Spark Interview Questions and Answers
Below, we have created a list of the most frequently asked Spark Interview Questions and Answers. Reading these can help you gain more knowledge and insights into this computing system. If you are looking for a job change or starting your career in Spark, this list of Spark Interview Questions can help you gain more confidence and eventually a job of your choice, in this field.
Apache Spark is a high-functioning, fast, and general-purpose cluster computing system. It provides high-functioning APIs in various programming languages such as Java, Python, Scala, and its prime purpose is to provide an optimized engine capable of supporting general execution graphs.
|What is Apache Spark?
|It is an open-source general-purpose cluster-computing framework. It gives over 80 high-level operators that make it handy to construct parallel apps and you can use it interactively from the Python, Scala, and SQL shells.
|2.4.4 released on 1st September 2019
|Python, Scala, Java, SQL
|Linux, Windows, macOS
|Apache License 2.0
Most Frequently Asked Spark Interview Questions
Spark has the following important features which help developers in many ways:
- Speed − It helps in the efficient workflow of a mobile application on the Hadoop cluster, having 100X memory speed and 10X Speed when running on a disk. By reducing the number of read/write operations on a disk and storing the intermediate processing data in memory, it saves valuable time.
- Support multiple languages − Spark comes with in-built APIs written in Java, Scala, or Python. Having more than 80 high-level operators for interactive-querying, Spark helps developers easily code in multiple languages.
- Advanced Analytics − Spark supports SQL queries, data streaming, Machine Learning, and Graphics algorithms along with total support for “Map” and “Reduce” functionalities.
Apache Spark is an open-source general-purpose distributed data processing engine used to process and analyze large amounts of data efficiently. It has a wide array of uses in ETL and SQL batch jobs, processing of data from sensors, IoT Data Management, Financial Systems and Machine Learning Tasks.
Tungsten is a codename for the project in Apache Spark whose main function is to make changes in the execution engine. Tungsten engine in Spark is used to exponentially increase the efficiency of memory and CPU for its native applications by pushing standard performance limits further as per hardware compatibility.
Parquet is a column-based file format which is used to optimize the speed of queries and is very efficient than a CSV or JSON file format. Spark SQL supports both read and write functions on parquet files which capture schema of original data automatically
Spark is faster than Hive because it does the processing of data in the main memory of worker nodes thus preventing unnecessary I/O operations within disks.
The PageRank Algorithm in Spark delivers an output probability distribution which is used to represent the chances of a person randomly clicking on links arriving on a particular page.
Spark Streaming is an extension of the core Spark API.Its main use is to allow data engineers and data scientists to process real-time data from multiple sources like Kafka, Amazon Kinesis and Flume. This processed data can be exported to file systems, databases and dashboards for further analysis.
|It's a Data Analytics Engine
|It is a Big Data Process Engine
|Used to Process real-time data, using real-time events like Twitter and Facebook
|Batch processing with a huge volume of data
|Has a Low latency computing
|Has a High latency computing
|Can process data extracted interactively
|Process the data extracted in batch mode
|It is easier to use, enables a user to process data using high-level operators through abstractions
|Hadoop's model is a bit complex, need to handle low-level APIs
|Has an in-memory computation, thus, no external scheduler is required
|The external job scheduler is required for memory computation
|It is a bit less secure as compare to Hadoop
|Costlier than Hadoop
In Spark, Actions are RDD’s operation whose value returns back to the spark driver programs which then kick off a job to be executed in a cluster. Reduce, Collect, Take, saves Textfile are common examples of actions in Apache Spark.
The optimizer used by Spark SQL is the Catalyst optimizer. Its main job is to optimize queries that are written in Spark SQL and DataFrame DSL. The Catalyst Optimizer runs queries much faster than its counterpart, RDD.