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Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
Question: Define a task in Apache Spark application?
Answer: A unit of work on a partition of a DataFrame.
Question: What are the main components of the YARN architecture?
Answer: A general architecture for running distributed applications. YARN specifies the following components:
- ResourceManager - A master daemon that authorizes submitted jobs to run, assigns an ApplicationMaster to them, and enforces resource limits.
- ApplicationMaster - A supervisory task that requests the resources needed for executor tasks. An ApplicationMaster runs on a different NodeManager for each application. The ApplicationMaster requests containers, which are sized by the resources a task requires to run.
- NodeManager - A worker daemon that launches and monitors the ApplicationMaster and task containers.
- JobHistory Server - Keeps track of completed applications.
The ApplicationMaster negotiates with the ResourceManager for cluster resourcesdescribed in terms of a number of containers, each with a certain memory limitand then runs application-specific processes in those containers. The containers are overseen by NodeManagers running on cluster nodes, which ensure that the application does not use more resources than it has been allocated.
Question: What is Zookeeper service?
Answer: A centralized service for maintaining configuration information, naming, and providing distributed synchronization and group services. In a CDH cluster, ZooKeeper coordinates the activities of high-availability services, including HDFS, Oozie, Hive, Solr, YARN, HBase, and Hue.
Question: What is the Case classes, and why do you use it in Spark?
Answer: Case classes are Scala way of creating Java Pojos. It is implicitly create getter and setter method for the Object member. This is used in Spark to assign Schema to a DataSet/DataFrame objects.
Question: What is a Catalyst optimizer?
Answer: It is a core part of SparkSQL, which is written using Scala. This helps Spark in
- Schema inference from JSON data
You can say that, it is helping Spark SQL to generate query plan, which can be easily converted to the Direct Acyclic Graph of RDDs. Once DAG is created it is ready to execute. It does lot of things before creating optimized query plan. Main purpose of this optimizer, is to create optimized DAG.
Both the query plans and optimized query plans are internally represented as trees. Catalyst optimizer has various other libraries, which help in working on this trees.
Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
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