<a id="cc-microsoft-sql-server-sink"></a>

# Microsoft SQL Server Sink (JDBC) Connector for Confluent Cloud

The fully managed Microsoft SQL Server Sink connector for Confluent Cloud moves data
from an Apache Kafka® topic to a Microsoft SQL Server database. It writes data from a
topic in Kafka to a table in the specified Microsoft SQL Server database. Table
auto-creation and limited auto-evolution are supported.

#### 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 Sink connector provides the following features:

* **Idempotent writes**: The default `insert.mode` is INSERT. If it is configured as UPSERT, the connector will use upsert semantics rather than plain insert statements. Upsert semantics refer to atomically adding a new row or updating the existing row if there is a primary key constraint violation, which provides idempotence.
* **SSL support**: Supports one-way SSL.
* **Schemas**: The connector supports Avro, JSON Schema, and Protobuf input **value** formats. The connector supports Avro, JSON Schema, Protobuf, and String input **key** formats. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format.
* **Primary key support**: Supported **PK modes** are `kafka`, `none`, `record_key`, and `record_value`. Used in conjunction with the **PK Fields** property.
* **Table and column auto-creation**: `auto.create` and `auto-evolve` are supported. If tables or columns are missing, they can be created automatically. Table names are created based on Kafka topic names.
* **At least once delivery**: This connector guarantees that records from the Kafka topic are delivered at least once.
* **Supports multiple tasks**: The connector supports running one or more tasks. More tasks may improve performance.
* **Provider integration support**: The connector supports Microsoft Entra ID-based authentication
  using Confluent Provider Integration. For more information about provider integration setup,
  see the [connector authentication](#cc-microsoft-sql-server-sink-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 Sink Connector](limits.md#cc-microsoft-sql-server-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 Microsoft SQL
Server Sink connector. The quick start provides the basics of selecting the
connector and configuring it to stream events to a Microsoft SQL Server
database.

#### NOTE
For configuring the Microsoft SQL Server Sink (JDBC) Connector with Azure
Private Link and Confluent Cloud Egress Private Link Endpoint, follow the steps in
[Egress Private Link Endpoints Setup Guide: First-Party Services on Azure for Confluent Cloud](networking/azure-eap-1st-party.md#cc-azure-eap-1st-party).

<a id="cc-microsoft-sql-server-sink-prereqs"></a>

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.
  - Authorized access to a Microsoft SQL Server database.
  - The database and Kafka cluster should be in the same region. If you use a different region, be aware that you may incur additional data transfer charges.
  - For networking considerations, see [Networking and DNS](overview.md#connect-internet-access-resources). To use a set of public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips).
  - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html).
  - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
  <br/>
  - 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 Sink** connector card.

![Microsoft SQL Server Sink Connector Card](images/ccloud-microsoft-sql-server-sink-icon.png)

<a id="cc-microsoft-sql-server-sink-setup-connection"></a>

#### Step 4: Enter the connector details

#### NOTE
* Ensure you have all your [prerequisites](#cc-microsoft-sql-server-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.

At the **Add Microsoft SQL Server Sink Connector** screen, complete the
following:

### Topic selection

If you’ve already populated your Kafka topics, select the topics you want
to connect from the **Topics** list.

To create a new topic, click **+Add new topic**.

### Kafka access

1. Select the way you want to provide **Kafka Cluster credentials**. You can
   choose one of the following options:
   - **My account**: This setting allows your connector to globally access everything
     that you have access to. With a user account, the connector uses an API key and
     secret to access the Kafka cluster. This option is not recommended for production.
   - **Service account**: This setting limits the access for your connector by using a
     [service account](service-account.md#s3-cloud-service-account). This option is recommended for
     production.
   - **Use an existing API key**: This setting allows you to specify an API key and a
     secret pair. You can use an existing pair or create a new one. This method is not
     recommended for production environments.

   #### NOTE
   Freight clusters support only service accounts for Kafka authentication.
2. Click **Continue**.

### Authentication

1. Configure the authentication properties:

   **Authentication method**
   - **Authentication method**: How Confluent Cloud authenticates with the Microsoft SQL Server database.

     Allowed values are:
     * `Password`
     * `Microsoft Entra ID application`
   - **Use secret manager**: Fetch sensitive configuration values from a secret manager.

   **Azure credentials**
   - **Provider Integration**: The Azure provider integration Confluent Cloud uses to generate Microsoft Entra ID application tokens.

   **Secret manager configuration**
   - **Secret manager**: Select the secret manager to use for retrieving sensitive data.
   - **Configurations from Secret manager**: Select the configurations whose values Confluent Cloud should
     fetch from the secret manager.
   - **Provider Integration**: The Azure provider integration Confluent Cloud uses to generate Microsoft Entra ID application tokens.

   **How should we connect to your database?**
   - **Connection host**: JDBC connection host.
   - **Connection port**: JDBC connection port.
   - **Connection user**: JDBC connection user.
   - **Connection password**: JDBC connection password.
   - **Database name**: JDBC database name.
   - **SSL mode**: The SSL mode to use to connect to your database.
   - **Trust store**: The trust store file containing the server CA
     certificate.
   - **Trust store password**: The trust store password containing the
     server CA certificate.
2. Click **Continue**.

### Configuration

#### NOTE
Configuration properties that are not shown in the
Cloud Console use the default values.  See
[Configuration Properties](#cc-sqlserver-sink-config-properties) for all
property values and definitions.

- **Input Kafka record value format**: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, or PROTOBUF. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.
- **Insert mode**: The insertion mode to use. INSERT uses the standard INSERT row function. An error occurs if the row already exists in the table. UPSERT mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided.

### **Show advanced configurations**

- **Schema context**: Select a schema context to use for this connector, if using
  a schema-based data format. This property defaults to the **Default** context,
  which configures the connector to use the default schema set up for Schema Registry in your
  Confluent Cloud environment. A schema context allows you to use separate schemas (like
  schema sub-registries) tied to topics in different Kafka clusters that share the
  same Schema Registry environment. For example, if you select a non-default context, a
  **Source** connector uses only that schema context to register a schema and a
  **Sink** connector uses only that schema context to read from. For more
  information about setting up a schema context, see [What are schema contexts and when should you use them?](../sr/faqs-cc.md#faq-schema-contexts).
- **Auto create table**: Whether to automatically create the
  destination table if it is missing.
- **Auto add columns**: Whether to automatically add columns in the
  table if they are missing.
- **Database timezone**: Name of the JDBC timezone that should be
  used in the connector when inserting time-based values.
- **Table name format**: A format string for the destination table
  name that may contain `${topic}` as a placeholder for the
  originating topic name. For example, to create a table named
  `kafka-orders` based on a Kafka topic named `orders`, you would
  enter `kafka-${topic}` in this field.
- **Timezone used for Date**: Name of the JDBC timezone that should be used in the connector when inserting DATE type values. Defaults to DB_TIMEZONE that uses the timezone set for db.timzeone configuration (to maintain backward compatibility). It is recommended to set this to UTC to avoid conversion for DATE type values.
- **Timestamp Precision Mode**: Converts the timestamp with precision. If set to microseconds, the timestamp is converted to microsecond precision. If set to nanoseconds, the timestamp is converted to nanosecond precision.
- **Timestamp Fields**: List of comma-separated record value timestamp field names that should be converted to timestamps. These fields are converted based on the precision mode specified in Timestamp Precision Mode. The timestamp fields included here should be Long or String type, and nested fields are not supported.
- **Table types**: The comma-separated types of database tables to
  which the sink connector can write.
- **Fields included**: List of comma-separated record value field
  names. If empty, all fields from the record value are used.
- **PK mode**: The primary key mode.
- **PK Fields**: List of comma-separated primary key field names.
- **When to quote SQL identifiers**: When to quote table names,
  column names, and other identifiers in SQL statements.
- **Max rows per batch**: Maximum number of rows to include in a
  single batch when polling for new data. This setting can be used
  to limit the amount of data buffered internally in the connector.
- **Input Kafka record key format**: Sets the input Kafka record key
  format. This need to be set to a proper format if using
  `pk.mode=record_key`. Valid entries are AVRO, JSON_SR, PROTOBUF,
  STRING. Note that you must have Confluent Cloud Schema Registry configured if
  using a schema-based message format like AVRO, JSON_SR, and
  PROTOBUF.
- **Delete on null**: Whether to treat null record
  values as deletes. This requires `pk.mode` to be `record_key`.
- **String Value Column Name**: Name of the destination table column
  when the Kafka record value written using
  `StringConverter` is sinked to the DB table

  The raw string value is written into this column.
  If the destination table is auto-created,
  the column is created with the name specified
  in this field. If the table already exists and
  the column is missing, the column is added using
  `ALTER` when `auto.evolve` is set to `true`.

  #### NOTE
  Databases that treat quoted identifiers as case-sensitive,
  like PostgreSQL, ensure that the column name in the
  existing table exactly matches this value.

  Defaults to `record_value`.
- **Date Calendar System**: Conversion of time since epoch value in Kafka topic record to `DATE` or `TIMESTAMP` depends on the calendar used to interpret it. If `LEGACY` is used, it will use the hybrid Gregorian/Julian calendar which was the default in the older java date time APIs. However, if `PROLEPTIC_GREGORIAN` is used, then it will use the proleptic gregorian calendar which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover. This matches the behavior of modern Java date/time APIs (`java.time`). This is defaulted to `LEGACY` for backward compatibility. The ideal setting for this depends on whether the values in source topic were populated using old or new java date time APIs. Changing this configuration on an existing connector might lead to a drift in the `DATE`/`TIMESTAMP` column’s values populated in the sink database.

**Additional Configs**

- **Value Converter Replace Null With Default**: Specifies whether to replace fields that have a default value and that are null to the default value. When set to `true`, the connector uses the default value; otherwise, it uses `null`. Applies to the `JSON` converter.
- **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
- **Value Converter Reference Subject Name Strategy**: Sets the subject reference name strategy for values. Valid entries are `DefaultReferenceSubjectNameStrategy` or `QualifiedReferenceSubjectNameStrategy`. You can use this strategy only with `PROTOBUF` format; the default strategy is `DefaultReferenceSubjectNameStrategy`.
- **Schema ID For Value Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Schemas Enable**: Includes schema within each of the serialized values. Input messages must contain `schema` and `payload` fields and must not contain additional fields. For plain `JSON` data, set this to `false`. Applies to the `JSON` converter.
- **Errors Tolerance**: Use this property to configure the connector’s error handling behavior.

  #### WARNING
  Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to `all`, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to `none`, the connector task fails on errant records.
- **Value Converter Ignore Default For Nullables**: When set to `true`, this property ensures that the corresponding record in Kafka is `null`, instead of showing the default column value. Applies to the `AVRO`, `PROTOBUF`, and `JSON_SR` converters.
- **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
- **Value Converter Decimal Format**: Specifies the `JSON` or `JSON_SR` serialization format for Connect `DECIMAL` logical type values with two allowed literals:
  `BASE64` to serialize `DECIMAL` logical types as base64 encoded binary data, and
  `NUMERIC` to serialize `DECIMAL` logical type values in `JSON` or `JSON_SR` as a number representing the decimal value.
- **Schema GUID For Key Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Schema GUID For Value Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
- **Value Converter Value Subject Name Strategy**: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- **Key Converter Key Subject Name Strategy**: Determines how to construct the subject name for key schema registration.
- **Schema ID For Key Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.

**Auto-restart policy**

- **Enable Connector Auto-restart**: Enables the auto-restart behavior of the connector and its
  task in the event of user-actionable errors. Defaults to `true`, enabling the connector to
  automatically restart in case of user-actionable errors. Set this property to `false` to
  disable auto-restart for failed connectors. If disabled, you must manually restart the connector.

**Consumer configuration**

- **Max poll interval(ms)**: Sets the maximum delay between subsequent consume requests to Kafka. Use this property to
  improve connector performance in cases when the connector cannot send records to the sink system.
  The default is 300,000 milliseconds (5 minutes).
- **Max poll records**: Sets the maximum number of records to consume from Kafka in a single request. Use this property to
  improve connector performance in cases when the connector cannot send records to the sink system.
  The default is 500 records.

**Transforms**

- **Single Message Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui).
  For more information about unsupported SMTs, see
  [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).

**Processing position**

- **Set offsets**: Click **Set offsets** to define a specific offset for
  this connector to begin procession data from. For more information
  on managing offsets, see [Manage offsets](offsets.md#connect-custom-offsets).

See [Configuration Properties](#cc-sqlserver-sink-config-properties) for all property values
and definitions.

- Click **Continue**.

### Sizing

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

1. To change the number of recommended tasks, enter the number of
   [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in
   the **Tasks** field.
2. Click **Continue**.

### Review and Launch

1. Verify the connection details.
2. Click **Launch**.

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

#### Step 5: Check the results in the database

Verify that new records are being added to the Microsoft SQL Server 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-microsoft-sql-server-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 <connector-plugin-name>
```

The command output shows the required and optional configuration properties.

#### Step 3: Create the connector configuration file

Create a JSON file that contains the connector configuration properties. The following example shows required and optional connector properties:

```none
{
  "topics": "sql_ratings",
  "input.data.format": "AVRO",
  "input.key.format": "AVRO",
  "connector.class": "MicrosoftSqlServerSink",
  "name": "MicrosoftSqlServerSinkConnector_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "****************************************************************",
  "connection.host": "connect-sqlserver-cdc.<host-id>.us-west-2.rds.amazonaws.com",
  "connection.port": "1433",
  "connection.user": "admin",
  "connection.password": "************",
  "db.name": "database-name",
  "insert.mode": "UPSERT",
  "auto.create": "true",
  "auto.evolve": "true",
  "tasks.max": "1",
  "pk.mode": "record_value",
  "pk.fields": "user_id"
}
```

Note the following property definitions. See the [Microsoft SQL Server Sink
configuration properties](#cc-sqlserver-sink-config-properties) for additional
property values and definitions.

* `"connector.class"`: Identifies the connector plugin name.
* `"name"`: Sets a name for your new connector.

* `"kafka.auth.mode"`: Identifies the connector authentication mode you want to use. There are two options: `SERVICE_ACCOUNT` or `KAFKA_API_KEY` (the default). To use an API key and secret, specify the configuration properties `kafka.api.key` and `kafka.api.secret`, as shown in the example configuration (above).  To use a [service account](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=<service-account-resource-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**, **JSON_SR** (JSON Schema), **PROTOBUF**, **JSON**, or **STRING**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.
* `"input.key.format"`: Sets the input record key format (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR** (JSON Schema), **PROTOBUF**, or **STRING**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.
* `"delete.on.null"`: Whether to treat null record values as deletes. Requires `pk.mode` to be `record_key`. Defaults to `false`.
* `"topics"`: Identifies the topic name or a comma-separated list of topic names.
* `"insert.mode"`: Enter one of the following modes:
  - `INSERT`: Use the standard `INSERT` row function. An error occurs if the row already exists in the table.
  - `UPSERT`: This mode is similar to `INSERT`. However, if the row already exists, the `UPSERT` function overwrites column values with the new values provided.
* `db.timezone`: Name of the time zone the connector uses when inserting time-based values. Defaults to UTC.
* `"auto.create"` (tables) and `"auto-evolve"` (columns): (Optional) Sets whether to automatically create tables or columns if they are missing relative to the input record schema. If not entered in the configuration, both default to `false`.
* `"pk.mode"`: Supported modes are listed below:
  - `kafka`: Kafka coordinates are used as the primary key. Must be used with the **PK Fields**.
  - `none`: No primary keys used.
  - `record_key`: Fields from the record key are used. May be a primitive or a struct.
  - `record_value`: Fields from the Kafka record value are used. Must be a struct type.
* `"pk.fields"`: A list of comma-separated primary key field names. The runtime interpretation of this property depends on the `pk.mode` selected. Options are listed below:
  - `kafka`: Must be three values representing the Kafka coordinates. If left empty, the coordinates default to `__connect_topic,__connect_partition,__connect_offset`.
  - `none`: PK Fields not used.
  - `record_key`: If left empty, all fields from the key struct are used. Otherwise, this is used to extract the fields in the property. A single field name must be configured for a primitive key.
  - `record_value`: Used to extract fields from the record value. If left empty, all fields from the value struct are used.
* `"tasks.max"`: Maximum number of tasks the connector can run. See Confluent Cloud [connector limitations](limits.md#cc-microsoft-sql-server-sink-limits) for additional task information.

**SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation.

See [Configuration Properties](#cc-sqlserver-sink-config-properties) for all property values and
definitions.

#### Step 4: Load the configuration file and create the connector

Enter the following command to load the configuration and start the connector:

```none
confluent connect cluster create --config-file <file-name>.json
```

For example:

```none
confluent connect cluster create --config-file microsoft-sql-server-sink-config.json
```

Example output:

```none
Created connector MicrosoftSqlServerSinkConnector_0 lcc-ix4dl
```

#### Step 5: Check the connector status

Enter the following command to check the connector status:

```none
confluent connect cluster list
```

Example output:

```none
ID          |       Name                         | Status  | Type
+-----------+------------------------------------+---------+------+
lcc-ix4dl   | MicrosoftSqlServerSinkConnector_0  | RUNNING | sink
```

#### Step 6: Check the results in the database.

Verify that new records are being added to the Microsoft SQL Server 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.

<a id="cc-sqlserver-sink-config-properties"></a>

## Configuration Properties

Use the following configuration properties with the fully managed connector. For
self-managed connector property definitions and other details, see the connector
docs in [Self-managed connectors for Confluent Platform](/platform/current/connect/kafka_connectors.html).

### Which topics do you want to get data from?

`topics.regex`
: A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.
  <br/>
  * Type: string
  * Importance: low

`topics`
: Identifies the topic name or a comma-separated list of topic names.
  <br/>
  * Type: list
  * Importance: high

`errors.deadletterqueue.topic.name`
: The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: dlq-${connector}
  * Importance: low

### Schema Config

`schema.context.name`
: Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.
  <br/>
  * Type: string
  * Default: default
  * Importance: medium

### Input messages

`input.data.format`
: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, or STRING. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF. JSON and STRING formats do not require Schema Registry. When JSON is selected, value.converter.schemas.enable must be set to true.
  <br/>
  * Type: string
  * Importance: high

`input.key.format`
: Sets the input Kafka record key format. This need to be set to a proper format if using pk.mode=record_key. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, or STRING. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF. JSON and STRING formats do not require Schema Registry. When pk.mode is set to record_key and JSON is selected, the record key must include an inline schema.
  <br/>
  * Type: string
  * Importance: high

`delete.enabled`
: Whether to treat null record values as deletes. Requires pk.mode to be record_key.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: low

### How should we connect to your data?

`name`
: Sets a name for your connector.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * Type: password
  * Importance: high

### Authentication method

`authentication.method`
: How Confluent Cloud authenticates with Azure. Allowed values - `Password` and `Microsoft Entra ID application`
  <br/>
  * Type: string
  * Default: Password
  * Valid Values: Microsoft Entra ID application, Password
  * Importance: high

`secret.manager.enabled`
: Fetch sensitive configuration values from a secret manager.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: high

### Azure credentials

`provider.integration.id`
: Azure provider-integration to mint Microsoft Entra ID application tokens.
  <br/>
  * Type: string
  * Importance: high

### Secret manager configuration

`secret.manager`
: Select the secret manager to use for retrieving sensitive data.
  <br/>
  * Type: string
  * Importance: high

`secret.manager.managed.configs`
: Select the configurations to fetch their values from the secret manager.
  <br/>
  * Type: list
  * Importance: high

`secret.manager.provider.integration.id`
: Select an existing provider integration that has access to your secret manager.
  <br/>
  * 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).
  <br/>
  * Type: string
  * Importance: high

`connection.port`
: JDBC connection port.
  <br/>
  * Type: int
  * Valid Values: [0,…,65535]
  * Importance: high

`connection.user`
: JDBC connection user.
  <br/>
  * Type: string
  * Importance: high

`connection.password`
: JDBC connection password.
  <br/>
  * Type: password
  * Importance: high

`db.name`
: JDBC database name.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: password
  * Default: [hidden]
  * Importance: low

### Database details

`insert.mode`
: The insertion mode to use. INSERT uses the standard INSERT row function. An error occurs if the row already exists in the table; UPSERT mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided.
  <br/>
  * Type: string
  * Default: INSERT
  * Importance: high

`table.name.format`
: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name.
  <br/>
  For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.
  <br/>
  * Type: string
  * Default: ${topic}
  * Importance: medium

`table.types`
: The comma-separated types of database tables to which the sink connector can write. By default this is `TABLE`, but any combination of `TABLE` and `VIEW` is allowed. Not all databases support writing to views, and when they do the sink connector will fail if the view definition does not match the records’ schemas (regardless of `auto.evolve`).
  <br/>
  * Type: list
  * Default: TABLE
  * Importance: low

`fields.whitelist`
: List of comma-separated record value field names. If empty, all fields from the record value are utilized, otherwise used to filter to the desired fields. Not applicable when input.data.format is set to STRING.
  <br/>
  * Type: list
  * Importance: medium

`timestamp.fields.list`
: List of comma-separated record value timestamp field names that should be converted to timestamps. These fields will be converted based on precision mode specified in Timestamp Precision Mode. The timestamp fields included here should be Long or String type and nested fields are not supported. Not applicable when input.data.format is set to STRING.
  <br/>
  * Type: list
  * Importance: medium

`string.output.value.column.name`
: Name of the destination table column to use when the Kafka record value written using StringConverter is sinked to the DB table (Input Kafka record value format = STRING). The raw string value is written into this single column. If the destination table is being auto-created, the column is created with this name; if the table already exists and the column is missing, the column is added via ALTER when ‘auto.evolve’ is true. Note: in databases that treat quoted identifiers as case-sensitive (e.g. PostgreSQL), make sure the column name in the existing table exactly matches this value (including case). Defaults to ‘record_value’.
  <br/>
  * Type: string
  * Default: record_value
  * Importance: low

`db.timezone`
: Name of the JDBC timezone used in the connector when querying with time-based criteria. Defaults to UTC.
  <br/>
  * Type: string
  * Default: UTC
  * Importance: medium

`date.timezone`
: Name of the JDBC timezone that should be used in the connector when inserting DATE type values. Defaults to DB_TIMEZONE that uses the timezone set for db.timzeone configuration (to maintain backward compatibility). It is recommended to set this to UTC to avoid conversion for DATE type values.
  <br/>
  * Type: string
  * Default: DB_TIMEZONE
  * Valid Values: DB_TIMEZONE, UTC
  * Importance: medium

`timestamp.precision.mode`
: Convert the Timestamp with precision. If set to microseconds the timestamp will be converted to microsecond precision. If set to nanoseconds the timestamp will be converted to nanoseconds precision. Not applicable when input.data.format is set to STRING.
  <br/>
  * Type: string
  * Default: microseconds
  * Importance: medium

`date.calendar.system`
: Conversion of time since epoch value in kafka topic record to DATE or TIMESTAMP depends on the calendar used to interpret it. If LEGACY is used, it will use the hybrid Gregorian/Julian calendar which was the default in the older java date time APIs. However, if ‘PROLEPTIC_GREGORIAN’ is used, then it will use the proleptic gregorian calendar which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover. This matches the behavior of modern Java date/time APIs (java.time). This is defaulted to LEGACY for backward compatibility. The ideal setting for this depends on whether the values in source topic were populated using old or new java date time APIs. Changing this configuration on an existing connector might lead to a drift in the DATE/TIMESTAMP column’s values populated in the sink database.
  <br/>
  * Type: string
  * Default: LEGACY
  * Importance: medium

### Primary Key

`pk.mode`
: The primary key mode, also refer to pk.fields documentation for interplay. Supported modes are:
  <br/>
  none: No keys utilized.
  <br/>
  kafka: Apache Kafka® coordinates are used as the PK.
  <br/>
  record_value: Field(s) from the record value are used, which must be a struct. This mode is not supported when input.data.format is set to STRING.
  <br/>
  record_key: Field(s) from the record key are used, which must be a struct.
  <br/>
  * Type: string
  * Valid Values: kafka, none, record_key, record_value
  * Importance: high

`pk.fields`
: List of comma-separated primary key field names. The runtime interpretation of this config depends on the pk.mode:
  <br/>
  none: Ignored as no fields are used as primary key in this mode.
  <br/>
  kafka: Must be a trio representing the Kafka coordinates, defaults to \_\_connect_topic,_\_connect_partition,_\_connect_offset if empty.
  <br/>
  record_value: If empty, all fields from the value struct will be used, otherwise used to extract the desired fields.
  <br/>
  * Type: list
  * Importance: high

### SQL/DDL Support

`auto.create`
: Whether to automatically create the destination table if it is missing.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: medium

`auto.evolve`
: Whether to automatically add columns in the table if they are missing.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: medium

`quote.sql.identifiers`
: When to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default is ‘always’.
  <br/>
  * Type: string
  * Default: ALWAYS
  * Valid Values: ALWAYS, NEVER
  * Importance: medium

### Connection details

`batch.sizes`
: Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.
  <br/>
  * Type: int
  * Default: 3000
  * Valid Values: [1,…,5000]
  * Importance: low

### Consumer configuration

`max.poll.interval.ms`
: The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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)
  <br/>
  * 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)
  <br/>
  * Type: string
  * Importance: low

`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: int
  * Importance: low

`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: all
  * Importance: low

`key.converter.key.schema.id.deserializer`
: The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.
  <br/>
  * 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.
  <br/>
  * 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:
  <br/>
  BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
  <br/>
  NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
  <br/>
  * Type: string
  * Default: BASE64
  * Importance: low

`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: low

`value.converter.schemas.enable`
: Must be set to true when input.data.format is JSON. Plain JSON without an inline schema is not supported. Applicable for JSON Converter.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

### Auto-restart policy

`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: medium

## Frequently asked questions

Find answers to frequently asked questions about the Microsoft SQL Server Sink connector for Confluent Cloud.

### How do I connect to a self-hosted SQL Server database over a private network?

To connect the fully managed Microsoft SQL Server Sink connector to a self-hosted SQL Server database that is not publicly accessible, 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 failing with `table does not exist` errors?

This error occurs when the connector attempts to write to a table that does not exist in the target database.

```text
com.microsoft.sqlserver.jdbc.SQLServerException: Invalid object name 'dbo.my_table'
```

Common causes and solutions:

1. **Auto-create disabled**: The `auto.create` property is set to `false`. Enable table auto-creation:
   ```json
   {
     "auto.create": "true"
   }
   ```

   With `auto.create` enabled, the connector automatically creates tables based on the Kafka topic schema.
2. **Table name mismatch**: The table name expected by the connector does not match the actual table name in SQL Server:

   Verify the table naming convention. By default, tables are created using the Kafka topic name.

   Use `table.name.format` to customize table names:
   ```json
   {
     "table.name.format": "${topic}"
   }
   ```
3. **Schema not specified**: If using a schema other than `dbo`, specify it in the table name:
   ```json
   {
     "table.name.format": "my_schema.${topic}"
   }
   ```
4. **Insufficient permissions**: The database user lacks `CREATE TABLE` permission:

   Grant table creation permission:
   ```sql
   GRANT CREATE TABLE TO connector_user;
   GO
   ```

#### NOTE
When `auto.create` is enabled, the connector creates tables during the first write operation. Subsequent writes use the existing table.

### Why is my connector failing with `primary key violation` errors?

This error indicates duplicate key values are being inserted into a table with a primary key constraint.

```text
Violation of PRIMARY KEY constraint. Cannot insert duplicate key in object 'dbo.my_table'
```

Common causes and solutions:

1. **Using INSERT mode with duplicate keys**: The default `insert.mode` is `insert`, which fails on duplicates:

   Switch to `UPSERT` mode to handle duplicates:
   ```json
   {
     "insert.mode": "upsert"
   }
   ```

   `UPSERT` mode performs INSERT or UPDATE operations based on primary key existence.
2. **Primary key not configured**: The connector does not know which fields constitute the primary key:

   Configure the primary key mode:
   ```json
   {
     "pk.mode": "record_key",
     "pk.fields": "id"
   }
   ```

   Primary key modes:
   * **\`\`record_key\`\`**: Use the Kafka record key as the primary key.
   * **\`\`record_value\`\`**: Extract primary key fields from the record value.
   * **\`\`kafka\`\`**: Use Kafka coordinates (topic, partition, offset) as the primary key.
   * **\`\`none\`\`**: No primary key
3. **Primary key fields mismatch**: The specified `pk.fields` do not match the table’s primary key:

   Verify the primary key fields:
   ```sql
   SELECT COLUMN_NAME
   FROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE
   WHERE TABLE_NAME = 'my_table' AND CONSTRAINT_NAME LIKE 'PK%';
   ```

   Update `pk.fields` to match:
   ```json
   {
     "pk.fields": "customer_id,order_id"
   }
   ```

#### IMPORTANT
For `UPSERT` mode to work, the table must have a primary key constraint. If the table was auto-created without a primary key, you must manually add one or recreate the table.

### What SQL Server user permissions are required for the connector?

The SQL Server database user specified in `Connection user` must have specific permissions to write data and manage tables.

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 write permissions**: Grant `INSERT`, `UPDATE`, and `DELETE` on tables:
   ```sql
   GRANT INSERT, UPDATE, DELETE ON SCHEMA::dbo TO connector_user;
   GO
   ```

   Alternatively, grant on specific tables:
   ```sql
   GRANT INSERT, UPDATE, DELETE ON dbo.my_table TO connector_user;
   GO
   ```
3. **Table creation permissions**: If using `auto.create`, grant CREATE TABLE:
   ```sql
   GRANT CREATE TABLE TO connector_user;
   GO
   ```
4. **Table modification permissions**: If using `auto.evolve`, grant ALTER:
   ```sql
   GRANT ALTER ON SCHEMA::dbo TO connector_user;
   GO
   ```
5. **Read table metadata**: Grant VIEW DEFINITION:
   ```sql
   GRANT VIEW DEFINITION TO connector_user;
   GO
   ```
6. **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_datawriter ADD MEMBER connector_user` for write permissions.

### Why is my connector failing with `data type mismatch` errors?

This error occurs when the Kafka message schema data type cannot be mapped to the SQL Server column data type.

```text
Error converting data type for column 'amount'
```

Common causes and solutions:

1. **Incompatible data types**: The Kafka schema field type does not have a compatible SQL Server type:

   Review the data type mapping. Common mappings:
   * **Kafka INT8, INT16, INT32**: SQL Server `INT`
   * **Kafka INT64**: SQL Server `BIGINT`
   * **Kafka FLOAT32**: SQL Server `REAL`
   * **Kafka FLOAT64**: SQL Server `FLOAT`
   * **Kafka STRING**: SQL Server `NVARCHAR(MAX)`
   * **Kafka BOOLEAN**: SQL Server `BIT`
   * **Kafka BYTES**: SQL Server `VARBINARY(MAX)`

   Ensure your Kafka schema uses appropriate types for SQL Server.
2. **String length exceeds column size**: String values are too long for the column:

   When using `auto.create`, the connector creates `NVARCHAR(MAX)` columns for strings. If the table was created manually with a fixed length, ensure it is large enough:
   ```sql
   ALTER TABLE dbo.my_table
   ALTER COLUMN description NVARCHAR(MAX);
   ```
3. **Null values in NOT NULL columns**: The Kafka message contains null values for a non-nullable column:

   Either make the column nullable:
   ```sql
   ALTER TABLE dbo.my_table
   ALTER COLUMN email NVARCHAR(255) NULL;
   ```

   Alternatively, ensure your data does not contain nulls.
4. **Decimal precision mismatch**: Decimal values exceed the column precision:

   Update the column precision:
   ```sql
   ALTER TABLE dbo.my_table
   ALTER COLUMN price DECIMAL(18, 4);
   ```

#### IMPORTANT
When using `auto.evolve`, the connector can add new columns but cannot modify existing column types. For type changes, you must manually alter the table schema.

### How can I optimize connector performance and throughput?

Batch size configuration, network latency, or database constraints can cause high latency or slow writes.

1. **Increase tasks**: Enable parallel processing with multiple tasks:
   ```json
   {
     "tasks.max": "4"
   }
   ```

   Each task can write to the database concurrently.
2. **Adjust batch sizes**: Process more records per database transaction:
   ```json
   {
     "batch.size": "3000"
   }
   ```

   Larger batch sizes reduce the number of database round trips but use more memory.
3. **Optimize polling intervals**: Control how frequently the connector polls for new records:
   ```json
   {
     "max.poll.interval.ms": "300000",
     "max.poll.records": "500"
   }
   ```
4. **Use UPSERT wisely**: UPSERT operations are slower than INSERT:

   If you know records are unique, use INSERT mode:
   ```json
   {
     "insert.mode": "insert"
   }
   ```
5. **Disable auto-evolve if not needed**: Schema evolution checks add overhead:
   ```json
   {
     "auto.evolve": "false"
   }
   ```
6. **Monitor SQL Server performance**: Check SQL Server metrics:
   * **Transaction log size**: Ensure log is not growing excessively.
   * **Lock waits**: Check for blocking queries.
   * **Disk I/O**: Verify adequate disk performance.
   * **CPU and memory**: Monitor resource utilization.
7. **Optimize table indexes**: Ensure appropriate indexes exist for UPSERT operations:
   ```sql
   CREATE CLUSTERED INDEX idx_pk ON dbo.my_table (id);
   ```
8. **Use connection pooling**: The connector uses connection pooling by default, but ensure SQL Server can handle concurrent connections:
   ```sql
   SELECT @@MAX_CONNECTIONS AS MaxConnections;
   ```

#### NOTE
The optimal `batch.size` depends on your record size and network latency. Start with 3000 and adjust based on observed performance.

### How do I configure SSL/TLS encryption for SQL Server connections?

The connector uses the `ssl.mode` property to configure SSL/TLS encryption.

The connector supports the following `ssl.mode` options:

* **\`\`prefer\`\`** (default): Use SSL if the server supports it; fall back to an unencrypted connection if not.
* **\`\`require\`\`**: Always use SSL. The connection fails if the server does not support SSL.
* **\`\`verify-ca\`\`**: Use SSL and verify the server certificate against a trusted certificate authority (CA).
* **\`\`verify-full\`\`**: Use SSL, verify the server certificate against a trusted CA, and verify that the server hostname matches the certificate.

**Require SSL without certificate verification**

```json
{
  "ssl.mode": "require"
}
```

**Verify server certificate against a trusted CA**

For `verify-ca` or `verify-full` modes, you must base64-encode the trust
store file before providing it as the `ssl.truststorefile` value.

1. Encode the trust store file:
   ```bash
   base64_truststore=$(cat /path/to/truststore.jks | base64)
   ```
2. Use the encoded value in the connector configuration:
   ```json
   {
     "ssl.mode": "verify-ca",
     "ssl.truststorefile": "data:text/plain;base64,<base64_encoded_content>",
     "ssl.truststorepassword": "your_truststore_password"
   }
   ```

   Replace `<base64_encoded_content>` with the output of the encoding command.

#### IMPORTANT
Use `verify-full` in production environments to prevent man-in-the-middle attacks. `verify-ca` validates the certificate authority but does not verify the server hostname.

### What should I do if my connector keeps failing or restarting?

Frequent connector failures indicate configuration issues, resource constraints, or database connectivity problems.

Common causes and solutions:

1. **Check connector logs**: In the Cloud Console, review error messages:
   * **Authentication failures**: Verify username and password.
   * **Network timeouts**: Check egress endpoint status.
   * **Table errors**: Verify table names and schema.
   * **Data type errors**: Check schema compatibility.
2. **Verify SQL Server connectivity**: Test that SQL Server is reachable and accepting connections:
   * **Check firewall rules**: Allow traffic on SQL Server port such as `1433`.
   * **Verify egress endpoint status**: Ensure status is `Ready`.
   * **Test credentials manually**: Connect using SQL Server Management Studio with the same credentials.
3. **Review table creation errors**: Check if tables can be created:
   ```sql
   SELECT name, is_read_only
   FROM sys.databases
   WHERE name = 'YourDatabase';
   ```

   Ensure the database is not read-only.
4. **Monitor connector resources**: Check if the connector is hitting limits:
   * **Reduce batch.size**: Lower the `batch.size` configuration property.
   * **Reduce tasks.max**: Decrease the number of concurrent tasks.
   * **Monitor memory usage**: Check connector task memory consumption.
5. **Verify schema compatibility**: Ensure Schema Registry is available if using Avro, JSON Schema, or Protobuf formats:

   Test that schemas are properly registered and compatible.
6. **Check for database locks**: Long-running transactions can cause timeouts:
   ```sql
   SELECT blocking_session_id, wait_type, wait_time, wait_resource
   FROM sys.dm_exec_requests
   WHERE blocking_session_id <> 0;
   ```

   Resolve blocking queries before restarting the connector.

#### IMPORTANT
For persistent failures, use the connector diagnostics 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.

[![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)