Common Apache Spark Interview Questions and Answers

What is Apache Spark and how does it differ from Hadoop MapReduce?
Apache Spark is a distributed computing framework that allows for fast and efficient processing of large-scale data sets. Spark uses in-memory processing to speed up computations, while Hadoop MapReduce relies on disk-based processing. Additionally, Spark provides a more user-friendly interface and supports a wider range of programming languages.

What are the key components of Apache Spark?
The key components of Apache Spark are Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX. Spark Core is the foundation of the Spark platform and provides APIs for distributed data processing. Spark SQL allows for the execution of SQL queries on Spark data. Spark Streaming allows for real-time processing of data streams. Spark MLlib provides machine learning algorithms for data analysis. Spark GraphX allows for the processing of graph data.

What is RDD and how does it work?
RDD stands for Resilient Distributed Dataset, which is the fundamental data structure in Spark. RDDs are immutable, partitioned collections of objects that can be processed in parallel across a cluster of machines. RDDs can be created from data stored in Hadoop Distributed File System (HDFS), local file systems, or other data sources. RDDs can be transformed using operations such as map, filter, and reduce, and can also be cached in memory for faster processing.

What is lazy evaluation in Spark?
Lazy evaluation in Spark refers to the fact that transformations on RDDs are not executed immediately, but instead are recorded and executed only when an action is called. This approach allows for more efficient processing of data, as it avoids unnecessary computations.

What is the difference between transformation and action in Spark?
Transformations in Spark are operations that produce a new RDD from an existing RDD, such as map, filter, and reduceByKey. Actions, on the other hand, are operations that trigger the computation of a result, such as count, collect, and save.

What is a Spark driver and executor?
The Spark driver is the program that coordinates the execution of tasks across the Spark cluster. The driver runs the main() function and creates RDDs, which are then distributed across the cluster. Executors are worker nodes in the Spark cluster that execute tasks on the data stored in RDDs.

What is a Spark SQL?
Spark SQL is a component of Spark that allows for the execution of SQL queries on Spark data. Spark SQL provides a DataFrame API for working with structured and semi-structured data, and also allows for the creation of temporary and permanent views on data.

What is Spark Streaming and how does it work?
Spark Streaming is a component of Spark that allows for the processing of real-time data streams. Spark Streaming works by dividing the incoming data stream into small batches, which are then processed using Spark's batch processing engine. Spark Streaming provides support for various data sources, such as Kafka, Flume, and Twitter, and allows for the execution of complex stream processing algorithms.

What is the difference between Apache Spark and Apache Flink?
Apache Flink is another open-source distributed computing framework that is designed for real-time data processing. While Spark provides support for batch processing, real-time processing, and machine learning, Flink focuses specifically on real-time processing and provides more advanced support for stream processing and event-driven applications.

What are the advantages of using Apache Spark?
The advantages of using Apache Spark include its ability to process data in-memory, its support for multiple programming languages, its efficient processing of batch and real-time data, and its support for advanced analytics and machine learning algorithms. Spark is also easy to use and has a large and active community of users and contributors.