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dataflow-connector-examples

README.md

Cloud Bigtable / Cloud Dataflow Connector examples

A starter set of examples for writing Google Cloud Dataflow programs using Cloud Bigtable.

Project setup

Provision your project for Cloud Dataflow

  • Follow the Cloud Dataflow getting started instructions. (if required) Including:
    • Create a project
    • Enable Billing
    • Enable APIs
    • Create a Google Cloud Storage Bucket
    • Development Environment Setup
      • Install Google Cloud SDK
      • Install Java
      • Install Maven
    • You may wish to also Run an Example Pipeline

Provision a Bigtable Instance

  • Create a Cloud Bigtable cluster using the Developer Console by clicking on the Storage > Cloud Bigtable > New Instance button. After that, enter the Instance name, ID, zone, and number of nodes. Once you have entered those values, click the Create button.

Create a Google Cloud Storage Bucket

  • Using the Developer Console click on Storage > Cloud Storage > Browser then click on the Create Bucket button. You will need a globally unique name for your bucket, such as your projectID.

Create a Pub/Sub topic

This step is required for the Pub / Sub sample.

  • Using the Developer Console click on Bigdata > Pub/Sub, then click on the New topic button. 'shakes' is a good topic name.

Create a Bigtable Table

Note - you may wish to keep the HBase shell open in a tab throughout.

Required Options for Cloud Bigtable

This pipeline needs to be configured with four command line options for Cloud Bigtable:

  • -Dbigtable.projectID=<projectID> - this will also be used for your Dataflow projectID
  • -Dbigtable.instanceID=<instanceID>
  • -Dgs=gs://my_bucket - A Google Cloud Storage bucket.

Optional Arguments

  • -Dbigtable.table=<Table to Read / Write> defaults to 'Dataflow_test'

HelloWorld - Writing Data

The HelloWorld examples take two strings, converts them to their upper-case representation and writes them to Bigtable.

HelloWorldWrite does a few Puts to show the basics of writing to Cloud Bigtable through Cloud Dataflow.

mvn package exec:exec \
    -DHelloWorldWrite \
    -Dbigtable.projectID=<projectID> \
    -Dbigtable.instanceID=<instanceID> \
    -Dgs=<Your bucket>

You can verify that the data was written by using HBase shell and typing scan 'Dataflow_test'. You can also remove the data, if you wish, using:

deleteall 'Dataflow_test', 'Hello'
deleteall 'Dataflow_test', 'World'

SourceRowCount - Reading from Cloud Bigtable

SourceRowCount shows the use of a Bigtable Source - a construct that knows how to scan a Bigtable Table. SourceRowCount performs a simple row count using the Cloud Bigtable Source and writes the count to a file in Google Storage.

mvn package exec:exec \
    -DSourceRowCount \
    -Dbigtable.projectID=<projectID> \
    -Dbigtable.instanceID=<instanceID> \
    -Dgs=<Your bucket>

You can verify the results by first typing:

gsutil ls gs://my_bucket/**

There should be a file that looks like count-XXXXXX-of-YYYYYY. Type:

gsutil cp gs://my_bucket/count-XXXXXX-of-YYYYYY .
cat count-XXXXXX-of-YYYYYY

CsvImport - Reading data from GCS and then Writing Data

Use the Hbase shell to add a column family to your table called 'csv' for this example

`alter 'Dataflow_test',  'csv'`

Required Options for CSV import

This pipeline needs to be configured with two additional command line options:

  • -Dheaders="id,header1,header2" - Comma separated list of headers
  • -DinputFile="gs://my_bucket/my_csv_file" - A Google Cloud Storage object.

The examples take a CSV file in a GCS bucket and writes each row to Bigtable.

mvn package exec:exec \
    -DCsvImport \
    -Dbigtable.projectID=<projectID> \
    -Dbigtable.instanceID=<instanceID> \
    -DinputFile="<Your file>" \
    -Dheaders="<Your headers>"

You can verify that the data was written by using HBase shell and typing scan 'Dataflow_test'. You can also delete the table, if you wish, using:

disable 'Dataflow_test'
drop 'Dataflow_test'

BigQueryBigtableTransfer - Copying records from BigQuery to Cloud Bigtable

BigQueryBigtableTransfer shows the use of BigQuery as a source, and writes the records into Bigtable. To make this sample generic, UUID is generated as the item key for each record. This has to be designed before putting into actual use.

mvn package exec:exec \
    -DBigQueryBigtableTransfer \
    -Dbigtable.projectID=<projectID> \
    -Dbigtable.instanceID=<instanceID> \
    -Dgs=<Your bucket> \
    -Dbq.query='<BigQuery SQL (Standard SQL)>'

You can verify the results by looking into BigTable:

gsutil ls gs://my_bucket/**