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    SparkContext Methods

    SparkContext Methods

    SparkContext Most Used Methods

    The following are the most commonly used methods of SparkContext. For the complete list, refer to Spark documentation.

    longAccumulator() – It creates an accumulator variable of a long data type. Only a driver can access accumulator variables.

    doubleAccumulator() – It creates an accumulator variable of a double data type. Only a driver can access accumulator variables.

    applicationId – Returns a unique ID of a Spark application.

    appName – Return an app name that was given when creating SparkContext

    broadcast – read-only variable broadcast to the entire cluster. You can broadcast a variable to a Spark cluster only once.

    emptyRDD – Creates an emptyRDD

    getPersistentRDDs – Returns all persisted RDDs

    getOrCreate() – Creates or returns a SparkContext

    hadoopFile – Returns an RDD of a Hadoop file

    master()–  Returns master that set while creating SparkContext

    newAPIHadoopFile – Creates an RDD for a Hadoop file with a new API InputFormat.

    sequenceFile – Get an RDD for a Hadoop SequenceFile with given key and value types.

    setLogLevel – Change log level to debug, info, warn, fatal, and error

    textFile – Reads a text file from HDFS, local or any Hadoop-supported file systems, and returns an RDD

    union – Union two RDDs

    wholeTextFiles – Reads a text file in the folder from HDFS, local, or any Hadoop-supported file systems and returns an RDD of Tuple2. The first element of the tuple consists file name and the second element consists context of the text file.

    Apache Spark Tutorial with Scala Examples — SparkSession & SparkContext

    Introduction to Apache Spark

    Setup Apache Spark Environment

    SparkSession & SparkContext

    Resilient Distributed Datasets (RDDs)

    Apache Spark Concepts

    Cache, Persistence and Shared Variables

    DataFrame Introduction

    DataFrame Narrow Transformation

    DataFrame Wider Transformation

    DataFrame Actions

    Spark SQL Functions

    Advanced DataFrame Features

    Using SQL Queries

    Spark Read & Write CSV Files

    Working with JSON Files

    Working with Parquet Files

    Spark Dataset

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