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Spark Interview questions

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What is Spark?

Spark is a parallel data processing framework. It allows to develop fast, unified big data application combine batch, streaming and interactive analytics.

Why Spark?

Spark is third generation distributed data processing platform. It’s unified bigdata solution for all bigdata processing problems such as batch , interacting, streaming processing.So it can ease many bigdata problems.

What is RDD?
Spark’s primary core abstraction is called Resilient Distributed Datasets. RDD is a collection of partitioned data that satisfies these properties. Immutable, distributed, lazily evaluated, catchable are common RDD properties.

What is Immutable?
Once created and assign a value, it’s not possible to change, this property is called Immutability. Spark is by default immutable, it’s not allows updates and modifications. Please note data collection is not immutable, but data value is immutable.

What is Distributed?
RDD can automatically the data is distributed across different parallel computing nodes.

What is Lazy evaluated?
If you execute a bunch of program, it’s not mandatory to evaluate immediately. Especially in Transformations, this Laziness is trigger.

What is Catchable?
keep all the data in-memory for computation, rather than going to the disk. So Spark can catch the data 100 times faster than Hadoop.

What is Spark engine responsibility?
Spark responsible for scheduling, distributing, and monitoring the application across the cluster.

What are common Spark Ecosystems?
Spark SQL(Shark) for SQL developers,
Spark Streaming for streaming data,
MLLib for machine learning algorithms,
GraphX for Graph computation,
SparkR to run R on Spark engine,
BlinkDB enabling interactive queries over massive data are common Spark ecosystems.  GraphX, SparkR and BlinkDB are in incubation stage.

spark ecosystems

What is Partitions?
partition is a logical division of the data, this idea derived from Map-reduce (split). Logical data specifically derived to process the data. Small chunks of data also it can support scalability and speed up the process. Input data, intermediate data and output data everything is Partitioned RDD.

How spark partition the data?

Spark use map-reduce API to do the partition the data. In Input format we can create number of partitions. By default HDFS block size is partition size (for best performance), but its’ possible to change partition size like Split.

How Spark store the data?
Spark is a processing engine, there is no storage engine. It can retrieve data from any storage engine like HDFS, S3 and other data resources.

Is it mandatory to start Hadoop to run spark application?
No not mandatory, but there is no separate storage in Spark, so it use local file system to store the data. You can load data from local system and process it, Hadoop or HDFS is not mandatory to run spark application.

spark interview questions

spark interview questions


What is SparkContext?
When a programmer creates a RDDs, SparkContext connect to the Spark cluster to create a new SparkContext object. SparkContext tell spark how to access the cluster. SparkConf is key factor to create programmer application.

What is SparkCore functionalities?
SparkCore is a base engine of apache spark framework. Memory management, fault tolarance, scheduling and monitoring jobs, interacting with store systems are primary functionalities of Spark.

How SparkSQL is different from HQL and SQL?
SparkSQL is a special component on the sparkCore engine that support SQL and HiveQueryLanguage without changing any syntax. It’s possible to join SQL table and HQL table.

When did we use Spark Streaming?
Spark Streaming is a real time processing of streaming data API. Spark streaming gather streaming data from different resources like web server log files, social media data, stock market data or Hadoop ecosystems like Flume, and Kafka.

How Spark Streaming API works?
Programmer set a specific time in the configuration, with in this time how much data gets into the Spark, that data separates as a batch. The input stream (DStream) goes into spark streaming. Framework breaks up into small chunks called batches, then feeds into the spark engine for processing. Spark Streaming API passes that batches to the core engine. Core engine can generate the final results in the form of streaming batches. The output also in the form of batches. It can allows streaming data and batch data for processing.

What is Spark MLlib?

Mahout is a machine learning library for Hadoop, similarly MLlib is a Spark library. MetLib provides different algorithms, that algorithms scale out on the cluster for data processing. Most of the data scientists use this MLlib library.

What is GraphX?

GraphX is a Spark API for manipulating Graphs and collections. It unifies ETL, other analysis, and iterative graph computation. It’s fastest graph system, provides fault tolerance and ease of use without special skills.

What is File System API?
FS API can read data from different storage devices like HDFS, S3 or local FileSystem. Spark uses FS API to read data from different storage engines.

Why Partitions are immutable?
Every transformation generate new partition.  Partitions uses HDFS API so that partition is immutable, distributed and fault tolerance. Partition also aware of data locality.

What is Transformation in spark?

Spark provides two special operations on RDDs called  transformations and Actions. Transformation follow lazy operation and temporary hold the data until unless called the Action. Each transformation generate/return new RDD. Example of transformations: Map, flatMap, groupByKey, reduceByKey, filter, co-group, join, sortByKey, Union, distinct, sample are common spark transformations.

What is Action in Spark?

Actions is RDD’s operation, that value return back to the spar driver programs, which kick off a job to execute on a cluster. Transformation’s output is input of Actions. reduce, collect, takeSample, take, first, saveAsTextfile, saveAsSequenceFile, countByKey, foreach are common actions in Apache spark.

What is RDD Lineage?
Lineage is a RDD process to reconstruct lost partitions. Spark not replicate the data in memory, if data lost, Rdd use linege to rebuild lost data.Each RDD remembers how the RDD build from other datasets.

What is Map and flatMap in Spark?

Map is a specific line or row to process that data. In FlatMap each input item can be mapped to multiple output items (so function should return a Seq rather than a single item). So most frequently used to return Array elements.

What are broadcast variables?
Broadcast variables let programmer keep a read-only variable cached on each machine, rather than shipping a copy of it with tasks. Spark supports 2 types of shared variables called broadcast variables (like Hadoop distributed cache) and accumulators (like Hadoop counters). Broadcast variables stored as Array Buffers, which sends read-only values to work nodes.

What are Accumulators in Spark?
Spark of-line debuggers called accumulators. Spark accumulators are similar to Hadoop counters, to count the number of events and what’s happening during job you can use accumulators. Only the driver program can read an accumulator value, not the tasks.

How RDD persist the data?
There are two methods to persist the data, such as persist() to persist permanently and cache() to persist temporarily in the memory. Different storage level options there such as MEMORY_ONLY, MEMORY_AND_DISK, DISK_ONLY and many more. Both persist() and cache() uses different options depends on the task.

To learn basic Spark video tutorials, just visit bigdata university website.

21 thoughts on “Spark Interview questions”

    1. Shortly ill release youtube video how to create accumulators … its easy pls follow my youtube channel for more info

    1. Hi, tell me wrong answers. what you have noticed
      pls note few answers changed based on versions

  1. Hey.
    Great article thanks for mentioning the interview questions as well as answers for apache sparks.Was searching for such question and answers.

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