Note that we only support officially supported Postgres versions. You can check the version list [here](https://www.postgresql.org/support/versioning/).
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.
- **username**: Specify the User to connect to Postgres. It should have enough privileges to read all the metadata.
- **password**: Password to connect to Postgres.
- **hostPort**: Enter the fully qualified hostname and port number for your Postgres deployment in the Host and Port field.
- **Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to Postgres during the connection. These details must be added as Key-Value pairs.
- **Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Postgres 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"`
- In case you authenticate with SSO using an external browser popup, then add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "externalbrowser"`
The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json):
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).
To ingest the Query Usage and Lineage information, the `serviceConnection` configuration will remain the same.
However, the `sourceConfig` is now modeled after this JSON Schema.
### 1. Define the YAML Config
This is a sample config for Postgres Usage:
```yaml
source:
type: postgres
serviceName: local_postgres
serviceConnection:
config:
type: Postgres
username: username
password: password
hostPort: localhost:5432
# database: database
sourceConfig:
config:
# Number of days to look back
queryLogDuration: 7
# This is a directory that will be DELETED after the usage runs
stageFileLocation: <pathtostorethestagefile>
# resultLimit: 1000
# If instead of getting the query logs from the database we want to pass a file with the queries
# queryLogFilePath: path-to-file
processor:
type: query-parser
config: {}
stage:
type: table-usage
config:
filename: /tmp/postgres_usage
bulkSink:
type: metadata-usage
config:
filename: /tmp/postgres_usage
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadatahostandport>
authProvider: <OpenMetadataauthprovider>
```
#### Source Configuration - Service Connection
You can find all the definitions and types for the `serviceConnection` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/postgresConnection.json).
They are the same as metadata ingestion.
#### Source Configuration - Source Config
The `sourceConfig` is defined [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.
-`resultLimit`: Configuration to set the limit for query logs
#### Processor, Stage and Bulk Sink
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.
#### Workflow Configuration
The same as the metadata ingestion.
### 2. Run with the CLI
There is an extra requirement to run the Usage pipelines. You will need to install:
- You can find all the definitions and types for the `serviceConnection` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/postgresConnection.json).
- The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceProfilerPipeline.json).
`tableConfig` allows you to set up some configuration at the table level.
All the properties are optional. `metrics` should be one of the metrics listed [here](https://docs.open-metadata.org/openmetadata/ingestion/workflows/profiler/metrics)
#### Workflow Configuration
The same as the metadata ingestion.
### 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:
```python
import yaml
from datetime import timedelta
from airflow import DAG
try:
from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
from airflow.operators.python_operator import PythonOperator
from airflow.utils.dates import days_ago
from metadata.orm_profiler.api.workflow import ProfilerWorkflow
