emitted by a PostgreSQL connector looks like. In this example, the PostgreSQL connector is configured to use JSON as the CloudEvents format envelope and also as the data format. 

{
  "id" : "name:test_server;lsn:29274832;txId:565",   1
  "source" : "/debezium/postgresql/test_server",     2
  "specversion" : "1.0",                             3
  "type" : "io.debezium.postgresql.datachangeevent", 4
  "time" : "2020-01-13T13:55:39.738Z",               5
  "datacontenttype" : "application/json",            6
  "iodebeziumop" : "r",                              7
  "iodebeziumversion" : "1.1.2.Final",        8
  "iodebeziumconnector" : "postgresql",
  "iodebeziumname" : "test_server",
  "iodebeziumtsms" : "1578923739738",
  "iodebeziumsnapshot" : "true",
  "iodebeziumdb" : "postgres",
  "iodebeziumschema" : "s1",
  "iodebeziumtable" : "a",
  "iodebeziumtxId" : "565",
  "iodebeziumlsn" : "29274832",
  "iodebeziumxmin" : null,
  "iodebeziumtxid": "565",                           9
  "iodebeziumtxtotalorder": "1",
  "iodebeziumtxdatacollectionorder": "1",
  "data" : {                                         10
    "before" : null,
    "after" : {
      "pk" : 1,
      "name" : "Bob"
    }
  }
}
1
Unique ID that the connector generates for the change event based on the change event’s content.
2
The source of the event, which is the logical name of the database as specified by the database.server.name property in the connector’s configuration.
3
The CloudEvents specification version.
4
Connector type that generated the change event. The format of this field is io.debezium.CONNECTOR_TYPE.datachangeevent. The value of CONNECTOR_TYPE is mongodb, mysql, postgresql, or sqlserver.
5
Time of the change in the source database.
6
Describes the content type of the data attribute, which is JSON.
7
An operation identifier. Possible values are r for read, c for create, u for update, or d for delete.
8
All source attributes that are known from Debezium change events are mapped to CloudEvents extension attributes by using the iodebezium prefix for the attribute name.
9
When enabled in the connector, each transaction attribute that is known from Debezium change events is mapped to a CloudEvents extension attribute by using the iodebeziumtx prefix for the attribute name.
10
The actual data change itself. Depending on the operation and the connector, the data might contain before, after and/or patch fields.

8.4.2. Example configuration

Configure io.debezium.converters.CloudEventsConverter in your Debezium connector configuration. Following is an example of configuring CloudEventsConverter. In this example, you could omit the specification of serializer.type because json is the default. 

...
"value.converter": "io.debezium.converters.CloudEventsConverter",
"value.converter.serializer.type" : "json",
...

CloudEventsConverter converts Kafka record values. In the same connector configuration, you can specify key.converter if you want to operate on record keys, for example you might specify StringConverter, LongConverter, or JsonConverter.

8.4.3. Configuration properties

When you configure a Debezium connector to use the CloudEvent converter you can specify the following properties.

Property

Default

Description

serializer.type

json

The encoding type to use for the CloudEvents envelope structure. json is the only supported value.

data.serializer.type

json

The encoding type to use for the data attribute. json is the only supported value.

json. ...

N/A

Any configuration properties to be passed through to the underlying converter when using JSON. The json. prefix is removed.

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