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    Cloud Storage Text to BigQuery (Stream) with Python UDF template

    Cloud Storage Text to BigQuery pipeline is a streaming pipeline that streams text files stored in Cloud Storage, transforms them using a Python user-defined function (UDF) that you provide, and appends the result to BigQuery.

    The pipeline runs indefinitely and needs to be terminated manually via a cancel and not a drain , due to its use of the Watch transform, which is a splittable DoFn that does not support draining.

    Pipeline requirements

    • Create a JSON file that describes the schema of your output table in BigQuery.

      Ensure that there is a top-level JSON array titled fields and that its contents follow the pattern {"name": "COLUMN_NAME", "type": "DATA_TYPE"} . For example:

       { 
  
 "fields" 
 : 
  
 [ 
  
 { 
  
 "name" 
 : 
  
 "name" 
 , 
  
 "type" 
 : 
  
 "STRING" 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "age" 
 , 
  
 "type" 
 : 
  
 "INTEGER" 
  
 } 
  
 ] 
 }
    • Create a Python ( .py ) file with your UDF function that supplies the logic to transform the lines of text. Your function must return a JSON string.

      The following example splits each line of a CSV file, creates a JSON object with the values, and returns a JSON string:

       import 
  
 json 
 def 
  
 process 
 ( 
 value 
 ): 
 data 
 = 
 value 
 . 
 split 
 ( 
 ',' 
 ) 
 obj 
 = 
 { 
 'name' 
 : 
 data 
 [ 
 0 
 ], 
 'age' 
 : 
 int 
 ( 
 data 
 [ 
 1 
 ]) 
 } 
 return 
 json 
 . 
 dumps 
 ( 
 obj 
 )

    Template parameters

    Required parameters

    • inputFilePattern: The gs:// path to the text in Cloud Storage you'd like to process. For example, gs://your-bucket/your-file.txt .
    • JSONPath: The gs:// path to the JSON file that defines your BigQuery schema, stored in Cloud Storage. For example, gs://your-bucket/your-schema.json .
    • outputTable: The location of the BigQuery table to use to store the processed data. If you reuse an existing table, it is overwritten. For example, <PROJECT_ID>:<DATASET_NAME>.<TABLE_NAME> .
    • bigQueryLoadingTemporaryDirectory: Temporary directory for BigQuery loading process. For example, gs://your-bucket/your-files/temp-dir .

    Optional parameters

    • outputDeadletterTable: Table for messages that failed to reach the output table. If a table doesn't exist, it is created during pipeline execution. If not specified, <outputTableSpec>_error_records is used. For example, <PROJECT_ID>:<DATASET_NAME>.<TABLE_NAME> .
    • useStorageWriteApiAtLeastOnce: This parameter takes effect only if Use BigQuery Storage Write API is enabled. If enabled the at-least-once semantics will be used for Storage Write API, otherwise exactly-once semantics will be used. Defaults to: false.
    • useStorageWriteApi: If true, the pipeline uses the BigQuery Storage Write API ( https://cloud.google.com/bigquery/docs/write-api ). The default value is false . For more information, see Using the Storage Write API ( https://beam.apache.org/documentation/io/built-in/google-bigquery/#storage-write-api ).
    • numStorageWriteApiStreams: When using the Storage Write API, specifies the number of write streams. If useStorageWriteApi is true and useStorageWriteApiAtLeastOnce is false , then you must set this parameter. Defaults to: 0.
    • storageWriteApiTriggeringFrequencySec: When using the Storage Write API, specifies the triggering frequency, in seconds. If useStorageWriteApi is true and useStorageWriteApiAtLeastOnce is false , then you must set this parameter.
    • pythonExternalTextTransformGcsPath: The Cloud Storage path pattern for the Python code containing your user-defined functions. For example, gs://your-bucket/your-function.py .
    • pythonExternalTextTransformFunctionName: The name of the function to call from your Python file. Use only letters, digits, and underscores. For example, 'transform' or 'transform_udf1' .

    User-defined function

    This template requires a UDF that parses the input files, as described in Pipeline requirements . The template calls the UDF for every line of text in each input file. For more information about creating UDFs, see Create user-defined functions for Dataflow templates .

    Function specification

    The UDF has the following specification:

    • Input: a single line of text from an input file.
    • Output: a JSON string that matches the schema of the BigQuery destination table.

    Run the template

    Console

    1. Go to the Dataflow Create job from template page.
      2. Go to Create job from template
    2. In the Job name field, enter a unique job name.
    3. Optional: For Regional endpoint , select a value from the drop-down menu. The default region is us-central1 .

      For a list of regions where you can run a Dataflow job, see Dataflow locations .

    4. From the Dataflow template drop-down menu, select the Cloud Storage Text to BigQuery (Stream) with Python UDF template.
    5. In the provided parameter fields, enter your parameter values.
    6. Click Run job .

    gcloud

    In your shell or terminal, run the template:

    gcloud  
dataflow  
flex-template  
run  
 JOB_NAME 
  
 \ 
  
--template-file-gcs-location  
gs://dataflow-templates- REGION_NAME 
/ VERSION 
/flex/Stream_GCS_Text_to_BigQuery_Xlang  
 \ 
  
--region  
 REGION_NAME 
  
 \ 
  
--staging-location  
 STAGING_LOCATION 
  
 \ 
  
--parameters  
 \ 
 pythonExternalTextTransformGcsPath 
 = 
 PATH_TO_PYTHON_UDF_FILE 
, \ 
 pythonExternalTextTransformFunctionName 
 = 
 PYTHON_FUNCTION 
, \ 
 JSONPath 
 = 
 PATH_TO_BIGQUERY_SCHEMA_JSON 
, \ 
 inputFilePattern 
 = 
 PATH_TO_TEXT_DATA 
, \ 
 outputTable 
 = 
 BIGQUERY_TABLE 
, \ 
 outputDeadletterTable 
 = 
 BIGQUERY_UNPROCESSED_TABLE 
, \ 
 bigQueryLoadingTemporaryDirectory 
 = 
 PATH_TO_TEMP_DIR_ON_GCS

    Replace the following:

    • JOB_NAME : a unique job name of your choice
    • REGION_NAME : the region where you want to deploy your Dataflow job—for example, us-central1
    • VERSION : the version of the template that you want to use

      You can use the following values:

    • STAGING_LOCATION : the location for staging local files (for example, gs://your-bucket/staging )
    • PYTHON_FUNCTION : The name of the Python user-defined function (UDF) that you want to use.
    • PATH_TO_BIGQUERY_SCHEMA_JSON : the Cloud Storage path to the JSON file containing the schema definition
    • PATH_TO_PYTHON_UDF_FILE : The Cloud Storage URI of the Python code file that defines the user-defined function (UDF) you want to use. For example, gs://my-bucket/my-udfs/my_file.py .
    • PATH_TO_TEXT_DATA : the Cloud Storage path to your text dataset
    • BIGQUERY_TABLE : your BigQuery table name
    • BIGQUERY_UNPROCESSED_TABLE : the name of your BigQuery table for unprocessed messages
    • PATH_TO_TEMP_DIR_ON_GCS : the Cloud Storage path to the temp directory

    API

    To run the template using the REST API, send an HTTP POST request. For more information on the API and its authorization scopes, see projects.templates.launch .

     POST 
  
 h 
 tt 
 ps 
 : 
 //dataflow.googleapis.com/v1b3/projects/ PROJECT_ID 
/locations/ LOCATION 
/flexTemplates:launch { 
  
 "launch_parameter" 
 : 
  
 { 
  
 "jobName" 
 : 
  
 " JOB_NAME 
" 
 , 
  
 "parameters" 
 : 
  
 { 
  
 "pythonExternalTextTransformFunctionName" 
 : 
  
 " PYTHON_FUNCTION 
" 
 , 
  
 "JSONPath" 
 : 
  
 " PATH_TO_BIGQUERY_SCHEMA_JSON 
" 
 , 
  
 "pythonExternalTextTransformGcsPath" 
 : 
  
 " PATH_TO_PYTHON_UDF_FILE 
" 
 , 
  
 "inputFilePattern" 
 : 
 " PATH_TO_TEXT_DATA 
" 
 , 
  
 "outputTable" 
 : 
 " BIGQUERY_TABLE 
" 
 , 
  
 "outputDeadletterTable" 
 : 
 " BIGQUERY_UNPROCESSED_TABLE 
" 
 , 
  
 "bigQueryLoadingTemporaryDirectory" 
 : 
  
 " PATH_TO_TEMP_DIR_ON_GCS 
" 
  
 }, 
  
 "containerSpecGcsPath" 
 : 
  
 "gs://dataflow-templates- LOCATION 
/ VERSION 
/flex/Stream_GCS_Text_to_BigQuery_Xlang" 
 , 
  
 } 
 }

    Replace the following:

    • PROJECT_ID : the Google Cloud project ID where you want to run the Dataflow job
    • JOB_NAME : a unique job name of your choice
    • LOCATION : the region where you want to deploy your Dataflow job—for example, us-central1
    • VERSION : the version of the template that you want to use

      You can use the following values:

    • STAGING_LOCATION : the location for staging local files (for example, gs://your-bucket/staging )
    • PYTHON_FUNCTION : The name of the Python user-defined function (UDF) that you want to use.
    • PATH_TO_BIGQUERY_SCHEMA_JSON : the Cloud Storage path to the JSON file containing the schema definition
    • PATH_TO_PYTHON_UDF_FILE : The Cloud Storage URI of the Python code file that defines the user-defined function (UDF) you want to use. For example, gs://my-bucket/my-udfs/my_file.py .
    • PATH_TO_TEXT_DATA : the Cloud Storage path to your text dataset
    • BIGQUERY_TABLE : your BigQuery table name
    • BIGQUERY_UNPROCESSED_TABLE : the name of your BigQuery table for unprocessed messages
    • PATH_TO_TEMP_DIR_ON_GCS : the Cloud Storage path to the temp directory

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