#sparkbasics

Apache Spark, the lightning-fast open-source computation platform, has become a cornerstone in big data technology. Developed by Matei Zaharia at UC Berkeley's AMPLab in 2009, Spark gained prominence within the Apache Foundation from 2014 onward. This article aims to equip you with the essential knowledge needed to succeed in Apache Spark interviews, covering key concepts, features, and critical questions.

Understanding Apache Spark: The Basics

Before delving into interview questions, let's revisit the fundamental features of Apache Spark:

1. Support for Multiple Programming Languages:

Java, Python, R, and Scala are the supported programming languages for writing Spark code.

High-level APIs in these languages facilitate seamless interaction with Spark.

2. Lazy Evaluation:

Spark employs lazy evaluation, delaying computation until absolutely necessary.

3. Machine Learning (MLlib):

MLlib, Spark's machine learning component, eliminates the need for separate engines for processing and machine learning.

4. Real-Time Computation:

Spark excels in real-time computation due to its in-memory cluster computing, minimizing latency.

5. Speed:

Up to 100 times faster than Hadoop MapReduce, Spark achieves this speed through controlled partitioning.

6. Hadoop Integration:

Smooth connectivity with Hadoop, acting as a potential replacement for MapReduce functions.

Top 30+ Interview Questions: Explained

Question 1: Key Features of Apache Spark

Apache Spark supports multiple programming languages, lazy evaluation, machine learning, multiple format support, real-time computation, speed, and seamless Hadoop integration.

Question 2: Advantages Over Hadoop MapReduce

Enhanced speed, multitasking, reduced disk-dependency, and support for iterative computation.

Question 3: Resilient Distributed Dataset (RDD)

RDD is a fault-tolerant collection of operational elements distributed and immutable in memory.

Question 4: Functions of Spark Core

Spark Core acts as the base engine for large-scale parallel and distributed data processing, including job distribution, monitoring, and memory management.

Question 5: Components of Spark Ecosystem

Spark Ecosystem comprises GraphX, MLlib, Spark Core, Spark Streaming, and Spark SQL.

Question 6: API for Implementing Graphs in Spark

GraphX is the API for implementing graphs and graph-parallel computing in Spark.

Question 7: Implementing SQL in Spark

Spark SQL modules integrate relational processing with Spark's functional programming API, supporting SQL and HiveQL.

Question 8: Parquet File

Parquet is a columnar format supporting read and write operations in Spark SQL.

Question 9: Using Spark with Hadoop

Spark can run on top of HDFS, leveraging Hadoop's distributed replicated storage for batch and real-time processing.

Question 10: Cluster Managers in Spark

Apache Mesos, Standalone, and YARN are cluster managers in Spark.

Question 11: Using Spark with Cassandra Databases

Spark Cassandra Connector allows Spark to access and analyze data in Cassandra databases.

Question 12: Worker Node

A worker node is a node capable of running code in a cluster, assigned tasks by the master node.

Question 13: Sparse Vector in Spark

A sparse vector stores non-zero entries using parallel arrays for indices and values.

Question 14: Connecting Spark with Apache Mesos

Configure Spark to connect with Mesos, place the Spark binary package in an accessible location, and set the appropriate configuration.

Question 15: Minimizing Data Transfers in Spark

Minimize data transfers by avoiding shuffles, using accumulators, and broadcast variables.

Question 16: Broadcast Variables in Spark

Broadcast variables store read-only cached versions of variables on each machine, reducing the need for shipping copies with tasks.

Question 17: DStream in Spark

DStream, or Discretized Stream, is the basic abstraction in Spark Streaming, representing a continuous stream of data.

Question 18: Checkpoints in Spark

Checkpoints in Spark allow programs to run continuously and recover from failures unrelated to application logic.

Question 19: Levels of Persistence in Spark

Spark offers various persistence levels for storing RDDs on disk, memory, or a combination of both.

Question 20: Limitations of Apache Spark

Limitations include the lack of a built-in file management system, higher latency, and no support for true real-time data stream processing.

Question 21: Defining Apache Spark

Apache Spark is an easy-to-use, highly flexible, and fast processing framework supporting cyclic data flow and in-memory computing.

Question 22: Purpose of Spark Engine

The Spark Engine schedules, monitors, and distributes data applications across the cluster.

Question 23: Partitions in Apache Spark

Partitions in Apache Spark split data logically for more efficient and smaller divisions, aiding in faster data processing.

Question 24: Operations of RDD

RDD operations include transformations and actions.

Question 25: Transformations in Spark

Transformations are functions applied to RDDs, creating new RDDs. Examples include Map() and filter().

Question 26: Map() Function

The Map() function repeats over every line in an RDD, splitting them into a new RDD.

Question 27: Filter() Function

The filter() function creates a new RDD by selecting elements from an existing RDD based on a specified function.

Question 28: Actions in Spark

Actions bring back data from an RDD to the local machine, including functions like reduce() and take().

Question 29: Difference Between reduce() and take()

reduce() repeatedly applies a function until only one value is left, while take() retrieves all values from an RDD to the local node.

Question 30: Coalesce() and Repartition() in MapReduce

Coalesce() and repartition() modify the number of partitions in an RDD, with Coalesce() being part of repartition().

Question 31: YARN in Spark

YARN acts as a central resource management platform, providing scalable operations across the cluster.

Question 32: PageRank in Spark

PageRank in Spark is an algorithm in GraphX measuring the importance of each vertex in a graph.

Question 33: Sliding Window in Spark

A Sliding Window in Spark specifies each batch of Spark streaming to be processed, setting batch intervals and processing several batches.

Question 34: Benefits of Sliding Window Operations

Sliding Window operations control data packet transfer, combine RDDs within a specific window, and support windowed computations.

Question 35: RDD Lineage

RDD Lineage is the process of reconstructing lost data partitions, aiding in data recovery.

Question 36: Spark Driver

Spark Driver is the program running on the master node, declaring transformations and actions on data RDDs.

Question 37: Supported File Systems in Spark

Spark supports Amazon S3, HDFS, and Local File System as file systems.

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