--- title: Run Postgres Connector using the CLI slug: /connectors/database/postgres/cli --- # Run Postgres using the metadata CLI {% multiTablesWrapper %} | Feature | Status | | :----------------- | :--------------------------- | | Stage | PROD | | Metadata | {% icon iconName="check" /%} | | Query Usage | {% icon iconName="check" /%} | | Data Profiler | {% icon iconName="check" /%} | | Data Quality | {% icon iconName="check" /%} | | Lineage | {% icon iconName="check" /%} | | DBT | {% icon iconName="check" /%} | | Supported Versions | Postgres>=11 | | Feature | Status | | :----------- | :--------------------------- | | Lineage | {% icon iconName="check" /%} | | Table-level | {% icon iconName="check" /%} | | Column-level | {% icon iconName="check" /%} | {% /multiTablesWrapper %} In this section, we provide guides and references to use the Postgres connector. Configure and schedule Postgres metadata and profiler workflows from the OpenMetadata UI: - [Requirements](#requirements) - [Metadata Ingestion](#metadata-ingestion) - [Query Usage](#query-usage) - [Data Profiler](#data-profiler) - [Lineage](#lineage) - [dbt Integration](#dbt-integration) ## Requirements {%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%} To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. **Note:** Note that we only support officially supported Postgres versions. You can check the version list [here](https://www.postgresql.org/support/versioning/). ### Usage and Lineage considerations When extracting lineage and usage information from Postgres we base our finding on the `pg_stat_statements` table. You can find more information about it on the official [docs](https://www.postgresql.org/docs/current/pgstatstatements.html#id-1.11.7.39.6). Another interesting consideration here is explained in the following SO [question](https://stackoverflow.com/questions/50803147/what-is-the-timeframe-for-pg-stat-statements). As a summary: - The `pg_stat_statements` has no time data embedded in it. - It will show all queries from the last reset (one can call `pg_stat_statements_reset()`). Then, when extracting usage and lineage data, the query log duration will have no impact, only the query limit. **Note:** For usage and lineage grant your user `pg_read_all_stats` permission. ```sql GRANT pg_read_all_stats TO your_user; ``` ### Python Requirements To run the Postgres ingestion, you will need to install: ```bash pip3 install "openmetadata-ingestion[postgres]" ``` ## Metadata Ingestion All connectors are defined as JSON Schemas. [Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/postgresConnection.json) you can find the structure to create a connection to Postgres. In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server. The workflow is modeled around the following [JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json) ### 1. Define the YAML Config This is a sample config for Postgres: {% codePreview %} {% codeInfoContainer %} #### Source Configuration - Service Connection {% codeInfo srNumber=1 %} **username**: Specify the User to connect to Postgres. It should have enough privileges to read all the metadata. {% /codeInfo %} {% codeInfo srNumber=2 %} **password**: Password to connect to Postgres. {% /codeInfo %} {% codeInfo srNumber=3 %} **hostPort**: Enter the fully qualified hostname and port number for your Postgres deployment in the Host and Port field. {% /codeInfo %} {% codeInfo srNumber=4 %} **database**: Initial Postgres database to connect to. If you want to ingest all databases, set ingestAllDatabases to true. {% /codeInfo %} {% codeInfo srNumber=5 %} **ingestAllDatabases**: Ingest data from all databases in Postgres. You can use databaseFilterPattern on top of this. {% /codeInfo %} #### Source Configuration - Source Config {% codeInfo srNumber=8 %} The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json): **markDeletedTables**: To flag tables as soft-deleted if they are not present anymore in the source system. **includeTables**: true or false, to ingest table data. Default is true. **includeViews**: true or false, to ingest views definitions. **databaseFilterPattern**, **schemaFilterPattern**, **tableFilternPattern**: Note that the they support regex as include or exclude. E.g., {% /codeInfo %} #### Sink Configuration {% codeInfo srNumber=9 %} To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`. {% /codeInfo %} #### Workflow Configuration {% codeInfo srNumber=10 %} The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation. For a simple, local installation using our docker containers, this looks like: {% /codeInfo %} #### Advanced Configuration {% codeInfo srNumber=6 %} **Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs. {% /codeInfo %} {% codeInfo srNumber=7 %} **Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs. - In case you are using Single-Sign-On (SSO) for authentication, add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "sso_login_url"` {% /codeInfo %} {% /codeInfoContainer %} {% codeBlock fileName="filename.yaml" %} ```yaml source: type: postgres serviceName: local_postgres serviceConnection: config: type: Postgres ``` ```yaml {% srNumber=6 %} username: username ``` ```yaml {% srNumber=6 %} password: password ``` ```yaml {% srNumber=6 %} hostPort: localhost:5432 ``` ```yaml {% srNumber=6 %} database: database ``` ```yaml {% srNumber=6 %} ingestAllDatabases: true ``` ```yaml {% srNumber=6 %} # connectionOptions: # key: value ``` ```yaml {% srNumber=7 %} # connectionArguments: # key: value ``` ```yaml {% srNumber=8 %} sourceConfig: config: type: DatabaseMetadata markDeletedTables: true includeTables: true includeViews: true # includeTags: true # databaseFilterPattern: # includes: # - database1 # - database2 # excludes: # - database3 # - database4 # schemaFilterPattern: # includes: # - schema1 # - schema2 # excludes: # - schema3 # - schema4 # tableFilterPattern: # includes: # - users # - type_test # excludes: # - table3 # - table4 ``` ```yaml {% srNumber=9 %} sink: type: metadata-rest config: {} ``` ```yaml {% srNumber=10 %} workflowConfig: openMetadataServerConfig: hostPort: "http://localhost:8585/api" authProvider: openmetadata securityConfig: jwtToken: "{bot_jwt_token}" ``` {% /codeBlock %} {% /codePreview %} ### Workflow Configs for Security Provider We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client). ## Openmetadata JWT Auth - JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details [here](/deployment/security/enable-jwt-tokens). ```yaml workflowConfig: openMetadataServerConfig: hostPort: "http://localhost:8585/api" authProvider: openmetadata securityConfig: jwtToken: "{bot_jwt_token}" ``` - You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc [link](/deployment/security/workflow-config-auth). ### 2. Run with the CLI First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: ```bash metadata ingest -c ``` Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources. ## Query Usage The Query Usage workflow will be using the `query-parser` processor. After running a Metadata Ingestion workflow, we can run Query Usage workflow. While the `serviceName` will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the `serviceConnection` details from the server. ### 1. Define the YAML Config This is a sample config for Postgres Usage: {% codePreview %} {% codeInfoContainer %} {% codeInfo srNumber=11 %} #### Source Configuration - Source Config You can find all the definitions and types for the `sourceConfig` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceQueryUsagePipeline.json). **queryLogDuration**: Configuration to tune how far we want to look back in query logs to process usage data. {% /codeInfo %} {% codeInfo srNumber=12 %} **stageFileLocation**: Temporary file name to store the query logs before processing. Absolute file path required. {% /codeInfo %} {% codeInfo srNumber=13 %} **resultLimit**: Configuration to set the limit for query logs {% /codeInfo %} {% codeInfo srNumber=14 %} **queryLogFilePath**: Configuration to set the file path for query logs {% /codeInfo %} {% codeInfo srNumber=15 %} #### Processor, Stage and Bulk Sink Configuration To specify where the staging files will be located. Note that the location is a directory that will be cleaned at the end of the ingestion. {% /codeInfo %} {% codeInfo srNumber=16 %} #### Workflow Configuration The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation. For a simple, local installation using our docker containers, this looks like: {% /codeInfo %} {% /codeInfoContainer %} {% codeBlock fileName="filename.yaml" %} ```yaml source: type: postgres-usage serviceName: sourceConfig: config: type: DatabaseUsage ``` ```yaml {% srNumber=11 %} # Number of days to look back queryLogDuration: 7 ``` ```yaml {% srNumber=12 %} # This is a directory that will be DELETED after the usage runs stageFileLocation: ``` ```yaml {% srNumber=13 %} # resultLimit: 1000 ``` ```yaml {% srNumber=14 %} # If instead of getting the query logs from the database we want to pass a file with the queries # queryLogFilePath: path-to-file ``` ```yaml {% srNumber=15 %} processor: type: query-parser config: {} stage: type: table-usage config: filename: /tmp/postgres_usage bulkSink: type: metadata-usage config: filename: /tmp/postgres_usage ``` ```yaml {% srNumber=16 %} workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` {% /codeBlock %} {% /codePreview %} ### 2. Run with the CLI There is an extra requirement to run the Usage pipelines. You will need to install: ```bash pip3 install --upgrade 'openmetadata-ingestion[postgres]' ``` After saving the YAML config, we will run the command the same way we did for the metadata ingestion: ```bash metadata ingest -c ``` ## Data Profiler The Data Profiler workflow will be using the `orm-profiler` processor. After running a Metadata Ingestion workflow, we can run Data Profiler workflow. While the `serviceName` will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the `serviceConnection` details from the server. ### 1. Define the YAML Config This is a sample config for the profiler: {% codePreview %} {% codeInfoContainer %} {% codeInfo srNumber=17 %} #### Source Configuration - Source Config You can find all the definitions and types for the `sourceConfig` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceProfilerPipeline.json). **generateSampleData**: Option to turn on/off generating sample data. {% /codeInfo %} {% codeInfo srNumber=18 %} **profileSample**: Percentage of data or no. of rows we want to execute the profiler and tests on. {% /codeInfo %} {% codeInfo srNumber=19 %} **threadCount**: Number of threads to use during metric computations. {% /codeInfo %} {% codeInfo srNumber=20 %} **processPiiSensitive**: Optional configuration to automatically tag columns that might contain sensitive information. {% /codeInfo %} {% codeInfo srNumber=21 %} **confidence**: Set the Confidence value for which you want the column to be marked {% /codeInfo %} {% codeInfo srNumber=22 %} **timeoutSeconds**: Profiler Timeout in Seconds {% /codeInfo %} {% codeInfo srNumber=23 %} **databaseFilterPattern**: Regex to only fetch databases that matches the pattern. {% /codeInfo %} {% codeInfo srNumber=24 %} **schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern. {% /codeInfo %} {% codeInfo srNumber=25 %} **tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern. {% /codeInfo %} {% codeInfo srNumber=26 %} #### Processor Configuration Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI: **tableConfig**: `tableConfig` allows you to set up some configuration at the table level. {% /codeInfo %} {% codeInfo srNumber=27 %} #### Sink Configuration To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`. {% /codeInfo %} {% codeInfo srNumber=28 %} #### Workflow Configuration The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation. For a simple, local installation using our docker containers, this looks like: {% /codeInfo %} {% /codeInfoContainer %} {% codeBlock fileName="filename.yaml" %} ```yaml source: type: postgres serviceName: local_postgres sourceConfig: config: type: Profiler ``` ```yaml {% srNumber=17 %} generateSampleData: true ``` ```yaml {% srNumber=18 %} # profileSample: 85 ``` ```yaml {% srNumber=19 %} # threadCount: 5 ``` ```yaml {% srNumber=20 %} processPiiSensitive: false ``` ```yaml {% srNumber=21 %} # confidence: 80 ``` ```yaml {% srNumber=22 %} # timeoutSeconds: 43200 ``` ```yaml {% srNumber=23 %} # databaseFilterPattern: # includes: # - database1 # - database2 # excludes: # - database3 # - database4 ``` ```yaml {% srNumber=24 %} # schemaFilterPattern: # includes: # - schema1 # - schema2 # excludes: # - schema3 # - schema4 ``` ```yaml {% srNumber=25 %} # tableFilterPattern: # includes: # - table1 # - table2 # excludes: # - table3 # - table4 ``` ```yaml {% srNumber=26 %} processor: type: orm-profiler config: {} # Remove braces if adding properties # tableConfig: # - fullyQualifiedName: # profileSample: # default # profileSample: # default will be 100 if omitted # profileQuery: # columnConfig: # excludeColumns: # - # includeColumns: # - columnName: # - metrics: # - MEAN # - MEDIAN # - ... # partitionConfig: # enablePartitioning: # partitionColumnName: # partitionInterval: # partitionIntervalUnit: ``` ```yaml {% srNumber=27 %} sink: type: metadata-rest config: {} ``` ```yaml {% srNumber=28 %} workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` {% /codeBlock %} {% /codePreview %} - You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from [here](/connectors/ingestion/workflows/profiler) ### 2. Prepare the Profiler DAG Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class: {% codePreview %} {% codeInfoContainer %} {% codeInfo srNumber=29 %} #### Import necessary modules The `ProfilerWorkflow` class that is being imported is a part of a metadata orm_profiler framework, which defines a process of extracting Profiler data. Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG. {% /codeInfo %} {% codeInfo srNumber=30 %} **Default arguments for all tasks in the Airflow DAG.** - Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout. {% /codeInfo %} {% codeInfo srNumber=31 %} - **config**: Specifies config for the profiler as we prepare above. {% /codeInfo %} {% codeInfo srNumber=32 %} - **metadata_ingestion_workflow()**: This code defines a function `metadata_ingestion_workflow()` that loads a YAML configuration, creates a `ProfilerWorkflow` object, executes the workflow, checks its status, prints the status to the console, and stops the workflow. {% /codeInfo %} {% codeInfo srNumber=33 %} - **DAG**: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements - For more Airflow DAGs creation details visit [here](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#declaring-a-dag). {% /codeInfo %} {% /codeInfoContainer %} {% codeBlock fileName="filename.py" %} ```python {% srNumber=30 %} import yaml from datetime import timedelta from airflow import DAG from metadata.orm_profiler.api.workflow import ProfilerWorkflow try: from airflow.operators.python import PythonOperator except ModuleNotFoundError: from airflow.operators.python_operator import PythonOperator from airflow.utils.dates import days_ago ``` ```python {% srNumber=31 %} default_args = { "owner": "user_name", "email_on_failure": False, "retries": 3, "retry_delay": timedelta(seconds=10), "execution_timeout": timedelta(minutes=60), } ``` ```python {% srNumber=32 %} config = """ """ ``` ```python {% srNumber=33 %} def metadata_ingestion_workflow(): workflow_config = yaml.safe_load(config) workflow = ProfilerWorkflow.create(workflow_config) workflow.execute() workflow.raise_from_status() workflow.print_status() workflow.stop() ``` ```python {% srNumber=34 %} with DAG( "profiler_example", default_args=default_args, description="An example DAG which runs a OpenMetadata ingestion workflow", start_date=days_ago(1), is_paused_upon_creation=False, catchup=False, ) as dag: ingest_task = PythonOperator( task_id="profile_and_test_using_recipe", python_callable=metadata_ingestion_workflow, ) ``` {% /codeBlock %} {% /codePreview %} ## Lineage You can learn more about how to ingest lineage [here](/connectors/ingestion/workflows/lineage). ## dbt Integration You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).