Cloud Storage Parquet to Bigtable template

The Cloud Storage Parquet to Bigtable template is a pipeline that reads data from Parquet files in a Cloud Storage bucket and writes the data to a Bigtable table. You can use the template to copy data from Cloud Storage to Bigtable.

Pipeline requirements

  • The Bigtable table must exist and have the same column families as exported in the Parquet files.
  • The input Parquet files must exist in a Cloud Storage bucket before running the pipeline.
  • Bigtable expects a specific schema from the input Parquet files.

Template parameters

Required parameters

  • bigtableProjectId : The Google Cloud project ID associated with the Bigtable instance.
  • bigtableInstanceId : The ID of the Cloud Bigtable instance that contains the table.
  • bigtableTableId : The ID of the Bigtable table to import.
  • inputFilePattern : The Cloud Storage path with the files that contain the data. (Example: gs://your-bucket/your-files/*.parquet).

Optional parameters

  • splitLargeRows : The flag for enabling splitting of large rows into multiple MutateRows requests. Note that when a large row is split between multiple API calls, the updates to the row are not atomic. .

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 region 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 Parquet Files on Cloud Storage to Cloud Bigtable 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 jobs run JOB_NAME \
    --gcs-location gs://dataflow-templates-REGION_NAME/VERSION/GCS_Parquet_to_Cloud_Bigtable \
    --region REGION_NAME \
    --parameters \
bigtableProjectId=BIGTABLE_PROJECT_ID,\
bigtableInstanceId=INSTANCE_ID,\
bigtableTableId=TABLE_ID,\
inputFilePattern=INPUT_FILE_PATTERN

Replace the following:

  • 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 region where you want to deploy your Dataflow job—for example, us-central1
  • BIGTABLE_PROJECT_ID: the ID of the Google Cloud project of the Bigtable instance that you want to read data from
  • INSTANCE_ID: the ID of the Bigtable instance that contains the table
  • TABLE_ID: the ID of the Bigtable table to export
  • INPUT_FILE_PATTERN: the Cloud Storage path pattern where data is located, for example, gs://mybucket/somefolder/prefix*

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/templates:launch?gcsPath=gs://dataflow-templates-LOCATION/VERSION/GCS_Parquet_to_Cloud_Bigtable
{
   "jobName": "JOB_NAME",
   "parameters": {
       "bigtableProjectId": "BIGTABLE_PROJECT_ID",
       "bigtableInstanceId": "INSTANCE_ID",
       "bigtableTableId": "TABLE_ID",
       "inputFilePattern": "INPUT_FILE_PATTERN",
   },
   "environment": { "zone": "us-central1-f" }
}

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 region where you want to deploy your Dataflow job—for example, us-central1
  • BIGTABLE_PROJECT_ID: the ID of the Google Cloud project of the Bigtable instance that you want to read data from
  • INSTANCE_ID: the ID of the Bigtable instance that contains the table
  • TABLE_ID: the ID of the Bigtable table to export
  • INPUT_FILE_PATTERN: the Cloud Storage path pattern where data is located, for example, gs://mybucket/somefolder/prefix*

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