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    RDD - Process Text Files

    RDD - Process Text Files

    Reading Text Files

    First, create a text file containing the text you want to analyze. For this example, let's assume you have a text file named test.txt with the following content:

    Project Gutenberg’s
    Alice’s Adventures in Wonderland
    Project Gutenberg’s
    Adventures in Wonderland
    Project Gutenberg’s
    

    Using sparkContext.textFile()

    For production applications, we mostly create RDD by using external storage systems like HDFS, S3, HBase, etc. So, in order to read from external sources and create an RDD, use sparkContext.textFile() method.

    To make it simple for this Spark tutorial, we are using files from the local system to create an RDD.

    // Create RDD from external data source
    val rdd2 = spark.sparkContext.textFile("/path/test.txt")
    
    // Collect RDD to driver
    val colData = rdd2.collect()
    
    // Print the collected data to console
    colData.foreach(data =>{
      println(data)
    })
    

    # Output:

    Project Gutenberg’s
    Alice’s Adventures in Wonderland
    Project Gutenberg’s
    Adventures in Wonderland
    Project Gutenberg’s
    

    Note: Besides using text files, we can also create RDD from CSV files, JSON, and more formats.

    Using sparkContext.wholeTextFiles()

    The wholeTextFiles() method returns a pairRDD with the key being the file path and the value being file content.

    // Reads entire file into a RDD as single record.
    val rdd3 = spark.sparkContext.wholeTextFiles("/path/test.txt")
    
    // Collect RDD to driver
    val colData = rdd3.collect()
    
    // Print the collected data to console
    colData.foreach(data =>{
        println("File Name : "+data._1)
        println("File Data : "+data._2)
    })
    

    # Output:

    File Name : file:/Users/admin/test.txt
    File Data : Project Gutenberg’s
    Alice’s Adventures in Wonderland
    Project Gutenberg’s
    Adventures in Wonderland
    Project Gutenberg’s
    

    Apache Spark Tutorial with Scala Examples — Resilient Distributed Datasets (RDDs)

    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

    What Next?

    Tutorial Not Found