IBM Db2 Source (JDBC) Connector for Confluent Cloud

The fully-managed IBM Db2 Source connector for Confluent Cloud reads data from an IBM Db2 database and writes records to Apache Kafka® topics. The connector can obtain a snapshot of existing data and then monitor and record all subsequent row-level changes. 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 Kafka topic. Note that deleted records are not captured.

This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see JDBC Connector (Source and Sink) for Confluent Platform.

If you require private networking for fully-managed connectors, make sure to set up the proper networking beforehand. For more information, see Manage Networking for Confluent Cloud Connectors.

Features

The IBM Db2 Source connector provides the following features:

  • At least once delivery: The connector guarantees that records are delivered at least once to the Kafka topic.

  • Topics created automatically: The connector automatically creates Kafka topics using the naming convention: <topic.prefix><tableName>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3.

  • Insert modes:

    • timestamp mode is enabled when only a timestamp column is specified when you enter database details.

    • timestamp+incrementing mode is enabled when both a timestamp column and incrementing column are specified when you enter database details.

      Important

      A timestamp column must not be nullable.

  • Record processing: Supports table and query modes. Use the query property to execute custom SQL queries for joining tables or selecting specific data subsets.

  • SSL support: Supports one-way SSL.

  • Client-side encryption (CSFLE) support: The connector supports Client-Side Field Level Encryption (CSFLE) for sensitive data. For more information about CSFLE setup, see the connector configuration.

  • Data format with or without a schema: The connector supports Avro, JSON Schema, Protobuf, JSON (schemaless), or Bytes. 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.

  • Select configuration properties:

    • db.timezone

    • poll.interval.ms

    • batch.max.rows

    • timestamp.delay.interval.ms

    • topic.prefix

    • schema.pattern

  • Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.

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

Limitations

For connector limitations, see IBM Db2 Source JDBC Connector.

Manage custom offsets

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

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": {
                "protocol": "1",
                "table": "{table_name}"
            },
            "offset": {
                "incrementing": 26
            }
        }
    ],
    "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.

  • In these examples, the curly braces around “{connector_name}” indicate a replaceable value.

To update the offset, make a POST request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies new offset and a patch type.

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

 {
     "type": "PATCH",
     "offsets": [
       {
         "partition": {
           "protocol": "1",
           "table": "{table_name}"
         },
         "offset": {
           "incrementing": 3
         }
       }
     ]
 }

Considerations:

  • You can only make one offset change at a time for a given connector.

  • This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.

  • For source connectors, the connector attempts to read from the position defined by the requested offsets.

Response:

Successful calls return HTTP 202 Accepted with a JSON payload that describes the offset.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
        {
            "partition": {
                "protocol": "1",
                "table": "{table_name}"
            },
            "offset": {
                "incrementing": 3
            }
        }
    ],
    "requested_at": "2024-03-28T17:58:45.606796307Z",
    "type": "PATCH"
}

Responses include the following information:

  • The requested position of the offsets in the source.

  • The time of the request to update the offset.

  • Information about the connector.

To delete the offset, make a POST request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies the delete type.

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

{
  "type": "DELETE"
}

Considerations:

  • Delete requests delete the offset for the provided partition and reset to the base state. A delete request is as if you created a fresh new connector.

  • This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.

  • Do not issue delete and patch requests at the same time.

  • For source connectors, the connector attempts to read from the position defined in the base state.

Response:

Successful calls return HTTP 202 Accepted with a JSON payload that describes the result.

{
  "id": "lcc-example123",
  "name": "{connector_name}",
  "offsets": [],
  "requested_at": "2024-03-28T17:59:45.606796307Z",
  "type": "DELETE"
}

Responses include the following information:

  • Empty offsets.

  • The time of the request to delete the offset.

  • Information about Kafka cluster and connector.

  • The type of request.

To get the status of a previous offset request, 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/request/status
Host: https://api.confluent.cloud

Considerations:

  • The status endpoint always shows the status of the most recent PATCH/DELETE operation.

Response:

Successful calls return HTTP 200 with a JSON payload that describes the result. The following is an example of an applied patch.

{
   "request": {
      "id": "lcc-example123",
      "name": "{connector_name}",
      "offsets": [
          {
              "partition": {
                  "protocol": "1",
                  "table": "{table_name}"
              },
              "offset": {
                  "incrementing": 3
              }
          }
      ],
      "requested_at": "2024-03-28T17:58:45.606796307Z",
      "type": "PATCH"
   },
   "status": {
      "phase": "APPLIED",
      "message": "The Connect framework-managed offsets for this connector have been altered successfully. However, if this connector manages offsets externally, they will need to be manually altered in the system that the connector uses."
   },
   "previous_offsets": [
       {
           "partition": {
               "protocol": "1",
               "table": "{table_name}"
           },
           "offset": {
               "incrementing": 26
           }
       }
   ],
   "applied_at": "2024-03-28T17:58:48.079141883Z"
}

Responses include the following information:

  • The original request, including the time it was made.

  • The status of the request: applied, pending, or failed.

  • The time you issued the status request.

  • The previous offsets. These are the offsets that the connector last updated prior to updating the offsets. Use these to try to restore the state of your connector if a patch update causes your connector to fail or to return a connector to its previous state after rolling back.

JDBC modes and offsets

You can run the JDBC source connectors in one of four modes. Each mode uses a different offset object in its JSON payload to track the progress of the connector. The provided samples show an offset object from a JBDC source connector in incrementing mode.

  • bulk - No offset. This is the default mode for JDBC source connectors.

  • incrementing - The offset is provided by the incrementing property in the offset object.

  • timestamp - The offset is provided by the timestamp and timestamp-nanos properties in the offset object.

  • timestamp+incrementing - The offset is provided by the incrementing, timestamp and timestamp-nanos properties in the offset object.

JSON payload

The table below offers a description of the unique fields in the JSON payload for managing offsets of the JDBC Source connectors, including:

  • IBM Db2 Source connector

  • Microsoft SQL Server Source connector

  • MySQL Source connector

  • Oracle Database Source connector

  • PostgreSQL Source connector

Field

Definition

Required/Optional

incrementing

Specifies the value of incrementing.column.name which identifies the current offset. The connector gets only values greater than the value in this field.

Available only in the following modes: incrementing, timestamp+incrementing.

Required

protocol

Specifies the protocol.

Available in the following modes: incrementing, timestamp, timestamp+incrementing.

Required

table

The name of the table.

Available in the following modes: incrementing, timestamp, timestamp+incrementing.

Required

timestamp

The number of milliseconds since January 1, 1970, 00:00:00 GMT represented by the Timestamp object of the column value.

Available only in the following modes: timestamp, timestamp+incrementing.

Required

timestamp_nanos

Fractional seconds component of the timestamp object.

Available only in the following modes: timestamp, timestamp+incrementing.

Required

Quick start

Use this quick start to get up and running with the Confluent Cloud IBM Db2 Source connector. The quick start provides the basics of selecting the connector and configuring it to read data from an IBM Db2 database and writing records to Kafka topics.

Prerequisites

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

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

  • An IBM Db2 for Linux, UNIX, and Windows (LUW) database. The connector supports only LUW-based IBM Db2 databases.

  • The connector automatically creates Kafka topics using the naming convention: <topic.prefix><tableName>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3. If you want to create topics with specific settings, create the topics before running this connector.

    Important

    If you are configuring granular access using a service account, and you leave the optional Topic prefix (topic.prefix) configuration property empty, you must grant ACL CREATE and WRITE access to all the Kafka topics or create RBAC role bindings. To add ACLs, you use the (*) wildcard in the ACL entries as shown in the following examples.

    confluent kafka acl create --allow --service-account
"<service-account-id>" --operation create --topic "*"

    confluent kafka acl create --allow --service-account
"<service-account-id>" --operation write --topic "*"

  • 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.

  • Make sure your connector can reach your service. Consider the following before running the connector:

  • 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

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

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 IBM Db2 Source connector card.

IBM Db2 Source Connector Card

Step 4: Enter the connector details

Complete the following steps to configure and launch the connector.

Note

  • Make sure you have all your prerequisites completed.

  • An asterisk ( * ) designates a required entry.

At the IBM Db2 Source Connector screen, complete the following:

In the Topic prefix field, define a topic prefix your connector uses to publish to Kafka topics. The connector creates Kafka topics using the following naming convention: <topic.prefix><tableName>.

Important

If you are configuring granular access using a service account, and you leave the optional Topic prefix (topic.prefix) configuration property empty, you must grant ACL CREATE and WRITE access to all the Kafka topics or create RBAC role bindings. To add ACLs, you use the (*) wildcard in the ACL entries as shown in the following examples.

confluent kafka acl create --allow --service-account
"<service-account-id>" --operation create --topic "*"

confluent kafka acl create --allow --service-account
"<service-account-id>" --operation write --topic "*"

  1. 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.

    Note

    Freight clusters support only service accounts for Kafka authentication.

  2. Click Continue.

  1. Configure the authentication properties:

    • Connection host: The host name or IP address of the database server. 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 host property. For example, database-1.abc234ec2.us-west.rds.amazonaws.com.

    • Connection port: The port number of the database server.

    • Connection user: The database user for the connection.

    • Connection password: The password for the database user.

    • Database name: The name of the database to connect to.

    • SSL mode: The SSL mode for connecting to the database. prefer allows the connection to be unencrypted and require encrypts the connection but does not perform certificate validation on the server. verify-ca and verify-full require you to provide a file containing the SSL CA certificate. The server certificate is verified as signed by one of these authorities. verify-ca verifies that the server certificate is issued by a trusted CA. verify-full verifies that the server certificate is issued by a trusted CA and that the server host name matches the name in the certificate. Client authentication is not performed.

    • Trust store: The binary trust store file that contains the server CA certificate. Required only if you use verify-ca or verify-full SSL mode. The connector supports files in JKS format. For REST API usage, you must base64-encode the binary trust store file and prefix it with data:text/plain;base64,. For example, first encode the file base64_truststore=$(cat /path/to/truststore.jks | base64) and then use data:text/plain;base64,$base64_truststore as the value.

    • Trust store password: The trust store password for the file containing the server CA certificate. Required only if you use verify-ca or verify-full SSL mode.

  2. Click Continue.

  • Table types: By default, the connector detects only tables with type TABLE from the source database. Use this property to specify a comma-separated list of table types to extract.

  • Database timezone: The JDBC timezone name used in the connector when querying with time-based criteria. Defaults to UTC.

  • Table include list: A comma-separated list of regular expressions that match the fully-qualified names of tables to copy. Table names are case-sensitive. For example, table.include.list: schema1.customer.*,schema2.order.*. If specified, you cannot set table.whitelist.

  • Table exclude list: A comma-separated list of regular expressions that match the fully-qualified names of tables to exclude from copying. Table names are case-sensitive. For example, table.exclude.list: schema1.customer.*,schema2.order.*. If specified, you cannot set table.whitelist.

Output messages

  • Select output record value format: Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON.

    Note

    You must have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, or PROTOBUF.

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?.

  • Mode: The mode for updating a table each time it is polled. BULK: performs a bulk load of the entire table each time it is polled. TIMESTAMP: uses a timestamp (or timestamp-like) column to detect new and modified rows. This assumes the column is updated with each write, and that values are monotonically incrementing, but not necessarily unique. INCREMENTING: uses a strictly incrementing column on each table to detect only new rows. This does not detect modifications or deletions of existing rows. TIMESTAMP AND INCREMENTING: uses two columns, a timestamp column that detects new and modified rows and a strictly incrementing column which provides a globally unique ID for updates so each row can be assigned a unique stream offset.

  • Table to timestamp columns mappings: A comma-separated list of table regex to timestamp column mappings. When you specify multiple timestamp columns, the connector uses the COALESCE SQL function to determine the effective timestamp for a row. Expected format is regex1:[col1|col2],regex2:[col3]. Regexes are matched against the fully-qualified table names. Identifier names are case-sensitive. Every table included for capture must match exactly one of the provided mappings. For example, .*\.customers.*:[updated_at|modified_at],.*\.orders.*:[changed_at].

  • Numeric Mapping: Maps NUMERIC values by precision and optionally scale to integral or decimal types. Use none if all NUMERIC columns are to be represented by the Connect DECIMAL logical type. Use best_fit if NUMERIC columns should be cast to Connect INT8, INT16, INT32, INT64, or FLOAT64 based upon the column precision and scale. Use best_fit_eager_double if, in addition to the properties of best_fit, you want to always cast NUMERIC columns with scale to Connect FLOAT64 type, despite potential loss in accuracy. Use precision_only to map NUMERIC columns based only on the column precision assuming the column scale is 0. The none option is the default, but may lead to serialization issues with Avro since the Connect DECIMAL type is mapped to its binary representation, and best_fit is often preferred since it maps to the most appropriate primitive type.

  • Table to incrementing column mappings: A comma-separated list of table regex to incrementing column mappings. Expected format is regex1:col1,regex2:col2. Regexes are matched against the fully-qualified table names. Identifier names are case-sensitive. Every table included for capture must match exactly one of the provided mappings. For example, .*\.customers.*:id,.*\.orders.*:order_id.

  • Schema pattern: The schema pattern used to fetch table metadata from the database.

  • Quote SQL Identifiers: Determines when to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default value is ALWAYS.

  • Timestamp column name (Deprecated): (Deprecated legacy configuration. Use timestamp.columns.mapping for new implementations.) A comma-separated list of one or more timestamp columns to detect new or modified rows using the COALESCE SQL function. Rows whose first non-null timestamp value is greater than the largest previous timestamp value seen are discovered with each poll. At least one column must not be nullable.

  • Initial timestamp: The epoch timestamp used for initial queries that use timestamp criteria. The value -1 sets the initial timestamp to the current time. If not specified, the connector retrieves all data. After the connector records a source offset, this property has no effect even if changed to a different value later.

  • Date Calendar System: The time elapsed from epoch populated in the end table topic for DATE or TIMESTAMP type columns can have two different values based upon the calendar used to interpret it. If LEGACY is used, the connector uses the hybrid Gregorian/Julian calendar which was the default in older Java date time APIs. If PROLEPTIC_GREGORIAN is used, the connector uses the proleptic Gregorian calendar which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover. This matches the behavior of modern Java date/time APIs (java.time). The default is LEGACY for backward compatibility.

    Warning

    Changing this configuration on an existing connector may lead to a drift in Kafka topic record values.

  • Transaction Isolation Level: The isolation level determines how transaction integrity is visible to other users and systems. DEFAULT: the default isolation level configured at the database server. READ_UNCOMMITTED: the lowest isolation level. At this level, one transaction may see dirty reads (that is, not-yet-committed changes made by other transactions). READ_COMMITTED: guarantees that any data read is already committed at the moment it is read. REPEATABLE_READ: in addition to the guarantees of READ_COMMITTED, also guarantees that any data read cannot change if the transaction reads the same data again. However, phantom reads are possible. SERIALIZABLE: the highest isolation level. In addition to everything REPEATABLE_READ guarantees, it also eliminates phantom reads.

  • Timestamp granularity for timestamp columns: Defines the granularity of the timestamp column. CONNECT_LOGICAL (default): represents timestamp values using Kafka Connect built-in representations. MICROS_LONG: represents timestamp values as microseconds since epoch. MICROS_STRING: represents timestamp values as microseconds since epoch in string. MICROS_ISO_DATETIME_STRING: uses ISO format for timestamps in microseconds. NANOS_LONG: represents timestamp values as nanoseconds since epoch. NANOS_STRING: represents timestamp values as nanoseconds since epoch in string. NANOS_ISO_DATETIME_STRING: uses ISO format for timestamps in nanoseconds.

  • Poll interval (ms): Set the time in milliseconds to wait for new change events when no data is returned. Default is 500 ms.

  • Max rows per batch: The maximum number of rows to include in a single batch when polling for new data. Use this setting to limit the amount of data buffered internally in the connector.

  • Delay interval (ms): How long to wait after a row with a certain timestamp appears before including it in the result. You can add some delay to allow transactions with an earlier timestamp to complete. The first execution fetches all available records (starting at timestamp 0) until current time minus the delay. Every subsequent execution gets data from the last time fetched until current time minus the delay.

Additional Configs

  • Value Converter Replace Null With Default: Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

  • 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.

  • Value Converter Schemas Enable: Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

  • Errors Tolerance: Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.

  • Value Converter Ignore Default For Nullables: When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.

  • Value Converter 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.

  • Key Converter Schema ID Serializer: The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.

  • Value Converter Connect Meta Data: Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.

  • Value Converter Value Subject Name Strategy: Determines how to construct the subject name under which the value schema is registered with Schema Registry.

  • Key Converter Key Subject Name Strategy: How to construct the subject name for key schema registration.

  • Value Converter Schema ID Serializer: The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.

Auto-restart policy

  • Enable Connector Auto-restart: Control the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. In such cases, you would need to manually restart the connector.

Database details

  • Query Config: If specified, the connector uses this custom SQL query to read source records, which allows for operations like joining tables or selecting subsets of data. Providing a query instructs the connector to read only the result set instead of performing a full table copy. This configuration supports different query modes with the incremental query properly constructed by appending a WHERE clause. For more information, see Incremental Query Modes. Only SELECT statements are supported.

    Note

    Always adhere to security best practices, like enforcing strict authorization through managed connector RBAC, applying appropriate network access controls for control plane APIs, and following the principle of least privilege when provisioning identities or credentials for any third-party systems.

Transforms

Data encryption

  • Enable Client-Side Field Level Encryption for data encryption. Specify a Service Account to access the Schema Registry and associated encryption rules or keys with that schema. For more information on CSFLE or CSPE setup, see Manage encryption for connectors.

Processing position

  • Set offsets: Click Set offsets to define a specific offset for this connector to begin procession data from. For more information on managing offsets, see Manage offsets.

For all property values and definitions, see Configuration properties.

  • Click Continue.

Based on the number of topic partitions you select, you are provided with a recommended number of tasks.

  1. To change the number of tasks, use the Range Slider to select the desired number of tasks.

  2. Click Continue.

  1. Verify the connection details by previewing the running configuration.

  2. After you validate that the properties are configured to your satisfaction, click Launch.

    The status for the connector should go from Provisioning to Running.

Step 5: Check the Kafka topic

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

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

{
    "name" : "confluent-ibmdb2-source",
    "connector.class": "IbmDb2Source",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.secret" : "<my-kafka-api-secret>",
    "topic.prefix" : "ibmdb2_",
    "ssl.mode" : "prefer",
    "connection.host" : "<my-database-endpoint>",
    "connection.port" : "50000",
    "connection.user" : "<my-database-user>",
    "connection.password": "<my-database-password>",
    "db.name": "<my-database-name>",
    "table.include.list": ".*<my_table>.*",
    "timestamp.columns.mapping": ".*<my_table>.*:[created_at]",
    "output.data.format": "JSON",
    "db.timezone": "UTC",
    "tasks.max" : "1"
}

Note the following property definitions:

  • "name": Sets a name for your new connector.

  • "connector.class": Identifies the connector plugin name.

  • "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

  • "topic.prefix": Enter a topic prefix. The connector automatically creates Kafka topics using the naming convention: <topic.prefix><tableName>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3. If you want to create topics with specific settings, create the topics before running this connector. If you are configuring granular access using a service account, you must set up ACLs for the topic prefix.

    Important

    If you are configuring granular access using a service account, and you leave the optional Topic prefix (topic.prefix) configuration property empty, you must grant ACL CREATE and WRITE access to all the Kafka topics or create RBAC role bindings. To add ACLs, you use the (*) wildcard in the ACL entries as shown in the following examples.

    confluent kafka acl create --allow --service-account
"<service-account-id>" --operation create --topic "*"

    confluent kafka acl create --allow --service-account
"<service-account-id>" --operation write --topic "*"

  • The following provides more information about how to use the ssl.mode property:

    • The default option prefer is enabled if 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.

    • 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.

    • verify-ca: similar to require, but also verifies the server TLS certificate against the configured Certificate Authority (CA) certificates. Fails if no valid matching CA certificates are found.

    • verify-full: similar to verify-ca, but also verifies that the server certificate matches the host to which the connection is attempted.

    If you choose verify-ca or verify-full, use the property ssl.rootcertfile and add the contents of the text certificate file for the property value. For example, "ssl.rootcertfile": "<certificate-text>".

  • The following provides more information about how to use the timestamp.columns.mapping and incrementing.column.name properties.

    • Enter a timestamp.columns.mapping value to enable timestamp mode. This mode uses a timestamp (or timestamp-like) column to detect new and modified rows. This assumes the column is updated with each write, and that values are monotonically incrementing, but not necessarily unique.

    • Enter both a timestamp.columns.mapping value and an incrementing.column.name to enable timestamp+incrementing mode. This mode uses two columns, a timestamp column that detects new and modified rows, and a strictly incrementing column which provides a globally unique ID for updates so each row can be assigned a unique stream offset. By default, the connector only detects table.types with type TABLE from the source database. Enter VIEW for virtual tables created from joining one or more tables.

  • If you define a schema pattern in your database, you need to enter the schema.pattern property to fetch table metadata from the database. "" retrieves table metadata for tables not using a schema. null (default) indicates that the schema name is not used to narrow the search and that all table metadata is fetched, regardless of the schema.

  • "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 message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

  • "db.timezone": Identifies the database timezone. This can be any valid database timezone. The default is UTC. For more information, see this list of database timezones.

Note

To enable CSFLE or CSPE for data encryption, specify the following properties:

  • csfle.enabled: Flag to indicate whether the connector honors CSFLE or CSPE rules.

  • sr.service.account.id: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema.

For more information on CSFLE or CSPE setup, see Manage encryption for connectors.

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

For all property values and definitions, see Configuration properties.

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 ibmdb2-source.json

Example output:

Created connector confluent-ibmdb2-source 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   | confluent-ibmdb2-source      | RUNNING | source

Step 6: Check the Kafka topic

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

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples 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, whenever possible.

  • Type: string

  • Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY

  • 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 do you want to prefix table names?

topic.prefix

Prefix to prepend to table names to generate the name of the Apache Kafka® topic to publish data to.

  • Type: string

  • 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 allows for the connection to not be encrypted 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 binary trust store file that contains the server’s CA certificate. Only required if you use verify-ca or verify-full ssl mode. The connector supports files in JKS format. For REST API usage, you must base64-encode the binary trust store file and prefix it with data:text/plain;base64,. For example, first, encode the file base64_truststore=<span>(cat /path/to/truststore.jks | base64) and then use data:text/plain;base64,</span>base64_truststore as the value.

  • 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

table.include.list

A comma-separated list of regular expressions that match the fully-qualified names of tables to be copied. Use a comma-separated list to specify multiple regular expressions. Table names are case-sensitive. For example, table.include.list: schema1.customer.*,schema2.order.*. If specified, table.whitelist cannot be set.

  • Type: list

  • Importance: medium

table.exclude.list

A comma-separated list of regular expressions that match the fully-qualified names of tables to be excluded from copying. Use a comma-separated list to specify multiple regular expressions. Table names are case-sensitive. For example, table.exclude.list: schema1.customer.*,schema2.order.*. If specified, table.whitelist cannot not be set.

  • Type: list

  • Importance: medium

query

If specified, the connector uses this custom SQL query to read source records, which allows for operations like joining tables or selecting subsets of data. Providing a query instructs the connector to read only the result set instead of performing a full table copy. This configuration supports different query modes with the incremental query properly constructed by appending a WHERE clause (For more information, Incremental Query Modes - <https://docs.confluent.io/kafka-connectors/jdbc/current/source-connector/overview.html#incremental-query-modes>). Note that only SELECT statements are supported. Always adhere to security best practices, like enforcing strict authorization via <https://docs.confluent.io/cloud/current/connectors/managed-connector-rbac.html#managed-connector-rbac>, applying appropriate :ref: network access controls - <https://docs.confluent.io/cloud/current/security/access-control/ip-filtering/manage-ip-filters.html> for control plane APIs, and following the principle of least privilege when provisioning identities or credentials for any third-party systems.

  • Type: password

  • Default: [hidden]

  • Importance: medium

table.types

By default, the JDBC connector will only detect tables with type TABLE from the source Database. This config allows a command separated list of table types to extract.

  • Type: list

  • Default: TABLE

  • Importance: medium

schema.pattern

Schema pattern to fetch table metadata from the database.

  • Type: string

  • Importance: high

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

numeric.mapping

Map NUMERIC values by precision and optionally scale to integral or decimal types. Use none if all NUMERIC columns are to be represented by Connect’s DECIMAL logical type. Use best_fit if NUMERIC columns should be cast to Connect’s INT8, INT16, INT32, INT64, or FLOAT64 based upon the column’s precision and scale. Use best_fit_eager_double if, in addition to the properties of best_fit described above, it is desirable to always cast NUMERIC columns with scale to Connect FLOAT64 type, despite potential of loss in accuracy. Use precision_only to map NUMERIC columns based only on the column’s precision assuming that column’s scale is 0. The none option is the default, but may lead to serialization issues with Avro since Connect’s DECIMAL type is mapped to its binary representation, and best_fit will often be preferred since it maps to the most appropriate primitive type.

  • Type: string

  • Default: none

  • Importance: low

timestamp.granularity

Define the granularity of the Timestamp column. CONNECT_LOGICAL (default): represents timestamp values using Kafka Connect built-in representations. MICROS_LONG: represents timestamp values as micros since epoch. MICROS_STRING: represents timestamp values as micros since epoch in string. MICROS_ISO_DATETIME_STRING: uses iso format for timestamps in micros. NANOS_LONG: represents timestamp values as nanos since epoch. NANOS_STRING: represents timestamp values as nanos since epoch in string. NANOS_ISO_DATETIME_STRING: uses iso format

  • Type: string

  • Default: CONNECT_LOGICAL

  • Importance: low

Mode

mode

The mode for updating a table each time it is polled. BULK: perform a bulk load of the entire table each time it is polled. TIMESTAMP: use a timestamp (or timestamp-like) column to detect new and modified rows. This assumes the column is updated with each write, and that values are monotonically incrementing, but not necessarily unique. INCREMENTING: use a strictly incrementing column on each table to detect only new rows. Note that this will not detect modifications or deletions of existing rows. TIMESTAMP AND INCREMENTING: use two columns, a timestamp column that detects new and modified rows and a strictly incrementing column which provides a globally unique ID for updates so each row can be assigned a unique stream offset.

  • Type: string

  • Default: “”

  • Importance: medium

timestamp.columns.mapping

A comma-separated list of table regex to timestamp columns mappings. On specifying multiple timestamp columns, COALESCE SQL function would be used to find out the effective timestamp for a row. Expected format is regex1:[col1|col2],regex2:[col3]. Regexes would be matched against the fully-qualified table names. Identifier names are case sensitive. Every table included for capture should match exactly one of the provided mappings. An example for a valid input would be .*\.customers.*:[updated_at|modified_at],.*\.orders.*:[changed_at].

  • Type: list

  • Importance: medium

incrementing.column.mapping

A comma-separated list of table regex to incrementing column mappings. Expected format is regex1:col1,regex2:col2. Regexes would be matched against the fully-qualified table names. Identifier names are case sensitive. Every table included for capture should match exactly one of the provided mappings. An example for a valid input would be .*\.customers.*:id,.*\.orders.*:order_id.

  • Type: list

  • Importance: medium

timestamp.column.name

(Deprecated legacy configuration. Use timestamp.columns.mapping for new implementations.) Comma separated list of one or more timestamp columns to detect new or modified rows using the COALESCE SQL function. Rows whose first non-null timestamp value is greater than the largest previous timestamp value seen will be discovered with each poll. At least one column should not be nullable.

  • Type: list

  • Importance: medium

quote.sql.identifiers

When to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default value is ALWAYS.

  • Type: string

  • Default: ALWAYS

  • Valid Values: ALWAYS, NEVER

  • Importance: medium

transaction.isolation.mode

Isolation level determines how transaction integrity is visible to other users and systems. DEFAULT: This is the default isolation level configured at the Database Server. READ_UNCOMMITTED: This is the lowest isolation level. At this level, one transaction may see dirty reads (that is, not-yet-committed changes made by other transactions). READ_COMMITTED: This level guarantees that any data read is already committed at the moment it is read. REPEATABLE_READ: In addition to the guarantees of the READ_COMMITTED level, this option also guarantees that any data read cannot change, if the transaction reads the same data again. However, phantom reads are possible. SERIALIZABLE: This is the highest isolation level. In addition to everything REPEATABLE_READ guarantees, it also eliminates phantom reads.

  • Type: string

  • Default: DEFAULT

  • Valid Values: DEFAULT, READ_COMMITTED, READ_UNCOMMITTED, REPEATABLE_READ, SERIALIZABLE

  • Importance: medium

timestamp.initial

The epoch timestamp used for initial queries that use timestamp criteria. The value -1 sets the initial timestamp to the current time. If not specified, the connector retrieves all data. Once the connector has managed to successfully record a source offset, this property has no effect even if changed to a different value later on.

  • Type: long

  • Valid Values: [-1,…]

  • Importance: medium

date.calendar.system

The time elapsed from epoch populated in the end table topic for DATE or TIMESTAMP type columns can have two different values based upon the Calendar used to interpret it. If LEGACY is used, it will use the hybrid Gregorian/Julian calendar which was the default in the older java date time APIs. However, if ‘PROLEPTIC_GREGORIAN’ is used, then it will use the proleptic gregorian calendar which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover. This matches the behavior of modern Java date/time APIs (java.time). This is defaulted to LEGACY for backward compatibility. Changing this configuration on an existing connector might lead to a drift in the kafka topic record values.

  • Type: string

  • Default: LEGACY

  • Importance: medium

Connection details

poll.interval.ms

Frequency in ms to poll for new data in each table.

  • Type: int

  • Default: 5000 (5 seconds)

  • Valid Values: [100,…]

  • Importance: high

batch.max.rows

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: 100

  • Valid Values: [1,…,5000]

  • Importance: low

timestamp.delay.interval.ms

How long to wait after a row with a certain timestamp appears before we include it in the result. You may choose to add some delay to allow transactions with an earlier timestamp to complete. The first execution will fetch all available records (starting at timestamp 0) until current time minus the delay. Every following execution will get data from the last time we fetched until current time minus the delay.

  • Type: int

  • Default: 0

  • Valid Values: [0,…]

  • Importance: high

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

  • Default: JSON

  • Importance: high

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

  • Type: int

  • Valid Values: [1,…]

  • Importance: high

Additional Configs

header.converter

The converter class for the headers. This is used to serialize and deserialize the headers of the messages.

  • Type: string

  • Importance: low

producer.override.compression.type

The compression type for all data generated by the producer. Valid values are none, gzip, snappy, lz4, and zstd.

  • Type: string

  • Importance: low

producer.override.linger.ms

The producer groups together any records that arrive in between request transmissions into a single batched request. More details can be found in the documentation: https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms.

  • Type: long

  • Valid Values: [100,…,1000]

  • Importance: low

value.converter.allow.optional.map.keys

Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.

  • Type: boolean

  • Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

  • Type: boolean

  • Importance: low

value.converter.connect.meta.data

Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.

  • Type: boolean

  • Importance: low

value.converter.enhanced.avro.schema.support

Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.

  • Type: boolean

  • Importance: low

value.converter.enhanced.protobuf.schema.support

Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.flatten.unions

Whether to flatten unions (oneofs). Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.generate.index.for.unions

Whether to generate an index suffix for unions. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.generate.struct.for.nulls

Whether to generate a struct variable for null values. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.int.for.enums

Whether to represent enums as integers. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.latest.compatibility.strict

Verify latest subject version is backward compatible when use.latest.version is true.

  • Type: boolean

  • Importance: low

value.converter.object.additional.properties

Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.

  • Type: boolean

  • Importance: low

value.converter.optional.for.nullables

Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.scrub.invalid.names

Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.use.latest.version

Use latest version of schema in subject for serialization when auto.register.schemas is false.

  • Type: boolean

  • Importance: low

value.converter.use.optional.for.nonrequired

Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.

  • Type: boolean

  • Importance: low

value.converter.wrapper.for.nullables

Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.wrapper.for.raw.primitives

Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

errors.tolerance

Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.

  • Type: string

  • Default: none

  • Importance: low

key.converter.key.schema.id.serializer

The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.

  • Type: string

  • Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer

  • Importance: low

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

  • Type: string

  • Default: TopicNameStrategy

  • Importance: low

value.converter.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

value.converter.flatten.singleton.unions

Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.

  • Type: boolean

  • Default: false

  • Importance: low

value.converter.ignore.default.for.nullables

When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.

  • Type: boolean

  • Default: false

  • 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: low

value.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

  • Type: boolean

  • Default: true

  • Importance: low

value.converter.schemas.enable

Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

  • Type: boolean

  • Default: false

  • Importance: low

value.converter.value.schema.id.serializer

The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.

  • Type: string

  • Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer

  • Importance: low

value.converter.value.subject.name.strategy

Determines how to construct the subject name under which the value schema is registered with Schema Registry.

  • Type: string

  • Default: TopicNameStrategy

  • Importance: low

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

  • Type: boolean

  • Default: true

  • Importance: medium

Next steps

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud for Apache Flink, 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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