OpenMetadata/openmetadata-docs/content/connectors/database/vertica/cli.md

title	slug
Run Vertica Connector using the CLI	/connectors/database/vertica/cli

Run Vertica using the metadata CLI

Stage	Metadata	Query Usage	Data Profiler	Data Quality	Lineage	DBT	Supported Versions
PROD	✅	✅	✅	✅	✅	✅	Vertica >= 9.2

Lineage	Table-level	Column-level
✅	✅	✅

In this section, we provide guides and references to use the Vertica connector.

Configure and schedule Vertica metadata and profiler workflows from the OpenMetadata UI:

• Requirements
• Metadata Ingestion
• Data Profiler
• dbt Integration

Requirements

To deploy OpenMetadata, check the Deployment guides.

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.

Permissions

To run the ingestion we need a user with SELECT grants on the schemas that you'd like to ingest, as well as to the V_CATALOG schema. You can grant those as follows for the schemas in your database:

CREATE USER openmetadata IDENTIFIED BY 'password';
GRANT SELECT ON ALL TABLES IN SCHEMA PUBLIC TO openmetadata;
GRANT SELECT ON ALL TABLES IN SCHEMA V_CATALOG TO openmetadata;

Note that these GRANTs won't be applied to any new table created on the schema unless the schema has Inherited Privileges

ALTER SCHEMA s1 DEFAULT INCLUDE PRIVILEGES;
-- If using the PUBLIC schema
ALTER SCHEMA "<db>.public" DEFAULT INCLUDE PRIVILEGES;

Lineage and Usage

If you also want to run the Lineage and Usage workflows, then the user needs to be granted permissions to the V_MONITOR schema:

GRANT SELECT ON ALL TABLES IN SCHEMA V_MONITOR TO openmetadata;

Note that this setting might only grant visibility to the queries executed by this user. A more complete approach will be to grant the SYSMONITOR role to the openmetadata user:

GRANT SYSMONITOR TO openmetadata;
ALTER USER openmetadata DEFAULT ROLE SYSMONITOR;

Profiler

To run the profiler, it's not enough to have USAGE permissions to the schema as we need to SELECT the tables in there. Therefore, you'll need to grant SELECT on all tables for the schemas:

GRANT SELECT ON ALL TABLES IN SCHEMA <schema> TO openmetadata;

Python Requirements

To run the Vertica ingestion, you will need to install:

pip3 install "openmetadata-ingestion[vertica]"

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Vertica.

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

1. Define the YAML Config

This is a sample config for Vertica:

source:
  type: vertica
  serviceName: local_vertica
  serviceConnection:
    config:
      type: Vertica
      username: username
      password: password
      hostPort: localhost:5432
      # database: database
  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:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
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 Vertica. It should have enough privileges to read all the metadata.
• password: Password to connect to Vertica.
• hostPort: Enter the fully qualified hostname and port number for your Vertica deployment in the Host and Port field.
• Connection Options (Optional): Enter the details for any additional connection options that can be sent to Vertica 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 Vertica 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:

• 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.,

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:

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. You can find the different implementation of the ingestion below.

Openmetadata JWT Auth

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: openmetadata
    securityConfig:
      jwtToken: '{bot_jwt_token}'

Auth0 SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: auth0
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Azure SSO

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

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Google SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: google
    securityConfig:
      secretKey: '{path-to-json-creds}'

Okta SSO

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

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

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

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

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.

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:

source:
  type: vertica
  serviceName: local_vertica
  serviceConnection:
    config:
      type: Vertica
      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.
• The sourceConfig is defined here.

Note that the filter patterns support regex as includes or excludes. E.g.,

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:

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

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:

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.