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Elastic Azure Blob Storage connector reference

Elastic Azure Blob Storage connector reference

The Elastic Azure Blob Storage connector is a connector for Azure Blob Storage.

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).

Elastic managed connector reference

View Elastic managed connector reference

Availability and prerequisites

This connector is available as a managed connector on Elastic Cloud, as of 8.9.1.

To use this connector natively in Elastic Cloud, satisfy all managed connector requirements.

Compatibility

This connector has not been tested with Azure Government. Therefore we cannot guarantee that it will work with Azure Government endpoints. For more information on Azure Government compared to Global Azure, refer to the official Microsoft documentation.

Create Azure Blob Storage connector

Use the UI

To create a new Azure Blob Storage 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 native Azure Blob Storage connector.

For additional operations, see Connectors UI in Kibana.

Use the API

You can use the Elasticsearch Create connector API to create a new native Azure Blob Storage connector.

For example:

resp = client.connector.put(
    connector_id="my-{service-name-stub}-connector",
    index_name="my-elasticsearch-index",
    name="Content synced from {service-name}",
    service_type="{service-name-stub}",
    is_native=True,
)
print(resp)

PUT _connector/my-azure_blob_storage-connector
{
  "index_name": "my-elasticsearch-index",
  "name": "Content synced from Azure Blob Storage",
  "service_type": "azure_blob_storage",
  "is_native": true
}

You’ll also need to create an API key for the connector to use.

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 id and encoded return values from the response:

resp = client.security.create_api_key(
    name="my-connector-api-key",
    role_descriptors={
        "my-connector-connector-role": {
            "cluster": [
                "monitor",
                "manage_connector"
            ],
            "indices": [
                {
                    "names": [
                        "my-index_name",
                        ".search-acl-filter-my-index_name",
                        ".elastic-connectors*"
                    ],
                    "privileges": [
                        "all"
                    ],
                    "allow_restricted_indices": False
                }
            ]
        }
    },
)
print(resp)

const response = await client.security.createApiKey({
  name: "my-connector-api-key",
  role_descriptors: {
    "my-connector-connector-role": {
      cluster: ["monitor", "manage_connector"],
      indices: [
        {
          names: [
            "my-index_name",
            ".search-acl-filter-my-index_name",
            ".elastic-connectors*",
          ],
          privileges: ["all"],
          allow_restricted_indices: false,
        },
      ],
    },
  },
});
console.log(response);

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

Use the encoded value to store a connector secret, and note the id return value from this response:

resp = client.connector.secret_post(
    body={
        "value": "encoded_api_key"
    },
)
print(resp)

const response = await client.connector.secretPost({
  body: {
    value: "encoded_api_key",
  },
});
console.log(response);

POST _connector/_secret
{
  "value": "encoded_api_key"
}

Use the API key id and the connector secret id to update the connector:

resp = client.connector.update_api_key_id(
    connector_id="my_connector_id>",
    api_key_id="API key_id",
    api_key_secret_id="secret_id",
)
print(resp)

const response = await client.connector.updateApiKeyId({
  connector_id: "my_connector_id>",
  api_key_id: "API key_id",
  api_key_secret_id: "secret_id",
});
console.log(response);

PUT /_connector/my_connector_id>/_api_key_id
{
  "api_key_id": "API key_id",
  "api_key_secret_id": "secret_id"
}

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

Usage

To use this connector as a managed connector, see Elastic managed connectors.

For additional operations, see Connectors UI in Kibana.

Configuration

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

Account name: Name of Azure Blob Storage account.
Account key: Account key for the Azure Blob Storage account.
Blob endpoint: Endpoint for the Blob Service.
Containers: List of containers to index. * will index all containers.

Documents and syncs

The connector will fetch all data available in the container.

Content from files bigger than 10 MB won’t be extracted. (Self-managed connectors can use the self-managed local extraction service to handle larger binary files.)
Permissions are not synced. All documents indexed to an Elastic deployment will be visible to all users with access to that Elastic Deployment.

Sync types

Full syncs are supported by default for all connectors.

This connector also supports incremental syncs.

Sync rules

Basic sync rules are identical for all connectors and are available by default.

Advanced sync rules are not available for this connector in the present version. Currently filtering is controlled via ingest pipelines.

Content extraction

See Content extraction.

Known issues

This connector has the following known issues:

lease data and tier fields are not updated in Elasticsearch indices

This is because the blob timestamp is not updated. Refer to Github issue.

Troubleshooting

See Troubleshooting.

Security

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

Self-managed connector

View self-managed connector reference

Availability and prerequisites

This connector is available as a self-managed self-managed connector. This self-managed connector is compatible with Elastic versions 8.6.0+. To use this connector, satisfy all self-managed connector requirements.

Compatibility

This connector has not been tested with Azure Government. Therefore we cannot guarantee that it will work with Azure Government endpoints. For more information on Azure Government compared to Global Azure, refer to the official Microsoft documentation.

Create Azure Blob Storage connector

Use the UI

To create a new Azure Blob Storage 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 Azure Blob Storage self-managed connector.

Use the API

You can use the Elasticsearch Create connector API to create a new self-managed Azure Blob Storage self-managed connector.

For example:

resp = client.connector.put(
    connector_id="my-{service-name-stub}-connector",
    index_name="my-elasticsearch-index",
    name="Content synced from {service-name}",
    service_type="{service-name-stub}",
)
print(resp)

PUT _connector/my-azure_blob_storage-connector
{
  "index_name": "my-elasticsearch-index",
  "name": "Content synced from Azure Blob Storage",
  "service_type": "azure_blob_storage"
}

You’ll also need to create an API key for the connector to use.

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:

resp = client.security.create_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
                }
            ]
        }
    },
)
print(resp)

const response = await client.security.createApiKey({
  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,
        },
      ],
    },
  },
});
console.log(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

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

Configuration

When using the self-managed connector workflow, initially these fields will use the default configuration set in the connector source code. These are set in the get_default_configuration function definition.

These configurable fields will be rendered with their respective labels in the Kibana UI. Once connected, you’ll be able to update these values in Kibana.

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

account_name: Name of Azure Blob Storage account.
account_key: Account key for the Azure Blob Storage account.
blob_endpoint: Endpoint for the Blob Service.
containers: List of containers to index. * will index all containers.
retry_count: Number of retry attempts after a failed call. Default value is 3.
concurrent_downloads: Number of concurrent downloads for fetching content. Default value is 100.
use_text_extraction_service: Requires a separate deployment of the Elastic Text Extraction Service. Requires that ingest pipeline settings disable text extraction. Default value is False.

Deployment using Docker

You can deploy the Azure Blob Storage connector as a self-managed connector using Docker. Follow these instructions.

Step 1: Download sample configuration file

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.

Step 2: Update the configuration file for your self-managed connector

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: azure_blob_storage
    api_key: <CONNECTOR_API_KEY_FROM_KIBANA> # 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.

Step 3: Run the Docker image

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.

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

The connector will fetch all data available in the container.

Content from files bigger than 10 MB won’t be extracted by default. You can use the self-managed local extraction service to handle larger binary files.
Permissions are not synced. All documents indexed to an Elastic deployment will be visible to all users with access to that Elastic Deployment.

Sync types

Full syncs are supported by default for all connectors.

This connector also supports incremental syncs.

Sync rules

Basic sync rules are identical for all connectors and are available by default.

Advanced sync rules are not available for this connector in the present version. Currently filtering is controlled via ingest pipelines.

Content extraction

See Content extraction.

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 Azure Blob Storage connector, run the following command:

$ make ftest NAME=azure_blob_storage

For faster tests, add the DATA_SIZE=small flag:

make ftest NAME=azure_blob_storage DATA_SIZE=small

Known issues

This connector has the following known issues:

lease data and tier fields are not updated in Elasticsearch indices

This is because the blob timestamp is not updated. Refer to Github issue.

Troubleshooting

See Troubleshooting.

Security

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