Top Apache Beam Interview Questions with Answers

1. What is Apache Beam?

Answer:- Apache Beam is an open-source, unified programming model for both batch and streaming data processing pipelines. It provides a high-level abstraction for describing data processing workflows and can be used to process data on a variety of execution engines, including Apache Flink, Apache Spark, and Google Cloud Dataflow.

2. What are the main features of Apache Beam?

Answer:- The main features of Apache Beam include:

• A unified programming model for batch and streaming data processing
• Support for a variety of execution engines
• Portability: code can be written once and run on any supported execution engine
• Scalability: Pipelines can be scaled to process large amounts of data
• Flexibility: Pipelines can be customized to meet specific needs
• Extensibility: New features and capabilities can be added easily

3. What are the different types of PCollections in Apache Beam?

Answer:- There are two types of PCollections in Apache Beam: bounded and unbounded.

Bounded PCollections are finite datasets that have a known upper bound on their size. For example, a PCollection of all the orders placed in a month is a bounded PCollection.
Unbounded PCollections are infinite datasets that do not have a known upper bound on their size. For example, a PCollection of all the tweets that are posted is an unbounded PCollection.

4. What are PTransforms in Apache Beam?

Answer:- PTransforms are the basic building blocks of Apache Beam pipelines. They represent operations that can be performed on PCollections. For example, a PTransform can be used to filter a PCollection, sort a PCollection, or join two PCollections.

5. What are the different types of PTransforms in Apache Beam?

Answer:- There are three types of PTransforms in Apache Beam:

• FlatMap: This type of PTransform takes a PCollection as input and produces a new PCollection with the same or a different schema.
• Map: This type of PTransform takes a PCollection as input and produces a new PCollection with the same schema.
• ParDo: This type of PTransform takes a PCollection as input and applies a user-defined function to each element of the PCollection.

6. What are the different runner types in Apache Beam?

Answer:- The runner type determines how a Beam pipeline is executed. The following are the supported runner types:

• DirectRunner: This local runner executes the pipeline on the same machine where it is written.
• Apache Flink Runner: This runner executes the pipeline on Apache Flink.
• Apache Spark Runner: This runner executes the pipeline on Apache Spark.
• Google Cloud Dataflow Runner: This runner executes the pipeline on the Google Cloud Platform.

7. What are the different windowing strategies in Apache Beam?

Answer:- Windowing strategies are used to group elements of a PCollection together for processing. The following are the supported windowing strategies:

• Fixed windows: Elements are grouped together into windows of a fixed size.
• Session windows: Elements are grouped together into windows based on their timestamps.
• Sliding windows: Elements are grouped together into windows that slide over the data stream.

8. What are watermarks in Apache Beam?

Answer:- Watermarks are used to indicate the end of a window. Elements with timestamps that are older than the watermark are considered to be out of order and are not processed.

9. What are the advantages of using Apache Beam?

Answer:- The advantages of using Apache Beam include:

• Portability: Code can be written once and run on any supported execution engine.
• Scalability: Pipelines can be scaled to process large amounts of data.
• Flexibility: Pipelines can be customized to meet specific needs.
• Extensibility: New features and capabilities can be added easily.

10. What are the disadvantages of using Apache Beam?

Answer:- The disadvantages of using Apache Beam include:

• It can be complex to learn and use.
• There is a steep learning curve.
• There are not as many resources available as for other data processing frameworks.

11. Explain the concept of a DoFn in Apache Beam.

Answer:- A DoFn (pronounced “do function”) is a user-defined function in Apache Beam that performs processing within transforms. It takes one or more input elements and produces zero or more output elements. DoFns are used to encapsulate custom processing logic.

12. What is the purpose of the GroupByKey transform in Apache Beam?

Answer:- The GroupByKey transform is used to group elements in a PCollection by a key, which is often used in combination with windowing. It allows you to aggregate or perform other operations on elements with the same key.

13. What is the difference between a batch pipeline and a streaming pipeline in Apache Beam?

Answer:- A batch pipeline processes data in finite, bounded datasets, while a streaming pipeline handles continuously arriving data unboundedly. Streaming channels are designed to handle data as it arrives, whereas batch pipelines process data that is already collected.

14. Explain how Apache Beam handles late data in streaming pipelines.

Answer:- Apache Beam provides mechanisms to handle late-arriving data in streaming pipelines, such as triggering window firings based on watermark progress and handling late data within allowed lateness bounds. This ensures that late data is correctly incorporated into processing results.

15. What is the purpose of the Apache Beam SDK Harness?

Answer:- The Apache Beam SDK Harness is responsible for managing and orchestrating the execution of pipeline stages on worker nodes. It handles tasks such as data serialization, task distribution, and coordination between pipeline components.

16. What is the role of a coder in Apache Beam?

Answer:- A coder in Apache Beam is responsible for serializing and deserializing data when it is passed between pipeline components. It defines how data is encoded and decoded during processing.

17. Explain how Apache Beam ensures fault tolerance in data processing.

Answer:- Apache Beam relies on the underlying processing engine (e.g., Spark, Flink) for fault tolerance. These engines provide mechanisms for checkpointing, state management, and fault recovery to ensure that data processing can resume after failures.

18. What are the different windowing strategies available in Apache Beam?

Answer:- Apache Beam supports several windowing strategies, including fixed windows, sliding windows, sessions, and global windows. These windowing strategies determine how data is grouped and processed over time.

19. What is the purpose of the ParDo transform in Apache Beam?

Answer:- The ParDo transform is a general-purpose transform in Apache Beam used to apply a user-defined function (DoFn) to elements in a PCollection. It is used for custom processing and transformations.

20. Explain how to handle late-arriving data in a windowed streaming pipeline.

Answer:- To handle late-arriving data in a windowed streaming pipeline, you can use the concept of allowed lateness. You specify a duration within which late data can be included in window computations. Late data is assigned to the appropriate window if it falls within the allowed lateness period.

21. What is DocumentRoot?

Answer:- DocumentRoot directive is the configuration where you can specify the folder location from where the static files will be served. It’s also called WebRoot.

22. What is a Directed Acyclic Graph in Spark?

Answer:- Directed Acyclic Graph or DAG is an arrangement of edges and vertices. As the name implies the graph is not cyclic. In this graph, the vertices represent RDDs, and the edges represent the operations applied to RDDs. This graph is unidirectional, which means it has only one flow. DAG is a scheduling layer that implements stage-oriented scheduling and converts a plan for logical execution to a physical execution plan.

23. What are deploy modes in Apache Spark?

Answer:- There are only two deploy modes in Apache Spark, client mode and cluster mode. The behavior of Apache Spark jobs depends on the driver component. If the driver component of Apache Spark will run on the machine from which the job is submitted, then it is the client mode. If the driver component of Apache Spark will run on Spark clusters and not on the local machine from which the job is submitted, then it is the cluster mode.

24. Roles of receivers in Apache Spark Streaming?

Answer:- Within Apache Spark Streaming Receivers are particular objects whose only goal is to consume data from different data sources and then move it to Spark. You can create receiver objects by streaming contexts as long-running tasks on various executors. There are two types of receivers. They are Reliable receivers: This receiver acknowledges data sources when data is received and replicated successfully in Apache Spark Storage. Unreliable receiver: These receivers do not acknowledge data sources even when they receive or replicate in Apache Spark Storage.

25. What is YARN?

Answer:- Similar to Hadoop, YARN is one of the key features in Spark, providing a central and resource management platform to deliver scalable operations across the cluster. Running Spark on YARN needs a binary distribution of Spark that is built on YARN support.

26. List the functions of Spark SQL.

Answer:- Spark SQL is capable of:

• Loading data from a variety of structured sources
• Querying data using SQL statements, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC), e.g., using Business Intelligence tools like Tableau
• Providing rich integration between SQL and the regular Python/Java/Scala code, including the ability to join RDDs and SQL tables, expose custom functions in SQL, and more.

27. What are the benefits of Spark over MapReduce?

Answer:- Due to the availability of in-memory processing, Spark implements data processing 10–100x faster than Hadoop MapReduce. MapReduce, on the other hand, makes use of persistence storage for any of the data processing tasks.
Unlike Hadoop, Spark provides in-built libraries to perform multiple tasks using batch processing, steaming, Machine Learning, and interactive SQL queries. However, Hadoop only supports batch processing.
Hadoop is highly disk-dependent, whereas Spark promotes caching and in-memory data storage.
Spark is capable of performing computations multiple times on the same dataset, which is called iterative computation. Whereas, there is no iterative computing implemented by Hadoop.

28. Is there any benefit of learning MapReduce?

Answer:- Yes, MapReduce is a paradigm used by many Big Data tools, including Apache Spark. It becomes extremely relevant to use MapReduce when data grows bigger and bigger. Most tools like Pig and Hive convert their queries into MapReduce phases to optimize them better.

You can always integrate Apache HTTP with Tomcat, however, based on the requirement you need to choose either one. If you need a proper web server, then Apache HTTP else Tomcat for JSP-Servlet Container.

29. What is the difference between Apache and Nginx web servers?

Answer:- Both are categorized as Web Server and here are some of the main differences.

• Nginx is an event-based web server whereas Apache is a process-based
• Nginx is known for better performance than Apache
• Apache supports a wide range of OS where Nginx doesn’t support OpenVMS and IBMi
• Apache has a large number of modules integration with backend application server where Nginx is still catching up
• Nginx is lightweight and capturing the market share rapidly. If you are new to Nginx, then you may be interested in checking out my articles on Nginx.

30. Name the types of Cluster Managers in Spark.

Answer:- The Spark framework supports three major types of Cluster Managers.

• Standalone: A basic Cluster Manager to set up a cluster
• Apache Mesos: A generalized/commonly-used Cluster Manager, running Hadoop MapReduce and other applications
• YARN: A Cluster Manager responsible for resource management in Hadoop