# Microsoft SQL Server Source (JDBC) Connector for Confluent Cloud The fully managed Microsoft SQL Server Source connector for Confluent Cloud captures a snapshot of the existing data in a Microsoft SQL Server database, then monitors and records all subsequent row-level changes. The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data formats. All of the events for each table are recorded in a separate Apache Kafka® topic. The events can then be easily consumed by applications and services. Note that deleted records are not captured. #### NOTE * This Quick Start is for the fully managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see [JDBC Connector (Source and Sink) for Confluent Platform](https://docs.confluent.io/kafka-connectors/jdbc/current/). * 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 Microsoft SQL Server Source connector provides the following features: * **At least once delivery**: The connector guarantees that records are delivered at least once to the Kafka topic. * **Topics created automatically**: The connector can automatically create Kafka topics. When creating topics, the connector uses the naming convention: ``. The tables are created with the properties: `topic.creation.default.partitions=1` and `topic.creation.default.replication.factor=3`. * **Insert modes:** - *timestamp* mode is enabled when only a timestamp column is specified when you enter database details. - *timestamp+incrementing* mode is enabled when both a timestamp column and incrementing column are specified when you enter database details. #### IMPORTANT * A timestamp column must not be nullable. * A timestamp column must use **datetime2** and not datetime. If the timestamp column uses datetime, the topic may receive numerous duplicates. * **Database authentication:** Supports password authentication and Microsoft Entra ID-based authentication using Confluent Provider Integration. For more information about provider integration setup, see the [connector authentication](#cc-microsoft-sql-server-source-setup-connection). * **Record processing**: Supports table and query modes. Use the `query` property to execute custom SQL queries for joining tables or selecting specific data subsets. * **SSL support**: Supports one-way SSL. * **Data formats:** The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data. [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). * **Select configuration properties:** - `db.timezone` - `poll.interval.ms` - `batch.max.rows` - `timestamp.delay.interval.ms` - `topic.prefix` - `schema.pattern` * **Offset management capabilities**: Supports offset management. For more information, see [Manage custom offsets](#cc-microsoft-sql-server-source-custom-offsets). * **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-microsoft-sql-server-source-setup-connection). * **Secret manager integration**: The connector supports secret manager integration. For `Password` based authentication, the connector can retrieve the following configurations from an integrated secret manager at runtime as needed. | **Secret manager managed configuration** | **Type** | |--------------------------------------------|------------| | `connection.user` | `STRING` | | `connection.password` | `PASSWORD` | For more information, see [Create a secret manager integration in Confluent Cloud](secret-manager-integration/overview.md#cloud-secret-manager-quickstart). 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 [Microsoft SQL Server Source (JDBC) Connector](limits.md#cc-microsoft-sql-server-source-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). ## Manage custom offsets You can manage the offsets for this connector. Offsets provide information on the point in the system from which the connector is accessing data. For more information, see [Manage Offsets for Fully Managed Connectors in Confluent Cloud](offsets.md#connect-custom-offsets). **To manage offsets**: - Manage offsets using Confluent Cloud APIs. For more information, see [Connect offsets API reference](https://docs.confluent.io/cloud/current/ccloud/offsets-connect-v-1/). ### Get the current offset To get the current offset, make a `GET` request that specifies the environment, Kafka cluster, and connector name. ```bash GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets Host: https://api.confluent.cloud ``` **Response:** Successful calls return HTTP `200` with a JSON payload that describes the offset. ```bash { "id": "lcc-example123", "name": "{connector_name}", "offsets": [ { "partition": { "protocol": "1", "table": "{table_name}" }, "offset": { "incrementing": 26 } } ], "metadata": { "observed_at": "2024-03-28T17:57:48.139635200Z" } } ``` Responses include the following information: - The position of latest offset. - The observed time of the offset in the metadata portion of the payload. The `observed_at` time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Use `observed_at` to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. Calling `GET` repeatedly will fetch more recently observed offsets. - Information about the connector. - In these examples, the curly braces around “{connector_name}” indicate a replaceable value. ### Update the offset To update the offset, make a `POST` request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies new offset and a patch type. ```bash POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request Host: https://api.confluent.cloud { "type": "PATCH", "offsets": [ { "partition": { "protocol": "1", "table": "{table_name}" }, "offset": { "incrementing": 3 } } ] } ``` **Considerations:** - You can only make one offset change at a time for a given connector. - This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see **Get the status of an offset request**. - For source connectors, the connector attempts to read from the position defined by the requested offsets. **Response:** Successful calls return HTTP `202 Accepted` with a JSON payload that describes the offset. ```bash { "id": "lcc-example123", "name": "{connector_name}", "offsets": [ { "partition": { "protocol": "1", "table": "{table_name}" }, "offset": { "incrementing": 3 } } ], "requested_at": "2024-03-28T17:58:45.606796307Z", "type": "PATCH" } ``` Responses include the following information: - The requested position of the offsets in the source. - The time of the request to update the offset. - Information about the connector. ### Delete the offset To delete the offset, make a `POST` request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies the delete type. ```bash POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request Host: https://api.confluent.cloud { "type": "DELETE" } ``` **Considerations:** - Delete requests delete the offset for the provided partition and reset to the base state. A delete request is as if you created a fresh new connector. - This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see **Get the status of an offset request**. - Do not issue delete and patch requests at the same time. - For source connectors, the connector attempts to read from the position defined in the base state. **Response**: Successful calls return HTTP `202 Accepted` with a JSON payload that describes the result. ```bash { "id": "lcc-example123", "name": "{connector_name}", "offsets": [], "requested_at": "2024-03-28T17:59:45.606796307Z", "type": "DELETE" } ``` Responses include the following information: - Empty offsets. - The time of the request to delete the offset. - Information about Kafka cluster and connector. - The type of request. ### Get the status of an offset request To get the status of a previous offset request, make a `GET` request that specifies the environment, Kafka cluster, and connector name. ```bash GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request/status Host: https://api.confluent.cloud ``` **Considerations:** - The status endpoint always shows the status of the most recent PATCH/DELETE operation. **Response**: Successful calls return HTTP `200` with a JSON payload that describes the result. The following is an example of an applied patch. ```bash { "request": { "id": "lcc-example123", "name": "{connector_name}", "offsets": [ { "partition": { "protocol": "1", "table": "{table_name}" }, "offset": { "incrementing": 3 } } ], "requested_at": "2024-03-28T17:58:45.606796307Z", "type": "PATCH" }, "status": { "phase": "APPLIED", "message": "The Connect framework-managed offsets for this connector have been altered successfully. However, if this connector manages offsets externally, they will need to be manually altered in the system that the connector uses." }, "previous_offsets": [ { "partition": { "protocol": "1", "table": "{table_name}" }, "offset": { "incrementing": 26 } } ], "applied_at": "2024-03-28T17:58:48.079141883Z" } ``` Responses include the following information: - The original request, including the time it was made. - The status of the request: applied, pending, or failed. - The time you issued the status request. - The previous offsets. These are the offsets that the connector last updated prior to updating the offsets. Use these to try to restore the state of your connector if a patch update causes your connector to fail or to return a connector to its previous state after rolling back. ### JDBC modes and offsets You can run the JDBC source connectors in one of four modes. Each mode uses a different offset object in its JSON payload to track the progress of the connector. The provided samples show an offset object from a JBDC source connector in incrementing mode. - `bulk` - No offset. This is the default mode for JDBC source connectors. - `incrementing` - The offset is provided by the `incrementing` property in the offset object. - `timestamp` - The offset is provided by the `timestamp` and `timestamp-nanos` properties in the offset object. - `timestamp+incrementing` - The offset is provided by the `incrementing`, `timestamp` and `timestamp-nanos` properties in the offset object. ### JSON payload The table below offers a description of the unique fields in the JSON payload for managing offsets of the JDBC Source connectors, including: - IBM Db2 Source connector - Microsoft SQL Server Source connector - MySQL Source connector - Oracle Database Source connector - PostgreSQL Source connector | Field | Definition | Required/Optional | |-------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------| | `incrementing` | Specifies the value of `incrementing.column.name` which identifies the current offset. The connector
gets only values greater than the value in this field.

Available only in the following modes: incrementing, timestamp+incrementing. | Required | | `protocol` | Specifies the protocol.

Available in the following modes: incrementing, timestamp, timestamp+incrementing. | Required | | `table` | The name of the table.

Available in the following modes: incrementing, timestamp, timestamp+incrementing. | Required | | `timestamp` | The number of milliseconds since `January 1, 1970, 00:00:00` GMT represented by the Timestamp object of the column value.

Available only in the following modes: timestamp, timestamp+incrementing. | Required | | `timestamp_nanos` | Fractional seconds component of the timestamp object.

Available only in the following modes: timestamp, timestamp+incrementing. | Required | ## Quick Start Use this quick start to get up and running with the Confluent Cloud Microsoft SQL Server Source connector. The quick start provides the basics of selecting the connector and configuring it to obtain a snapshot of the existing data in a Microsoft SQL Server database and then monitoring and recording all subsequent row-level changes. 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. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). - The connector automatically creates Kafka topics using the naming convention: ``. The tables are created with the properties: `topic.creation.default.partitions=1` and `topic.creation.default.replication.factor=3`. If you want to create topics with specific settings, create the topics before running this connector.
#### IMPORTANT If you are configuring granular access using a [service account](service-account.md#s3-cloud-service-account), and you leave the optional **Topic prefix** (`topic.prefix`) configuration property empty, you must grant ACL `CREATE` and `WRITE` access to all the Kafka topics or create [RBAC role bindings](managed-connector-rbac.md#managed-connector-rbac). To add ACLs, you use the (\*) wildcard in the ACL entries as shown in the following examples. ```bash confluent kafka acl create --allow --service-account "" --operation create --topic "*" ```
```bash confluent kafka acl create --allow --service-account "" --operation write --topic "*" ``` - [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). - Make sure your connector can reach your service. Consider the following before running the connector: * Depending on the service environment, certain network access limitations may exist. See [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud) for details. * 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). For additional fully managed connector networking details, see [Networking and DNS](overview.md#connect-internet-access-resources). * Do not include `jdbc:xxxx://` in the connection hostname property. An example of a connection hostname property is `database.example.endpoint.com`. For example, `mydatabase.abc123ecs2.us-west.rds.amazonaws.com`. * Clients from Azure Virtual Networks are not allowed to access the server by default. Ensure your Azure Virtual Network is correctly configured and that **Allow access to Azure Services** is enabled. * See your specific cloud platform documentation for how to configure security rules for your VPC. - A database table timestamp column must not be nullable and must use **datetime2** and not datetime.
- 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 **Microsoft SQL Server Source** connector card.  #### Step 4: Enter the connector details #### NOTE * Make sure you have all your [prerequisites](#cc-microsoft-sql-server-source-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Microsoft SQL Server Source Connector** screen, complete the following: ### Define a topic prefix In the **Topic prefix** field, define a topic prefix your connector will use to publish to Kafka topics. The connector will Kafka topics using the following naming convention: ``. #### IMPORTANT If you are configuring granular access using a [service account](service-account.md#s3-cloud-service-account), and you leave the optional **Topic prefix** (`topic.prefix`) configuration property empty, you must grant ACL `CREATE` and `WRITE` access to all the Kafka topics or create [RBAC role bindings](managed-connector-rbac.md#managed-connector-rbac). To add ACLs, you use the (\*) wildcard in the ACL entries as shown in the following examples. ```bash confluent kafka acl create --allow --service-account "" --operation create --topic "*" ``` ```bash confluent kafka acl create --allow --service-account "" --operation write --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: **Authentication method** - **Authentication method**: Select how you want to authenticate with your database. Currently, `Password` is the only valid option. - **Use secret manager**: Enable this setting to fetch sensitive configuration values, such as the `Password`, from a secret manager. **Azure credentials** - **Provider Integration**: Select an existing integration that has access to your resource such as the secret manager. **Secret manager configuration** - **Secret manager**: Select the secret manager that Confluent Cloud should use to retrieve sensitive data. - **Configurations from Secret manager**: Select the configurations whose values Confluent Cloud should fetch from the secret manager. - **Provider Integration**: Select an existing integration that has access to your resource such as the secret manager. **How should we connect to your database?** - **Connection host**: The JDBC connection host. Do not include `jdbc:xxxx://` in the connection hostname property. An example of a connection hostname property is `database-1.123abc456ecs2.us-west-2.rds.amazonaws.com`. Depending on the service environment, certain network access limitations may exist. For details, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud). - **Connection port**: JDBC connection port for Microsoft SQL Server. - **Connection user**: JDBC connection user for Microsoft SQL Server. - **Connection password**: JDBC connection password for Microsoft SQL Server. - **Database name**: JDBC database name for Microsoft SQL Server. - **SSL mode**: The SSL mode to use to connect to your database. - **Trust store**: Upload the trust store file that contains the Certificate Authority (CA) information. - **Trust store password**: The trust store password containing server CA certificate. Only required if using `verify-ca` or `verify-full` SSL mode. 2. Click **Continue**. ### Configuration - **Table include list**: List of tables to include when copying data. Use comma-separated list of regular expressions or fully qualified table names to specify multiple tables (for example, `mydb.dbo.users, mydb.dbo.orders` or `.*users.*, .*orders.*`). For SQL Server, use `database.schema.table` format (for example, `mydb.dbo.users`). - **Table exclude list**: A comma-separated list of regular expressions that match the fully qualified names of tables to be excluded from copying. Use a comma-separated list to specify multiple regular expressions. Table names are case-sensitive. For example, `table.exclude.list: schema1.customer.*,schema2.order.*`. If specified, `table.whitelist` cannot be set. **Output messages** - **Select output record value format**: Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF. **Database details** - **Table names (Deprecated)**: (Deprecated) List of tables to include when copying data. Use a comma-separated list to specify multiple tables (for example: "User, Address, Email"). This is deprecated. Use table.include.list. - **Table types**: By default, the JDBC connector will only detect tables with type `TABLE` from the source database. This config allows a command separated list of table types to extract. - **Database timezone**: Name of the JDBC timezone used in the connector when querying with time-based criteria. Defaults to `UTC`. **Data encryption** - Enable **Client-Side Field Level Encryption** for data encryption. Specify a **Service Account** to access the Schema Registry and associated encryption rules or keys with that schema. For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](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). - **Mode**: The mode for updating a table each time it is polled. Defaults to `bulk` mode. - **Table to timestamp columns mappings**: A comma-separated list of table regex to timestamp columns mappings. On specifying multiple timestamp columns, COALESCE SQL function would be used to find out the effective timestamp for a row. Expected format is `regex1:[col1|col2],regex2:[col3]`. Regexes would be matched against the fully qualified table names. Identifier names are case sensitive. Every table included for capture should match exactly one of the provided mappings. An example for a valid input would be `.*\.customers.*:[updated_at|modified_at],.*\.orders.*:[changed_at]`. - **Numeric Mapping**: Map NUMERIC values by precision and optionally scale to integral or decimal types. - **Transaction Isolation Level**: Isolation level determines how transaction integrity is visible to other users and systems. `DEFAULT` is the default isolation level configured at the database server. `READ_UNCOMMITTED` is the lowest isolation level. At this level, a transaction may see changes that are not committed (that is, dirty reads) made by other transactions. `READ_COMMITTED` guarantees that any data read is already committed at the moment it is read. `REPEATABLE_READ` adds to the guarantees of the `READ_COMMITTED` level with the addition of also guaranteeing that any data read cannot change, if the transaction reads the same data again. However, phantom reads are possible. `SERIALIZABLE` is the highest isolation level. In addition to everything `REPEATABLE_READ` guarantees, `SERIALIZABLE` also eliminates phantom reads. `SQL_SERVER_SNAPSHOT` achieves the same results as `SERIALIZABLE`. However, instead of using locks, it uses row versioning. - **Table to incrementing column mappings**: A comma-separated list of table regex to incrementing column mappings. Expected format is `regex1:col1,regex2:col2`. Regexes would be matched against the fully qualified table names. Identifier names are case sensitive. Every table included for capture should match exactly one of the provided mappings. An example for a valid input would be `.*\.customers.*:id,.*\.orders.*:order_id`. - **Quote SQL Identifiers**: When to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default value is `ALWAYS`. - **Timestamp column name (Deprecated)**: (Deprecated legacy configuration. Use timestamp.columns.mapping for new implementations.) Comma separated list of one or more timestamp columns to detect new or modified rows using the COALESCE SQL function. Rows whose first non-null timestamp value is greater than the largest previous timestamp value seen will be discovered with each poll. At least one column should not be nullable. - **Initial timestamp**: The epoch timestamp used for initial queries that use timestamp criteria. The value -1 sets the initial timestamp to the current time. If not specified, the connector retrieves all data. Once the connector has managed to successfully record a source offset, this property has no effect even if changed to a different value later on. - **Date Calendar System**: The time elapsed from epoch populated in the end table topic for `DATE` or `TIMESTAMP` type columns can have two different values based upon the Calendar used to interpret it. If `LEGACY` is used, it will use the hybrid Gregorian/Julian calendar which was the default in the older java date time APIs. However, if ‘PROLEPTIC_GREGORIAN’ is used, then it will use the proleptic gregorian calendar which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover. This matches the behavior of modern Java date/time APIs (java.time). This is defaulted to LEGACY for backward compatibility. Changing this configuration on an existing connector might lead to a drift in the Kafka topic record values. - **Incrementing column name (Deprecated)**: (Deprecated legacy configuration. Use incrementing.column.mapping for new implementations.) The name of the strictly incrementing column to use to detect new rows. Any empty value indicates the column should be autodetected by looking for an auto-incrementing column. This column may not be nullable. **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 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`. - **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. - **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. - **Key Converter Schema ID Serializer**: The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers. - **Value Converter Connect Meta Data**: 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. - **Value Converter Schema ID Serializer**: The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers. **Auto-restart policy** - **Enable Connector Auto-restart**: 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. **Database details** - **Query Config**: If specified, the connector uses this custom SQL query to read source records, which allows for operations like joining tables or selecting subsets of data. Providing a query instructs the connector to read only the result set instead of performing a full table copy. This configuration supports different query modes with the incremental query properly constructed by appending a `WHERE` clause (for more information, see [Incremental Query Modes](https://docs.confluent.io/kafka-connectors/jdbc/current/source-connector/overview.html#incremental-query-modes)). Note that only `SELECT` statements are supported. Always adhere to security best practices, like enforcing strict authorization using [managed connector RBAC](https://docs.confluent.io/cloud/current/connectors/managed-connector-rbac.html#managed-connector-rbac), applying appropriate [network access controls](https://docs.confluent.io/cloud/current/security/access-control/ip-filtering/manage-ip-filters.html) for control plane APIs, and following the principle of least privilege when provisioning identities or credentials for any third-party systems. - **Schema pattern**: Schema pattern to fetch table metadata from the database. - **Timestamp granularity for timestamp columns**: Defines the granularity of the Timestamp column. `CONNECT_LOGICAL` (default) represents timestamp values using Connect’s built-in representations. `MICROS_LONG` represents timestamp values as microseconds since the epoch (UNIX epoch time). `MICROS_STRING` represents timestamp values as microseconds since the epoch in string format. `MICROS_ISO_DATETIME_STRING` represents timestamp values in ISO format `yyyy-MM-dd'T'HH:mm:ss.SSSSSS`. `NANOS_LONG` represents timestamp values as nanoseconds (ns) since the epoch (UNIX epoch time). `NANOS_STRING` represents timestamp values as ns since the epoch in string format. `NANOS_ISO_DATETIME_STRING` represents timestamp values in ISO format `yyyy-MM-dd'T'HH:mm:ss.n`. **Connection details** - **Poll interval (ms)**: Set the time in milliseconds to wait for new change events when no data is returned. Default is `500` ms. - **Max rows per batch**: The maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector. - **Delay interval (ms)**: The amount of time to wait after a row with a certain timestamp appears before the connector includes it in the result. Add a delay to allow transactions with an earlier timestamp to complete. **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). For all property values and definitions, see [Configuration Properties](#cc-microsoft-sql-source-config-properties). - 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 tasks, use the Range Slider to select the desired number of tasks. 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**. The status for the connector should go from **Provisioning** to **Running**.  #### Step 5: Check the Kafka topic After the connector is running, verify that messages are populating your Kafka topic. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](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-microsoft-sql-server-source-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. ```none { "name" : "confluent-microsoft-sql-source", "connector.class": "MicrosoftSqlServerSource", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "", "kafka.api.secret" : "", "topic.prefix" : "microsoftsql_", "connection.host" : "", "connection.port" : "1433", "connection.user" : "", "connection.password": "", "db.name": "ms-sql-test", "table.include.list": ".*passengers.*", "timestamp.columns.mapping": ".*passengers.*:[created_at]", "output.data.format": "JSON", "db.timezone": "UTC", "tasks.max" : "1" } ``` Note the following property definitions: * `"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 ``` * `"topic.prefix"`: Enter a topic prefix. The connector automatically creates Kafka topics using the naming convention: ``. The tables are created with the properties: `topic.creation.default.partitions=1` and `topic.creation.default.replication.factor=3`. If you want to create topics with specific settings, create the topics before running this connector. If you are configuring granular access using a service account, you must set up [ACLs for the topic prefix](service-account.md#cloud-service-account-jdbc-mongo-acls). #### IMPORTANT If you are configuring granular access using a [service account](service-account.md#s3-cloud-service-account), and you leave the optional **Topic prefix** (`topic.prefix`) configuration property empty, you must grant ACL `CREATE` and `WRITE` access to all the Kafka topics or create [RBAC role bindings](managed-connector-rbac.md#managed-connector-rbac). To add ACLs, you use the (\*) wildcard in the ACL entries as shown in the following examples. ```bash confluent kafka acl create --allow --service-account "" --operation create --topic "*" ``` ```bash confluent kafka acl create --allow --service-account "" --operation write --topic "*" ``` * The following provides more information about how to use the `ssl.mode` property: - The default option `prefer` is enabled if `ssl.mode` is not added to the connector configuration. When `prefer` is enabled, the connector attempts to use an encrypted connection to the database server. - `prefer` and `require`: use a secure (encrypted) connection. The connector fails if a secure connection cannot be established. These modes do not do Certification Authority (CA) validation. - `verify-ca`: similar to `require`, but also verifies the server TLS certificate against the configured Certificate Authority (CA) certificates. Fails if no valid matching CA certificates are found. - `verify-full`: similar to `verify-ca`, but also verifies that the server certificate matches the host to which the connection is attempted. If you choose `verify-ca` or `verify-full`, use the property `ssl.rootcertfile` and add the contents of the text certificate file for the property value. For example, `"ssl.rootcertfile": ""`. * The following provides more information about how to use the `timestamp.column.name` and `incrementing.column.name` properties. - Enter a `timestamp.column.name` to enable *timestamp* mode. This mode uses a timestamp (or timestamp-like) column to detect new and modified rows. This assumes the column is updated with each write, and that values are monotonically incrementing, but not necessarily unique. - Enter both a `timestamp.column.name` and an `incrementing.column.name` to enable *timestamp+incrementing* mode. This mode uses two columns, a timestamp column that detects new and modified rows, and a strictly incrementing column which provides a globally unique ID for updates so each row can be assigned a unique stream offset. By default, the connector only detects `table.types` with type `TABLE` from the source database. Enter `VIEW` for virtual tables created from joining one or more tables. Enter `ALIAS` for tables with a shortened or temporary name. * If you define a schema pattern in your database, you need to enter the `schema.pattern` property to fetch table metadata from the database. `""` retrieves table metadata for tables not using a schema. `null` (default) indicates that the schema name is not used to narrow the search and that all table metadata is fetched, regardless of the schema. * `"output.data.format"`: Sets the output Kafka record value format (data coming from the connector). Valid entries are **AVRO**, **JSON_SR**, **PROTOBUF**, **JSON**, or **STRING**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"db.timezone"`: Identifies the database timezone. This can be any valid database timezone. The default is **UTC**. For more information, see this [list of database timezones](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones). #### NOTE To enable CSFLE or CSPE for data encryption, specify the following properties: * `csfle.enabled`: Flag to indicate whether the connector honors CSFLE or CSPE rules. * `sr.service.account.id`: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema. For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](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-microsoft-sql-source-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 microsoft-sql-source.json ``` Example output: ```none Created connector confluent-microsoft-sql-source 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-microsoft-sql-source | RUNNING | source ``` #### Step 6: Check the Kafka topic. After the connector is running, verify that messages are populating your Kafka topic. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](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 ### Kafka Cluster credentials `kafka.auth.mode` : Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode, whenever possible.
* Type: string * Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY * Importance: high `kafka.api.key` : Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.
* Type: password * Importance: high `kafka.service.account.id` : The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.
* Type: string * Importance: high `kafka.api.secret` : Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
* Type: password * Importance: high ### Schema Config `schema.context.name` : Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.
* Type: string * Default: default * Importance: medium ### How do you want to prefix table names? `topic.prefix` : Prefix to prepend to table names to generate the name of the Apache Kafka® topic to publish data to.
* Type: string * Importance: high ### Authentication method `authentication.method` : Select how you want to authenticate with your database.
* Type: string * Default: Password * Importance: high `secret.manager.enabled` : Fetch sensitive configuration values from a secret manager.
* Type: boolean * Default: false * Importance: high ### Azure credentials `provider.integration.id` : Azure provider-integration to mint Microsoft Entra ID application tokens.
* Type: string * Importance: high ### Secret manager configuration `secret.manager` : Select the secret manager to use for retrieving sensitive data.
* Type: string * Importance: high `secret.manager.managed.configs` : Select the configurations to fetch their values from the secret manager.
* Type: list * Importance: high `secret.manager.provider.integration.id` : Select an existing provider integration that has access to your secret manager.
* Type: string * Importance: high ### How should we connect to your database? `connection.host` : Depending on the service environment, certain network access limitations may exist. Make sure the connector can reach your service. Do not include [jdbc:xxxx://](jdbc:xxxx://) in the connection hostname property (e.g. database-1.abc234ec2.us-west.rds.amazonaws.com).
* Type: string * Importance: high `connection.port` : JDBC connection port.
* Type: int * Valid Values: [0,…,65535] * Importance: high `connection.user` : JDBC connection user.
* Type: string * Importance: high `connection.password` : JDBC connection password.
* Type: password * Importance: high `db.name` : JDBC database name.
* Type: string * Importance: high `ssl.mode` : What SSL mode should we use to connect to your database. `prefer` allows for the connection to not be encrypted and `require` allows for the connection to be encrypted but does not do certificate validation on the server. `verify-ca` and `verify-full` require a file containing SSL CA certificate to be provided. The server’s certificate will be verified to be signed by one of these authorities.\`\`verify-ca\`\` will verify that the server certificate is issued by a trusted CA. `verify-full` will verify that the server certificate is issued by a trusted CA and that the server hostname matches that in the certificate. Client authentication is not performed.
* Type: string * Default: prefer * Importance: high `ssl.truststorefile` : The binary trust store file that contains the server’s CA certificate. Only required if you use verify-ca or verify-full ssl mode. The connector supports files in JKS format. For REST API usage, you must base64-encode the binary trust store file and prefix it with `data:text/plain;base64,`. For example, first, encode the file `base64_truststore=$(cat /path/to/truststore.jks | base64)` and then use `data:text/plain;base64,$base64_truststore` as the value.
* Type: password * Default: [hidden] * Importance: low `ssl.truststorepassword` : The trust store password containing server CA certificate. Only required if using verify-ca or verify-full ssl mode.
* Type: password * Default: [hidden] * Importance: low ### Database details `table.whitelist` : (Deprecated) List of tables to include in copying. Use a comma-separated list to specify multiple tables (for example: “User, Address, Email”). This is deprecated, please use table.include.list.
* Type: list * Importance: medium `table.include.list` : A comma-separated list of regular expressions that match the fully-qualified names of tables to be copied. Use a comma-separated list to specify multiple regular expressions. Table names are case-sensitive. For example, `table.include.list: schema1.customer.*,schema2.order.*`. If specified, `table.whitelist` cannot be set. For PostgreSQL, use `schema.table` format and do not include database name in the fully-qualified name (for example, `public.customers`).
* Type: list * Importance: medium `table.exclude.list` : A comma-separated list of regular expressions that match the fully-qualified names of tables to be excluded from copying. Use a comma-separated list to specify multiple regular expressions. Table names are case-sensitive. For example, `table.exclude.list: schema1.customer.*,schema2.order.*`. If specified, `table.whitelist` cannot not be set. For PostgreSQL, use `schema.table` format and do not include database name in the fully-qualified name (for example, `public.customers`).
* Type: list * Importance: medium `query` : If specified, the connector uses this custom SQL query to read source records, which allows for operations like joining tables or selecting subsets of data. Providing a query instructs the connector to read only the result set instead of performing a full table copy. This configuration supports different query modes with the incremental query properly constructed by appending a WHERE clause (For more information, Incremental Query Modes - ). When specified with the different query modes, please do not add any or ORDER BY or GROUP BY clauses in the outer SELECT query as the connector adds them by default the incrementing or timestamp columns specified. Note that only SELECT statements are supported. Always adhere to security best practices, like enforcing strict authorization via , applying appropriate :ref: network access controls - for control plane APIs, and following the principle of least privilege when provisioning identities or credentials for any third-party systems.
* Type: password * Default: [hidden] * Importance: medium `table.types` : By default, the JDBC connector will only detect tables with type TABLE from the source Database. This config allows a command separated list of table types to extract.
* Type: list * Default: TABLE * Importance: medium `schema.pattern` : Schema pattern to fetch table metadata from the database.
* Type: string * Importance: medium `db.timezone` : Name of the JDBC timezone used in the connector when querying with time-based criteria. Defaults to UTC.
* Type: string * Default: UTC * Importance: medium `numeric.mapping` : Map NUMERIC values by precision and optionally scale to integral or decimal types. Use `none` if all NUMERIC columns are to be represented by Connect’s DECIMAL logical type. Use `best_fit` if NUMERIC columns should be cast to Connect’s INT8, INT16, INT32, INT64, or FLOAT64 based upon the column’s precision and scale. Use `best_fit_eager_double` if, in addition to the properties of best_fit described above, it is desirable to always cast NUMERIC columns with scale to Connect FLOAT64 type, despite potential of loss in accuracy. Use `precision_only` to map NUMERIC columns based only on the column’s precision assuming that column’s scale is 0. The `none` option is the default, but may lead to serialization issues with Avro since Connect’s DECIMAL type is mapped to its binary representation, and `best_fit` will often be preferred since it maps to the most appropriate primitive type.
* Type: string * Default: none * Importance: low `timestamp.granularity` : Define the granularity of the Timestamp column. CONNECT_LOGICAL (default): represents timestamp values using Kafka Connect built-in representations. MICROS_LONG: represents timestamp values as micros since epoch. MICROS_STRING: represents timestamp values as micros since epoch in string. MICROS_ISO_DATETIME_STRING: uses iso format for timestamps in micros. NANOS_LONG: represents timestamp values as nanos since epoch. NANOS_STRING: represents timestamp values as nanos since epoch in string. NANOS_ISO_DATETIME_STRING: uses iso format
* Type: string * Default: CONNECT_LOGICAL * Importance: low ### Mode `mode` : The mode for updating a table each time it is polled. `BULK`: perform a bulk load of the entire table each time it is polled. `TIMESTAMP`: use a timestamp (or timestamp-like) column to detect new and modified rows. This assumes the column is updated with each write, and that values are monotonically incrementing, but not necessarily unique. `INCREMENTING`: use a strictly incrementing column on each table to detect only new rows. Note that this will not detect modifications or deletions of existing rows. `TIMESTAMP AND INCREMENTING`: use two columns, a timestamp column that detects new and modified rows and a strictly incrementing column which provides a globally unique ID for updates so each row can be assigned a unique stream offset.
* Type: string * Default: “” * Importance: medium `timestamp.columns.mapping` : A comma-separated list of table regex to timestamp columns mappings. On specifying multiple timestamp columns, COALESCE SQL function would be used to find out the effective timestamp for a row. Expected format is `regex1:[col1|col2],regex2:[col3]`. Regexes would be matched against the fully-qualified table names. Identifier names are case sensitive. Every table included for capture should match exactly one of the provided mappings. An example for a valid input would be `.*\.customers.*:[updated_at|modified_at],.*\.orders.*:[changed_at]`.
* Type: list * Importance: medium `incrementing.column.mapping` : A comma-separated list of table regex to incrementing column mappings. Expected format is `regex1:col1,regex2:col2`. Regexes would be matched against the fully-qualified table names. Identifier names are case sensitive. Every table included for capture should match exactly one of the provided mappings. An example for a valid input would be `.*\.customers.*:id,.*\.orders.*:order_id`.
* Type: list * Importance: medium `transaction.isolation.mode` : Isolation level determines how transaction integrity is visible to other users and systems. DEFAULT: This is the default isolation level configured at the Database Server. READ_UNCOMMITTED: This is the lowest isolation level. At this level, one transaction may see dirty reads (that is, not-yet-committed changes made by other transactions). READ_COMMITTED: This level guarantees that any data read is already committed at the moment it is read. REPEATABLE_READ: In addition to the guarantees of the READ_COMMITTED level, this option also guarantees that any data read cannot change, if the transaction reads the same data again. However, phantom reads are possible. SERIALIZABLE: This is the highest isolation level. In addition to everything REPEATABLE_READ guarantees, it also eliminates phantom reads. SQL_SERVER_SNAPSHOT: This option achieves the same results as SERIALIZABLE. However, instead of using locks, it uses row versioning.
* Type: string * Default: DEFAULT * Valid Values: DEFAULT, READ_COMMITTED, READ_UNCOMMITTED, REPEATABLE_READ, SERIALIZABLE, SQL_SERVER_SNAPSHOT * Importance: medium `timestamp.column.name` : (Deprecated legacy configuration. Use timestamp.columns.mapping for new implementations.) Comma separated list of one or more timestamp columns to detect new or modified rows using the COALESCE SQL function. Rows whose first non-null timestamp value is greater than the largest previous timestamp value seen will be discovered with each poll. At least one column should not be nullable.
* Type: list * Importance: medium `quote.sql.identifiers` : When to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default value is ALWAYS.
* Type: string * Default: ALWAYS * Valid Values: ALWAYS, NEVER * Importance: medium `incrementing.column.name` : (Deprecated legacy configuration. Use incrementing.column.mapping for new implementations.) The name of the strictly incrementing column to use to detect new rows. Any empty value indicates the column should be autodetected by looking for an auto-incrementing column. This column may not be nullable.
* Type: string * Default: “” * Importance: medium `timestamp.initial` : The epoch timestamp used for initial queries that use timestamp criteria. The value `-1` sets the initial timestamp to the current time. If not specified, the connector retrieves all data. Once the connector has managed to successfully record a source offset, this property has no effect even if changed to a different value later on.
* Type: long * Valid Values: [-1,…] * Importance: medium `date.calendar.system` : The time elapsed from epoch populated in the end table topic for DATE or TIMESTAMP type columns can have two different values based upon the Calendar used to interpret it. If `LEGACY` is used, it will use the hybrid Gregorian/Julian calendar which was the default in the older java date time APIs. However, if `PROLEPTIC_GREGORIAN` is used, then it will use the proleptic gregorian calendar which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover. This matches the behavior of modern Java date/time APIs (java.time). This is defaulted to LEGACY for backward compatibility. Changing this configuration on an existing connector might lead to a drift in the kafka topic record values.
* Type: string * Default: LEGACY * Importance: medium ### Connection details `poll.interval.ms` : Frequency in ms to poll for new data in each table.
* Type: int * Default: 5000 (5 seconds) * Valid Values: [100,…] * Importance: high `batch.max.rows` : Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.
* Type: int * Default: 100 * Valid Values: [1,…,5000] * Importance: low `timestamp.delay.interval.ms` : How long to wait after a row with a certain timestamp appears before we include it in the result. You may choose to add some delay to allow transactions with an earlier timestamp to complete. The first execution will fetch all available records (starting at timestamp 0) until current time minus the delay. Every following execution will get data from the last time we fetched until current time minus the delay.
* Type: int * Default: 0 * Valid Values: [0,…] * Importance: high ### Output messages `output.data.format` : Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, 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
* Type: string * Default: JSON * Importance: high ### Number of tasks for this connector `tasks.max` : Maximum number of tasks for the connector.
* Type: int * Valid Values: [1,…] * Importance: high ### Additional Configs `header.converter` : The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* Type: string * Importance: low `producer.override.compression.type` : The compression type for all data generated by the producer. Valid values are none, gzip, snappy, lz4, and zstd.
* Type: string * Importance: low `producer.override.linger.ms` : The producer groups together any records that arrive in between request transmissions into a single batched request. More details can be found in the documentation: [https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms](https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms).
* Type: long * Valid Values: [100,…,1000] * Importance: low `value.converter.allow.optional.map.keys` : Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.
* Type: boolean * Importance: low `value.converter.auto.register.schemas` : Specify if the Serializer should attempt to register the Schema.
* Type: boolean * Importance: low `value.converter.connect.meta.data` : Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.
* Type: boolean * Importance: low `value.converter.enhanced.avro.schema.support` : Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
* Type: boolean * Importance: low `value.converter.enhanced.protobuf.schema.support` : Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.flatten.unions` : Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.generate.index.for.unions` : Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.generate.struct.for.nulls` : Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.int.for.enums` : Whether to represent enums as integers. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.latest.compatibility.strict` : Verify latest subject version is backward compatible when use.latest.version is true.
* Type: boolean * Importance: low `value.converter.object.additional.properties` : Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
* Type: boolean * Importance: low `value.converter.optional.for.nullables` : Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.optional.for.proto2` : Whether proto2 optionals are supported. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.scrub.invalid.names` : Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.
* Type: boolean * Importance: low `value.converter.use.latest.version` : Use latest version of schema in subject for serialization when auto.register.schemas is false.
* Type: boolean * Importance: low `value.converter.use.optional.for.nonrequired` : Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
* Type: boolean * Importance: low `value.converter.wrapper.for.nullables` : Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `value.converter.wrapper.for.raw.primitives` : Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.
* Type: boolean * Importance: low `errors.tolerance` : Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.
* Type: string * Default: none * Importance: low `key.converter.key.schema.id.serializer` : The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer * Importance: low `key.converter.key.subject.name.strategy` : How to construct the subject name for key schema registration.
* Type: string * Default: TopicNameStrategy * Importance: low `value.converter.decimal.format` : Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:
BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
* Type: string * Default: BASE64 * Importance: low `value.converter.flatten.singleton.unions` : Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
* Type: boolean * Default: false * Importance: low `value.converter.ignore.default.for.nullables` : When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.
* Type: boolean * Default: false * Importance: low `value.converter.reference.subject.name.strategy` : Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.
* Type: string * Default: DefaultReferenceSubjectNameStrategy * Importance: low `value.converter.replace.null.with.default` : Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.
* Type: boolean * Default: true * Importance: low `value.converter.schemas.enable` : When true, the JsonConverter writes each record to Kafka as a {schema, payload} envelope so downstream consumers can interpret the value with its schema. When false, only the payload (plain JSON) is written. Applicable for JSON Converter.
* Type: boolean * Default: false * Importance: low `value.converter.value.schema.id.serializer` : The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer * Importance: low `value.converter.value.subject.name.strategy` : Determines how to construct the subject name under which the value schema is registered with Schema Registry.
* Type: string * Default: TopicNameStrategy * Importance: low ### Auto-restart policy `auto.restart.on.user.error` : Enable connector to automatically restart on user-actionable errors.
* Type: boolean * Default: true * Importance: medium ## Frequently asked questions Find answers to frequently asked questions about the Microsoft SQL Server Source connector for Confluent Cloud. ### How do I connect to a self-hosted SQL Server database from Confluent Cloud? To connect the fully managed Microsoft SQL Server Source connector to a self-hosted SQL Server database, you must configure private networking using egress endpoints: 1. **Create a PrivateLink egress endpoint**: In the Cloud Console, navigate to your environment and create an egress PrivateLink endpoint that connects to your SQL Server database. 2. **Configure the endpoint service**: Ensure your SQL Server database is accessible through an Amazon PrivateLink endpoint service, Azure Private Link service, or Google Cloud Private Service Connect. 3. **Update the connector configuration**: In the `Connection host` property, use the DNS name of the egress endpoint instead of a public IP address or hostname. 4. **Verify connectivity**: Ensure the egress endpoint status is `Ready` before creating the connector. You can verify this in the Cloud Console under **Networking > Egress**. #### IMPORTANT The egress endpoint must be in the same region as your Kafka cluster. Cross-region egress is not supported. For detailed setup instructions, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud). ### Why is my connector receiving duplicate records? The connector can produce duplicate records in the Kafka topic when using timestamp mode with an incorrect column type or nullable timestamp columns. Common causes and solutions: 1. **Using datetime instead of datetime2**: On SQL Server 2016 and later, the connector rejects `datetime` columns configured as timestamp columns and throws the following error: `A DATETIME column is configured for timestamp.column.name with Sql Server. DATETIME is not supported. Use DATETIME2 instead.` This restriction exists because a precision casting mismatch between JDBC and SQL Server 2016+ causes the connector to loop infinitely on the same record. JDBC casts to higher precision non-recursively, for example `3.330000` ms, while SQL Server 2016+ casts recursively, for example `3.333333` ms. This disparity prevents the connector from advancing past the current offset. Verify the column type: ```sql SELECT COLUMN_NAME, DATA_TYPE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = 'your_table' AND COLUMN_NAME = 'your_timestamp_column'; ``` If the column uses `datetime`, alter it to `datetime2`: ```sql ALTER TABLE your_table ALTER COLUMN your_timestamp_column datetime2; ``` 2. **Nullable timestamp column**: If you configure a single timestamp column, it should be `NOT NULL`. A `NULL` value causes the row to be skipped rather than duplicated, because `NULL` cannot satisfy the `> last_timestamp` comparison in the connector’s WHERE clause. If you configure multiple timestamp columns, the connector uses the SQL `COALESCE` function to select the first non-null value. In this case, at least one of the configured columns should not be nullable. Verify and update the column constraint: ```sql ALTER TABLE your_table ALTER COLUMN your_timestamp_column datetime2 NOT NULL; ``` 3. **Future timestamps**: The connector uses the database server’s current time minus `timestamp.delay.interval.ms` as the upper bound of its query. Records with timestamps ahead of the database clock are excluded from query results until the clock catches up, which appears as a delivery delay rather than duplicate records. Ensure your application does not write future timestamps to the timestamp column, and verify that `db.timezone` is configured to match the timezone used by your SQL Server instance. #### IMPORTANT On SQL Server 2016 and later, `datetime` columns are not supported as timestamp columns. The connector validates the column type at startup and rejects `datetime` with a `ConnectException`. Use `datetime2` instead, which provides nanosecond-level precision compatible with the JDBC driver. ### Why is my connector not detecting new or updated records? The connector may miss records if you have not configured the timestamp or incrementing columns correctly. Common causes and solutions: 1. **Timestamp column not updated**: The timestamp column must be updated every time a row is modified: Ensure your application or database triggers update the timestamp column on every `UPDATE` operation. Verify with a test update: ```sql UPDATE your_table SET your_column = 'value' WHERE id = 1; SELECT your_timestamp_column FROM your_table WHERE id = 1; ``` If the timestamp did not change, add a trigger or modify your application to update it. 2. **Wrong timestamp mode**: Using `timestamp` mode when you need `timestamp+incrementing` mode. `timestamp` mode configuration: ```json { "timestamp.column.name": "updated_at" } ``` `timestamp+incrementing` mode configuration: ```json { "timestamp.column.name": "updated_at", "incrementing.column.name": "id" } ``` Use `timestamp+incrementing` mode to ensure each row receives a globally unique stream offset. This is required when multiple rows can share the same timestamp value and you need to guarantee that no rows are missed or duplicated during ingestion. 3. **Poll interval too long**: The `poll.interval.ms` property controls how frequently the connector queries the database: Reduce the poll interval to check more frequently: ```json { "poll.interval.ms": "5000" } ``` The default is `5000` milliseconds. 4. **Timestamp delay interval**: The `timestamp.delay.interval.ms` property adds a delay before querying recent records: If this value is too high, recent records may appear delayed. Adjust it: ```json { "timestamp.delay.interval.ms": "0" } ``` #### NOTE The `timestamp.delay.interval.ms` property subtracts the configured delay from the database server’s current time to set the query’s upper bound. This allows in-flight transactions with earlier timestamps to commit before the connector reads them, preventing missed records due to uncommitted writes. #### NOTE When using timestamp mode, the timestamp column must be monotonically increasing and updated on every row modification. The connector does not detect `DELETE` operations in any mode. ### What SQL Server user permissions are required for the connector? The SQL Server database user specified in `Connection user` must have specific permissions to read data and table metadata. Required permissions: 1. **Database access**: Grant connection and basic permissions: ```sql USE YourDatabase; GO CREATE LOGIN connector_user WITH PASSWORD = 'YourPassword'; GO CREATE USER connector_user FOR LOGIN connector_user; GO ``` 2. **Table read permissions**: Grant `SELECT` on tables or schemas: ```sql GRANT SELECT ON SCHEMA::dbo TO connector_user; GO ``` Alternatively, grant on specific tables: ```sql GRANT SELECT ON dbo.your_table TO connector_user; GO ``` 3. **Metadata access**: The connector uses standard JDBC metadata APIs — `DatabaseMetaData.getTables`, `getColumns`, and `getPrimaryKeys` — and queries `sys.synonyms` for synonym support. These operations require `SELECT` permission on the tables being accessed and membership in the `public` database role. In restrictive environments, you may also need to grant access to the synonyms view: ```sql GRANT SELECT ON sys.synonyms TO connector_user; GO ``` `VIEW DEFINITION` is generally not required unless your security policy restricts access to system catalog views. 4. **Verify permissions**: Check granted permissions: ```sql SELECT USER_NAME(grantee_principal_id) AS UserName, permission_name, state_desc FROM sys.database_permissions WHERE USER_NAME(grantee_principal_id) = 'connector_user'; ``` #### NOTE For Azure SQL Database, permissions are managed similarly. Use `ALTER ROLE db_datareader ADD MEMBER connector_user` for read permissions. ### How do I filter which tables the connector reads from? Use the `table.include.list` property to specify which tables the connector should monitor. Table filtering configuration: 1. **Include specific tables**: Provide a comma-separated list of regular expressions that match the fully-qualified names of the tables to include. Table names are case-sensitive. Because values are treated as regular expressions, use `\\.` to match a literal dot. An unescaped `.` matches any character. ```json { "table.include.list": "dbo\\.customers,dbo\\.orders" } ``` This includes only the `customers` and `orders` tables from the `dbo` schema. 2. **Include tables with pattern matching**: Use regex patterns: ```json { "table.include.list": ".*customers.*,.*orders.*" } ``` This includes all tables containing `customers` or `orders` in the name. 3. **Exclude tables**: Use `table.exclude.list` to exclude specific tables: ```json { "table.include.list": "dbo.*", "table.exclude.list": "dbo.temp.*,dbo.audit.*" } ``` This includes all tables in `dbo` schema except those starting with `temp` or `audit`. #### NOTE The `table.exclude.list` property requires `table.include.list` to also be specified. It cannot be used alone to exclude tables. If `table.include.list` is specified, the legacy `table.whitelist` and `table.blacklist` properties cannot be set. 4. **Use schema pattern**: If you have multiple schemas, filter by schema: ```json { "schema.pattern": "dbo", "table.include.list": "customers,orders" } ``` The `schema.pattern` property narrows the search to specific schemas. #### IMPORTANT The `schema.pattern` property uses JDBC metadata pattern matching with `%` and `_` wildcards, not regular expressions. The default value is `null` (all schemas), which is applied by omitting the property from the connector configuration. Use `""` (empty string) to match only tables without a schema. For SQL Server, the connector defaults to the `dbo` schema when no schema is specified in table identifiers. ### Why are my Kafka topics not being created automatically? The connector creates topics automatically using the naming convention ``, but topic creation can fail due to permissions or configuration issues. Common causes and solutions: 1. **Missing topic prefix**: The `topic.prefix` property is required: Configure a topic prefix: ```json { "topic.prefix": "sqlserver_" } ``` Topics are created as `sqlserver_tablename`. 2. **Insufficient ACL permissions**: The service account needs permissions to create topics: If using a service account with granular ACLs, grant `CREATE` permission on topic prefix. For details, see [JDBC-based Source Connectors and the MongoDB Atlas Source Connector](service-account.md#cloud-service-account-jdbc-mongo-acls). 3. **Empty topic prefix with ACLs**: If `topic.prefix` is empty and you use granular ACLs, additional configuration is required: For empty topic prefix ACL requirements, see the [prerequisites](#cc-microsoft-sql-server-source-prereqs). 4. **Table name character restrictions**: The connector constructs the topic name by concatenating `topic.prefix` with the table name. If the resulting topic name contains characters that are invalid for Kafka topics (only ASCII alphanumerics, `.`, `_`, and `-` are permitted), the connector fails to produce records. Rename the source table or use a custom query with an explicit `topic.prefix` to avoid this issue. 5. **Topic already exists with different settings**: If topics were created manually with different partition or replication settings: The default topic settings are determined by the Confluent Cloud platform configuration. Pre-create topics if you need specific partition counts or replication settings. #### NOTE Topics are created automatically during the first poll. Check the connector logs if topics are not appearing. ### How can I optimize connector performance and reduce database load? You can tune connector performance using poll interval, batch size, and timestamp delay settings. Performance optimization: 1. **Adjust poll interval**: Control how frequently the connector queries the database: ```json { "poll.interval.ms": "10000" } ``` Increase the interval to reduce database load. The default is `5000` milliseconds. 2. **Increase batch size**: Fetch more rows per query: ```json { "batch.max.rows": "500" } ``` Larger batch sizes reduce the number of queries but use more memory. The default is `100` rows. 3. **Use timestamp delay**: Add a delay before querying recent records to avoid missing updates: ```json { "timestamp.delay.interval.ms": "1000" } ``` This delays querying records by 1 second to ensure all updates are committed. 4. **Optimize database indexes**: Ensure indexes exist on timestamp and incrementing columns: ```sql CREATE INDEX idx_timestamp ON your_table(updated_at); CREATE INDEX idx_id ON your_table(id); ``` 5. **Use table filtering**: Only monitor tables you need: ```json { "table.include.list": "critical_table1,critical_table2" } ``` Reduces the number of tables the connector polls. 6. **Monitor database performance**: Check SQL Server metrics: * **Query execution time**: Monitor query duration in SQL Server logs. * **Lock waits**: Check for blocking queries. * **Transaction log size**: Ensure adequate log space. * **CPU and memory**: Monitor resource utilization. #### NOTE Balance poll frequency with database load. For high-volume tables, consider increasing `batch.max.rows` and `poll.interval.ms`. ### How do I reset or modify connector offsets? The connector supports offset management to reset or modify the starting point for data ingestion. To view, reset, or modify connector offsets, see [Manage custom offsets](#cc-microsoft-sql-server-source-custom-offsets). #### IMPORTANT Resetting offsets causes the connector to re-read data, which may result in duplicate records in your Kafka topics. Use offset reset carefully. ### What should I do if my connector keeps failing or restarting? Frequent connector failures indicate configuration issues, network problems, or database connectivity errors. Common causes and solutions: 1. **Check connector logs**: In the Cloud Console, review error messages: * **Authentication failures**: Verify username and password. * **Connection timeouts**: Check egress endpoint status and network connectivity. * **Table access errors**: Verify table names and user permissions. * **Timestamp column errors**: Check column type and nullable constraints. 2. **Verify SQL Server connectivity**: Test that SQL Server is reachable: * **Check firewall rules**: Allow traffic on SQL Server port `1433`, which is the default. * **Verify egress endpoint status**: Ensure status is `Ready`. * **Test credentials manually**: Connect using SQL Server Management Studio with the same credentials. 3. **Validate timestamp columns**: Ensure timestamp columns meet requirements: ```sql SELECT COLUMN_NAME, DATA_TYPE, IS_NULLABLE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = 'your_table' AND COLUMN_NAME = 'your_timestamp_column'; ``` The column should use `datetime2` (required on SQL Server 2016 and later) and preferably `NOT NULL` to ensure no rows are skipped during ingestion. 4. **Check table filtering**: Verify `table.include.list` or `schema.pattern` matches existing tables: ```sql SELECT TABLE_SCHEMA, TABLE_NAME FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_TYPE = 'BASE TABLE'; ``` 5. **Verify Schema Registry availability**: If using Avro, JSON Schema, or Protobuf formats, ensure Schema Registry is enabled and accessible. 6. **Monitor connector resources**: Check if the connector is hitting resource limits: * **Reduce batch.max.rows**: Lower the batch size. * **Increase poll.interval.ms**: Reduce query frequency. * **Check database query performance**: Look for slow queries or locks. #### IMPORTANT For persistent failures, use the connector diagnostics feature and share logs with Confluent Support. ## 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)