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doc: added 0.13.3 documentation (#11271)
2023-04-25 16:58:47 +02:00
---
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 %}
Fix DB yaml (#11376)
2023-05-02 11:32:28 +05:30
sourceConfig:
config:
Fix Docs (#11386)
2023-05-02 16:36:52 +05:30
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
doc: added 0.13.3 documentation (#11271)
2023-04-25 16:58:47 +02:00
```
```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 <path-to-yaml>
```
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: <service name>
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: <path to store the stage file>
```
```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: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
```
{% /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 <path-to-yaml>
```
## 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: <table fqn>
# profileSample: <number between 0 and 99> # default
# profileSample: <number between 0 and 99> # default will be 100 if omitted
# profileQuery: <query to use for sampling data for the profiler>
# columnConfig:
# excludeColumns:
# - <column name>
# includeColumns:
# - columnName: <column name>
# - metrics:
# - MEAN
# - MEDIAN
# - ...
# partitionConfig:
# enablePartitioning: <set to true to use partitioning>
# partitionColumnName: <partition column name. Must be a timestamp or datetime/date field type>
# partitionInterval: <partition interval>
# partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>
```
```yaml {% srNumber=27 %}
sink:
type: metadata-rest
config: {}
```
```yaml {% srNumber=28 %}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
```
{% /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 = """
<your YAML configuration>
"""
```
```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).
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