BigQuery export to Parquet (via Storage API) template

The BigQuery export to Parquet template is a batch pipeline that reads data from a BigQuery table and writes it to a Cloud Storage bucket in Parquet format. This template utilizes the BigQuery Storage API to export the data.

Pipeline requirements

  • The input BigQuery table must exist before running the pipeline.
  • The output Cloud Storage bucket must exist before running the pipeline.

Template parameters

Parameter Description
tableRef The BigQuery input table location. For example, <my-project>:<my-dataset>.<my-table>.
bucket The Cloud Storage folder in which to write the Parquet files. For example, gs://mybucket/exports.
numShards (Optional) The number of output file shards. The default value is 1.
fields (Optional) A comma-separated list of fields to select from the input BigQuery table.

Run the template

Console

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

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

  5. From the Dataflow template drop-down menu, select the BigQuery export to Parquet (via Storage API) template.
  6. In the provided parameter fields, enter your parameter values.
  7. Click Run job.

gcloud

In your shell or terminal, run the template:

gcloud dataflow flex-template run JOB_NAME \
    --project=PROJECT_ID \
    --template-file-gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/flex/BigQuery_to_Parquet \
    --region=REGION_NAME \
    --parameters \
tableRef=BIGQUERY_TABLE,\
bucket=OUTPUT_DIRECTORY,\
numShards=NUM_SHARDS,\
fields=FIELDS

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
  • VERSION: the version of the template that you want to use

    You can use the following values:

  • REGION_NAME: the regional endpoint where you want to deploy your Dataflow job—for example, us-central1
  • BIGQUERY_TABLE: your BigQuery table name
  • OUTPUT_DIRECTORY: your Cloud Storage folder for output files
  • NUM_SHARDS: the desired number of output file shards
  • FIELDS: the comma-separated list of fields to select from the input BigQuery table

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 https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/flexTemplates:launch
{
   "launch_parameter": {
      "jobName": "JOB_NAME",
      "parameters": {
          "tableRef": "BIGQUERY_TABLE",
          "bucket": "OUTPUT_DIRECTORY",
          "numShards": "NUM_SHARDS",
          "fields": "FIELDS"
      },
      "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/BigQuery_to_Parquet",
   }
}

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
  • VERSION: the version of the template that you want to use

    You can use the following values:

  • LOCATION: the regional endpoint where you want to deploy your Dataflow job—for example, us-central1
  • BIGQUERY_TABLE: your BigQuery table name
  • OUTPUT_DIRECTORY: your Cloud Storage folder for output files
  • NUM_SHARDS: the desired number of output file shards
  • FIELDS: the comma-separated list of fields to select from the input BigQuery table

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