Most frequently asked Apache Beam Interview Questions

What is Apache Beam?
What are the Concepts used by Apache Beam?
What is the process of Apache Beam?
Explain the implementation of Apache Beam?
Explain Machine Learning with Apache Beam?
How can we combine streaming data with large history data set in Apache Beam?
What is the Functional Model in Apache Beam?
How can we import Apache Beam metaclass conflict?
What is Data Flow in Apache Beam?
How do we write to multiple files in Apache Beam?

What is Apache Beam?

Apache Beam is an unified programming model used in defining and executing data processing pipeline.These Pipelines consists ETL, batches and stream processing.Beam is an open source based by Apache Software Foundation.Apache Beam is used in simplifying the mechanics in large scale data processing, by using Apache Beam SDKs, we can build program which defines pipeline.

What are the Concepts used by Apache Beam?

Concepts used by Apache Beam is as follows:

PCollection is used in representing data sets that can be fixed batch or stream of data.
PTransform used in taking one or more PCollections and also helps in the output of PCollection.
Pipeline used in representing a grapg of PCollection and PTransform for encapsulating the data processing job.
PipelineRunner helps in executing pipeline in distributing process backend.

What is the process of Apache Beam?

Apache Beam is used in transforming PCollection as input and output in each step of the Pipeline.It also can hold the dataset of a fixed size from updating the data source.

Explain the implementation of Apache Beam?

Apache Beam is used in utilizing MapReduce programming paradigm.

PipelineOptions = PipelineFactory.create();
Pipeline p = pipeline.create(abcd);

Explain Machine Learning with Apache Beam?

Apache Beam is used in the preprocessing phase and the prediction phase,it also use TensorFlow Transform Library.

How can we combine streaming data with large history data set in Apache Beam?

We can combine streaming data with large history from the side input by specifying the users ID.

public static class MetricFn extends DoFn<login, String> {

    final PCollectionView<Map<String, Iterable<LogLine>>> pHistoryView;

    public MetricFn(PCollectionView<Map<String, Iterable<LogLine>>> historyView) {
        this.pHistoryView = historyView;
    }

    @Override
    public void Process(ProcessContext processContext) throws Exception {
        Map<String, Iterable<LogLine>> historyLogData = processContext.sideInput(pHistoryView);

        final LogLine currentLogLine = processContext.element();
        final Iterable<LogLine> userHistory = historyLogData.get(currentLogLine.getUserId());
        final String outputMetric = calculateMetricWithUserHistory(currentLogLine, userHistory);
        processContext.output(outputMetric);
    }
}

What is the Functional Model in Apache Beam?

Functional Model is used in concentrating on the dynamic processes.

How can we import Apache Beam metaclass conflict?

We can create a new virtual environment by installing GCP with pipeline.

>>> import apache beam
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/toor/pfff/local/lib/python2.7/site-packages/apache_beam/__init__.py", line 78, in <module>
    from apache_beam import io
  File "/home/toor/pfff/local/lib/python2.7/site-packages/apache_beam/io/__init__.py", line 21, in <module>
    ...
    from apitools.base.protorpclite import messages
  File "/home/toor/pfff/local/lib/python2.7/site-packages/apitools/base/protorpclite/messages.py", line 1165, in <module>
    class Field(six.with_metaclass(_FieldMeta, object)):
TypeError: Error when calling  metaclass bases
    metaclass conflict

What is Data Flow in Apache Beam?

Data Flow is the representation the flow and exchange of information in a system.It is also used in representing the information by describing the process in transferring data from file storage and reports generation.

How do we write to multiple files in Apache Beam?

We can write multiple files by connecting cloud storage and also by running high mem instances.

PCollection<KV<String, List<String>>> readyToWrite = groupedByFirstLetter
                .apply(Combine.perKey(AccumulatorOfWords.getCombineFn()));
        readyToWrite.apply(
                new PTransformWriteToGCS("dataflow-experiment", TonyWordGrouper::derivePath));