Microsoft SQL Server Source (JDBC) Connector for Confluent Cloud

The fully-managed Microsoft SQL Server Source connector for Confluent Cloud can obtain a snapshot of the existing data in a Microsoft SQL Server database and then monitor and record all subsequent row-level changes to that data. 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

Features

The Microsoft SQL Server Source connector provides the following features:

  • Topics created automatically: The connector can automatically create Kafka topics. When creating topics, the connector uses the naming convention: <topic.prefix><tableName>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3.

  • Insert modes:

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

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

      Important

      • A timestamp column must not be nullable.
      • A timestamp column must use datetime2 and not datetime. If the timestamp column uses datetime, the topic may receive numerous duplicates.

  • Database authentication: password authentication.

  • SSL support: Supports one-way SSL.

  • Data formats: The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

  • Select configuration properties:

    • db.timezone
    • poll.interval.ms
    • batch.max.rows
    • timestamp.delay.interval.ms
    • topic.prefix
    • schema.pattern

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

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Limitations

Be sure to review the following information.

Manage custom offsets

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

To manage offsets:

To get the current offset, make a GET request that specifies the environment, Kafka cluster, and connector name.

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

Response:

Successful calls return HTTP 200 with a JSON payload that describes the offset.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
        {
            "partition": {
                "protocol": "1",
                "table": "{table_name}"
            },
            "offset": {
                "incrementing": 26
            }
        }
    ],
    "metadata": {
        "observed_at": "2024-03-28T17:57:48.139635200Z"
    }
}

Responses include the following information:

  • The position of latest offset.
  • The observed time of the offset in the metadata portion of the payload. The observed_at time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Use observed_at to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. Calling GET repeatedly will fetch more recently observed offsets.
  • Information about the connector.
  • In these examples, the curly braces around “{connector_name}” indicate a replaceable value.

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:

  • 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 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.

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

    Important

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

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

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

  • Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

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

    • Depending on the service environment, certain network access limitations may exist. See Manage Networking for Confluent Cloud Connectors for details.
    • To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors. For additional fully-managed connector networking details, see Networking and DNS.
    • 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 resource ID.
    • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
    • Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

See the Quick Start for Confluent Cloud for installation instructions.

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.

Microsoft SQL Server Source Connector Card

Step 4: Enter the connector details

Note

  • Make sure you have all your prerequisites completed.
  • An asterisk ( * ) designates a required entry.

At the Microsoft SQL Server Source Connector screen, complete the following:

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: <topic.prefix><tableName>.

Important

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

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

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

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 Managed and Custom Connectors section.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

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

{
    "name" : "confluent-microsoft-sql-source",
    "connector.class": "MicrosoftSqlServerSource",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret" : "<my-kafka-api-secret>",
    "topic.prefix" : "microsoftsql_",
    "connection.host" : "<my-database-endpoint>",
    "connection.port" : "1433",
    "connection.user" : "<database-username>",
    "connection.password": "<database-password>",
    "db.name": "ms-sql-test",
    "table.whitelist": "passengers",
    "timestamp.column.name": "created_at",
    "output.data.format": "JSON",
    "db.timezone": "UCT",
    "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, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

    confluent iam service-account list

    For example:

    confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2

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

    Important

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

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

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

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

    • The default option prefer is enabled if ssl.mode is not added to the connector configuration. When prefer is enabled, the connector attempts to use an encrypted connection to the database server.
    • prefer and require: use a secure (encrypted) connection. The connector fails if a secure connection cannot be established. These modes do not do Certification Authority (CA) validation.
    • verify-ca: similar to require, but also verifies the server TLS certificate against the configured Certificate Authority (CA) certificates. Fails if no valid matching CA certificates are found.
    • verify-full: similar to verify-ca, but also verifies that the server certificate matches the host to which the connection is attempted.

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

  • The following provides more information about how to use the timestamp.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, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

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

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

See Configuration 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:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file microsoft-sql-source.json

Example output:

Created connector confluent-microsoft-sql-source lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |            Name                | Status  |  Type
+-----------+--------------------------------+---------+-------+
lcc-ix4dl   | confluent-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 Managed and Custom Connectors section.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

How should we connect to your data?

name

Sets a name for your connector.

  • Type: string
  • Valid Values: A string at most 64 characters long
  • Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

  • Type: string
  • Default: KAFKA_API_KEY
  • Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
  • Importance: high
kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password
  • Importance: high
kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

  • Type: string
  • Importance: high
kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password
  • Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

  • Type: string
  • Default: default
  • Importance: medium

How do you want to prefix table names?

topic.prefix

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

  • Type: string
  • Importance: high

How should we connect to your database?

connection.host

Depending on the service environment, certain network access limitations may exist. Make sure the connector can reach your service. Do not include jdbc:xxxx:// in the connection hostname property (e.g. database-1.abc234ec2.us-west.rds.amazonaws.com).

  • Type: string
  • Importance: high
connection.port

JDBC connection port.

  • Type: int
  • Valid Values: [0,…,65535]
  • Importance: high
connection.user

JDBC connection user.

  • Type: string
  • Importance: high
connection.password

JDBC connection password.

  • Type: password
  • Importance: high
db.name

JDBC database name.

  • Type: string
  • Importance: high
ssl.mode

What SSL mode should we use to connect to your database. prefer 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 trust store containing server CA certificate. Only required if using verify-ca or verify-full ssl mode.

  • 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

List of tables to include in copying. Use a comma-separated list to specify multiple tables (for example: “User, Address, Email”).

  • Type: list
  • Importance: medium
timestamp.column.name

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
incrementing.column.name

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
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. 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
quote.sql.identifiers

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

  • Type: string
  • Default: ALWAYS
  • Valid Values: ALWAYS, NEVER
  • Importance: medium
transaction.isolation.mode

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

Connection details

poll.interval.ms

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

  • Type: int
  • Default: 5000 (5 seconds)
  • Valid Values: [100,…]
  • Importance: high
batch.max.rows

Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.

  • Type: int
  • Default: 100
  • Valid Values: [1,…,5000]
  • Importance: low
timestamp.delay.interval.ms

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

  • Type: int
  • Default: 0
  • Valid Values: [0,…]
  • Importance: high

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

  • Type: string
  • Default: JSON
  • Importance: high

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

  • Type: int
  • Valid Values: [1,…]
  • Importance: high

Next Steps

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

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