Streaming Pipeline in Dataflow to Bigtable Python

Question

I wanted to read the pubsub topic and write data to BigTable with the dataflow code written in Python. I could find the sample code in JAVA but not in Python. How can we assign columns in a row from pubsub to different column families and write the data to Bigtable?

Answer 1

To write to Bigtable in a Dataflow pipeline, you'll need to create direct rows and pass them to the WriteToBigTable doFn. Here is a brief example that just passes in the row keys and adds one cell for each key nothing too fancy:

import datetime
import apache_beam as beam

from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io.gcp.bigtableio import WriteToBigTable
from google.cloud.bigtable import row


class MyOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):
        parser.add_argument(
            '--bigtable-project',
            help='The Bigtable project ID, this can be different than your '
                 'Dataflow project',
            default='bigtable-project')
        parser.add_argument(
            '--bigtable-instance',
            help='The Bigtable instance ID',
            default='bigtable-instance')
        parser.add_argument(
            '--bigtable-table',
            help='The Bigtable table ID in the instance.',
            default='bigtable-table')


class CreateRowFn(beam.DoFn):
    def process(self, key):
        direct_row = row.DirectRow(row_key=key)
        direct_row.set_cell(
            "stats_summary",
            b"os_build",
            b"android",
            datetime.datetime.now())
        return [direct_row]


def run(argv=None):
    """Build and run the pipeline."""
    options = MyOptions(argv)
    with beam.Pipeline(options=options) as p:
        p | beam.Create(["phone#4c410523#20190501",
                         "phone#4c410523#20190502"]) | beam.ParDo(
            CreateRowFn()) | WriteToBigTable(
            project_id=options.bigtable_project,
            instance_id=options.bigtable_instance,
            table_id=options.bigtable_table)


if __name__ == '__main__':
    run()

I am just starting to explore this now and can link to a more polished version on GitHub once it's complete. Hope this helps you get started.

Answer 2

Building on top of what was proposed and adding PubSub, here’s a working version..

Pre requisites

• GCS Bucket created (for Dataflow temp/staging files)
• PubSub topic created
• PubSub subscription created
• BigTable instance created
• BigTable table created
• BigTable column family must be created (no visible error otherwise !)

Example of the latter with cbt:

cbt -instance test-instance createfamily test-table cf1

Code

Define and run the Dataflow pipeline.

# Packages

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io.gcp.bigtableio import WriteToBigTable
from google.cloud import pubsub_v1

# Classes
 
class CreateRowFn(beam.DoFn):

    def __init__(self, pipeline_options):
        
        self.instance_id = pipeline_options.bigtable_instance
        self.table_id = pipeline_options.bigtable_table
  
    def process(self, key):
        
        from google.cloud.bigtable import row
        import datetime

        direct_row = row.DirectRow(row_key=key)
        direct_row.set_cell(
            'cf1',
            'field1',
            'value1',
            timestamp=datetime.datetime.now())
        
        yield direct_row

# Options

class XyzOptions(PipelineOptions):
    
    @classmethod
    def _add_argparse_args(cls, parser):
        parser.add_argument('--bigtable_project', default='nested'),
        parser.add_argument('--bigtable_instance', default='instance'),
        parser.add_argument('--bigtable_table', default='table')

pipeline_options = XyzOptions(
    save_main_session=True, streaming=True,
    runner='DataflowRunner',
    project=PROJECT,
    region=REGION,
    temp_location=TEMP_LOCATION,
    staging_location=STAGING_LOCATION,
    requirements_file=REQUIREMENTS_FILE,
    bigtable_project=PROJECT,
    bigtable_instance=INSTANCE,
    bigtable_table=TABLE)

# Pipeline

def run (argv=None):
    
    with beam.Pipeline(options=pipeline_options) as p:
       
       input_subscription=f"projects/{PROJECT}/subscriptions/{SUBSCRIPTION}"

        _ = (p
                | 'Read from Pub/Sub' >> beam.io.ReadFromPubSub(subscription=input_subscription).with_output_types(bytes)
                | 'Conversion UTF-8 bytes to string' >> beam.Map(lambda msg: msg.decode('utf-8'))
                | 'Conversion string to row object' >> beam.ParDo(CreateRowFn(pipeline_options)) 
                | 'Writing row object to BigTable' >> WriteToBigTable(project_id=pipeline_options.bigtable_project,
                                  instance_id=pipeline_options.bigtable_instance,
                                  table_id=pipeline_options.bigtable_table))

if __name__ == '__main__':
    run()

Publish a message b"phone#1111" to PubSub topic (e.g. using the Python PublisherClient()).

Table content (using happybase)

b'phone#1111': {b'cf1:field1': b'value1'}
Row length: 1