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MySQL CDC Source (Debezium) Connector for Confluent Cloud

Note

• MariaDB is not currently supported. See the Debezium docs for more information.
• This is a Quick Start for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see Debezium MySQL Source connector for Confluent Platform.

The Kafka Connect MySQL Change Data Capture (CDC) Source (Debezium) connector for Confluent Cloud can obtain a snapshot of the existing data in a MySQL database and then monitor and record all subsequent row-level changes to that data. The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data formats. All of the events for each table are recorded in a separate Apache Kafka® topic. The events can then be easily consumed by applications and services.

Features

The MySQL CDC Source (Debezium) connector provides the following features:

• Topics created automatically: The connector automatically creates Kafka topics using the naming convention: <database.server.name>.<schemaName>.<tableName>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3. For more information, see Maximum message size.
• Databases included and Databases excluded: Sets whether a database is or is not monitored for changes. By default, the connector monitors every database on the server.
• Tables included and Tables excluded: Sets whether a table is or is not monitored for changes.
• Output formats: The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output Kafka record value format. It supports Avro, JSON Schema, Protobuf, JSON (schemaless), and String output record key format. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
• Tasks per connector: Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").
• Snapshot mode: Specifies the criteria for running a snapshot.
• Tombstones on delete: Sets whether a tombstone event is generated after a delete event. Default is true.
• Database authentication: Uses password authentication.
• SSL support: Supports one-way SSL.
• Data formats: Supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

Note

database.server.id is set to a random number between 5400 and 6400.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Limitations

Be sure to review the following information.

• For connector limitations, see MySQL CDC Source Connector (Debezium) limitations.
• If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
• If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

Maximum message size

This connector creates topics automatically. When it creates topics, the internal connector configuration property max.message.bytes is set to the following:

• Basic cluster: 2 MB
• Standard cluster: 2 MB
• Dedicated cluster: 20 MB

For more information about Confluent Cloud clusters, see Confluent Cloud Features and Limits by Cluster Type.

Log retention during snapshot

When launched, the CDC connector creates a snapshot of the existing data in the database to capture the nominated tables. To do this, the connector executes a “SELECT *” statement. Completing the snapshot can take a while if one or more of the nominated tables is very large.

During the snapshot process, the database server must retain redo logs and transaction logs so that when the snapshot is complete, the CDC connector can start processing database changes that have completed since the snapshot process began. These logs are retained in a binary log (binlog) on the database server.

If one or more of the tables are very large, the snapshot process could run longer than the binlog retention time set on the database server (that is, expire_logs_days = <number-of-days>). To capture very large tables, you should temporarily retain the binlog for longer than normal by increasing the expire_logs_days number.

Quick Start

Use this quick start to get up and running with the MySQL CDC Source (Debezium) connector. The quick start provides the basics of selecting the connector and configuring it to obtain a snapshot of the existing data in a MySQL database and then monitoring and recording all subsequent row-level changes.

Prerequisites

• Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud Platform (GCP).

• The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.

• Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

• Public access may be required for your database. See Networking, DNS, and Service Endpoints for details. The example below shows the AWS Management Console when setting up a MySQL database.

  

  Public access enabled

• For networking considerations, see Networking, DNS, and service endpoints. To use static egress IPs, see Static Egress IP Addresses. The example below shows the AWS Management Console when setting up security group rules for the VPC.

  

  Open inbound traffic

  Note

  See your specific cloud platform documentation for how to configure security rules for your VPC.

• Kafka cluster credentials. The following lists the different ways you can provide credentials.

  • Enter an existing service account resource ID.
  • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
  • Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

• Update the following settings for the MySQL database.

  1. Turn on backup for the database.

  2. Create a new parameter group and set the following parameters:

     binlog_format=ROW
     binlog_row_image=full
     

  3. Apply the new parameter group to the database.

  4. Reboot the database.

  The following example screens are from Amazon RDS:

  
  
  

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster.

See the Quick Start for Confluent Cloud for installation instructions.

Step 2: Add a connector.

In the left navigation menu, click Data integration, and then click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector.

Click the MySQL CDC Source connector card.

Step 4: Enter the connector details.

Note

• Make sure you have all your prerequisites completed.
• An asterisk ( * ) designates a required entry.

At the MySQL CDC Source (Debezium) Connector screen, complete the following:

Kafka credentialsAuthenticationConfigurationSizingReview and Launch

1. Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
   • Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
   • Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
   • Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
2. Click Continue.

Step 5: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

Note

A topic named dbhistory.<database.server.name>.<connect-id> is automatically created for database.history.kafka.topic with one partition.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

• Make sure you have all your prerequisites completed.
• The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe MySqlCdcSource

Example output:

Following are the required configs:
connector.class: MySqlCdcSource
name
kafka.auth.mode
kafka.api.key
kafka.api.secret
database.hostname
database.user
database.server.name
output.data.format
tasks.max

Step 3: Create the connector configuration file.

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

{
  "connector.class": "MySqlCdcSource",
  "name": "MySqlCdcSourceConnector_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "****************************************************************",
  "database.hostname": "database-2.<host-ID>.us-west-2.rds.amazonaws.com",
  "database.port": "3306",
  "database.user": "admin",
  "database.password": "**********",
  "database.server.name": "mysql",
  "database.whitelist": "employee",
  "table.includelist":"employees.departments,
  "snapshot.mode": "initial",
  "output.data.format": "AVRO",
  "tasks.max": "1"
}

Note the following property definitions:

• "connector.class": Identifies the connector plugin name.
• "name": Sets a name for your new connector.

• "kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

  confluent iam service-account list
  

  For example:

  confluent iam service-account list
  
     Id     | Resource ID |       Name        |    Description
  +---------+-------------+-------------------+-------------------
     123456 | sa-l1r23m   | sa-1              | Service account 1
     789101 | sa-l4d56p   | sa-2              | Service account 2
  

• "database.ssl.mode": The default option prefer is enabled if database.ssl.mode is not added to the connector configuration. When prefer is enabled, the connector attempts to use an encrypted connection to the database server. The options prefer and require use a secure (encrypted) connection. The connector fails if a secure connection cannot be established. These modes do not do Certification Authority (CA) validation.

• "table.includelist": (Optional) Enter a comma-separated list of fully-qualified table identifiers for the connector to monitor. By default, the connector monitors all non-system tables. A fully-qualified table name is in the form schemaName.tableName.

• "column.exclude.list": (Optional) A comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event record values. Fully-qualified names for columns are in the form databaseName.tableName.columnName.

• "snapshot.mode": (Optional) Specifies the criteria for performing a database snapshot when the connector starts.

  • The default option is initial. When selected, the connector takes a snapshot of the structure and data from captured tables. This is useful if you want the topics populated with a complete representation of captured table data when the connector starts.
  • never: Specifies that the connector should never perform snapshots, and that when starting for the first time, the connector starts reading from where it last left off.
  • when_needed: Specifies that the connector performs a snapshot when it considers a snapshot is needed.
  • schema_only: Specifies that the connector completes a snapshot of the schemas and not the data. This option is useful when you do not need the topics to contain a consistent snapshot of the data, but need them to have only the changes since the connector was started.
  • schema_only_recovery: A recovery option for a connector that has already been capturing changes. When you restart the connector, it recovers a corrupted or lost database history topic. You might use this option periodically to clean up a database history topic that has been growing unexpectedly.

• "snapshot.locking.mode": (Optional) Controls how long the connector holds a global read lock as it performs a snapshot. The default is minimal, which means the connector holds the global read lock (preventing any updates) for just the initial portion of the snapshot, while the database schemas and other metadata are being read. The remaining work in a snapshot involves selecting all rows from each table. This is accomplished using a REPEATABLE READ transaction, even when the read lock is off and other operations are updating the database. (Use minimal_percona for a Percona server.) In some cases it may be desirable to block all writes for the entire duration of the snapshot. In a case like this, set this property to extended. The option none prevents the connector from acquiring any table locks during the snapshot. While this setting is allowed with all snapshot modes, it is safe to use only if no schema changes are happening while the snapshot is running. Note that for tables defined with the MyISAM engine, the tables are locked regardless of this property’s setting, since MyISAM acquires a table lock. This behavior is unlike the InnoDB engine, which acquires row level locks.

• "output.data.format": Sets the output Kafka record value format (data coming from the connector). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based record format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

• "after.state.only": (Optional) Defaults to true, which results in the Kafka record having only the record state from change events applied. Enter false to maintain the prior record states after applying the change events.

• "json.output.decimal.format": (Optional) Defaults to BASE64. Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

  • BASE64 to serialize DECIMAL logical types as base64 encoded binary data.
  • NUMERIC to serialize Connect DECIMAL logical type values in JSON or JSON_SR as a number representing the decimal value.

• "tasks.max": Enter the number of tasks in use by the connector. Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all properties and definitions.

Step 4: Load the properties file and create the connector.

Enter the following command to load the configuration and start the connector:

confluent connect create --config <file-name>.json

For example:

confluent connect create --config mysql-cdc-source.json

Example output:

Created connector MySqlCdcSourceConnector_0 lcc-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

confluent connect list

Example output:

ID          |            Name             | Status  |  Type
+-----------+-----------------------------+---------+-------+
lcc-ix4dl   | MySqlCdcSourceConnector_0   | RUNNING | source

Step 6: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

Note

A topic named dbhistory.<database.server.name>.<connect-id> is automatically created for database.history.kafka.topic with one partition.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Configuration Properties

Use the following configuration properties with this connector.

Note

These are properties for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see Debezium MySQL Source connector for Confluent Platform.

How should we connect to your data?

name

Sets a name for your connector.

• Type: string
• Valid Values: A string at most 64 characters long
• Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

• Type: string
• Default: KAFKA_API_KEY
• Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
• Importance: high

kafka.api.key

• Type: password
• Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

• Type: string
• Importance: high

kafka.api.secret

• Type: password
• Importance: high

How should we connect to your database?

database.hostname

The address of the MySQL server.

• Type: string
• Importance: high

database.port

Port number of the MySQL server.

• Type: int
• Valid Values: [0,…,65535]
• Importance: high

database.user

The name of the MySQL server user that has the required authorization.

• Type: string
• Importance: high

database.password

The password for the MySQL server user that has the required authorization.

• Type: password
• Importance: high

database.server.name

The logical name of the MySQL server cluster. This logical name forms a namespace and is used in all Kafka topic names and Kafka Connect schema names. The logical name is also used for the namespaces of the corresponding Avro schema, if Avro data format is used. Kafka topics must (and will be) created with the prefix database.server.name. Only alphanumeric characters, underscores, hyphens and dots are allowed.

• Type: string
• Importance: high

database.ssl.mode

What SSL mode should we use to connect to your database. The default preferred option establishes an encrypted connection if the server supports secure connections. If the server does not support secure connections, it falls back to an unencrypted connection. The required option establishes an encrypted connection or fails if one cannot be made for any reason.

• Type: string
• Default: preferred
• Importance: low

Database details

database.include.list

An optional comma-separated list of strings that match database names to be monitored. Any database name not included in the list is excluded from monitoring. By default all databases are monitored. May not be used with database.exclude.list.

• Type: list
• Importance: medium

database.exclude.list

An optional comma-separated list of strings that match database names to be excluded from monitoring. Any database name not included in the list is monitored. May not be used with database.include.list.

• Type: list
• Importance: medium

table.include.list

An optional comma-separated list of strings that match fully-qualified table identifiers for tables to be monitored. Any table not included in this config property is excluded from monitoring. Each identifier is in the form schemaName.tableName. By default the connector monitors every non-system table in each monitored schema. May not be used with “Table excluded”.

• Type: list
• Importance: medium

database.connectionTimeZone

The value must be a valid ZoneId.

• Type: string
• Importance: low

table.exclude.list

An optional comma-separated list of strings that match fully-qualified table identifiers for tables to be excluded from monitoring. Any table not included in this config property is monitored. Each identifier is in the form schemaName.tableName. May not be used with “Table included”.

• Type: list
• Importance: medium

datatype.propagate.source.type

A comma-separated list of regular expressions matching the database-specific data type names that adds the data type’s original type and original length as parameters to the corresponding field schemas in the emitted change records.

• Type: list
• Importance: low

snapshot.mode

Specifies the criteria for running a snapshot when the connector starts. The default setting is initial and specifies that 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 necessary; typically 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 when the connector starts with a logical server name, the connector should read from the beginning of the binlog. Use the never option with care, as it is only valid when the binlog is guaranteed to contain the entire history of the database. The schema_only option performs a snapshot of the schemas and not the data. This setting is useful when you do not need the topics to contain a consistent snapshot of the data but need them to have only the changes since the connector was started. The schema_only_recovery option is a recovery setting for a connector that has already been capturing changes. When you restart the connector, this setting enables recovery of a corrupted or lost database history topic. You might set it periodically to “clean up” a database history topic that has been growing unexpectedly. Database history topics require infinite retention.

• Type: string
• Default: initial
• Valid Values: initial, never, schema_only, schema_only_recovery, when_needed
• Importance: low

snapshot.locking.mode

Controls how long the connector holds onto the global read lock while it is performing a snapshot. The default is minimal, which means the connector holds the global read lock (and thus prevents any updates) for just the initial portion of the snapshot, while the database schemas and other metadata are being read. The remaining work in a snapshot involves selecting all rows from each table. This is accomplished using a REPEATABLE READ transaction, even when the lock is no longer held and other operations are updating the database. However, in some cases it may be desirable to block all writes for the entire duration of the snapshot. In this situation, set this property to extended. Using a value of none prevents the connector from acquiring any table locks during the snapshot process. While this setting is allowed with all snapshot modes, it is safe to use if and only if no schema changes are happening while the snapshot is running.

• Type: string
• Default: minimal
• Valid Values: extended, minimal, minimal_percona, none
• Importance: low

tombstones.on.delete

Controls whether a tombstone event should be generated after a delete event. When set to true, the delete operations are represented by a delete event and a subsequent tombstone event. When set to 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.

• Type: boolean
• Default: true
• Importance: high

column.exclude.list

Regular expressions matching columns to exclude from change events

• Type: list
• Importance: medium

Connection details

poll.interval.ms

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.

• Type: int
• Default: 1000 (1 second)
• Valid Values: [1,…]
• Importance: low

max.batch.size

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

• Type: int
• Default: 1000
• Valid Values: [1,…,5000]
• Importance: low

event.processing.failure.handling.mode

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

• Type: string
• Default: fail
• Valid Values: fail, skip, warn
• Importance: low

heartbeat.interval.ms

Controls how frequently the connector sends heartbeat messages to a Kafka topic. The behavior of default value 0 is that the connector does not send heartbeat messages.

• Type: int
• Default: 0
• Valid Values: [0,…]
• Importance: low

database.history.skip.unparseable.ddl

A Boolean value that specifies whether the connector should ignore malformed or unknown database statements (true), or stop processing so a human can fix the issue (false). Defaults to false. Consider setting this to true to ignore unparseable statements.

• Type: boolean
• Default: false
• Importance: low

event.deserialization.failure.handling.mode

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

• Type: string
• Default: fail
• Valid Values: fail, skip, warn
• Importance: medium

inconsistent.schema.handling.mode

Specifies how the connector should react to binlog events that belong to a table missing from internal schema representation.

• Type: string
• Default: fail
• Valid Values: fail, skip, warn
• Importance: medium

Connector details

provide.transaction.metadata

Stores transaction metadata information in a dedicated topic and enables the transaction metadata extraction together with event counting.

• Type: boolean
• Default: false
• Importance: low

decimal.handling.mode

Specifies how DECIMAL and NUMERIC columns should be represented in change events, including: ‘precise’ (the default) 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; ‘string’ uses string to represent values; ‘double’ represents values using Java’s ‘double’, which may not offer the precision but will be far easier to use in consumers.

• Type: string
• Default: precise
• Valid Values: double, precise, string
• Importance: medium

binary.handling.mode

Specifies how binary (blob, binary, etc.) columns should be represented in change events, including: ‘bytes’ (the default) represents binary data as byte array; ‘base64’ represents binary data as base64-encoded string; ‘hex’ represents binary data as hex-encoded (base16) string.

• Type: string
• Default: bytes
• Valid Values: base64, bytes, hex
• Importance: low

time.precision.mode

Time, date, and timestamps can be represented with different kinds of precisions, including: ‘adaptive_time_microseconds’ TIME fields always use microseconds precision; ‘connect’ (the default) always represents time, date, 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.

• Type: string
• Default: connect
• Valid Values: adaptive_time_microseconds, connect
• Importance: medium

cleanup.policy

Set the topic cleanup policy

• Type: string
• Default: delete
• Valid Values: compact, delete
• Importance: medium

bigint.unsigned.handling.mode

Specifies how BIGINT UNSIGNED columns should be represented in change events.

• Type: string
• Default: long
• Valid Values: long, precise
• Importance: medium

enable.time.adjuster

Specifies if the year value conversion is adjusted by the connector or delegated to the database.

• Type: boolean
• Default: true
• Importance: medium

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

• Type: string
• Importance: high

output.key.format

Sets the output Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

• Type: string
• Default: JSON
• Valid Values: AVRO, JSON, JSON_SR, PROTOBUF, STRING
• Importance: high

after.state.only

Controls whether the generated Kafka record should contain only the state after applying change events.

• Type: boolean
• Default: true
• Importance: low

json.output.decimal.format

Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

BASE64 to serialize DECIMAL logical types as base64 encoded binary data and

NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.

• Type: string
• Default: BASE64
• Importance: low

Number of tasks for this connector

tasks.max

• Type: int
• Valid Values: [1,…,1]
• Importance: high

Next Steps

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.