This tutorial walks you through the process of creating a connector client for a PostgreSQL data source. You’ll be using the connector client workflow in the Kibana UI. This means you’ll be deploying the connector on your own infrastructure. Refer to the Elastic PostgreSQL connector reference for more information about this connector.
You’ll use the Python connector framework to create the connector. In this exercise, you’ll be working in both the terminal (or your IDE) and the Kibana UI.
If you want to deploy a connector client for another data source, use this tutorial as a blueprint. Refer to the list of available connector clients.
You can test the PostgreSQL connector client in an isolated environment, using a real PostgreSQL source and a Dockerized Elasticsearch instance. This requires no configuration and is as simple as running a single command in your terminal.
First, ensure you satisfy the prerequisites for building a connector. Importantly, your Enterprise Search service needs at least 4GB RAM per zone.
- PostgreSQL version 11+.
- Tables must be owned by a PostgreSQL user.
superuserprivileges are required to index all database tables.
You should enable recording of the commit time of PostgreSQL transactions.
Otherwise, all data will be indexed in every sync.
Enable this by running the following command on the PosgreSQL server command line:
ALTER SYSTEM SET track_commit_timestamp = on;
Then restart the PostgreSQL server.
To complete this tutorial, you’ll need to complete the following steps:
Create an Elasticsearch indexedit
Elastic connectors enable you to create searchable, read-only replicas of your data sources in Elasticsearch. The first step in setting up your connector client is to create an index.
In the Kibana UI go to Enterprise Search > Content > Elasticsearch indices.
Create a new connector index:
- Under Select an ingestion method choose Connector.
- Choose PostgreSQL from the list of connectors.
Name your index and optionally change the language analyzer to match the human language of your data source.
(The index name you provide is automatically prefixed with
- Save your changes.
The index is created and ready to configure.
Set up the connectoredit
Once you’ve created an index, you can set up the connector. You will be guided through this process in the UI.
- Edit the name and description for the connector. This will help your team identify the connector.
Clone and edit the connector service code. For this example, we’ll use the Python framework. Follow these steps:
Clone or fork that repository locally with the following command:
git clone https://github.com/elastic/connectors-python.
Native mode must be set to false to use this as a connector client.
"is_native": Falseis set in
config.ymlconfiguration file in your editor of choice.
Replace the values for
connector_idwith the values you gathered earlier. Use the
postgresqlfor this connector.
Expand to see an example
Replace the values for
connector_idwith your own values. Use the
postgresqlfor this connector.
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 # native_service_types: # - mongodb # - mysql # - network_drive # - s3 # - google_cloud_storage # - azure_blob_storage # - postgresql # - oracle # - dir # - sharepoint # - mssql # - jira # Connector client settings # Uncomment and update the following settings to use the connector client 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
- Clone or fork that repository locally with the following command:
Run the connector serviceedit
Now that you’ve configured the connector code, you can run the connector service.
In your terminal or IDE:
cdinto the root of your
Run the following command:
The connector service should now be running. The UI will let you know that the connector has successfully connected to Enterprise Search.
Here we’re working locally. In production setups, you’ll deploy the connector service to your own infrastructure. If you prefer to use Docker, refer to the repo docs for instructions.
Sync your PostgreSQL data sourceedit
Enter your PostgreSQL data source detailsedit
Once you’ve configured the connector, you can use it to index your data source.
You can now enter your PostgreSQL instance details in the Kibana UI.
Enter the following information:
- Host. Server host address for your PostgreSQL instance.
- Port. Port number for your PostgreSQL instance.
- Username. Username of the PostgreSQL account.
- Password. Password for that user.
- Database. Name of the PostgreSQL database.
Comma-separated list of tables.
*will fetch data from all tables in the configured database.
Once you’ve entered all these details, select Save configuration.
Launch a syncedit
If you navigate to the Overview tab in the Kibana UI, you can see the connector’s ingestion status. This should now have changed to Configured.
It’s time to launch a sync by selecting the Sync button.
If you navigate to the terminal window where you’re running the connector service, you should see output like the following:
[FMWK][13:22:26][INFO] Fetcher <create: 499 update: 0 |delete: 0> [FMWK][13:22:26][INF0] Fetcher <create: 599 update: 0 |delete: 0> [FMWK][13:22:26][INFO] Fetcher <create: 699 update: 0 |delete: 0> ... [FMWK][23:22:28][INF0] [oRXQwYYBLhXTs-qYpJ9i] Sync done: 3864 indexed, 0 deleted. (27 seconds)
This confirms the connector has fetched records from your PostgreSQL table(s) and transformed them into documents in your Elasticsearch index.
Verify your Elasticsearch documents in the Documents tab in the Kibana UI.
If you’re happy with the results, set a recurring sync schedule in the Scheduling tab. This will ensure your searchable data in Elasticsearch is always up to date with changes to your PostgreSQL data source.
Intro to Kibana
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