MySQL Sink (JDBC) Connector for Confluent Cloud¶
The fully-managed MySQL Sink connector for Confluent Cloud exports data from Kafka topics to a MySQL database.
Note
This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see JDBC Connector (Source and Sink) for Confluent Platform.
Features¶
The MySQL Sink connector provides the following features:
- Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
- Table and column auto-creation:
auto.create
andauto-evolve
are supported. If tables or columns are missing, they can be created automatically. Table names are created based on Kafka topic names. - Database authentication: username/password authentication.
- Schemas: The connector supports Avro, JSON Schema, and Protobuf input data formats. Schema Registry must be enabled to use a Schema Registry-based format.
- Modes: This connector inserts and upserts Kafka records into a MySQL database.
- Primary key support: Supported PK modes are
kafka
,none
,record_key
, andrecord_value
. Used in conjunction with the PK Fields property.
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.
- For connector limitations, see MySQL Sink Connector 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.
Quick Start¶
Use this quick start to get up and running with the Confluent Cloud MySQL sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a MySQL database.
- Prerequisites
- Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
- Access to a MySQL database.
- 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.
- For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
- 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.
- 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.
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 4: Enter the connector details¶
Note
- Ensure you have all your prerequisites completed.
- An asterisk ( * ) designates a required entry.
At the Add MySQL Sink Connector screen, complete the following:
If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.
To create a new topic, click +Add new topic.
- Select the way you want to provide Kafka Cluster credentials. You can
choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
- Click Continue.
- Enter the following Databricks Delta Lake connection details:
- Connection host: The JDBC connection host.
- Connection port: The JDBC connection port.
- Connection user: The JDBC connection user.
- Connection password: The JDBC connection password.
- Database name: The JDBC database name.
- SSL mode: The SSL mode to use to connect to your database.
- Trust store: The trust store file that contains the server CA certificate.
- Trust store password: The password for the trust store file that contains the server CA certificate.
- Click Continue.
Note
Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.
Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format.
Select an insert mode:
INSERT
: Use the standardINSERT
row function. An error occurs if the row already exists in the table.UPSERT
: This mode is similar toINSERT
. However, if the row already exists, theUPSERT
function overwrites column values with the new values provided.
Show advanced configurations
Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
Auto create table: Whether to automatically create the destination table if it is missing.
Auto add columns: Whether to automatically add columns in the table if they are missing.
Database timezone: Name of the JDBC timezone that should be used in the connector when inserting time-based values.
Table name format: A format string for the destination table name, which may contain
${topic}
as a placeholder for the originating topic name. For example, to create a table namedkafka-orders
based on a Kafka topic namedorders
, you would enterkafka-${topic}
in this field.Table types: The comma-separated types of database tables to which the sink connector can write.
Fields included: List of comma-separated record value field names. If empty, all fields from the record value are used.
PK mode: The primary key mode.
PK Fields: List of comma-separated primary key field names.
When to quote SQL identifiers: When to quote table names, column names, and other identifiers in SQL statements.
Max rows per batch: Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.
Input Kafka record key format: Sets the input Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING. A valid schema must be available in Schema Registry to use a schema-based message format.
Delete on null: Whether to treat null record values as deletes. Requires
pk.mode
to berecord_key
.For Transforms and Predicates, see the Single Message Transforms (SMT) documentation for details.
For all property values and definitions, see Configuration Properties.
Click Continue.
Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.
- To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
- Click Continue.
Verify the connection details.
Click Launch.
The status for the connector should go from Provisioning to Running.
Step 5: Check the results in the database¶
Verify that new records are being added to the MySQL database.
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.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.
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 required and optional connector properties:
{
"connector.class": "MySqlSink",
"name": "MySqlSinkConnector_0",
"topics": "pageviews",
"input.data.format": "AVRO",
"input.key.format": "AVRO",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "****************",
"kafka.api.secret": "****************************************************************",
"connection.host": "dev-testing-temp.abcdefghijk.us-west-7.rds.amazonaws.com",
"connection.port": "3306",
"connection.user": "admin",
"connection.password": "**********",
"db.name": "test",
"insert.mode": "INSERT",
"auto.create": "true",
"auto.evolve": "true",
"tasks.max": "1"
}
Note the following property definitions:
"connector.class"
: Identifies the connector plugin name."name"
: Sets a name for your new connector."topics"
: Identifies the topic name or a comma-separated list of topic names."input.data.format"
: Sets the input record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR (JSON Schema), or PROTOBUF. You must have Confluent Cloud Schema Registry configured if using a schema-based message format."input.key.format"
: Sets the input record key format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR (JSON Schema), PROTOBUF, or STRING. You must have Confluent Cloud Schema Registry configured if using a schema-based message format."delete.on.null"
: Whether to treat null record values as deletes. Requirespk.mode
to berecord_key
. Defaults tofalse
.
"kafka.auth.mode"
: Identifies the connector authentication mode you want to use. There are two options:SERVICE_ACCOUNT
orKAFKA_API_KEY
(the default). To use an API key and secret, specify the configuration propertieskafka.api.key
andkafka.api.secret
, as shown in the example configuration (above). To use a service account, specify the Resource ID in the propertykafka.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
"insert.mode"
: Enter one of the following modes:INSERT
: Use the standardINSERT
row function. An error occurs if the row already exists in the table.UPSERT
: This mode is similar toINSERT
. However, if the row already exists, theUPSERT
function overwrites column values with the new values provided.
"auto.create"
(tables) and"auto-evolve"
(columns): (Optional) Sets whether to automatically create tables or columns if they are missing relative to the input record schema. If not entered in the configuration, both default tofalse
."tasks.max"
: Maximum number of tasks the connector can run. See Confluent Cloud connector limitations for additional task information.
The following lists a few optional properties. See the MySQL Sink configuration properties for other property values and definitions.
"pk.mode"
: Supported modes are listed below:kafka
: Kafka coordinates are used as the primary key. Must be used with the PK Fields.none
: No primary keys used.record_key
: Fields from the record key are used. May be a primitive or a struct.record_value
: Fields from the Kafka record value are used. Must be a struct type.
"pk.fields"
: A list of comma-separated primary key field names. The runtime interpretation of this property depends on thepk.mode
selected. Options are listed below:kafka
: Must be three values representing the Kafka coordinates. If left empty, the coordinates default to__connect_topic,__connect_partition,__connect_offset
.none
: PK Fields not used.record_key
: If left empty, all fields from the key struct are used. Otherwise, this is used to extract the fields in the property. A single field name must be configured for a primitive key.record_value
: Used to extract fields from the record value. If left empty, all fields from the value struct are used.
Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.
See Configuration Properties for all property values and definitions.
Step 4: Load the configuration 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-server-sink-config.json
Example output:
Created connector MySqlSinkConnector_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 | MySqlSinkConnector_0 | RUNNING | sink
Step 6: Check the results in the database.¶
Verify that new records are being added to the MySQL database.
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.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.
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.
Which topics do you want to get data from?¶
topics
Identifies the topic name or a comma-separated list of topic names.
- Type: list
- 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
Input messages¶
input.data.format
Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, or PROTOBUF. 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
input.key.format
Sets the input Kafka record key format. This need to be set to a proper format if using pk.mode=record_key. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING. 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
delete.enabled
Whether to treat null record values as deletes. Requires pk.mode to be record_key.
- Type: boolean
- Default: false
- Importance: low
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
How should we connect to your database?¶
connection.host
Depending on the service environment, certain network access limitations may exist. Make sure the connector can reach your service. Do not include jdbc:xxxx:// in the connection hostname property (e.g. database-1.abc234ec2.us-west.rds.amazonaws.com).
- Type: string
- Importance: high
connection.port
JDBC connection port.
- Type: int
- Valid Values: [0,…,65535]
- Importance: high
connection.user
JDBC connection user.
- Type: string
- Importance: high
connection.password
JDBC connection password.
- Type: password
- Importance: high
db.name
JDBC database name.
- Type: string
- Importance: high
ssl.mode
What SSL mode should we use to connect to your database. prefer and require allows for the connection to be encrypted but does not do certificate validation on the server. verify-ca and verify-full require a file containing SSL CA certificate to be provided. The server’s certificate will be verified to be signed by one of these authorities.`verify-ca` will verify that the server certificate is issued by a trusted CA. verify-full will verify that the server certificate is issued by a trusted CA and that the server hostname matches that in the certificate. Client authentication is not performed.
- Type: string
- Default: prefer
- Importance: high
ssl.truststorefile
The trust store containing server CA certificate. Only required if using verify-ca or verify-full ssl mode.
- Type: password
- Default: [hidden]
- Importance: low
ssl.truststorepassword
The trust store password containing server CA certificate. Only required if using verify-ca or verify-full ssl mode.
- Type: password
- Default: [hidden]
- Importance: low
Database details¶
insert.mode
The insertion mode to use. INSERT uses the standard INSERT row function. An error occurs if the row already exists in the table; UPSERT mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided.
- Type: string
- Default: INSERT
- Importance: high
table.name.format
A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name.
For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.
- Type: string
- Default: ${topic}
- Importance: medium
table.types
The comma-separated types of database tables to which the sink connector can write. By default this is
TABLE
, but any combination ofTABLE
andVIEW
is allowed. Not all databases support writing to views, and when they do the sink connector will fail if the view definition does not match the records’ schemas (regardless ofauto.evolve
).- Type: list
- Default: TABLE
- Importance: low
fields.whitelist
List of comma-separated record value field names. If empty, all fields from the record value are utilized, otherwise used to filter to the desired fields.
- Type: list
- Importance: medium
db.timezone
Name of the JDBC timezone used in the connector when querying with time-based criteria. Defaults to UTC.
- Type: string
- Default: UTC
- Importance: medium
date.timezone
Name of the JDBC timezone that should be used in the connector when inserting DATE type values. Defaults to DB_TIMEZONE that uses the timezone set for db.timzeone configuration (to maintain backward compatibility). It is recommended to set this to UTC to avoid conversion for DATE type values.
- Type: string
- Default: DB_TIMEZONE
- Valid Values: DB_TIMEZONE, UTC
- Importance: medium
Primary Key¶
pk.mode
The primary key mode, also refer to pk.fields documentation for interplay. Supported modes are:
none: No keys utilized.
kafka: Apache Kafka® coordinates are used as the PK.
record_value: Field(s) from the record value are used, which must be a struct.
record_key: Field(s) from the record key are used, which must be a struct.
- Type: string
- Valid Values: kafka, none, record_key, record_value
- Importance: high
pk.fields
List of comma-separated primary key field names. The runtime interpretation of this config depends on the pk.mode:
none: Ignored as no fields are used as primary key in this mode.
kafka: Must be a trio representing the Kafka coordinates, defaults to __connect_topic,__connect_partition,__connect_offset if empty.
record_value: If empty, all fields from the value struct will be used, otherwise used to extract the desired fields.
- Type: list
- Importance: high
SQL/DDL Support¶
auto.create
Whether to automatically create the destination table if it is missing.
- Type: boolean
- Default: false
- Importance: medium
auto.evolve
Whether to automatically add columns in the table if they are missing.
- Type: boolean
- Default: false
- Importance: medium
quote.sql.identifiers
When to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default is ‘always’.
- Type: string
- Default: ALWAYS
- Valid Values: ALWAYS, NEVER
- Importance: medium
Connection details¶
batch.sizes
Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.
- Type: int
- Default: 3000
- Valid Values: [1,…,5000]
- Importance: low
Consumer configuration¶
max.poll.interval.ms
The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).
- Type: long
- Default: 300000 (5 minutes)
- Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
- Importance: low
max.poll.records
The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.
- Type: long
- Default: 500
- Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
- Importance: low
Number of tasks for this connector¶
tasks.max
Maximum number of tasks for the connector.
- Type: int
- Valid Values: [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.