# AlloyDB Sink Connector for Confluent Cloud The Kafka Connect AlloyDB Sink connector for Confluent Cloud moves data from an Apache Kafka® topic to an AlloyDB database. It writes data from a topic in Kafka to a table in the specified AlloyDB database. Table auto-creation and limited auto-evolution are supported. #### NOTE 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](networking/internet-resource.md#clusters-connect-cloud). ## Features The AlloyDB Sink connector provides the following features: * **Idempotent writes**: The default `insert.mode` is INSERT. If it is configured as UPSERT, the connector will use upsert semantics rather than plain insert statements. Upsert semantics refer to atomically adding a new row or updating the existing row if there is a primary key constraint violation, which provides idempotence. * **Schemas**: The connector supports Avro, JSON Schema, and Protobuf input **value** formats. The connector supports Avro, JSON Schema, Protobuf, and String input **key** formats. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format. * **Primary key support**: Supported **PK modes** are `kafka`, `none`, `record_key`, and `record_value`. These are used in conjunction with the **PK Fields** property. * **Table and column auto-creation**: `auto.create` and `auto-evolve` are supported. If tables or columns are missing, they can be created automatically. Table names are created based on Kafka topic names. For more information, see [Table names and Kafka topic names](#cc-alloydb-sink-truncation-behavior). * **At least once delivery**: This connector guarantees that records from the Kafka topic are delivered at least once. * **Supports multiple tasks**: The connector supports running one or more tasks. More tasks may improve performance. * **PostgreSQL JSON and JSONB**: The connector supports sinking to AlloyDB tables containing data stored as JSON or JSONB (JSON binary format). JSON or JSONB should be stored as STRING type in Kafka and matching columns should be defined as JSON or JSONB in AlloyDB. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section. ## Limitations Be sure to review the following information. * For connector limitations, see [AlloyDB Sink Connector](limits.md#cc-alloydb-sink-limits) limitations. * If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations). ## Table names and Kafka topic names You can configure the connector to combine the value for `table.name.format` and the Kafka topic name. If the resulting combined value (table name) exceeds the maximum-permitted identifier length for the database version in use, the connector truncates the value to the permitted identifier length. For example, PostgreSQL 14 (fully compatible with AlloyDB) uses 63 bytes as its default identifier length setting. If the value used for `table.name.format` and the Kafka topic name exceeds 63 characters, only the first 63 characters from the combined name are used. For this reason, you should not run the connector with very long Kafka topic names and table names. If the table name is truncated, and the connector receives records from different upstream topics, the records map to the same table name after truncation takes place. This results in a duplicate table name collision. #### NOTE You can expect this connector behavior for any interactions with the database, both DDL (table creation and evolution) and DML (insert, upsert, and delete). ## Quick Start Use this quick start to get up and running with the Confluent Cloud AlloyDB sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to an AlloyDB database. Prerequisites : - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Google Cloud. - Authorized access to a AlloyDB database via [AlloyDB Auth Proxy](https://cloud.google.com/alloydb/docs/auth-proxy/connect) running on an intermediary VM accessible over a public IP. - The database and Kafka cluster should be in the same region. - For networking considerations, see [Networking and DNS](overview.md#connect-internet-access-resources). To use a set of public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips). - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
- Kafka cluster credentials. The following lists the different ways you can provide credentials. - Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID. - Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). 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](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *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](../get-started/index.md#cloud-create-kafka-cluster). #### 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 **AlloyDB Sink** connector card.  #### Step 4: Enter the connector details #### NOTE * Ensure you have all your [prerequisites](#cc-alloydb-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add AlloyDB Sink Connector** screen, complete the following: ### Topic selection 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**. ### Kafka access 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](service-account.md#s3-cloud-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**. ### Authentication 1. Configure the authentication properties: - **Connection host**: The hostname or the IP address of the VM running the AlloyDB Auth Proxy. - **Connection port**: The AlloyDB database connection port. Defaults to `5432`. - **Connection user**: The AlloyDB database user name. - **Connection password**: The AlloyDB database password. - **Database name**: The AlloyDB database name. 2. Click **Continue**. ### Configuration #### NOTE Configuration properties that are not shown in the Cloud Console use the default values. See [Configuration Properties](#cc-alloydb-sink-config-properties) for all property values and definitions. - **Input Kafka record value format**: Select an input Kafka record value format (data coming from the Kafka topic). Valid entries AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format. - **Insert mode**: Select an **insert mode** (insertion mode) to use: - `INSERT`: Use the standard `INSERT` row function. An error occurs if the row already exists in the table. - `UPSERT`: This mode is similar to `INSERT`. However, if the row already exists, the `UPSERT` 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?](../sr/faqs-cc.md#faq-schema-contexts). - **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. #### NOTE Auto create tables and Auto add columns are optional. These properties set whether to automatically create tables or columns if they are missing relative to the input record schema. If not used, both default to `false`. When Auto create tables is set to `true`, the connector creates a table name using `${topic}` (that is, the Kafka topic name). For more information, see [Table names and Kafka topic names](#cc-alloydb-sink-truncation-behavior) and the [AlloyDB Sink configuration properties](#cc-alloydb-sink-config-properties). - **Database timezone**: Name of the timezone used in the connector when querying with time-based criteria. Defaults to `UTC`. - **Table name format**: A format string for the destination table name, which may contain `${topic}` as a placeholder for the originating topic name. - **Timezone used for Date**: 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. - **Table types**: The comma-separated types of database tables to which the sink connector can write. - **Timestamp Precision Mode**: Convert the Timestamp with precision. If set to microseconds the timestamp will be converted to microsecond precision. If set to nanoseconds the timestamp will be converted to nanoseconds precision. - **Timestamp Fields**: List of comma-separated record value timestamp field names that should be converted to timestamps. These fields will be converted based on precision mode specified in Timestamp Precision Mode. The timestamp fields included here should be Long or String type and nested fields are not supported. - **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. Options are: - `kafka`: Kafka coordinates are used as the primary key. Must be used with the **PK Fields** property. - `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**: List of comma-separated primary key field names. Options are: - `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. - **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. 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 must have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF. - **Delete on null**: Whether to treat null record values as deletes. Requires `pk.mode` to be `record_key`. - **String Value Column Name**: Name of the destination table column when the Kafka record value written using `StringConverter` is sinked to the DB table The raw string value is written into this column. If the destination table is auto-created, the column is created with the name specified in this field. If the table already exists and the column is missing, the column is added using `ALTER` when `auto.evolve` is set to `true`. #### NOTE Databases that treat quoted identifiers as case-sensitive, like PostgreSQL, ensure that the column name in the existing table exactly matches this value. Defaults to `record_value`. - **Date Calendar System**: Conversion of time since epoch value in kafka topic record to DATE or TIMESTAMP depends on 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. The ideal setting for this depends on whether the values in source topic were populated using old or new java date time APIs. Changing this configuration on an existing connector might lead to a drift in the DATE/TIMESTAMP column’s values populated in the sink database. **Additional Configs** - **Value Converter Replace Null With Default**: Specifies whether to replace fields that have a default value and that are null to the default value. When set to `true`, the connector uses the default value; otherwise, it uses `null`. Applies to the `JSON` converter. - **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers. - **Value Converter Reference Subject Name Strategy**: Sets the subject reference name strategy for values. Valid entries are `DefaultReferenceSubjectNameStrategy` or `QualifiedReferenceSubjectNameStrategy`. You can use this strategy only with `PROTOBUF` format; the default strategy is `DefaultReferenceSubjectNameStrategy`. - **Schema ID For Value Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. - **Value Converter Schemas Enable**: Includes schema within each of the serialized values. Input messages must contain `schema` and `payload` fields and must not contain additional fields. For plain `JSON` data, set this to `false`. Applies to the `JSON` converter. - **Errors Tolerance**: Use this property to configure the connector’s error handling behavior. #### WARNING Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to `all`, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to `none`, the connector task fails 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. Applies to the `AVRO`, `PROTOBUF`, and `JSON_SR` converters. - **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers. - **Value Converter Decimal Format**: Specifies the `JSON` or `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 `DECIMAL` logical type values in `JSON` or `JSON_SR` as a number representing the decimal value. - **Schema GUID For Key Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. - **Schema GUID For Value Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. - **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to 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**: Determines how to construct the subject name for key schema registration. - **Schema ID For Key Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. **Auto-restart policy** - **Enable Connector Auto-restart**: Enables 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. If disabled, you must manually restart the connector. **Consumer configuration** - **Max poll interval(ms)**: Sets the maximum delay between subsequent consume requests to Kafka. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 300,000 milliseconds (5 minutes). - **Max poll records**: Sets the maximum number of records to consume from Kafka in a single request. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 500 records. **Transforms** - **Single Message Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui). For more information about unsupported SMTs, see [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). **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](offsets.md#connect-custom-offsets). See [Configuration Properties](#cc-alloydb-sink-config-properties) for all property values and definitions. - Click **Continue**. ### Sizing Based on the number of topic partitions you select, you will be provided with a recommended number of tasks. 1. To change the number of recommended tasks, enter the number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in the **Tasks** field. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details. 2. Click **Launch**. The status for the connector should go from **Provisioning** to **Running**. #### Step 5: Check the results in AlloyDB Verify that new records are being added to the AlloyDB database. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section. For an example that shows fully managed Confluent Cloud connectors in action with Confluent Cloud for Apache Flink, see the [Cloud ETL Demo](/platform/current/tutorials/examples/cloud-etl/docs/index.html). This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud. [](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html) ### 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](#cc-alloydb-sink-prereqs) completed. #### Step 1: List the available connectors Enter the following command to list available connectors: ```none confluent connect plugin list ``` #### Step 2: List the connector configuration properties Enter the following command to show the connector configuration properties: ```none confluent connect plugin describe ``` 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: ```none { "connector.class": "AlloyDbSink", "name": "AlloyDbSinkConnector_0", "input.data.format": "AVRO", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "****************", "kafka.api.secret": "****************************************************************", "connection.host": "34.27.121.137", "connection.port": "5432", "connection.user": "postgres", "connection.password": "**************", "db.name": "postgres", "topics": "postgresql_ratings", "insert.mode": "UPSERT", "db.timezone": "UTC", "auto.create": "true", "auto.evolve": "true", "pk.mode": "record_value", "pk.fields": "user_id", "tasks.max": "1" } ``` Note the following property definitions. See the [AlloyDB Sink configuration properties](#cc-alloydb-sink-config-properties) for additional property values and 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](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=`. To list the available service account resource IDs, use the following command: ```bash confluent iam service-account list ``` For example: ```bash 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 ``` * `"connection.host"`: The hostname or the IP address of the VM running the AlloyDB Auth Proxy. * `"connection.port"`: The AlloyDB database connection port. Defaults to `5432`. * `"connection.user"`: The AlloyDB database user name. * `"connection.password"`: The AlloyDB database password. * `"db.name"`: The AlloyDB database name. * `"input.data.format"`: Sets the input Kafka 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. Defaults to `false`. Requires `pk.mode` to be `record_key`. Defaults to `false`. * `"topics"`: Identifies the topic name or a comma-separated list of topic names. * `"insert.mode"`: Enter one of the following modes: - `INSERT`: Use the standard `INSERT` row function. An error occurs if the row already exists in the table. - `UPSERT`: This mode is similar to `INSERT`. However, if the row already exists, the `UPSERT` function overwrites column values with the new values provided. * `db.timezone`: Name of the time zone the connector uses when inserting time-based values. Defaults to UTC. * `"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 to `false`. When\`\`auto.create\`\` is set to `true`, the connector creates a table name using `${topic}` (that is, the Kafka topic name). For more information, see [Table names and Kafka topic names](#cc-alloydb-sink-truncation-behavior) and the [AlloyDB Sink configuration properties](#cc-alloydb-sink-config-properties). * `"pk.mode"`: Supported modes are listed below: - `kafka`: Kafka coordinates are used as the primary key. Must be used with the `"pk.fields"` property. - `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 the `pk.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. * `"tasks.max"`: Maximum number of tasks the connector can run. See Confluent Cloud [connector limitations](limits.md#cc-alloydb-sink-limits) for additional task information. **SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation. See [Configuration Properties](#cc-alloydb-sink-config-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: ```none confluent connect cluster create --config-file .json ``` For example: ```none confluent connect cluster create --config-file alloydb-sink-config.json ``` Example output: ```none Created connector AlloyDbSinkConnector_0 lcc-ix4dl ``` #### Step 5: Check the connector status Enter the following command to check the connector status: ```none confluent connect cluster list ``` Example output: ```none ID | Name | Status | Type +-----------+--------------------------+---------+------+ lcc-ix4dl | AlloyDbSinkConnector_0 | RUNNING | sink ``` #### Step 6: Check the results in AlloyDB. Verify that new records are being added to the AlloyDB database. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) 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](/platform/current/connect/kafka_connectors.html). ### Which topics do you want to get data from? `topics.regex` : A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.
* Type: string * Importance: low `topics` : Identifies the topic name or a comma-separated list of topic names.
* Type: list * Importance: high `errors.deadletterqueue.topic.name` : The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
* Type: string * Default: dlq-${connector} * Importance: low ### 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, PROTOBUF, JSON, or 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. JSON and STRING formats do not require Schema Registry. When JSON is selected, value.converter.schemas.enable must be set to true.
* 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, JSON, or 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. JSON and STRING formats do not require Schema Registry. When pk.mode is set to record_key and JSON is selected, the record key must include an inline schema.
* 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, 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 ### How should we connect to your database? `connection.host` : Hostname or IP address of the virtual machine running the AlloyDB Auth Proxy. Make sure the connector can reach your service. Do not include [jdbc:xxxx://](jdbc:xxxx://) in the connection hostname property.
* Type: string * Importance: high `connection.port` : Connection port for the AlloyDB database.
* Type: int * Default: 5432 * Valid Values: [0,…,65535] * Importance: high `connection.user` : User of the AlloyDB database.
* Type: string * Importance: high `connection.password` : Password of the AlloyDB database.
* Type: password * Importance: high `db.name` : AlloyDB database name.
* Type: string * Importance: high ### 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 of `TABLE`, `PARTITIONED TABLE` and `VIEW` 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 of `auto.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. Not applicable when input.data.format is set to STRING.
* Type: list * Importance: medium `timestamp.fields.list` : List of comma-separated record value timestamp field names that should be converted to timestamps. These fields will be converted based on precision mode specified in Timestamp Precision Mode. The timestamp fields included here should be Long or String type and nested fields are not supported. Not applicable when input.data.format is set to STRING.
* Type: list * Importance: medium `string.output.value.column.name` : Name of the destination table column to use when the Kafka record value written using StringConverter is sinked to the DB table (Input Kafka record value format = STRING). The raw string value is written into this single column. If the destination table is being auto-created, the column is created with this name; if the table already exists and the column is missing, the column is added via ALTER when ‘auto.evolve’ is true. Note: in databases that treat quoted identifiers as case-sensitive (e.g. PostgreSQL), make sure the column name in the existing table exactly matches this value (including case). Defaults to ‘record_value’.
* Type: string * Default: record_value * Importance: low `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 `timestamp.precision.mode` : Convert the Timestamp with precision. If set to microseconds the timestamp will be converted to microsecond precision. If set to nanoseconds the timestamp will be converted to nanoseconds precision. Not applicable when input.data.format is set to STRING.
* Type: string * Default: microseconds * Importance: medium `date.calendar.system` : Conversion of time since epoch value in kafka topic record to DATE or TIMESTAMP depends on 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. The ideal setting for this depends on whether the values in source topic were populated using old or new java date time APIs. Changing this configuration on an existing connector might lead to a drift in the DATE/TIMESTAMP column’s values populated in the sink database.
* Type: string * Default: LEGACY * 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. This mode is not supported when input.data.format is set to STRING.
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 ### Additional Configs `consumer.override.auto.offset.reset` : Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: [https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset)
* Type: string * Importance: low `consumer.override.isolation.level` : Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. More details: [https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level)
* Type: string * Importance: low `header.converter` : The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* Type: string * Importance: low `key.converter.use.schema.guid` : The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: string * Importance: low `key.converter.use.schema.id` : The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: int * 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.use.schema.guid` : The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: string * Importance: low `value.converter.use.schema.id` : The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: int * 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: all * Importance: low `key.converter.key.schema.id.deserializer` : The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer * 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` : Must be set to true when input.data.format is JSON. Plain JSON without an inline schema is not supported. Applicable for JSON Converter.
* Type: boolean * Default: false * Importance: low `value.converter.value.schema.id.deserializer` : The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer * 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 ## Frequently asked questions Find answers to frequently asked questions about the AlloyDB Sink connector for Confluent Cloud. ### Connection and networking #### Why do I see `connection.host: Could not connect to database. Connection timed out` errors? This error indicates the connector cannot establish a connection to the AlloyDB instance. ##### Common causes * Network misconfiguration between Confluent Cloud and AlloyDB. * Firewall rules blocking inbound connections. * AlloyDB Auth Proxy not running or not accessible. * Region mismatch between the Confluent Cloud cluster and the AlloyDB instance. ##### Resolution 1. Ensure the AlloyDB Auth Proxy is running and accessible. 2. Verify that the AlloyDB instance and Kafka cluster are in the same region. 3. Check firewall rules to allow connections from Confluent Cloud egress IPs. 4. For Private Service Connect setups, verify that DNS records and service endpoints are correctly configured. #### How do I connect using Google Cloud Private Service Connect? The connector supports Private Service Connect for secure, private connectivity to AlloyDB. Ensure that proper DNS records and service endpoints are configured. Network configuration changes may take time to propagate. For networking details, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud). ### Configuration and setup #### Why do I get errors related to table auto-creation or auto-evolution? This error occurs when the connector cannot create or alter tables in AlloyDB. ##### Common causes * The database user lacks sufficient permissions. * Table name truncation due to long topic names. ##### Resolution 1. Ensure the database user has `CREATE` and `ALTER` permissions. 2. Ensure the combined `table.name.format` and topic name don’t exceed the 63-character identifier length limit in PostgreSQL (AlloyDB). Exceeding this limit causes name truncation, which can lead to collisions. #### What insert modes does the connector support? The connector supports `INSERT` and `UPSERT` modes: - `INSERT` fails if a row with the same primary key already exists. - `UPSERT` atomically adds or updates rows based on primary key constraints, providing idempotent writes. Configure the insert mode using the `insert.mode` property. ### Data type handling #### Does the connector support PostgreSQL JSON and JSONB data types? Yes. JSON or JSONB data should be stored as `STRING` type in Kafka. The matching columns in AlloyDB should be defined as `JSON` or `JSONB`. #### Why do I see data type mapping errors? The AlloyDB Sink connector is based on the PostgreSQL Sink connector (JDBC sink family). Data type mismatches between Kafka schemas and AlloyDB table columns can cause failures. ##### Resolution 1. Verify schema compatibility between your Kafka topic and AlloyDB table. 2. Use Schema Registry to manage schemas. 3. For complex or custom PostgreSQL types, ensure the Kafka schema uses compatible types. For information on compatible types, see [Features](#cc-alloydb-sink-features). ### Performance #### Why is my connector in a degraded state with some tasks failing? A degraded state means some connector tasks are running while others have failed. ##### Common causes * Authentication issues affecting a subset of tasks. * Configuration errors. * Network connectivity problems. ##### Resolution 1. Check connector task-level status for specific error messages. 2. Verify that credentials are valid. 3. Restarting the connector might help, but only if the underlying issue is resolved first. ## 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](/platform/current/tutorials/examples/cloud-etl/docs/index.html). This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud. [](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)