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:
- **awsAccessKeyId** &**awsSecretAccessKey**: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have
permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and
authorize your requests ([docs](https://docs.aws.amazon.com/IAM/latest/UserGuide/security-creds.html)).
Access keys consist of two parts: An **access key ID** (for example, `AKIAIOSFODNN7EXAMPLE`), and a **secret access key** (for example, `wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY`).
You must use both the access key ID and secret access key together to authenticate your requests.
You can find further information on how to manage your access keys [here](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html).
**awsSessionToken**: If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID
and AWS Secrets Access Key. Also, these will include an AWS Session Token.
**awsRegion**: Each AWS Region is a separate geographic area in which AWS clusters data centers ([docs](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.RegionsAndAvailabilityZones.html)).
As AWS can have instances in multiple regions, we need to know the region the service you want reach belongs to.
Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the
services programmatically, there are different ways in which we can extract and use the rest of AWS configurations.
You can find further information about configuring your credentials [here](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials).
**endPointURL**: To connect programmatically to an AWS service, you use an endpoint. An *endpoint* is the URL of the
entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically use the
default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests.
Find more information on [AWS service endpoints](https://docs.aws.amazon.com/general/latest/gr/rande.html).
**profileName**: A named profile is a collection of settings and credentials that you can apply to a AWS CLI command.
When you specify a profile to run a command, the settings and credentials are used to run that command.
Multiple named profiles can be stored in the config and credentials files.
You can inform this field if you'd like to use a profile other than `default`.
Find here more information about [Named profiles for the AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-profiles.html).
**assumeRoleArn**: Typically, you use `AssumeRole` within your account or for cross-account access. In this field you'll set the
`ARN` (Amazon Resource Name) of the policy of the other account.
A user who wants to access a role in a different account must also have permissions that are delegated from the account
administrator. The administrator must attach a policy that allows the user to call `AssumeRole` for the `ARN` of the role in the other account.
This is a required field if you'd like to `AssumeRole`.
Find more information on [AssumeRole](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html).
**assumeRoleSessionName**: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role
is assumed by different principals or for different reasons.
By default, we'll use the name `OpenMetadataSession`.
Find more information about the [Role Session Name](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html#:~:text=An%20identifier%20for%20the%20assumed%20role%20session.).
**assumeRoleSourceIdentity**: The source identity specified by the principal that is calling the `AssumeRole` operation. You can use source identity
information in AWS CloudTrail logs to determine who took actions with a role.
Find more information about [Source Identity](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html#:~:text=Required%3A%20No-,SourceIdentity,-The%20source%20identity).
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 filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/database)
**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.
**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"`
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: <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: <pathtostorethestagefile>
```
```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: <OpenMetadatahostandport>
authProvider: <OpenMetadataauthprovider>
```
{% /codeBlock %}
{% /codePreview %}
### 2. Run with the CLI
There is an extra requirement to run the Usage pipelines. You will need to install:
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: <tablefqn>
# profileSample: <numberbetween0and99> # default
# profileSample: <numberbetween0and99> # default will be 100 if omitted
- 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).
