Connector clients
editConnector clients
editConnector clients are Elastic connectors that you self-manage on your own infrastructure. This means they run outside of your Elastic deployment.
You can run them from source or from a Docker container. Deploying your self-managed connector details the options.
For a concrete end-to-end example, see PostgreSQL connector client tutorial.
The following connectors are available as connector clients:
Availability and Elastic prerequisites
editExpand to see the Elastic prerequisites
Elastic 8.4.0 or later is required.
Your Elastic deployment must include the following Elastic services:
- Elasticsearch
- Kibana
- Enterprise Search
(A new Elastic Cloud deployment includes these services by default.)
Support and licensing
Depending on how you use connector clients, support and licensing requirements will vary.
Refer to the following subscriptions pages for details. Find your connector of interest in the Elastic Search section under Client Integrations:
Note the following information regarding support for connector clients:
- A converted but unmodified native connector is supported by Elastic.
- A converted but customized native connector is not supported by Elastic.
You must have access to Kibana and have write
indices privileges for the .elastic-connectors
index.
You must additionally deploy the connector service. See Deploying your self-managed connector.
In order to setup, configure, and run a connector you’ll be moving between your third-party service, the Kibana UI, and your terminal.
Initial setup in Kibana
editFollow the Connector workflow in the Kibana UI to select the Connector ingestion method.
Next, complete these steps:
- Choose which third-party service you’d like to use by selecting a data source.
- Create and name a new Elasticsearch index.
- Generate a new API key and save it somewhere safe.
- Name your connector and provide an optional description
- Convert native connector to a connector client (Only applicable if connector is also available natively). This action is irreversible.
-
Copy the configuration block from the example shown on the screen. You’ll use this in a later step:
# ... api_key: '<YOUR-API-KEY>' connector_id: '<YOUR-CONNECTOR-ID>' service_type: 'postgresql' # The third party service you've selected
Deploying your self-managed connector
editTo use connector clients, you must deploy the connector service.
The source code is hosted in the elastic/connectors-python
repository.
You have two deployment options:
Option 1: Run from source
editThis is a good option if you’re comfortable working with Python and want to iterate quickly locally.
Clone the repository and edit config.yml
editOnce you’ve created an index, and entered the access details for your data source, you’re ready to deploy the connector service.
First, you need to clone the elastic/connectors-python
repository.
Follow these steps:
-
Clone or fork the
connectors-python
repository locally with the following command:git clone https://github.com/elastic/connectors-python
. -
Open the
config.yml
configuration file in your editor of choice. -
Replace the values for
host
(your Elasticsearch endpoint),api_key
,connector_id
, andservice_type
.Expand to see an example
config.yml
fileReplace the values for
api_key
,connector_id
, andservice_type
with the values you copied earlier.elasticsearch: host: <https://<my-elastic-deployment.es.us-west2.gcp.elastic-cloud.com>> # Your Elasticsearch endpoint api_key: '<YOUR-API-KEY>' # Your API key ssl: true bulk: queue_max_size: 1024 queue_max_mem_size: 25 display_every: 100 chunk_size: 1000 max_concurrency: 5 chunk_max_mem_size: 5 concurrent_downloads: 10 request_timeout: 120 max_wait_duration: 120 initial_backoff_duration: 1 backoff_multiplier: 2 log_level: info service: idling: 30 heartbeat: 300 max_errors: 20 max_errors_span: 600 max_concurrent_syncs: 1 job_cleanup_interval: 300 log_level: INFO # Connector client settings connector_id: '<YOUR-CONNECTOR-ID>' # Your connector ID service_type: 'postgresql' # The service type for your connector sources: # mongodb: connectors.sources.mongo:MongoDataSource # s3: connectors.sources.s3:S3DataSource # dir: connectors.sources.directory:DirectoryDataSource # mysql: connectors.sources.mysql:MySqlDataSource # network_drive: connectors.sources.network_drive:NASDataSource # google_cloud_storage: connectors.sources.google_cloud_storage:GoogleCloudStorageDataSource # azure_blob_storage: connectors.sources.azure_blob_storage:AzureBlobStorageDataSource postgresql: connectors.sources.postgresql:PostgreSQLDataSource # oracle: connectors.sources.oracle:OracleDataSource # sharepoint: connectors.sources.sharepoint:SharepointDataSource # mssql: connectors.sources.mssql:MSSQLDataSource # jira: connectors.sources.jira:JiraDataSource
Run the connector service
editOnce you’ve configured the connector code, you can run the connector service.
In your terminal or IDE:
-
cd
into the root of yourconnectors-python
clone/fork. -
Run the following commands to compile and run the connector service:
make install make run
The connector service should now be running. The UI will let you know that the connector has successfully connected to your Elasticsearch instance.
Here we’re working locally. In a production setup, you’ll deploy the connector service to your own infrastructure.
Option 2: Run with Docker
editYou can also deploy connector clients using Docker. This is a good option if you don’t want to worry about your local Python environment.
To do this you will:
- Create a Docker network.
- Create a directory to be mounted into the Docker image.
-
Download the sample configuration file (
conf.yml
) into that directory. -
Update the config file with your specific values:
-
host
(your Elasticsearch endpoint) -
api_key
-
connector_id
-
service_type
.
-
- Run the Docker image with the Connector Service
Refer to DOCKER.md
in the repo for a more detailed guide.
Enter data source details in Kibana
editOnce the connector service is running, it’s time to head back to the Kibana UI to finalize the connector configuration. In this step, you need to add the specific connection details about your data source instance, like URL, authorization credentials, etc. As mentioned above, these details will vary based on the third-party data source you’re connecting to.
For example, the PostgreSQL connector requires the following details:
- Host
- Port
- Username
- Password
- Database
- Comma-separated list of tables
You’re now ready to run a sync. Select the Full sync button in the Kibana UI to start ingesting documents into Elasticsearch.
Tutorial
editOur PostgreSQL connector client tutorial is a concrete example of running a self-managed connector service and a connector client. This example uses the PostgreSQL connector but the basic process is the same for all connector clients.
Connector testing
editThe connector framework enables you to run end-to-end (E2E) tests on your connector clients, against a real data source.
To avoid tampering with a real Elasticsearch instance, E2E tests run an isolated Elasticsearch instance in Docker.
Configuration values are set in your docker-compose.yml
file.
Docker Compose manages the setup of the development environment, including both the mock Elastic instance and mock data source.
E2E tests use default configuration values for the connector. Find instructions about testing in each connector’s documentation.
Connector framework
editThe Elastic Python connector framework enables you to:
- Customize existing connector clients.
- Build your own connector clients.
Refer to Elastic connector framework for more information.