--- title: Run Redshift Connector using the CLI slug: /connectors/database/redshift/cli --- # Run Redshift using the metadata CLI In this section, we provide guides and references to use the Redshift connector. Configure and schedule Redshift 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 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. Redshift user must grant `SELECT` privilege on table [SVV_TABLE_INFO](https://docs.aws.amazon.com/redshift/latest/dg/r_SVV_TABLE_INFO.html) to fetch the metadata of tables and views. For more information visit [here](https://docs.aws.amazon.com/redshift/latest/dg/c_visibility-of-data.html). ### Python Requirements To run the Redshift ingestion, you will need to install: ```bash pip3 install "openmetadata-ingestion[redshift]" ``` If you want to run the Usage Connector, you'll also need to install: ```bash pip3 install "openmetadata-ingestion[redshift-usage]" ``` ## 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/redshiftConnection.json) you can find the structure to create a connection to Redshift. 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) During the metadata ingestion for redshift, the tables in which the distribution style i.e `DISTSTYLE` is not `AUTO` will be marked as partitioned tables ### 1. Define the YAML Config This is a sample config for Redshift: ```yaml source: type: redshift serviceName: aws_redshift serviceConnection: config: type: Redshift hostPort: cluster.name.region.redshift.amazonaws.com:5439 username: username password: password database: dev # If we want to iterate over all databases, set it to true # ingestAllDatabases: true 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: authProvider: ``` #### Source Configuration - Service Connection

Source Configuration - Service Connection

- **username**: Specify the User to connect to Snoflake. It should have enough privileges to read all the metadata. - **password**: Password to connect to Redshift. - **database**: The database of the data source is an optional parameter, if you would like to restrict the metadata reading to a single database. If left blank, OpenMetadata ingestion attempts to scan all the databases. - **Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to Redshift 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 Redshift 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. ### 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}' ``` ### 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 ``` 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 Redshift Usage: ```yaml source: type: redshift-usage serviceName: serviceConnection: config: type: Redshift hostPort: cluster.name.region.redshift.amazonaws.com:5439 username: username password: password database: dev # If we want to iterate over all databases, set it to true # ingestAllDatabases: true sourceConfig: config: # Number of days to look back queryLogDuration: 7 # This is a directory that will be DELETED after the usage runs stageFileLocation: # 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/redshift_usage bulkSink: type: metadata-usage config: filename: /tmp/redshift_usage workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` #### 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/bigQueryConnection.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[redshift-usage]' ``` After saving the YAML config, we will run the command the same way we did for the metadata ingestion: ```bash metadata ingest -c ``` ## 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: redshift serviceName: serviceConnection: config: type: Redshift hostPort: cluster.name.region.redshift.amazonaws.com:5439 username: username password: password database: dev 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: # profileSample: # default will be 100 if omitted # profileQuery: # columnConfig: # excludeColumns: # - # includeColumns: # - columnName: # - metrics: # - MEAN # - MEDIAN # - ... # partitionConfig: # enablePartitioning: # partitionColumnName: # partitionInterval: # partitionIntervalUnit: sink: type: metadata-rest config: {} workflowConfig: openMetadataServerConfig: hostPort: authProvider: ``` #### 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/redshiftConnection.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:
profileSample: partitionConfig: partitionField: partitionQueryDuration: partitionValues: profileQuery: columnConfig: excludeColumns: - includeColumns: - columnName: - 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 ``` Note how instead of running `ingest`, we are using the `profile` command to select the Profiler workflow. ## SSL Configuration In order to integrate SSL in the Metadata Ingestion Config, the user will have to add the SSL config under connectionArguments which is placed in the source. ```yaml --- source: type: redshift serviceName: serviceConnection: config: type: Redshift hostPort: cluster.name.region.redshift.amazonaws.com:5439 username: username ... ... ... connectionArguments: sslmode: ``` ### SSL Modes There are couple of types of SSL modes that Redshift supports which can be added to ConnectionArguments, they are as follows: - **disable**: SSL is disabled and the connection is not encrypted. - **allow**: SSL is used if the server requires it. - **prefer**: SSL is used if the server supports it. Amazon Redshift supports SSL, so SSL is used when you set sslmode to prefer. - **require**: SSL is required. - **verify-ca**: SSL must be used and the server certificate must be verified. - **verify-full**: SSL must be used. The server certificate must be verified and the server hostname must match the hostname attribute on the certificate. For more information, you can visit [Redshift SSL documentation](https://docs.aws.amazon.com/redshift/latest/mgmt/connecting-ssl-support.html) ## DBT Integration You can learn more about how to ingest DBT models' definitions and their lineage from [here](https://docs.open-metadata.org/openmetadata/ingestion/workflows/metadata/dbt).