Preparing for an Apache Spark interview can either be very straightforward or difficult. If done right, you can pass the interview with no issues; however, it is necessary to understand how an Apache Spark interview works. So, this article focuses on providing common Apache Spark interview questions along with short sample answers to help you figure out the right way to answer.
There are surely going to be two types of questions that will be asked during your Apache Spark interview. Behavioral questions focus on bringing out your true self as a person and technical ones focus on understanding the amount of knowledge you have in regards to the job. Both types of questions will be used to confirm whether you are the right person for the job or not.
Apache Spark interview questions with sample answers
What are the implications of Spark’s Resilient Distributed Datasets?
Apache Spark’s core data structure is Resilient Distributed Datasets. It’s a part of Spark Core. RDDs are distributed collections of items that are unchangeable, fault-tolerant, and maybe worked on in parallel. RDDs are divided into divisions and can be run on various cluster nodes.
RDDs are produced by transforming existing RDDs or importing a dataset from a stable storage system such as HDFS or HBase.
In Spark, what is a lazy evaluation?
Spark remembers the instructions it receives while working with a dataset. When you call a transformation on an RDD, such as map(), the action does not happen immediately. The lazy evaluation mainly focuses on optimizing the whole data, and thus, it is only evaluated after the action is executed.
What makes Spark particularly well suited to low-latency tasks such as graph processing and machine learning?
For quicker processing and the creation of machine learning models, Apache Spark keeps data in memory. To develop an ideal model, machine learning algorithms require numerous iterations and distinct conceptual phases. Graph algorithms are required to go over the edges and nodes to build a graph. To build a graph, graph algorithms run over all of the nodes and edges. Low-latency workloads that need several iterations can result in improved performance.
What is a Parquet file and what are the benefits of using one?
The columnar format Parquet is supported by several data processing platforms. Spark has algorithms that can read and even write data from any Parquet file.
Given below are the main benefits of having a Parquet file:
- It allows you to access certain columns by retrieving them.
- It takes up less space.
- It adheres to the type-specific encoding method.
- It can handle a limited number of I/O operations.
What is a Lineage Graph, and how does it work?
A dependency graph between the old RDD and the new RDD is called a lineage graph. It implies that instead of the original data, all of the RDD’s dependencies will be stored in a graph.
When we need to construct a new RDD or recover data from a lost persistent RDD, we require an RDD lineage graph. Data replication in memory is not supported by Spark. As a result, if any data is lost, RDD lineage may be used to reconstruct it.
Explain Spark Streaming caching.
Caching, often known as persistence, is a Spark computation efficiency method. DStreams, like RDDs, allows developers to save data from streams in memory. That is, using the persist() function on DStream causes every RDD in that DStream to be saved in memory. It is beneficial to store intermediate partial findings in order to utilize them in later phases.
The persistence level is set to duplicate all data into two nodes to increase the fault tolerance, so input streams can easily receive data.
What role do broadcast variables have in Spark?
Programmers are accessible to broadcast variables so they can keep a copy of a read-only variable on each computer rather than supplying them separately. They may be used to efficiently distribute copies of a big input dataset to each node. Spark reduces communication costs by distributing broadcast variables using efficient broadcast algorithms.
Is there a checkpoint feature in Apache Spark?
This is another common Spark interview question so, you need to elaborate on your answer. Don’t simply say yes or no while replying; provide as much information as you know.
Yes, there is an API for adding and maintaining checkpoints in Apache Spark. The practice of checkpointing makes streaming applications more robust to errors. You can store the data and metadata in a checkpointing directory. In the event of a failure, the spark may retrieve this data and resume where it left off.
In Spark, there are two categories of data for which checkpointing may be used.
Metadata Checkpointing: Metadata refers to information about information. This refers to metadata that is stored in fault-tolerant systems. Configurations, DStream actions, and unfinished batches are all examples of metadata.
Data Checkpointing: We store the RDD to reliable storage since some of the stateful transformations require it. In this scenario, the RDD for the next batch is determined by the RDDs from prior batches.
What exactly do you mean when you say “sliding window operation”?
The flow of data packets through and from computer networks is controlled using the sliding window. The Spark Streaming library supports windowed computations, which apply RDD transformations to a sliding window of data.
What role do accumulators play in Spark?
Accumulators are variables that are used to aggregate data among executors. API diagnostic holds the number of damaged records along with the number of times a library API was used.
What are the many sorts of operators that the Apache GraphX library offers?
Explain these kinds of interview questions; don’t simply name the operators without telling the interviewer how they function.
Property Operators: They generate a new graph using the user-defined map function by altering vertices or edge properties.
Structural Operator: Alter structures of input graphs to create new ones.
Operators to Join: Operators to Join Data may be added to graphs, and new graphs can be created.
What analytic methods does Apache Spark GraphX provide?
GraphX is the API for graphs and graph-parallel computing in Apache Spark. To make analytics jobs easier, GraphX offers a collection of graph algorithms. The algorithms are in the org.apache.spark.graphx.lib package and may be accessed directly as GraphOps methods.
PageRank uses parallel algorithms as part of its graph to find out the importance of every vertex in a graph. For example, you may use PageRank to determine which Wikipedia pages are the most significant.
Connected Components: The linked components algorithm assigns the ID of the graph’s lowest-numbered vertex to each connected component. In a social network, for example, linked components can resemble clusters.
Triangle Counting: A vertex is a component of a triangle if it is connected to two other vertices by an edge. The TriangleCount object in GraphX is responsible for providing triangular counting methods to determine the exact number of triangles that pass through every vortex thus providing a measure of clustering.
In Apache Spark GraphX, what is the PageRank algorithm?
It’s a plus if you can explain this spark interview question in detail and provide an example! The significance of each vertex in a network is measured using PageRank, which assumes that an edge from u vindicates u’s support of v’s importance.
Larry Page and Sergey Brin created the PageRank algorithm to help Google rank websites. In any network graph, it may be used to calculate the impact of vertices. PageRank uses variables such as the quality and number of links that lead to the page to determine the importance of a website. It is assumed that more significant websites would acquire more links from other websites.
Bottom Line
This article will help you ace your Apache Spark interview. It consists of the most common interview questions in this niche, along with sample answers for all of them to help you better understand the right flow. You can go through this article and prepare for the interview with ease.
FAQs
Why is RDD so sluggish?
Transformations are sluggish by nature, which means that when we call an RDD operation, it does not run instantly. We may run operations at any moment by executing an action on data since transformations are lazy by nature. As a result, data is not loaded until it is required in lazy evaluation.
What is the duration of a spark hiring interview?
Due to the virus, interviews were substituted to a 45-minute scheduled phone interview with a 15-minute one-way video interview using Spark Hire. The candidate creates the films in their spare time, which are then evaluated by the recruiting team at their leisure.
What is Spark RDD and how does it work?
Since its debut, RDD has been Spark’s principal user-facing API. An RDD is a distributed immutable collection of data items partitioned among nodes in your cluster that can be managed in parallel with a low-level API that enables transformations and actions.
