Convert Apache Beam Job from JSON Files to Elasticsearch: PyFlink Equivalent

In this article, we will discuss how to convert an Apache Beam pipeline job, which runs on Flink EMR + EKS Kubernetes cluster using the Flink Kubernetes operator, from Java to PyFlink. The discussion will cover key concepts and provide a detailed context on the topic, with subtitles and paragraphs to help you understand the content better.

What is Apache Beam?

Apache Beam is an open-source, unified programming model for both batch and streaming data processing. It provides a simple and powerful API for building parallel data processing pipelines. With Apache Beam, you can write batch and streaming data processing jobs that can run on various execution engines such as Apache Flink, Apache Spark, and Google Cloud Dataflow.

What is PyFlink?

PyFlink is a Python API for running Apache Flink programs. It allows you to write Flink programs using Python, instead of Java or Scala. With PyFlink, you can write the same Flink programs that you would write in Java or Scala, using a more concise and expressive syntax.

Why Convert Beam Pipeline Jobs from Java to PyFlink?

There are several reasons why you might want to convert your Beam pipeline jobs from Java to PyFlink:

• Ease of use: PyFlink has a more concise and expressive syntax than Java, making it easier to write Flink programs.
• Rapid development: pyFlink's interactive shell allows for rapid development and testing of Flink programs.
• Leverage existing Python libraries: With PyFlink, you can leverage existing Python libraries in your Flink programs.

Converting Beam Pipeline Jobs from Java to PyFlink

Converting Beam pipeline jobs from Java to PyFlink consists of the following steps:

1. Convert the Beam Java pipeline code to a Python pipeline code.
2. Convert the runner and options used in the Java code to PyFlink equivalents.
3. Test the PyFlink pipeline to ensure it produces the same results as the Java pipeline.

Converting Beam Pipeline Code

Converting the Beam pipeline code from Java to PyFlink is a straightforward process. The pipeline structure and transforms remain the same, but the API used for creating the pipeline and transforms change from Java to Python.

Converting the Runner and Options

When converting Beam pipeline jobs from Java to PyFlink, you need to convert the runner and options used in the Java code to PyFlink equivalents. In this case, you are running the pipeline on an EMR + EKS Kubernetes cluster using the Flink Kubernetes operator. The following code snippet shows how you can convert the runner and options in a Java Beam pipeline to PyFlink:

import apache_beam as beam

# Java code
PipelineOptions options = PipelineOptions.fromArgs(argv)
        .setRunner(FlinkRunner.class);
Pipeline pipeline = Pipeline.create(options);

# PyFlink code
from pyflink.beam import BeamExecutionEnvironment

options = BeamExecutionEnvironment.get_options()
options.set_runner('FlinkKubernetesRunner')
options.set_configuration_property('kubernetes.cluster.name', 'flink-cluster')
options.set_configuration_property('kubernetes.namespace', 'flink')
options.set_configuration_property('kubernetes.job.name', 'job-name')
options.set_configuration_property('kubernetes.job.id', 'job-id')
options.set_configuration_property('kubernetes.job.entrypoint', 'python ./pipeline.py')
options.set_configuration_property('kubernetes.job.pod.template.image', 'flink:1.13.2')
options.set_configuration_property('flink.jobmanager.memory.process.size', '4096m')
options.set_configuration_property('flink.taskmanager.memory.process.size', '8192m')

env = BeamExecutionEnvironment.create(options)
\ 

Testing the PyFlink Pipeline

To test the PyFlink pipeline, you can use the following code snippet:

p = beam.Pipeline(env)

# Define the pipeline here

result = p.run()

# Use a checkpoint to verify that the results match between
# the Java and PyFlink pipelines.
result.wait_until_finish()
\ 

Converting Beam pipeline jobs from Java to PyFlink can provide several benefits, such as ease of use, rapid development, and the ability to leverage existing Python libraries. With careful consideration given to converting the pipeline code, runner, and options, you can ensure that the PyFlink pipeline produces the same results as the Java pipeline.

References

• Apache Beam website
• PyFlink documentation
• Flink Kubernetes