# Get Started with the ClickHouse Sink Connector for Confluent Cloud The fully managed ClickHouse Sink connector for Confluent Cloud moves data from an Apache Kafka® topic to the ClickHouse database. It writes data from a topic in Kafka to a table in the specified ClickHouse database. Ensure that the table exists in the ClickHouse database with the appropriate schema before you run the connector. #### 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 connector offers the following features: * **Client-side encryption (CSFLE and CSPE) support**: The connector supports CSFLE and CSPE for sensitive data. For more information about CSFLE or CSPE setup, see the [connector configuration](#cc-clickhouse-sink-setup-connection). * **Supports multiple tasks:** The connector supports running one or more tasks. More tasks may improve performance. * **Database authentication:** Uses password authentication. * **Input Data Format with or without a Schema:** The connector supports input data from Kafka topics in Avro, JSON Schema (JSON_SR), Protobuf, JSON (schemaless), or Bytes format. [Schema Registry](../../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format. * **Offset management capabilities**: The connector supports offset management. For more information, see [Manage offsets for Sink Connectors](../offsets.md#custom-offsets-sink-proc). 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 [ClickHouse](../limits.md#clickhouse-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). ## Quick Start Use this quick start to get up and running with the Confluent Cloud ClickHouse Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream Kafka events to an ClickHouse DB container. ### Prerequisites - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud. - The Confluent CLI installed and configured for the cluster. For more information, 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). - Access to a ClickHouse database. - If you have a VPC-peered cluster in Confluent Cloud, consider configuring a [PrivateLink Connection](https://clickhouse.com/docs/cloud/security/private-link-overview). between ClickHouse and the VPC. For additional 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). - 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 **ClickHouse Sink** connector card. ![ClickHouse Source Connector Card](images/ccloud-clickhouse-sink-icon.png) #### Step 4: Enter the connector details At the **ClickHouse Sink Connector** screen, complete the following: ### 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: - **ClickHouse hostname**: ClickHouse hostname or IP address of the ClickHouse server. - **ClickHouse port**: ClickHouse connection port. Defaults to 8443 (for HTTPS in cloud), and 8123 (for HTTP). - **Enable SSL**: Enable SSL connection to ClickHouse. - **SSL socket SNI**: Overrides the SNI hostname sent during the TLS handshake. This property is required only when ClickHouse sits behind a proxy or load balancer whose certificate uses a different hostname than the connection URL. For typical setups, leave this property empty. - **ClickHouse username**: ClickHouse database username - **ClickHouse password**: ClickHouse connection password. When entering the password, make sure that any special characters are URL encoded. - **Database name**: ClickHouse database name. 2. Click **Continue**. ### Configuration - **Input Kafka record value format**: Select the input Kafka record value format (data coming from the Kafka topic). Valid entires are AVRO, BYTES, JSON, 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. - **Topic to Table Mapping**: Enter a comma-separated list that maps Kafka topic names to ClickHouse DB table names (e.g. “topic1=table1, topic2=table2, etc.). **Data decryption** - Enable **Client-Side Field Level Encryption** for data decryption. Specify a **Service Account** to access the Schema Registry and associated encryption rules or keys with that schema. Select the connector behavior (`ERROR` or `NONE`) on data decryption failure. If set to `ERROR`, the connector fails and writes the encrypted data in the DLQ. If set to `NONE`, the connector writes the encrypted data in the target system without decryption. For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](../csfle.md#connect-csfle). ### **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). - **Input Kafka record key format**: Sets the input Kafka record key format. Valid entries are: Avro, Bytes, JSON, JSON Schema, Protobuf, or String. A valid schema must be available in [Schema Registry](../../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format. **Additional Configs** - **Table Refresh Interval**: Time (in seconds) to refresh the table definition cache. - **Keeper Cluster Configuration**: Allows configuration of ON CLUSTER parameter for self-hosted instances (for example, ON CLUSTER clusterNameInConfigFileDefinition) for exactly-once connect_state table. - **Bypass RowBinary**: Allows disabling use of RowBinary and RowBinaryWithDefaults for Schema-based data (for example, Avro, Protobuf, etc.) - should only be used when data will have missing columns, and Nullable/Default are unacceptable. - **DateTime Formats**: Date time formats for parsing DateTime64 schema fields, separated by ; (for example, someDateField=yyyy-MM-dd HH:mm:ss.SSSSSSSSS;someOtherDateField=yyyy-MM-dd HH:mm:ss). - **Auto evolve schema**: Automatically issues an `ALTER TABLE ADD COLUMN IF NOT EXISTS` statement when a record contains a field that does not exist as a column in the target table. This feature requires the connector’s ClickHouse user to have `ALTER` privileges. - **Auto evolve struct (JSON)**: Acts as a companion to the `auto.evolve` property. When a new field is a nested object, this property decides whether the connector stores the new column as a single JSON column (`true`) or flattens it into multiple columns (`false`). This property has no effect unless you enable the `auto.evolve` property. - **Auto evolve DDL refresh retries**: Specifies the number of times the connector retries fetching the updated table description while it waits for the added column to appear. This has no effect unless you enable the `auto.evolve` property and the property triggers an `ALTER TABLE` command that adds a column to the table. - **JDBC Connection Properties**: Connection properties when connecting to ClickHouse. Must start with `?` and joined by & between param=value - **Exactly Once**: Enable **Exactly Once** feature. - **Error Tolerance**: Connector error tolerance. Supported values are none, all - **Dead Letter Queue Topic**: If set with errors.tolerance=all, a DLQ will be used for failed batches. For details, see Troubleshooting. - **Enable DLQ Context Headers**: Adds additional headers for the DLQ - **ClickHouse Settings**: A comma-separated list of ClickHouse settings, for example, “insert_quorum=2, etc…”. - **Tolerate State Mismatch**: Allows the connector to drop records “earlier” than the current offset stored AFTER_PROCESSING (for example, if offset 5 is sent, and offset 250 was the last recorded offset). - **Report inserted offsets**: When set to `true`, the connector reports only the offsets of successfully inserted records back to Kafka during a commit, rather than reporting all received offsets. This setting prevents a silent data-loss path that can occur during partition rebalances. - **Connector retry timeout**: Specifies the retry budget in milliseconds for failed record inserts. After this elapses, the connector routes a failing record to the dead-letter queue (DLQ) if configured, or fails the task. The connector logs a `WARN` message if you set this value below 10 seconds. - **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. - **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-clickhouse-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. One task can handle up to 100 partitions. 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 by previewing the running configuration. 2. After you’ve validated that the properties are configured to your satisfaction, click **Launch**. 3. Verify the connection details and click **Launch**. The status for the connector should go from **Provisioning** to **Running**. It may take a few minutes. #### Step 5: Check ClickHouse After the connector is running, verify that new records are populating the ClickHouse 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. ### 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-clickhouse-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 the required connector properties. ```json { "connector.class": "ClickHouseSink", "name": "", "schema.context.name": "default", "input.data.format": "JSON", "input.key.format": "JSON", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "", "kafka.api.secret": "", "topics": "", "hostname": "", "port": "8443", "username": "", "password": "", "database": "", "topic2TableMap": "topic1=table1", "tableRefreshInterval": "0", "bypassRowBinary": "false", "exactlyOnce": "false", "errors.tolerance": "none", "errors.deadletterqueue.context.headers.enable": "false", "tolerateStateMismatch": "false", "max.poll.interval.ms": "300000", "max.poll.records": "500", "tasks.max": "1", "value.converter.decimal.format": "BASE64", "value.converter.replace.null.with.default": "true", "value.converter.reference.subject.name.strategy": "DefaultReferenceSubjectNameStrategy", "value.converter.schemas.enable": "false", "value.converter.value.subject.name.strategy": "TopicNameStrategy", "key.converter.key.subject.name.strategy": "TopicNameStrategy", "value.converter.ignore.default.for.nullables": "false", "auto.restart.on.user.error": "true" } ``` Note the following property definitions: * `"connector.class"`: Identifies the connector plugin name. * `"name"`: Sets a name for your new connector. * `"kafka.auth.mode"`: Identifies the connector authentication mode you want to use. There are two options: `SERVICE_ACCOUNT` or `KAFKA_API_KEY` (the default). To use an API key and secret, specify the configuration properties `kafka.api.key` and `kafka.api.secret`, as shown in the example configuration (above). To use a [service account](../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 ``` * `"input.data.format"`: Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are **AVRO**, **BYTES**, **JSON**, **JSON_SR** (JSON Schema), or **PROTOBUF**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"topics"`: Identifies the topic name or a comma-separated list of topic names. * `"hostname"`: The hostname of the ClickHouse server. Use a hostname address and not a full URL. For example: `btnmsdpy5r.us-east-2.aws.clickhouse.cloud`. The ClickHouse hostname address must provide a service record (SRV). A standard connection string does not work. * `"password"`: ClickHouse database password. * `"database"`: ClickHouse database name. * `"topic2TableMap"`: Comma-separated list that maps topic names to table names (e.g. "topic1=table1, topic2=table2, etc…"). * `"tasks.max"`: The connector supports running multiple tasks. * `"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). * `"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. #### 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. * `csfle.onFailure`: Configures the connector behavior (`ERROR` or `NONE`) on data decryption failure. If set to `ERROR`, the connector fails and writes the encrypted data in the DLQ. If set to `NONE`, the connector writes the encrypted data in the target system without decryption. When using CSFLE or CSPE with connectors that route failed messages to a Dead Letter Queue (DLQ), be aware that data sent to the DLQ is written in unencrypted plaintext. This poses a significant security risk as sensitive data that should be encrypted may be exposed in the DLQ. Do not use DLQ with CSFLE or CSPE in the current version. If you need error handling for CSFLE- or CSPE-enabled data, use alternative approaches such as: * Setting the connector behavior to `ERROR` to throw exceptions instead of routing to DLQ * Implementing custom error handling in your applications * Using `NONE` to pass encrypted data through without decryption For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](../csfle.md#connect-csfle). **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-clickhouse-sink-config-properties) for all property values and definitions. #### Step 4: Load the properties 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 clickhouse-sink.json ``` Example output: ```none Created connector confluent-clickhouse-sink 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 | confluent-clickhouse-sink | RUNNING | sink ``` #### Step 6: Check ClickHouse After the connector is running, verify that records are populating your ClickHouse 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). ### 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 ### 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 BYTES. 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 `input.key.format` : Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, 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
* Type: string * Default: JSON * Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING * 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 ### 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 ### How should we connect to your ClickHouse? `hostname` : The hostname or IP address of the ClickHouse server
* Type: string * Importance: high `port` : The ClickHouse port - default is 8443 (for HTTPS in the cloud), but for HTTP (the default for self-hosted) it should be 8123
* Type: int * Default: 8443 * Importance: high `ssl` : Enable SSL connection to ClickHouse
* Type: boolean * Default: true * Importance: high `username` : ClickHouse database username
* Type: string * Default: default * Importance: high `ssl.socket.sni` : Overrides the SNI hostname sent during the TLS handshake. This property is required only when ClickHouse sits behind a proxy or load balancer whose certificate uses a different hostname than the connection URL. For typical setups, leave this property empty.
* Type: string * Default: “” * Importance: low `password` : ClickHouse database password
* Type: password * Importance: high `database` : ClickHouse database name
* Type: string * Default: default * Importance: high ### Database details `topic2TableMap` : Comma-separated list that maps topic names to table names (e.g. “topic1=table1, topic2=table2, etc…”)
* Type: string * Default: “” * Importance: medium `tableRefreshInterval` : Time (in seconds) to refresh the table definition cache
* Type: int * Default: 0 * Importance: medium `keeperOnCluster` : Allows configuration of ON CLUSTER parameter for self-hosted instances (e.g. ON CLUSTER clusterNameInConfigFileDefinition) for exactly-once connect_state table
* Type: string * Default: “” * Importance: medium `bypassRowBinary` : Allows disabling use of RowBinary and RowBinaryWithDefaults for Schema-based data (Avro, Protobuf, etc.) - should only be used when data will have missing columns, and Nullable/Default are unacceptable
* Type: boolean * Default: false * Importance: medium `dateTimeFormats` : Date time formats for parsing DateTime64 schema fields, separated by ; (e.g. someDateField=yyyy-MM-dd HH:mm:ss.SSSSSSSSS;someOtherDateField=yyyy-MM-dd HH:mm:ss)
* Type: string * Default: “” * Importance: medium `auto.evolve` : Automatically issues an ALTER TABLE ADD COLUMN IF NOT EXISTS statement when a record contains a field that does not exist as a column in the target table. This feature requires the connector’s ClickHouse user to have ALTER privileges.
* Type: boolean * Default: false * Importance: medium `auto.evolve.struct.to.json` : Acts as a companion to `auto.evolve` property. When a new field is a nested object, this property decides whether the connector stores the new column as a single JSON column (`true`) or flattens it into multiple columns (`false`). This property has no effect unless you enable `auto.evolve` property.
* Type: boolean * Default: false * Importance: low `auto.evolve.ddl.refresh.retries` : Specifies the number of times the connector retries fetching the updated table description while waiting for a column added by an `auto.evolve`-issued ALTER TABLE statement to become visible. This has no effect unless you enable `auto.evolve` property.
* Type: int * Default: 3 * Importance: low ### Connection details `jdbcConnectionProperties` : Connection properties when connecting to ClickHouse. Must start with ? and joined by & between param=value
* Type: string * Default: “” * Importance: medium `exactlyOnce` : Exactly Once Enabled
* Type: boolean * Default: false * Importance: medium `errors.tolerance` : Connector Error Tolerance. Supported: none, all
* Type: string * Default: none * Importance: high `errors.deadletterqueue.topic.name` : If set (with errors.tolerance=all), a DLQ will be used for failed batches (see Troubleshooting)
* Type: string * Default: “” * Importance: medium `errors.deadletterqueue.context.headers.enable` : Adds additional headers for the DLQ
* Type: boolean * Default: false * Importance: medium `clickhouseSettings` : Comma-separated list of ClickHouse settings (e.g. “insert_quorum=2, etc…”)
* Type: string * Default: “” * Importance: medium `tolerateStateMismatch` : Allows the connector to drop records “earlier” than the current offset stored AFTER_PROCESSING (e.g. if offset 5 is sent, and offset 250 was the last recorded offset)
* Type: boolean * Default: false * Importance: medium `report.inserted.offsets` : When set to `true`, the connector reports only the offsets of successfully inserted records back to Kafka during a commit, rather than reporting all received offsets. This setting prevents a silent data-loss path that can occur during partition rebalances.
* Type: boolean * Default: true * Importance: medium `errors.retry.timeout` : Specifies the retry budget, in milliseconds, for failed record inserts. After this elapses, the connector routes a failing record to the dead-letter queue (DLQ), if configured, or fails the task. The connector logs a WARN message if you set this value below 10 seconds.
* Type: int * Default: 30000 * Valid Values: [0,…,300000] * Importance: medium ### 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 `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 `key.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 Key Converter.
* Type: boolean * Default: true * Importance: low `key.converter.schemas.enable` : Include schemas within each of the serialized keys. Input message keys must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Key Converter.
* Type: boolean * Default: false * 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.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 ClickHouse Sink connector. ### Why does my connector fail with `Connection to ClickHouse is not active` error? This error occurs when: * **The ClickHouse database does not exist**: Pre-create the database in ClickHouse before deploying the connector. Use the database name specified in your connector configuration. * **Network connectivity issues**: Verify your ClickHouse instance is reachable from Confluent Cloud. Check firewall rules, security groups, and network policies. For private instances, ensure VPC peering or PrivateLink is configured. * **Incorrect connection details**: Verify the hostname, port, username, password, and database name in your connector configuration. * **Authentication fails**: Ensure the ClickHouse user has `INSERT` and `SELECT` permissions on the target database and tables. Review the connector logs in the Confluent Cloud Console for connection error details. ### Why is my connector stuck in `PROVISIONING` state? If your connector remains in `PROVISIONING` state indefinitely without logs or errors, this indicates: * **Network connectivity problems**: The connector cannot reach your ClickHouse instance. During provisioning, the connector validates the connection. Network timeouts may cause the connector to hang. * **DNS resolution issues**: Verify the ClickHouse hostname resolves correctly. For PrivateLink or VPC-peered deployments, ensure DNS records are configured. * **Firewall blocking**: Verify your ClickHouse firewall or security groups allow inbound connections from Confluent Cloud egress IP addresses. For more information, see [Public Egress IP Addresses for Confluent Cloud Connectors](../static-egress-ip.md#cc-static-egress-ips). If the connector remains stuck after resolving connectivity issues, delete and recreate the connector with the corrected configuration. ### Why doesn’t my connector use the custom DLQ topic name? For fully managed connectors in Confluent Cloud, configuring `errors.deadletterqueue.topic.name` through the Confluent Cloud Console may not work as expected. The connector may create a DLQ topic with the default naming pattern `dlq-lcc-`. To use a custom DLQ topic name: * **Use the Confluent CLI or REST API**: Configure the connector using a JSON configuration file with the `errors.deadletterqueue.topic.name` property. The Confluent Cloud Console may not apply this configuration for some connectors. * **Pre-create the topic**: Create the custom DLQ topic before launching the connector. * **Configure ACLs**: Ensure the connector’s service account has permissions to write to the custom DLQ topic. For more information, see [Sink connector service account](../service-account.md#cloud-service-account-sink-connectors). Example CLI command: ```none confluent connect cluster create --config-file clickhouse-sink-config.json ``` For more information about DLQ, see [View Connector Dead Letter Queue Errors in Confluent Cloud](../dead-letter-queue.md#ccloud-dlq-topics). ### Why am I seeing `Data schema validation failed` errors? Schema validation errors occur when the data schema from Kafka does not match the ClickHouse table schema: * **Column name mismatch**: Column names in your ClickHouse table must match the field names in your Kafka messages. Configure the `topic2TableMap` property to map topics to tables correctly. * **Data type incompatibility**: Kafka data types must be convertible to the corresponding ClickHouse data types. For example, nested JSON structures may require specific ClickHouse data types like `JSON` or `Nested`. * **Missing columns**: Kafka messages may contain fields that do not exist in the ClickHouse table. Add the missing columns to your table, or use SMTs to filter fields. * **Incorrect input format**: The `input.data.format` setting must match the actual format of messages in your Kafka topic, such as `AVRO`, `JSON_SR`, `PROTOBUF`, `JSON`, or `BYTES`. Check the connector logs and DLQ messages to identify the specific schema mismatch. Set `errors.tolerance=all` and configure a DLQ to capture problematic records for investigation. ### What is the correct hostname format for ClickHouse Cloud? For ClickHouse Cloud instances, use the hostname address only, not a full URL: * **Correct format**: `btnmsdpy5r.us-east-2.aws.clickhouse.cloud` * **Incorrect format**: `https://btnmsdpy5r.us-east-2.aws.clickhouse.cloud:8443` The hostname must provide a service record (SRV). A standard connection string URL does not work. Configure the port separately using the `port` property. For ClickHouse Cloud, use port `8443`. For PrivateLink connections, use the private endpoint hostname that ClickHouse provides after you configure the connection. ### How can I improve connector performance? To optimize the connector performance: * **Increase the number of tasks**: Set a higher `tasks.max` value to process more partitions in parallel. One task handles up to 100 partitions. More tasks improve throughput. * **Tune batch settings**: * Increase `max.poll.records` to consume more records from Kafka in a single request. Default is `500`. * Adjust `max.poll.interval.ms` if the connector needs more time to write large batches to ClickHouse. Default is `300000` ms. * **Optimize ClickHouse table schema**: * Use appropriate MergeTree family engines for your use case. * Configure optimal ordering keys and partition keys. * Enable compression for better write performance. Monitor connector metrics in the Confluent Cloud Console to identify bottlenecks. ### Why do I see duplicate records in ClickHouse? The connector provides at-least-once delivery semantics by default: * **Duplicate records can occur**: When the connector experiences transient failures, such as network issues, task rebalances, or offset commit timeouts, records may be replayed and inserted multiple times. * **Not routed to DLQ**: Duplicate records from replays are not considered errors and are not sent to the Dead Letter Queue. To handle duplicates: * **Configure ClickHouse for idempotency**: Use ClickHouse table engines that support deduplication, such as `ReplacingMergeTree` or `CollapsingMergeTree`, with appropriate primary keys. * **Enable exactly-once semantics**: Set `exactlyOnce=true` in the connector configuration. This requires proper primary key configuration in ClickHouse. * **Handle duplicates in queries**: Structure your queries to handle duplicate records appropriately for your use case. Monitor the connector’s consumer lag and error rates to detect when replays occur. ## 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. [![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)