Top 20 Apache Spark Interview Questions and Answers

As a Big Data specialist, it's critical that you understand all of the words and technologies associated with the area, including Apache Spark, which is one of the most prominent and in-demand Big Data technologies. With the IT industry’s increasing need to calculate big data at high speeds, it’s no wonder that the Apache Spark mechanism has earned the industry’s trust. Apache Spark is one of the most common, general-purpose, and cluster-computing frameworks. There are many common interview questions which is related to apache sparks. Let's have a look at each of them with explanations. 

We will discuss below 20 questions which is related to apache sparks.

   1. What is Apache Spark?

   2. Explain the key features of Spark.

   3. What is MapReduce?

   4. Compare MapReduce with Spark.

   5. Explain what is SchemaRDD.

   6. What does a Spark Engine do?

   7. Define Partitions.

   8. Explain the advantage of a lazy evaluation.

   9. Does Spark also contain the storage layer?

   10. Explain the idle appraisal in Spark.

  11. Explain the lineage chart.

and more 

20 Apache Spark Questions from Interviews with Answers

So let's look at each of the questions with great explanations below:

1. What is Apache Spark?

This is one of the most frequently asked Apache Spark interview questions. Apache Spark is a data processing framework that can perform processing tasks on extensive data sets quickly. This is an open-source analytics engine that was developed by using Scala, Python, Java, and R.

2. Explain the key features of Spark.

There are many features that are related to apache spark. so we will go through them one by one in his question. 

• Apache Spark supports stream processing in real-time. 
• Spark helps in achieving a very high processing speed of data, which it achieves by reducing the read or write operations to disk. 
• Apache Spark codes can be reused for data streaming, running ad-hoc queries, batch processing, etc. 
• Spark is considered a better cost-efficient solution when compared to Hadoop. 
• Spark consists of RDDs (Resilient Distributed Datasets), which can be cached across the computing nodes in a cluster
• Apache Spark allows integrating with Hadoop.

3. What is MapReduce?

It is a programming approach and software platform for handling large datasets. MapReduce is divided into two sections: Map and Reduce. Reduce covers data shuffling and reduction, whereas Map handles data splitting and mapping.

4. Compare MapReduce with Spark.

 Map Reduce 

  1. Processing Speed is average

  2. Data Caching is in the hard disk

  3. Performing iterative jobs average.

  4. There can have a dependency on Hadoop.

Spark

  1. Processing speed is excellent.

  2. In-memory data caching is available when compared with map-reduce.

  3. Performing iterative jobs excellent.

  4. No dependency on Hadoop

So this is the comparison between the map-reduce and apache spark. let's move into another question that may ask in the interview.

5. Explain what is SchemaRDD.

In a typical relational database, a SchemaRDD is equivalent to a table. A SchemaRDD can be built from an existing RDD, Parquet file, JSON dataset, or data saved in Apache Hive using HiveQL.

6.  What does a Spark Engine do?

A Spark engine is responsible for scheduling, distributing, and monitoring the data application across the cluster. Spark Engine is used to run mappings in Hadoop clusters. It is suitable for wide-ranging circumstances. It includes SQL batch and ETL jobs in Spark, streaming data from sensors, IoT, ML, etc.

7. Define Partitions.

Partition is a logical division of records, based on the Map-reduce (split) concept, in which logical data is obtained directly to process data. Small pieces of data can also aid scalability and speed up the process. Partitioned RDDs are used to store input, output, and intermediate data.

8. Explain the advantage of a lazy evaluation.

If you generate an RDD from an existing RDD or a data source, park implements a feature that prevents the RDD from materializing until it needs to be dealt with. This is to ensure that needless memory and CPU utilization, which might arise as a result of human error, is avoided, especially in the case of Big Data Analytics. So this will expand the program's manageability and features. 

 9. Does Spark also contain the storage layer?

Spark doesn't have a storage layer, but it does let you use a variety of data sources.

10. Explain the idle appraisal in Spark.

The idle assessment also called call by use, is a compliance method that postpones compliance until a benefit is required.

11. Describe Accumulator in detail in Apache Spark

The accumulator is a shared variable in Apache Spark, used to aggregate information across the cluster. When we use a function inside the operation like map(), filter(), etc these functions can use the variables defined outside these function scope in the driver program. When we submit the task to the cluster, each task running on the cluster gets a new copy of these variables, and updates from this variable do not propagate back to the driver program

12. Explain about the different cluster managers in Apache Spark?

Apache Spark supports three alternative cluster managers:

    1. YARN

    2. Apache Mesos - Has extensive resource scheduling capabilities, making it ideal for running Spark alongside other applications. When numerous users utilize interactive shells, it is advantageous since the CPU allocation between commands is scaled down.

    3. Standalone deployments — Ideal for fresh deployments that just need to run once and are simple to set up.

13. Is it possible to run Spark and Mesos along with Hadoop?

Yes, Spark and Mesos can be used with Hadoop if they are launched as distinct services on the workstations. Mesos serves as a centralized scheduler that distributes work to Spark or Hadoop.

14. What is the significance of Sliding Window operation?

The transport of data packets between various computer networks is controlled by Sliding Window. The Spark Streaming library supports windowed computations, which apply RDD transformations to a sliding window of data.

15. What are the common mistakes developers make when running Spark applications?

Developers often make the mistake of-

1. Hitting the web service several times by using multiple clusters.

2. Run everything on the local node instead of distributing it.

Developers need to be careful with this, as Spark makes use of memory for processing.

16. What is the difference between persist() and cache()?

The user can select the storage level with to persist (), whereas cache () utilizes the default storage level.

17. How Spark handles monitoring and logging in Standalone mode?

In standalone mode, Spark offers a web-based user interface for monitoring the cluster, which displays cluster and task data. The output of each job is written to the slave nodes' work directory.

18. How can you launch Spark jobs inside Hadoop MapReduce?

Users can run any spark job within MapReduce using SIMR (Spark in MapReduce) without requiring admin permissions.

19. What do you understand by SchemaRDD?

An RDD consists of row objects (wrappers around basic string or integer arrays) with schema information about the type of data in each column.

20. Does Apache Spark provide checkpointing?
RDDs can always be recovered using lineage graphs, however, this can take a long time if the RDDs have long lineage chains. Spark provides a checkpointing API, which includes a REPLICATE flag that persists.

That's all about popular Apache Spark Interview Questions with Answers for beginners and 2 to 3 years experienced Java developers and IT professionals. So these are some common questions you might ask in the interview. Having a good understanding about the apache spark is a good way of learning. So hope to see you in the next tutorial. Until then bye.