MySQL CDC Source (Debezium) [Legacy] Connector for Confluent Cloud

Tip

Confluent recommends using version 2 of this connector. For more information, see MySQL CDC Source Connector V2 (Debezium) for Confluent Cloud and Moving from V1 to V2.

The fully-managed MySQL Change Data Capture (CDC) Source (Debezium) [Legacy] 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.

Note

Features

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

  • Topics created automatically: The connector automatically creates Kafka topics using the naming convention: <database.server.name>.<schemaName>.<tableName>. The topics 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.
  • Incremental snapshot: Supports incremental snapshotting via signaling.
  • Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.

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 Managed and Custom Connectors section.

Limitations

Be sure to review the following information.

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: 8 MB
  • Standard cluster: 8 MB
  • Enterprise cluster: 8 MB
  • Dedicated cluster: 20 MB

For more information about Confluent Cloud clusters, see Kafka Cluster Types in Confluent Cloud.

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.

Manage custom offsets

Custom offsets for managed connectors is Early Access

Confluent uses Early Access releases to gather feedback. This service should be used only for evaluation and non-production testing purposes, or to provide feedback to Confluent, particularly as it becomes more widely available in follow-on preview editions.

Early Access is intended for evaluation use in development and testing environments only and not for production use. The warranty, SLA, and Support Services provisions of your agreement with Confluent do not apply to Early Access. Confluent considers Early Access to be a Proof of Concept as defined in the Confluent Cloud Terms of Service. Confluent may discontinue providing preview releases of the Early Access releases at any time at the sole discretion of Confluent.

You can manage the offsets for this connector. Offsets provide information on the point in the source system from which the connector accesses data. For more information, see Manage Offsets for Fully-Managed Connectors.

To manage offsets:

To get the current offset, make a GET request that specifies the environment, Kafka cluster, and connector name.

GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets
Host: https://api.confluent.cloud

Response:

Successful calls return HTTP 200 with a JSON payload that describes the offset.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
        {
            "partition": {
                "server": "server_01"
            },
            "offset": {
                "event": 2,
                "file": "mysql-bin.000598",
                "pos": 2326,
                "row": 1,
                "server_id": 1,
                "transaction_id": null,
                "ts_sec": 1711648627
            }
        }
    ],
    "metadata": {
        "observed_at": "2024-03-28T17:57:48.139635200Z"
    }
}

Responses include the following information:

  • The position of latest offset.
  • The observed time of the offset in the metadata portion of the payload. The observed_at time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Use observed_at to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. Calling get repeatedly will fetch more recently observed offsets.
  • Information about the connector.

JSON payload

The table below offers a description of the unique fields in the JSON payload for managing offsets of the MySQL Change Data Capture (CDC) Source connector.

Field Definition Required/Optional
event The number of rows and events to skip while starting from this file and position. Use event and row together but only use these fields if you understand which rows and events to skip. For most cases, you only need to provide file and position. Optional
file The file from the last processed binlog. Use file and pos together. Required
pos The position from the last processed binlog. Use pos and file together. Required
row The number of rows and events to skip while starting from this file and position. Use event and row together but only use these fields if you understand which rows and events to skip. For most cases, you only need to provide file and position. Optional
server_id The id of the server from which the event originated. For more information, see MySQL documentation. Optional
transaction_id Mostly null, provided only when provide.transaction.metadata is set to true in the config. For more information, see Debezium Documentation. Optional
ts_sec The timestamp at which the event at this pos was executed in the database. Optional

Important

Do not reset the offset to an arbitrary number. Use only offsets found in the binlog file. To find offsets in a binlog file, use the mysqlbinlog utility. Offsets appear in this format: # at <offset>

Quick Start

Use this quick start to get up and running with the MySQL CDC Source (Debezium) [Legacy] 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

  • 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:

    Set database backup
    Set database binlog
    Set database binlog row image

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 Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector

Click the MySQL CDC Source (Debezium) [Legacy] connector card.

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) [Legacy] Connector screen, complete the following:

  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 Managed and Custom Connectors section.

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.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

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.

  • "signal.data.collection": Fully-qualified name of the data collection that is used to send signals to the connector. The collection name is of the form databaseName.tableName. These signals can be used to perform incremental snapshotting.

  • "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. For additional information about the Debezium SMTs ExtractNewRecordState and EventRouter (Debezium), see Debezium transformations.

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 cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file 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 cluster 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 Managed and Custom Connectors section.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors 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

Kafka API Key. Required when kafka.auth.mode==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

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password
  • Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

  • Type: string
  • Default: default
  • Importance: medium

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

signal.data.collection

Fully-qualified name of the data collection that needs to be used to send signals to the connector. Use the following format to specify the fully-qualified collection name: databaseName.tableName

  • Type: string
  • Importance: medium
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
value.converter.reference.subject.name.strategy

Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

  • Type: string
  • Default: DefaultReferenceSubjectNameStrategy
  • Importance: high
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
key.converter.reference.subject.name.strategy

Set the subject reference name strategy for key. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

  • Type: string
  • Default: DefaultReferenceSubjectNameStrategy
  • Importance: high

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

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

Next Steps

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.

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