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analyzer
coerce
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doc_values
dynamic
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enabled
format
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index.mapping.ignore_above
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index
index_options
index_phrases
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fields
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- ILM: Manage the index lifecycle
- Tutorial: Customize built-in policies
- Tutorial: Automate rollover
- Index management in Kibana
- Overview
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- Active Directory user authentication
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- Kerberos authentication
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- Integrating with other authentication systems
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- Looking up users without authentication
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Role restriction
- Security privileges
- Document level security
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- Troubleshooting
- Some settings are not returned via the nodes settings API
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- Limitations
- Watcher
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- elasticsearch-certgen
- elasticsearch-certutil
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- elasticsearch-croneval
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- elasticsearch-node
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- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
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- Troubleshooting corruption
- Fix data nodes out of disk
- Fix master nodes out of disk
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- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Troubleshooting broken repositories
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- Troubleshooting transforms
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- Capture diagnostics
- REST APIs
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- REST API compatibility
- Autoscaling APIs
- Behavioral Analytics APIs
- Compact and aligned text (CAT) APIs
- cat aliases
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- Cluster APIs
- Cluster allocation explain
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- Cluster health
- Health
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- Cluster Info
- Pending cluster tasks
- Remote cluster info
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- Create or update desired nodes
- Get desired nodes
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- Get desired balance
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- Cross-cluster replication APIs
- Connector APIs
- Create connector
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- Data stream APIs
- Document APIs
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- EQL APIs
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- Features APIs
- Fleet APIs
- Graph explore API
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- Alias exists
- Aliases
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- Delete index template (legacy)
- Exists
- Field usage stats
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- Get index template (legacy)
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- Import dangling index
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- List dangling indices
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- Remove policy
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- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
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- Inference APIs
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- AlibabaCloud AI Search inference service
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- Create data frame analytics jobs
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- Bulk update API keys
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- Activate user profile
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- Get user profiles
- Suggest user profile
- Update user profile data
- Has privileges user profile
- Create Cross-Cluster API key
- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Text structure APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 8.18.0
- Elasticsearch version 8.17.0
- Elasticsearch version 8.16.1
- Elasticsearch version 8.16.0
- Elasticsearch version 8.15.5
- Elasticsearch version 8.15.4
- Elasticsearch version 8.15.3
- Elasticsearch version 8.15.2
- Elasticsearch version 8.15.1
- Elasticsearch version 8.15.0
- Elasticsearch version 8.14.3
- Elasticsearch version 8.14.2
- Elasticsearch version 8.14.1
- Elasticsearch version 8.14.0
- Elasticsearch version 8.13.4
- Elasticsearch version 8.13.3
- Elasticsearch version 8.13.2
- Elasticsearch version 8.13.1
- Elasticsearch version 8.13.0
- Elasticsearch version 8.12.2
- Elasticsearch version 8.12.1
- Elasticsearch version 8.12.0
- Elasticsearch version 8.11.4
- Elasticsearch version 8.11.3
- Elasticsearch version 8.11.2
- Elasticsearch version 8.11.1
- Elasticsearch version 8.11.0
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Elastic Azure Blob Storage connector reference
editElastic Azure Blob Storage connector reference
editThe 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 8.x, compatible with Elastic 8.17).
Elastic managed connector reference
editView Elastic managed connector reference
Availability and prerequisites
editThis 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
editThis 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
editUse the UI
editTo 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
editYou 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)
const response = await 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, }); console.log(response);
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
andencoded
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 theid
return value from this response:resp = client.connector.secret_post( body={ "value": "encoded_api_key" }, ) print(resp)
const response = await client.transport.request({ method: "POST", path: "/_connector/_secret", body: { value: "encoded_api_key", }, }); console.log(response);
POST _connector/_secret { "value": "encoded_api_key" }
-
Use the API key
id
and the connector secretid
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
editTo use this connector as a managed connector, see Elastic managed connectors.
For additional operations, see Connectors UI in Kibana.
Configuration
editThe 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
editThe 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
editFull syncs are supported by default for all connectors.
This connector also supports incremental syncs.
Sync rules
editBasic 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
editSee Content extraction.
Known issues
editThis connector has the following known issues:
-
lease data
andtier
fields are not updated in Elasticsearch indicesThis is because the blob timestamp is not updated. Refer to Github issue.
Troubleshooting
editSee Troubleshooting.
Security
editSee Security.
View the source code for this connector (branch 8.x, compatible with Elastic 8.17)
Self-managed connector
editView self-managed connector reference
Availability and prerequisites
editThis 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
editThis 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
editUse the UI
editTo 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
editYou 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)
const response = await 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}", }); console.log(response);
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 keyencoded
value.
Refer to the Elasticsearch API documentation for details of all available Connector APIs.
Usage
editTo use this connector as a self-managed connector, see Self-managed connectors For additional usage operations, see Connectors UI in Kibana.
Configuration
editWhen 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
editYou 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/enterprise-search/elastic-connectors:8.17.0.0 \ /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
editThe 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
editFull syncs are supported by default for all connectors.
This connector also supports incremental syncs.
Sync rules
editBasic 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
editSee Content extraction.
End-to-end testing
editThe 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
editThis connector has the following known issues:
-
lease data
andtier
fields are not updated in Elasticsearch indicesThis is because the blob timestamp is not updated. Refer to Github issue.
Troubleshooting
editSee Troubleshooting.
Security
editSee Security.
On this page
- Elastic managed connector reference
- Availability and prerequisites
- Compatibility
- Create Azure Blob Storage connector
- Use the UI
- Use the API
- Usage
- Configuration
- Documents and syncs
- Sync types
- Sync rules
- Content extraction
- Known issues
- Troubleshooting
- Security
- Self-managed connector
- Availability and prerequisites
- Compatibility
- Create Azure Blob Storage connector
- Use the UI
- Use the API
- Usage
- Configuration
- Deployment using Docker
- Documents and syncs
- Sync types
- Sync rules
- Content extraction
- End-to-end testing
- Known issues
- Troubleshooting
- Security