Translate SQL queries with the translation API

This document describes how to use the translation API in BigQuery to translate scripts written in other SQL dialects into GoogleSQL queries. The translation API can simplify the process of migrating workloads to BigQuery.

Before you begin

Before you submit a translation job, complete the following steps:

1. Ensure that you have all the required permissions.
2. Enable the BigQuery Migration API.
3. Collect the source files containing the SQL scripts and queries to be translated.
4. Upload the source files to Cloud Storage.

Required permissions

To get the permissions that you need to create translation jobs using the translation API, ask your administrator to grant you the MigrationWorkflow Editor (roles/bigquerymigration.editor) IAM role on the parent resource. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the permissions required to create translation jobs using the translation API. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to create translation jobs using the translation API:

• bigquerymigration.workflows.create
• bigquerymigration.workflows.get

You might also be able to get these permissions with custom roles or other predefined roles.

Enable the BigQuery Migration API

If your Google Cloud CLI project was created before February 15, 2022, enable the BigQuery Migration API as follows:

1. In the Google Cloud console, go to the BigQuery Migration API page.

   Go to BigQuery Migration API

2. Click Enable.

Note: Projects created after February 15, 2022 have this API enabled automatically.

Upload input files to Cloud Storage

If you want to use the Google Cloud console or the BigQuery Migration API to perform a translation job, you must upload the source files containing the queries and scripts you want to translate to Cloud Storage. You can also upload any metadata files or configuration YAML files to the same Cloud Storage bucket containing the source files. For more information about creating buckets and uploading files to Cloud Storage, see Create buckets and Upload objects from a filesystem.

Supported task types

The translation API can translate the following SQL dialects into GoogleSQL:

• Amazon Redshift SQL - Redshift2BigQuery_Translation
• Apache HiveQL and Beeline CLI - HiveQL2BigQuery_Translation
• Apache Spark SQL - SparkSQL2BigQuery_Translation
• Azure Synapse T-SQL - AzureSynapse2BigQuery_Translation
• Greenplum SQL - Greenplum2BigQuery_Translation
• IBM Db2 SQL - Db22BigQuery_Translation
• IBM Netezza SQL and NZPLSQL - Netezza2BigQuery_Translation
• MySQL SQL - MySQL2BigQuery_Translation
• Oracle SQL, PL/SQL, Exadata - Oracle2BigQuery_Translation
• PostgreSQL SQL - Postgresql2BigQuery_Translation
• Presto or Trino SQL - Presto2BigQuery_Translation
• Snowflake SQL - Snowflake2BigQuery_Translation
• SQLite - SQLite2BigQuery_Translation
• SQL Server T-SQL - SQLServer2BigQuery_Translation
• Teradata and Teradata Vantage - Teradata2BigQuery_Translation
• Vertica SQL - Vertica2BigQuery_Translation

Locations

The translation API is available in the following processing locations:

	Region description	Region name	Details
Asia Pacific
	Tokyo	asia-northeast1
	Mumbai	asia-south1
	Singapore	asia-southeast1
	Sydney	australia-southeast1
Europe
	EU multi-region	eu
	Warsaw	europe-central2
	Finland	europe-north1	Low CO2
	Madrid	europe-southwest1	Low CO2
	Belgium	europe-west1	Low CO2
	London	europe-west2	Low CO2
	Frankfurt	europe-west3	Low CO2
	Netherlands	europe-west4	Low CO2
	Zürich	europe-west6	Low CO2
	Paris	europe-west9	Low CO2
	Turin	europe-west12
Americas
	Québec	northamerica-northeast1	Low CO2
	São Paulo	southamerica-east1	Low CO2
	US multi-region	us
	Iowa	us-central1	Low CO2
	South Carolina	us-east1
	Northern Virginia	us-east4
	Columbus, Ohio	us-east5
	Dallas	us-south1	Low CO2
	Oregon	us-west1	Low CO2
	Los Angeles	us-west2
	Salt Lake City	us-west3

Submit a translation job

To submit a translation job using the translation API, use the projects.locations.workflows.create method and supply an instance of the MigrationWorkflow resource with a supported task type.

Once the job is submitted, you can issue a query to get results.

Create a batch translation

The following curl command creates a batch translation job where the input and output files are stored in Cloud Storage. The source_target_mapping field contains a list that maps the source literal entries to an optional relative path for the target output.

curl -d "{
  \"tasks\": {
      string: {
        \"type\": \"TYPE\",
        \"translation_details\": {
            \"target_base_uri\": \"TARGET_BASE\",
            \"source_target_mapping\": {
              \"source_spec\": {
                  \"base_uri\": \"BASE\"
              }
            },
            \"target_types\": \"TARGET_TYPES\",
        }
      }
  }
  }" \
  -H "Content-Type:application/json" \
  -H "Authorization: Bearer TOKEN" -X POST https://bigquerymigration.googleapis.com/v2alpha/projects/PROJECT_ID/locations/LOCATION/workflows

Replace the following:

• TYPE: the task type of the translation, which determines the source and target dialect.
• TARGET_BASE: the base URI for all translation outputs.
• BASE: the base URI for all files read as sources for translation.

• TARGET_TYPES (optional): the generated output types. If not specified, SQL is generated.

  • sql (default): The translated SQL query files.
  • suggestion: AI generated suggestions.

  The output is stored in a subfolder in the output directory. The subfolder is named based on the value in TARGET_TYPES.

• TOKEN: the token for authentication. To generate a token, use the gcloud auth print-access-token command or the OAuth 2.0 playground (use the scope https://www.googleapis.com/auth/cloud-platform).

• PROJECT_ID: the project to process the translation.

• LOCATION: the location where the job is processed.

The preceding command returns a response that includes a workflow ID written in the format projects/PROJECT_ID/locations/LOCATION/workflows/WORKFLOW_ID.

Example batch translation

To translate the Teradata SQL scripts in the Cloud Storage directory gs://my_data_bucket/teradata/input/ and store the results in the Cloud Storage directory gs://my_data_bucket/teradata/output/, you might use the following query:

{
  "tasks": {
     "task_name": {
       "type": "Teradata2BigQuery_Translation",
       "translation_details": {
         "target_base_uri": "gs://my_data_bucket/teradata/output/",
           "source_target_mapping": {
             "source_spec": {
               "base_uri": "gs://my_data_bucket/teradata/input/"
             }
          },
       }
    }
  }
}

Note: The string "task_name" in this example is an identifier for the translation task and can be set to any value you prefer.

This call will return a message containing the created workflow ID in the "name" field:

{
  "name": "projects/123456789/locations/us/workflows/12345678-9abc-def1-2345-6789abcdef00",
  "tasks": {
    "task_name": { /*...*/ }
  },
  "state": "RUNNING"
}

To get the updated status for the workflow, run a GET query. The job sends outputs to Cloud Storage as it progresses. The job state changes to COMPLETED after all the requested target_types are generated. If the task succeeds, you can find the translated SQL query in gs://my_data_bucket/teradata/output.

Example batch translation with AI suggestions

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Note: The translation API can call Gemini using BigQuery Vertex AI integration to generate suggestions to your translated SQL query based on your AI configuration YAML file.

The following example translates the Teradata SQL scripts located in the gs://my_data_bucket/teradata/input/ Cloud Storage directory and stores results in the Cloud Storage directory gs://my_data_bucket/teradata/output/ with additional AI suggestion:

{
  "tasks": {
     "task_name": {
       "type": "Teradata2BigQuery_Translation",
       "translation_details": {
         "target_base_uri": "gs://my_data_bucket/teradata/output/",
           "source_target_mapping": {
             "source_spec": {
               "base_uri": "gs://my_data_bucket/teradata/input/"
             }
          },
          "target_types": "suggestion",
       }
    }
  }
}

Note: To generate AI suggestions, the Cloud Storage source directory must contain at least one configuration YAML file with a suffix of .ai_config.yaml. To learn how to write the configuration YAML file for AI suggestions, see Create a Gemini-based configuration YAML file.

After the task runs successfully, AI suggestions can be found in gs://my_data_bucket/teradata/output/suggestion Cloud Storage directory.

Create an interactive translation job with string literal inputs and outputs

The following curl command creates a translation job with string literal inputs and outputs. The source_target_mapping field contains a list that maps the source directories to an optional relative path for the target output.

curl -d "{
  \"tasks\": {
      string: {
        \"type\": \"TYPE\",
        \"translation_details\": {
        \"source_target_mapping\": {
            \"source_spec\": {
              \"literal\": {
              \"relative_path\": \"PATH\",
              \"literal_string\": \"STRING\"
              }
            }
        },
        \"target_return_literals\": \"TARGETS\",
        }
      }
  }
  }" \
  -H "Content-Type:application/json" \
  -H "Authorization: Bearer TOKEN" -X POST https://bigquerymigration.googleapis.com/v2alpha/projects/PROJECT_ID/locations/LOCATION/workflows

Replace the following:

• TYPE: the task type of the translation, which determines the source and target dialect.
• PATH: the identifier of the literal entry, similar to a filename or path.
• STRING: string of literal input data (for example, SQL) to be translated.
• TARGETS: the expected targets that the user wants to be directly returned in the response in the literal format. These should be in the target URI format (for example, GENERATED_DIR + target_spec.relative_path + source_spec.literal.relative_path). Anything not in this list is not returned in the response. The generated directory, GENERATED_DIR for general SQL translations is sql/.
• TOKEN: the token for authentication. To generate a token, use the gcloud auth print-access-token command or the OAuth 2.0 playground (use the scope https://www.googleapis.com/auth/cloud-platform).
• PROJECT_ID: the project to process the translation.
• LOCATION: the location where the job is processed.

The preceding command returns a response that includes a workflow ID written in the format projects/PROJECT_ID/locations/LOCATION/workflows/WORKFLOW_ID.

When your job completes, you can view the results by by querying the job and examining the inline translation_literals field in the response after the workflow completes.

Example Interactive Translation

To translate the Hive SQL string select 1 interactively, you might use the following query:

"tasks": {
  string: {
    "type": "HiveQL2BigQuery_Translation",
    "translation_details": {
      "source_target_mapping": {
        "source_spec": {
          "literal": {
            "relative_path": "input_file",
            "literal_string": "select 1"
          }
        }
      },
      "target_return_literals": "sql/input_file",
    }
  }
}

Note: The string "task_name" in this example is an identifier for the translation task and can be set to any value you prefer.

You can use any relative_path you would like for your literal, but the translated literal will only appear in the results if you include sql/$relative_path in your target_return_literals. You can also include multiple literals in a single query, in which case each of their relative paths must be included in target_return_literals.

This call will return a message containing the created workflow ID in the "name" field:

{
  "name": "projects/123456789/locations/us/workflows/12345678-9abc-def1-2345-6789abcdef00",
  "tasks": {
    "task_name": { /*...*/ }
  },
  "state": "RUNNING"
}

To get the updated status for the workflow, run a GET query. The job is complete when "state" changes to COMPLETED. If the task succeeds, you will find the translated SQL in the response message:

{
  "name": "projects/123456789/locations/us/workflows/12345678-9abc-def1-2345-6789abcdef00",
  "tasks": {
    "string": {
      "id": "0fedba98-7654-3210-1234-56789abcdef",
      "type": "HiveQL2BigQuery_Translation",
      /* ... */
      "taskResult": {
        "translationTaskResult": {
          "translatedLiterals": [
            {
              "relativePath": "sql/input_file",
              "literalString": "-- Translation time: 2023-10-05T21:50:49.885839Z\n-- Translation job ID: projects/123456789/locations/us/workflows/12345678-9abc-def1-2345-6789abcdef00\n-- Source: input_file\n-- Translated from: Hive\n-- Translated to: BigQuery\n\nSELECT\n    1\n;\n"
            }
          ],
          "reportLogMessages": [
            ...
          ]
        }
      },
      /* ... */
    }
  },
  "state": "COMPLETED",
  "createTime": "2023-10-05T21:50:49.543221Z",
  "lastUpdateTime": "2023-10-05T21:50:50.462758Z"
}

Explore the translation output

After running the translation job, retrieve the results by specifying the translation job workflow ID using the following command:

curl \
-H "Content-Type:application/json" \
-H "Authorization:Bearer TOKEN" -X GET https://bigquerymigration.googleapis.com/v2alpha/projects/PROJECT_ID/locations/LOCATION/workflows/WORKFLOW_ID

Replace the following:

• TOKEN: the token for authentication. To generate a token, use the gcloud auth print-access-token command or the OAuth 2.0 playground (use the scope https://www.googleapis.com/auth/cloud-platform).
• PROJECT_ID: the project to process the translation.
• LOCATION: the location where the job is processed.
• WORKFLOW_ID: the ID generated when you create a translation workflow.

The response contains the status of your migration workflow, and any completed files in target_return_literals.

The response will contain the status of your migration workflow, and any completed files in target_return_literals. You can poll this endpoint to check your workflow's status.