The ML.TRANSLATE function

This document describes the ML.TRANSLATE function, which lets you translate text stored in BigQuery tables by using the Cloud Translation API.

Syntax

ML.TRANSLATE(
  MODEL `PROJECT_ID.DATASET.MODEL`,
  { TABLE `PROJECT_ID.DATASET.TABLE` | (QUERY_STATEMENT) },
  STRUCT('MODE_NAME' AS translate_mode [, 'TARGET_LANGUAGE_CODE' AS target_language_code ])
)

Arguments

ML.TRANSLATE takes the following arguments:

  • PROJECT_ID: the project that contains the resource.

  • DATASET: the dataset that contains the resource.

  • MODEL: the name of a remote model with a REMOTE_SERVICE_TYPE of CLOUD_AI_TRANSLATE_V3.

  • TABLE: the name of the BigQuery table that contains text data. The text analysis is applied on the column named text_content in this table. If your table does not have a text_content column, use a SELECT statement for this argument to provide an alias for an existing table column, as shown in the following example:

    SELECT * from ML.TRANSLATE(
  MODEL `mydataset.mymodel`,
  (SELECT comment AS text_content from mydataset.mytable),
  STRUCT('translate_text' AS translate_mode, 'en' AS target_language_code)
);

    An error occurs if no text_content column is available.

  • QUERY_STATEMENT: a query whose result contains the text data. The text analysis is applied on the column in the query named text_content. You can alias an existing table column as text_content if necessary. For information about the supported SQL syntax of the QUERY_STATEMENT clause, see GoogleSQL query syntax.

  • MODE_NAME: a STRING value that specifies one of the following supported translation modes:

  • TARGET_LANGUAGE_CODE: a STRING value that specifies a supported language code for translation. This argument is only required when you use the TRANSLATE_TEXT translation mode.

Output

ML.TRANSLATE returns the input table plus the following columns:

  • ml_translate_result: a JSON value that contains the translation result from Cloud Translation API.
  • ml_translate_status: a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful.

Quotas

See Cloud AI service functions quotas and limits.

Known issues

Sometimes after a query job that uses this function finishes successfully, some returned rows contain the following error message:

A retryable error occurred: RESOURCE EXHAUSTED error from <remote endpoint>

This issue occurs because BigQuery query jobs finish successfully even if the function fails for some of the rows. The function fails when the volume of API calls to the remote endpoint exceeds the quota limits for that service. This issue occurs most often when you are running multiple parallel batch queries. BigQuery retries these calls, but if the retries fail, the resource exhausted error message is returned.

To iterate through inference calls until all rows are successfully processed, you can use the BigQuery remote inference SQL scripts or the BigQuery remote inference pipeline Dataform package.

Locations

ML.TRANSLATE must run in the same region as the remote model that the function references. For more information about supported locations for models based on the Cloud Translation API, see Locations for remote models.

Example

Example 1

The following example applies text translation on the column name text_content on the bq table mybqtable in mydataset to Chinese.

# Create model
CREATE OR REPLACE MODEL
`myproject.mydataset.mytranslatemodel`
REMOTE WITH CONNECTION `myproject.myregion.myconnection`
OPTIONS (remote_service_type = 'cloud_ai_translate_v3')
 
# Translate text
SELECT * FROM ML.TRANSLATE(
  MODEL `mydataset.mytranslatemodel`,
  TABLE `mydataset.mybqtable`,
  STRUCT('translate_text' AS translate_mode, 'zh-CN' AS target_language_code));

The output is similar to the following:

ml_translate_result  | ml_translate_status | text_content |
------- | -------- | --------
{"glossary_translations":[],"translation_memory_translations":[],"translations":[{"detected_language_code":"en","translated_text":"苹果"}]} | | apple

Example 2

The following example translates the text in the column text_content in the table mybqtable to Chinese, and parses the JSON results into separate columns.

# Translate text and parse the json
CREATE TABLE
  `mydataset.translate_result` AS (
  SELECT
    STRING(ml_translate_result.translations[0].detected_language_code) AS `Original Language`,
    text_content AS `Original Text`,
    "zh-CN" AS `Destination Language`,
    STRING(ml_translate_result.translations[0].translated_text) AS Translation,
    ml_translate_status as `Status`
  FROM ML.TRANSLATE(
    MODEL `mydataset.mytranslatemodel`,
    TABLE `mydataset.mybqtable`,
    STRUCT('translate_text' AS translate_mode, 'zh-CN' AS target_language_code)));

SELECT * FROM `mydataset.translate_result`;

The output is similar to the following:

Original Language  | Original Text | Destination Language | Translation  |  Status  |
------- | -------- | -------- | -------- | --------
en  |  apple  |  zh-cn. |  苹果   | |

If you get an error like query limit exceeded, you might have exceeded the quota for this function, which can leave you with unprocessed rows. Use the following query to complete processing the unprocessed rows:

CREATE TABLE
  `mydataset.translate_result_next` AS (
  SELECT
    STRING(ml_translate_result.translations[0].detected_language_code) AS `Original Language`,
    text_content AS `Original Text`,
    'zh-CN' AS `Destination Language`,
    STRING(ml_translate_result.translations[0].translated_text) AS Translation,
    ml_translate_status as `Status`
  FROM ML.TRANSLATE(
    MODEL `mydataset.mytranslatemodel`,
    (SELECT `Original Text` AS text_content
     FROM `mydataset.translate_result`
     WHERE Status != ''),
    STRUCT('translate_text' AS translate_mode, 'zh-CN' AS target_language_code)));

SELECT * FROM `mydataset.translate_result_next`;

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