Spark is that analytics engine that can process a large volume of data. It is because of this that makes it popular and very much in demand. Big companies like Amazon, Alibaba, eBay, Yahoo, Shopify, etc., all use Spark. It is one of the hottest skills in demand right now. And if you want to shine in your career, sound knowledge about Spark helps immensely. Here’s a list of the top 10 most frequently asked Spark interview questions for beginners. It will give you the gist of how interviews are.
What is Apache Spark? (This will generally be the first Spark interview question)
Apache Spark is an open-source computing framework. It caters to real-time processing. Currently, this is the most active Apache project. It is extensively put to use in Big data processing as well.
What are the features of Spark?
Some of the key features of Spark are as follows –
In comparison to Hadoop, Spark is way faster. In some cases, it runs at a speed that is 100 times the speed of Hadoop. It is because of controlled partitioning that Spark achieves such high speed. With this high speed, it is possible to process large data. It also ensures minimal network traffic.
Spark talks about lazy evaluation. Well, this is not a disadvantage rather one of the best features. This lazy evaluation is one of the prime reasons behind the high computational speed of Spark. It is only when drivers request data that the Directed Acyclic Graph (DAG) gets executed.
Apache Spark is easily compatible with Hadoop. Spark holds the potential to replace the MapReduce function of Hadoop. It can also run on top of an existing Hadoop cluster. It does this by using YARN. Hadoop integration is the greatest advantage for those who started their career with Hadoop.
Spark has the advantage of supporting multiple data sources. Some of them are JSON, Cassandra, Parquet, Hive, etc. The point worth noting is that data sources do not take up the role of pipes alone. They can convert data and pull it into Spark as well. These data sources can access structured data using Spark SQL.
Spark allows a high-level Application Programming Interface. They are Python, Java, R, and Scala. With this feature, it is possible to write Spark in any of these languages.
Spark allows big data processing as well. Thanks to Spark’s MLib. With this, one need not use multiple tools for processing and ML. Spark is a unified platform for both.
A result of an in-memory computation is real-time computation. Spark supports various computational models.
What are the advantages of Spark over Hadoop?
There are some areas where Spark outperforms Hadoop. Some areas are –
- Spark performs better real-time queuing of data.
- For detecting frauds in live streams, Spark is a better option.
- Spark processes log better than Hadoop.
- Sensor data processing is better observed in Spark. The reason is “in-memory computation.” It is because data is retrieved and combined from several sources.
What are the major libraries that you find in the Spark ecosystem?
There are four main libraries that you can find here. They are –
- Spark Streaming: Its main job is to process real-time streaming data.
- Spark SQL: This library executes SQL queries. It is done using either Business Intelligence tools or using standard visualization.
- Spark MLib: This library caters to algorithms like regression, clustering, classification, etc.
- Spark GraphX: This supports many basic operators like subgraph, joinVertices, etc.
What is YARN?
YARN is a distributed container manager. YARN is that feature in Spark that paves the way for a central and resource management platform. It aims to deliver scalable operations. Running Spark on YARN requires a binary distribution of Spark.
What are the different cluster managers in Spark?
The different cluster managers in Spark are –
- Hadoop YARN: It is the cluster resource manager of Hadoop 2.
- Apache Mesos: This is an open-source project cluster manager. When Spark is deployed with Mesos, the advantages are –
- Dynamic partitioning between Spark and other frameworks
- Scalable partitioning
- Kubernetes: This caters to-
- Automatic deployment
- Management of containerized applications
4. Standalone Mode: One can launch this cluster either manually or using launch scripts. The application submitted to the standalone mode will run in FIFO fashion by default. Also, each application tries to use all the available nodes.
What is a Parquet file?
It is a columnar format supported by Spark. A lot of data processing systems supports it. Using this format, Spark can perform both operations – read and write.
Which file systems does Spark support?
The files supported by Spark are-
- The Hadoop Distributed File System (HDFS)
- Local file system
- Amazon S3
What is RDD?
RDD stands for Resilient Distribution Datasets. It is a collection of operational elements running parallel to each other. It is distributed, fault-tolerant and immutable. These RDDs are those parts that are stored in the memory. They are distributed across various nodes. There are two types of RDD – Parallelized collections and Hadoop datasets.
- Parallelized collections: The existing RDDs run parallel to each other.
- Hadoop datasets: These datasets perform functions on each file in either Hadoop Distributed File System (HDFS) or other storage systems.
What is an “action” in Spark?
An “action” brings the data from RDD to the local mention. The tasks performed are –
- Load data into original RDD
- Carry out all intermediate transformations
- Return final results to Driver program or write it out to file system
This guide focuses on the most frequently asked Spark interview questions. A beginner can delve deeper into these concepts to have the edge over others. These are some of the major spark interview questions that will help you to get entry level jobs.