Top 40 Apache Spark Interview Questions and Answers
Apache Spark is a free open source search engine like Google, Yahoo and other search engines. It is an analytical search engine for the processing of the user’s important data scale. SPARK provides the interface for programming users. Spark features include default tolerance. It was developed at the University of California, Berkeleys Amplbb and he had worked very hard to get this. The Spark CodeBase was then given to the most eligible software development association that was Apache Software Foundation. And the association maintains it properly. Spark provides all the features of the DataFrames API. The entire idea of using an SQL interface as the spark is that there are lots of knowledge that will be represented as during the process of relational model.
Aggregations are at the center of the enormous large-scale data processing effort, as all this generally depends on dashboards and ml, which both require an aggregation of one sorting or the opposite. Using the SPARKSQL library, you will mainly get everything the user can get during a traditional electronic database or knowledge warehouse query engine. Apache is a unified analytics engine for the processing of large files which cannot be possible in any other means. The spark interview questions are in the following ones:
- What are the important components of the spark ecosystem?
Ans: Apache Spark has different main categories that comprise its Spark ecosystem. Those are:
Language support: Spark can be integrate with different language to the applications and the different performance analytics. Those language are Java, Python, Scala and etc.
Core concept: Spark supports 5 main Core components. These are some of the most important concepts which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib and GraphX.
Cluster Management: Spark can be run in 3 different environments. Those environment are Standalone cluster, Apache Mesos and YARN.
- Explain how Spark runs applications with the help of its architecture.
Ans: Spark applications run as independent process that are coordinate by the SparkSession object in the driver program. The resource manager or cluster manager assigns tasks to the worker nodes with the task which is assigned to the software. Algorithms can be apply as the operations are repeatedly to the data so they can be benefited from getting back the datasets across iterations. A task applies its unit of work to the dataset in each partition and find outs new outputs a new partition dataset. The results are sent back to the original driver application.
- What are the different cluster managers available in Apache Spark?
Ans: Standalone Mode: By default, applications submitted to the standalone mode cluster will run in First in first out order and each application will try to use all available nodes. It can be launched either manually or by starting a master and workers by hand or use provided launch scripts. It is also possible to run the demons on a single machine for the testing.
Apache mesos: It is an open-source project to manage computer cluster and can also run Hadoop applications. The advantages of deploying Spark with mesos which includes the dynamic Partitioning between spark and other frameworks as well as scalable partitioning between multiple instances of spark.
Hadoop YARN: Apache YARN is the cluster resource manager of the application called hadoop 2. Spark can be run YARN as well.
Kubernetes: Kubernetes is an open source system for automating deployment, scaling and management of specialised applications.
- What is the significance of Resilient Distributed Datasets in Spark?
Ans: Resilent 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 executed on different nodes of a cluster. RDDS are created by either the transformation of existing RDDS or by loading an external dataset from stable storage like HDFS or HBase.
- What is a lazy evaluation in Spark?
Ans: When spark operates on any dataset, it remembers the instructions which are implied by the user. When a transformation such as a map() is called RDD, the operation is not performed at the right moment. Transformation is the Spark which are not evaluated until the user perform any of the action, which aids in the optimization in overall data processing workflow which is called as LAZY Evaluation.
- What is a Parquet file?
Ans: Parquet file is columnar format which supports the several data which process systems. With the parquet file spark can be perform the both reading and writing ability in the work.
