Elastic Microsoft SQL connector reference

The Elastic Microsoft SQL connector is a connector for Microsoft SQL databases. This connector is written in Python using the Elastic connector framework.

View the source code for this connector (branch main, compatible with Elastic 9.0).

Important

As of Elastic 9.0, managed connectors on Elastic Cloud Hosted are no longer available. All connectors must be self-managed.

Self-managed connector

Availability and prerequisites

This connector is available as a self-managed connector. To use this connector, satisfy all self-managed connector requirements.

Create a Microsoft SQL connector

Use the UI

To create a new Microsoft SQL connector:

In the Kibana UI, navigate to the Search → Content → Connectors page from the main menu, or use the global search field.
Follow the instructions to create a new Microsoft SQL self-managed connector.

Use the API

You can use the Elasticsearch Create connector API to create a new self-managed Microsoft SQL self-managed connector.

For example:

		 PUT _connector/my-mssql-connector {
  "index_name": "my-elasticsearch-index",
  "name": "Content synced from Microsoft SQL",
  "service_type": "mssql"
}

	

Note

The user needs the cluster privileges manage_api_key, manage_connector and write_connector_secrets to generate API keys programmatically.

To create an API key for the connector:

Run the following command, replacing values where indicated. Note the encoded return values from the response:

		 POST /_security/api_key {
  "name": "connector_name-connector-api-key",
  "role_descriptors": {
    "connector_name-connector-role": {
      "cluster": [
        "monitor",
        "manage_connector"
      ],
      "indices": [
        {
          "names": [
            "index_name",
            ".search-acl-filter-index_name",
            ".elastic-connectors*"
          ],
          "privileges": [
            "all"
          ],
          "allow_restricted_indices": false
        }
      ]
    }
  }
}

	

Update your config.yml file with the API key encoded value.

Refer to the Elasticsearch API documentation for details of all available Connector APIs.

Usage

Users require the sysadmin server role. Note that SQL Server Authentication is required. Windows Authentication is not supported.

To use this connector as a self-managed connector, see Self-managed connectors For additional usage operations, see Connectors UI in Kibana.

Compatibility

The following are compatible with Elastic connector frameworks:

Microsoft SQL Server versions 2017, 2019
Azure SQL
Amazon RDS for SQL Server

Configuration

The following configuration fields are required to set up the connector:

host

The server host address where the Microsoft SQL Server is hosted. Default value is 127.0.0.1. Examples:

192.158.1.38
demo.instance.demo-region.demo.service.com

port

The port where the Microsoft SQL Server is hosted. Default value is 9090.

username

The username of the account for Microsoft SQL Server. (SQL Server Authentication only)

password

The password of the account to be used for the Microsoft SQL Server. (SQL Server Authentication only)

database

Name of the Microsoft SQL Server database. Examples:

employee_database
customer_database

tables

Comma-separated list of tables. The Microsoft SQL connector will fetch data from all tables present in the configured database, if the value is * . Default value is *. Examples:

table_1, table_2
*

This field can be bypassed by advanced sync rules.

fetch_size

Rows fetched per request.

retry_count

The number of retry attempts per failed request.

schema

Name of the Microsoft SQL Server schema. Default value is dbo.

Examples:

dbo
custom_schema

ssl_enabled

SSL verification enablement. Default value is False.

ssl_ca

Content of SSL certificate. If SSL is disabled, the ssl_ca value will be ignored.

validate_host

Host validation enablement. Default value is False.

Deployment using Docker

You can deploy the Microsoft SQL connector as a self-managed connector using Docker. Follow these instructions.

Download the sample configuration file. You can either download it manually or run the following command:

		curl https://raw.githubusercontent.com/elastic/connectors/main/config.yml.example --output ~/connectors-config/config.yml

	

Remember to update the --output argument value if your directory name is different, or you want to use a different config file name.

Update the configuration file with the following settings to match your environment:

elasticsearch.host
elasticsearch.api_key
connectors

If you’re running the connector service against a Dockerized version of Elasticsearch and Kibana, your config file will look like this:

		# When connecting to your cloud deployment you should edit the host value
elasticsearch.host: http://host.docker.internal:9200
elasticsearch.api_key: <ELASTICSEARCH_API_KEY>

connectors:
  -
    connector_id: <CONNECTOR_ID_FROM_KIBANA>
    service_type: mssql
    api_key: <CONNECTOR_API_KEY_FROM_KIBANA>1

	

Optional. If not provided, the connector will use the elasticsearch.api_key instead

Using the elasticsearch.api_key is the recommended authentication method. However, you can also use elasticsearch.username and elasticsearch.password to authenticate with your Elasticsearch instance.

Note: You can change other default configurations by simply uncommenting specific settings in the configuration file and modifying their values.

Run the Docker image with the Connector Service using the following command:

		docker run \
-v ~/connectors-config:/config \
--network "elastic" \
--tty \
--rm \
docker.elastic.co/integrations/elastic-connectors:9.0.0-beta1.0 \
/app/bin/elastic-ingest \
-c /config/config.yml

	

Refer to DOCKER.md in the elastic/connectors repo for more details.

Find all available Docker images in the official registry.

Tip

We also have a quickstart self-managed option using Docker Compose, so you can spin up all required services at once: Elasticsearch, Kibana, and the connectors service. Refer to this README in the elastic/connectors repo for more information.

Documents and syncs

Tables with no primary key defined are skipped.
If the last_user_update of sys.dm_db_index_usage_stats table is not available for a specific table and database then all data in that table will be synced.

Note

Files bigger than 10 MB won’t be extracted.
Permissions are not synced. All documents indexed to an Elastic deployment will be visible to all users with access to that Elastic Deployment.

Sync rules

Basic sync rules are identical for all connectors and are available by default. For more information read sync rules.

Advanced sync rules

This connector supports advanced sync rules for remote filtering. These rules cover complex query-and-filter scenarios that cannot be expressed with basic sync rules. Advanced sync rules are defined through a source-specific DSL JSON snippet.

Note

A full sync is required for advanced sync rules to take effect.

Here are a few examples of advanced sync rules for this connector.

emp_id	name	age
3	John	28
10	Jane	35
14	Alex	22

c_id	name	age
2	Elm	24
6	Pine	30
9	Oak	34

Example: Two queries

These rules fetch all records from both the employee and customer tables. The data from these tables will be synced separately to Elasticsearch.

		[
  {
    "tables": [
      "employee"
    ],
    "query": "SELECT * FROM employee"
  },
  {
    "tables": [
      "customer"
    ],
    "query": "SELECT * FROM customer"
  }
]

	

Example: One WHERE query

This rule fetches only the records from the employee table where the emp_id is greater than 5. Only these filtered records will be synced to Elasticsearch.

		[
  {
    "tables": ["employee"],
    "query": "SELECT * FROM employee WHERE emp_id > 5"
  }
]

	

Example: One JOIN query

This rule fetches records by performing an INNER JOIN between the employee and customer tables on the condition that the emp_id in employee matches the c_id in customer. The result of this combined data will be synced to Elasticsearch.

		[
  {
    "tables": ["employee", "customer"],
    "query": "SELECT * FROM employee INNER JOIN customer ON employee.emp_id = customer.c_id"
  }
]

	

Warning

When using advanced rules, a query can bypass the configuration field tables. This will happen if the query specifies a table that doesn’t appear in the configuration. This can also happen if the configuration specifies * to fetch all tables while the advanced sync rule requests for only a subset of tables.

End-to-end testing

The connector framework enables operators to run functional tests against a real data source. Refer to Connector testing for more details.

To perform E2E testing for the Microsoft SQL connector, run the following command:

make ftest NAME=mssql

For faster tests, add the DATA_SIZE=small flag:

make ftest NAME=mssql DATA_SIZE=small

View additional code specific to this data source (branch main, compatible with Elastic 9.0).

Elastic Microsoft SQL connector reference

Self-managed connector

Availability and prerequisites

Create a Microsoft SQL connector

Use the UI

Use the API

Usage

Compatibility

Configuration

Deployment using Docker

Documents and syncs

Sync rules

Advanced sync rules

End-to-end testing

Known issues

Troubleshooting

Security