Here are 30 Apache Flink interview questions along with their answers:

1. What is Apache Flink?

Ans: Apache Flink is an open-source stream processing and batch processing framework designed for big data processing and analytics. It provides fault tolerance, high throughput, and low-latency processing of large-scale data streams.

2. What are the key features of Apache Flink?

• Stream and batch processing capabilities
• Event time and processing time semantics
• Support for exactly-once processing guarantees
• Stateful processing with fault tolerance
• Support for various data sources and sinks
• Extensive windowing operations
• Dynamic scaling of resources

3. How does Apache Flink handle fault tolerance?

Ans: Apache Flink achieves fault tolerance through checkpointing, which periodically takes consistent snapshots of the application’s state. In case of a failure, the system can recover the state from the latest successful checkpoint and resume processing from there.

4. What is event time processing in Apache Flink?

Ans: Event time processing in Apache Flink refers to the ability to process events based on the time they occurred in the real world. It allows for handling out-of-order events and provides accurate results even when events arrive late or early.

5. Explain the concept of the watermark in Apache Flink.

Ans: Watermarks are used in Apache Flink to track the progress of event time. They represent a threshold for event times and indicate that no events with timestamps earlier than the watermark should arrive any longer. They help define when window computations should be considered complete.

6. What is the difference between Apache Flink and Apache Kafka?

Ans: Apache Flink is a stream processing and batch processing framework, whereas Apache Kafka is a distributed streaming platform. Apache Kafka provides scalable and fault-tolerant publish-subscribe messaging, while Apache Flink provides processing capabilities on the received data streams.

7. How does Apache Flink achieve exactly-once processing semantics?

Ans: Apache Flink achieves exactly-once processing semantics by combining the use of checkpoints for fault tolerance and an atomic commit protocol for sinks. Checkpoints ensure that the state is consistently saved and recovered, while the atomic commit protocol guarantees that sink operations are either fully committed or fully rolled back.

8. What is the role of the Flink Job Manager?

Ans: The Flink Job Manager is responsible for coordinating the distributed execution of Flink applications. It handles job submission, scheduling, and resource management. It also provides fault tolerance by supervising checkpoints and recovery.

9. How does Apache Flink handle state management?

Ans: Apache Flink supports managed states through various types of state backend implementations. It allows for local state storage, distributed storage (such as Apache Hadoop HDFS or Apache Cassandra), or even off-heap storage. The state backend handles state serialization, checkpointing, and recovery.

10. What is a Flink Task Manager?

Ans: A Flink Task Manager is responsible for executing tasks assigned by the Job Manager. It manages the parallel execution of tasks by coordinating the data exchange and processing between different operators.

11. How does Flink handle windowing in stream processing?

Ans: Flink supports various windowing operations, such as tumbling windows, sliding windows, and session windows. Windowing in Flink allows you to group and process events based on time or count constraints. It provides flexibility in defining window sizes and slide intervals.

12. What are Flink’s deployment modes?

Ans: Flink supports different deployment modes, including local mode (for development and testing on a single machine), standalone mode (for running a Flink cluster on multiple machines), and YARN or Mesos mode (for running Flink on a cluster managed by YARN or Mesos).

13. How does Spark store the data?

Ans: Spark is a processing engine, there is no storage engine. It can retrieve data from any storage engine like HDFS, S3, and other data resources.

14. Is it mandatory to start Hadoop to run the Spark application?

Ans: No not mandatory, but there is no separate storage in Spark, so it uses a local file system to store the data. You can load data from the local system and process it, Hadoop or HDFS is not mandatory to run the spark application.

15. What is SparkContext?

Ans: When a programmer creates an RDDs, SparkContext connects to the Spark cluster to create a new SparkContext object. SparkContext tells Spark how to access the cluster. SparkConf is a key factor to create a programming application.

16. What are SparkCore functionalities?

Ans: SparkCore is a base engine of the Apache spark framework. Memory management, fault tolerance, scheduling, and monitoring jobs, interacting with store systems are primary functionalities of Spark.

17. How SparkSQL is different from HQL and SQL?

Ans: SparkSQL is a special component on the spark core engine that supports SQL and HiveQueryLanguage without changing any syntax. It’s possible to join the SQL table and HQL table.

18. When did we use Spark Streaming?

Ans: Spark Streaming is the real-time processing of streaming data API. Spark streaming gathers streaming data from different resources like web server log files, social media data, stock market data, or Hadoop ecosystems like Flume, and Kafka.

19. How does Spark Streaming API work?

Ans: The programmer sets a specific time in the configuration, within this time how much data gets into the Spark, and that data is separate as a batch. The input stream (DStream) goes into spark streaming. The framework breaks up into small chunks called batches, then feeds into the spark engine for processing.

Spark Streaming API passes those batches to the core engine. The core engine can generate the final results in the form of streaming batches. The output is also in the form of batches. It can allow streaming data and batch data for processing.

20. What is Spark MLlib?

Ans: Mahout is a machine learning library for Hadoop, similarly, MLlib is a Spark library. MetLib provides different algorithms, that algorithms scale out on the cluster for data processing. Most data scientists use this MLlib library.

21. What file systems does Apache Spark support?

Ans: Apache Spark is a powerful distributed data processing engine that processes data coming from multiple data sources. The file systems that Apache Spark supports are:

• Hadoop Distributed File System (HDFS)
• Local file system
• Amazon S3
• HBase
• Cassandra, etc.

22. What is a Directed Acyclic Graph in Spark?

Ans: 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?

Ans: 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?

Ans: Within Apache Spark Streaming Receivers are special 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?

Ans: 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.

Ans: 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?

Ans:

• 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.
• For more insights, read on Spark vs MapReduce!

28. Is there any benefit of learning MapReduce?

Ans: 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.

29. What is a Spark Executor?

Ans: When SparkContext connects to Cluster Manager, it acquires an executor on the nodes in the cluster. Executors are Spark processes that run computations and store data on worker nodes. The final tasks by SparkContext are transferred to executors for their execution.

30. Name the types of Cluster Managers in Spark.

Ans: 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