Apache Spark is a unified analytics engine for processing large volumes of data. It can run workloads 100 times faster and offers over 80 high-level operators that make it easy to build parallel apps. Spark can run on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud, and can access data from multiple sources.

And this article covers the most important Apache Spark Interview questions that you might face in a Spark interview. The Spark interview questions have been segregated into different sections based on the various components of Apache Spark and surely after going through this article you will be able to answer most of the questions asked in your next Spark interview.

To learn more about Apache Spark interview questions, you can also watch the below video.

Apache Spark

MapReduce

Spark processes data in batches as well as in real-time

MapReduce processes data in batches only

Spark runs almost 100 times faster than Hadoop MapReduce

Hadoop MapReduce is slower when it comes to large scale data processing

Spark stores data in the RAM i.e. in-memory. So, it is easier to retrieve it

Hadoop MapReduce data is stored in HDFS and hence takes a long time to retrieve the data

Spark provides caching and in-memory data storage

Hadoop is highly disk-dependent

Apache Spark has 3 main categories that comprise its ecosystem. Those are:

This is one of the most frequently asked spark interview questions, and the interviewer will expect you to give a thorough answer to it.

Spark applications run as independent processes that are coordinated by the SparkSession object in the driver program. The resource manager or cluster manager assigns tasks to the worker nodes with one task per partition. Iterative algorithms apply operations repeatedly to the data so they can benefit from caching datasets across iterations. A task applies its unit of work to the dataset in its partition and outputs a new partition dataset. Finally, the results are sent back to the driver application or can be saved to the disk.

Resilient Distributed Datasets are the fundamental data structure of Apache Spark. It is embedded in Spark Core. RDDs are immutable, fault-tolerant, distributed collections of objects that can be operated on in parallel.RDDs are split into partitions and can be executed on different nodes of a cluster.

RDDs are created by either transformation of existing RDDs or by loading an external dataset from stable storage like HDFS or HBase.

Here is how the architecture of RDD looks like:

So far, if you have any doubts regarding the apache spark interview questions and answers, please comment below.

When Spark operates on any dataset, it remembers the instructions. When a transformation such as a map() is called on an RDD, the operation is not performed instantly. Transformations in Spark are not evaluated until you perform an action, which aids in optimizing the overall data processing workflow, known as lazy evaluation.

Also Read: What Are the Skills Needed to Learn Hadoop?

Apache Spark stores data in-memory for faster processing and building machine learning models. Machine Learning algorithms require multiple iterations and different conceptual steps to create an optimal model. Graph algorithms traverse through all the nodes and edges to generate a graph. These low latency workloads that need multiple iterations can lead to increased performance.

To trigger the clean-ups, you need to set the parameter spark.cleaner.ttlx.

There are a total of 4 steps that can help you connect Spark to Apache Mesos.

Parquet is a columnar format that is supported by several data processing systems. With the Parquet file, Spark can perform both read and write operations.

Some of the advantages of having a Parquet file are:

Shuffling is the process of redistributing data across partitions that may lead to data movement across the executors. The shuffle operation is implemented differently in Spark compared to Hadoop.

Shuffling has 2 important compression parameters:

spark.shuffle.compress checks whether the engine would compress shuffle outputs or not spark.shuffle.spill.compress decides whether to compress intermediate shuffle spill files or not

It occurs while joining two tables or while performing byKey operations such as GroupByKey or ReduceByKey

Spark uses a coalesce method to reduce the number of partitions in a DataFrame.

Suppose you want to read data from a CSV file into an RDD having four partitions.

This is how a filter operation is performed to remove all the multiple of 10 from the data.

The RDD has some empty partitions. It makes sense to reduce the number of partitions, which can be achieved by using coalesce.

This is how the resultant RDD would look like after applying to coalesce.

Consider the following cluster information:

Here is the number of core identification:

To calculate the number of executor identification:

Spark Core is the engine for parallel and distributed processing of large data sets. The various functionalities supported by Spark Core include:

There are 2 ways to convert a Spark RDD into a DataFrame:

import com.mapr.db.spark.sql._

val df = sc.loadFromMapRDB()

.where(field(first_name) === Peter)

.select(_id, first_name).toDF()

You can convert an RDD[Row] to a DataFrame by

calling createDataFrame on a SparkSession object

def createDataFrame(RDD, schema:StructType)

Resilient Distributed Dataset (RDD) is a rudimentary data structure of Spark. RDDs are the immutable Distributed collections of objects of any type. It records the data from various nodes and prevents it from significant faults.

The Resilient Distributed Dataset (RDD) in Spark supports two types of operations. These are:

The transformation function generates new RDD from the pre-existing RDDs in Spark. Whenever the transformation occurs, it generates a new RDD by taking an existing RDD as input and producing one or more RDD as output. Due to its Immutable nature, the input RDDs don't change and remain constant.

Along with this, if we apply Spark transformation, it builds RDD lineage, including all parent RDDs of the final RDDs. We can also call this RDD lineage as RDD operator graph or RDD dependency graph. RDD Transformation is the logically executed plan, which means it is a Directed Acyclic Graph (DAG) of the continuous parent RDDs of RDD.

The RDD Action works on an actual dataset by performing some specific actions. Whenever the action is triggered, the new RDD does not generate as happens in transformation. It depicts that Actions are Spark RDD operations that provide non-RDD values. The drivers and external storage systems store these non-RDD values of action. This brings all the RDDs into motion.

If appropriately defined, the action is how the data is sent from the Executor to the driver. Executors play the role of agents and the responsibility of executing a task. In comparison, the driver works as a JVM process facilitating the coordination of workers and task execution.

This is another frequently asked spark interview question. A Lineage Graph is a dependencies graph between the existing RDD and the new RDD. It means that all the dependencies between the RDD will be recorded in a graph, rather than the original data.

The need for an RDD lineage graph happens when we want to compute a new RDD or if we want to recover the lost data from the lost persisted RDD. Spark does not support data replication in memory. So, if any data is lost, it can be rebuilt using RDD lineage. It is also called an RDD operator graph or RDD dependency graph.

A Discretized Stream (DStream) is a continuous sequence of RDDs and the rudimentary abstraction in Spark Streaming. These RDDs sequences are of the same type representing a constant stream of data. Every RDD contains data from a specific interval.

The DStreams in Spark take input from many sources such as Kafka, Flume, Kinesis, or TCP sockets. It can also work as a data stream generated by converting the input stream. It facilitates developers with a high-level API and fault tolerance.

Caching also known as Persistence is an optimization technique for Spark computations. Similar to RDDs, DStreams also allow developers to persist the streams data in memory. That is, using the persist() method on a DStream will automatically persist every RDD of that DStream in memory. It helps to save interim partial results so they can be reused in subsequent stages.

The default persistence level is set to replicate the data to two nodes for fault-tolerance, and for input streams that receive data over the network.

Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used to give every node a copy of a large input dataset in an efficient manner. Spark distributes broadcast variables using efficient broadcast algorithms to reduce communication costs.

scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))

broadcastVar: org.apache.spark.broadcast.Broadcast[Array[Int]] = Broadcast(0)

scala> broadcastVar.value

res0: Array[Int] = Array(1, 2, 3)

So far, if you have any doubts regarding the spark interview questions for beginners, please ask in the comment section below.

Moving forward, let us understand the spark interview questions for experienced candidates

DataFrame can be created programmatically with three steps:

1. map(func)

2. transform(func)

3. filter(func)

4. count()

The correct answer is c) filter(func).

This is one of the most frequently asked spark interview questions where the interviewer expects a detailed answer (and not just a yes or no!). Give as detailed an answer as possible here.

Yes, Apache Spark provides an API for adding and managing checkpoints. Checkpointing is the process of making streaming applications resilient to failures. It allows you to save the data and metadata into a checkpointing directory. In case of a failure, the spark can recover this data and start from wherever it has stopped.

There are 2 types of data for which we can use checkpointing in Spark.

Metadata Checkpointing: Metadata means the data about data. It refers to saving the metadata to fault-tolerant storage like HDFS. Metadata includes configurations, DStream operations, and incomplete batches.

Data Checkpointing: Here, we save the RDD to reliable storage because its need arises in some of the stateful transformations. In this case, the upcoming RDD depends on the RDDs of previous batches.

Controlling the transmission of data packets between multiple computer networks is done by the sliding window. Spark Streaming library provides windowed computations where the transformations on RDDs are applied over a sliding window of data.

DISK_ONLY - Stores the RDD partitions only on the disk

MEMORY_ONLY_SER - Stores the RDD as serialized Java objects with a one-byte array per partition

MEMORY_ONLY - Stores the RDD as deserialized Java objects in the JVM. If the RDD is not able to fit in the memory available, some partitions wont be cached

OFF_HEAP - Works like MEMORY_ONLY_SER but stores the data in off-heap memory

MEMORY_AND_DISK - Stores RDD as deserialized Java objects in the JVM. In case the RDD is not able to fit in the memory, additional partitions are stored on the disk

MEMORY_AND_DISK_SER - Identical to MEMORY_ONLY_SER with the exception of storing partitions not able to fit in the memory to the disk

See the rest here:

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024 - Simplilearn