Bigtable to Cloud Storage Parquet template

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

Pipeline requirements

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

Template parameters

Required parameters

  • bigtableProjectId : The ID of the Google Cloud project of the Cloud Bigtable instance that you want to read data from.
  • bigtableInstanceId : The ID of the Cloud Bigtable instance that contains the table.
  • bigtableTableId : The ID of the Cloud Bigtable table to export.
  • outputDirectory : The path and filename prefix for writing output files. Must end with a slash. DateTime formatting is used to parse directory path for date & time formatters. (Example: gs://your-bucket/your-path).
  • filenamePrefix : The prefix of the Parquet file name. For example, "table1-". Defaults to: part.

Optional parameters

  • numShards : The maximum number of output shards produced when writing. A higher number of shards means higher throughput for writing to Cloud Storage, but potentially higher data aggregation cost across shards when processing output Cloud Storage files. Default value is decided by Dataflow.

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 Cloud Bigtable to Parquet Files on Cloud Storage 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/Cloud_Bigtable_to_GCS_Parquet \
    --region REGION_NAME \
    --parameters \
bigtableProjectId=BIGTABLE_PROJECT_ID,\
bigtableInstanceId=INSTANCE_ID,\
bigtableTableId=TABLE_ID,\
outputDirectory=OUTPUT_DIRECTORY,\
filenamePrefix=FILENAME_PREFIX,\
numShards=NUM_SHARDS

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
  • OUTPUT_DIRECTORY: the Cloud Storage path where data is written, for example, gs://mybucket/somefolder
  • FILENAME_PREFIX: the prefix of the Parquet filename, for example, output-
  • NUM_SHARDS: the number of Parquet files to output, for example, 1

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/Cloud_Bigtable_to_GCS_Parquet
{
   "jobName": "JOB_NAME",
   "parameters": {
       "bigtableProjectId": "BIGTABLE_PROJECT_ID",
       "bigtableInstanceId": "INSTANCE_ID",
       "bigtableTableId": "TABLE_ID",
       "outputDirectory": "OUTPUT_DIRECTORY",
       "filenamePrefix": "FILENAME_PREFIX",
       "numShards": "NUM_SHARDS"
   },
   "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
  • OUTPUT_DIRECTORY: the Cloud Storage path where data is written, for example, gs://mybucket/somefolder
  • FILENAME_PREFIX: the prefix of the Parquet filename, for example, output-
  • NUM_SHARDS: the number of Parquet files to output, for example, 1

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