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OpenMetadata/openmetadata-docs/content/connectors/database/postgres/cli.md
Onkar Ravgan 14fa96958f
Added dbt workflow docs (#9493) 
* Added dbt workflow docs

* added dbt small case

* Fixed review comments
2022-12-22 13:11:18 +00:00

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---
title: Run Postgres Connector using the CLI
slug: /connectors/database/postgres/cli
---
# Run Postgres using the metadata CLI
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 and Lineage Ingestion](#query-usage-and-lineage-ingestion)
- [Data Profiler](#data-profiler)
- [dbt Integration](#dbt-integration)
## Requirements
<InlineCallout color="violet-70" icon="description" bold="OpenMetadata 0.12 or later" href="/deployment">
To deploy OpenMetadata, check the <a href="/deployment">Deployment</a> 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/).
</Note>
### 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.
### 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:
```yaml
source:
type: postgres
serviceName: local_postgres
serviceConnection:
config:
type: Postgres
username: username
password: password
hostPort: localhost:5432
# database: database
sourceConfig:
config:
markDeletedTables: true
includeTables: true
includeViews: true
# includeTags: true
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
# tableFilterPattern:
# includes:
# - table1
# - table2
# excludes:
# - table3
# - table4
# For dbt, choose one of Cloud, Local, HTTP, S3 or GCS configurations
# dbtConfigSource:
# # For cloud
# dbtCloudAuthToken: token
# dbtCloudAccountId: ID
# # For Local
# dbtCatalogFilePath: path-to-catalog.json
# dbtManifestFilePath: path-to-manifest.json
# # For HTTP
# dbtCatalogHttpPath: http://path-to-catalog.json
# dbtManifestHttpPath: http://path-to-manifest.json
# # For S3
# dbtSecurityConfig: # These are modeled after all AWS credentials
# awsAccessKeyId: KEY
# awsSecretAccessKey: SECRET
# awsRegion: us-east-2
# dbtPrefixConfig:
# dbtBucketName: bucket
# dbtObjectPrefix: "dbt/"
# # For GCS
# dbtSecurityConfig: # These are modeled after all GCS credentials
# type: My Type
# projectId: project ID
# privateKeyId: us-east-2
# privateKey: |
# -----BEGIN PRIVATE KEY-----
# Super secret key
# -----END PRIVATE KEY-----
# clientEmail: client@mail.com
# clientId: 1234
# authUri: https://accounts.google.com/o/oauth2/auth (default)
# tokenUri: https://oauth2.googleapis.com/token (default)
# authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
# clientX509CertUrl: https://cert.url (URI)
# dbtPrefixConfig:
# dbtBucketName: bucket
# dbtObjectPrefix: "dbt/"
sink:
type: metadata-rest
config: {}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>2. Configure service settings
```
#### Source Configuration - Service Connection
- **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"`
#### 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/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.,
```yaml
tableFilterPattern:
includes:
- users
- type_test
```
#### Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
#### 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:
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: '{bot_jwt_token}'
```
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).
You can find the different implementation of the ingestion below.
<Collapse title="Configure SSO in the Ingestion Workflows">
### Openmetadata JWT Auth
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: '{bot_jwt_token}'
```
### Auth0 SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: auth0
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### Azure SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: azure
securityConfig:
clientSecret: '{your_client_secret}'
authority: '{your_authority_url}'
clientId: '{your_client_id}'
scopes:
- your_scopes
```
### Custom OIDC SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### Google SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: google
securityConfig:
secretKey: '{path-to-json-creds}'
```
### Okta SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: http://localhost:8585/api
authProvider: okta
securityConfig:
clientId: "{CLIENT_ID - SPA APP}"
orgURL: "{ISSUER_URL}/v1/token"
privateKey: "{public/private keypair}"
email: "{email}"
scopes:
- token
```
### Amazon Cognito SSO
The ingestion can be configured by [Enabling JWT Tokens](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens)
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: auth0
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### OneLogin SSO
Which uses Custom OIDC for the ingestion
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### KeyCloak SSO
Which uses Custom OIDC for the ingestion
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
</Collapse>
### 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 and Lineage Ingestion
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: <path to store the stage file>
# 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: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
```
#### 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:
```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.
While the `serviceConnection` will still be the same to reach the source system, the `sourceConfig` will be
updated from previous configurations.
### 1. Define the YAML Config
This is a sample config for the profiler:
```yaml
source:
type: postgres
serviceName: local_postgres
serviceConnection:
config:
type: Postgres
username: username
password: password
hostPort: localhost:5432
# database: database
sourceConfig:
config:
type: Profiler
# generateSampleData: true
# profileSample: 85
# threadCount: 5 (default)
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
# tableFilterPattern:
# includes:
# - table1
# - table2
# excludes:
# - table3
# - table4
processor:
type: orm-profiler
config: {} # Remove braces if adding properties
# tableConfig:
# - fullyQualifiedName: <table fqn>
# 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>
sink:
type: metadata-rest
config: {}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
```
#### Source Configuration
- 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).
Note that the filter patterns support regex as includes or excludes. E.g.,
```yaml
tableFilterPattern:
includes:
- *users$
```
#### Processor
Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI:
```yaml
processor:
type: orm-profiler
config:
tableConfig:
- fullyQualifiedName: <table fqn>
profileSample: <number between 0 and 99>
partitionConfig:
partitionField: <field to use as a partition field>
partitionQueryDuration: <for date/datetime partitioning based set the offset from today>
partitionValues: <values to uses as a predicate for the query>
profileQuery: <query to use for sampling data for the profiler>
columnConfig:
excludeColumns:
- <column name>
includeColumns:
- columnName: <column name>
- metrics:
- MEAN
- MEDIAN
- ...
```
`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. Run with the CLI
After saving the YAML config, we will run the command the same way we did for the metadata ingestion:
```bash
metadata profile -c <path-to-yaml>
```
Note how instead of running `ingest`, we are using the `profile` command to select the Profiler workflow.
## 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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