Read Text File from Hadoop in Zeppelin through Spark Context

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zeppelin spark hadoop rdd

In this page

Background

This page provides an example to load text file from HDFS through SparkContext in Zeppelin (sc).

Reference

The details about this method can be found at:

SparkContext.textFile

https://spark.apache.org/docs/2.2.1/api/java/org/apache/spark/SparkContext.html#textFile-java.lang.String-int-

SqlContext

https://spark.apache.org/docs/2.2.1/api/java/org/apache/spark/sql/SQLContext.html

Prerequisites

Hadoop and Zeppelin

Refer to the following page to install Zeppelin and Hadoop in your environment if you don’t have one to play with.

Install Big Data Tools (Spark, Zeppelin, Hadoop) in Windows for Learning and Practice

Sample text file

In this example, I am going to use the file created in this tutorial:

Create a local CSV file

Step by step guide

Create a new note

Create a new note in Zeppelin with Note Name as ‘Test HDFS’:

image

Create data frame using RDD.toDF function

%spark
import spark.implicits._

// Read file as RDD
val rdd=sc.textFile("hdfs://0.0.0.0:19000/Sales.csv")

// Convert rdd to dataframe using toDF
val df = rdd.toDF
z.show(df)

The output:

image

As shown in the above screenshot, each line is converted to one row.

Let’s convert the string rows to string tuples.

Read CSV using spark.read

%spark
val df = spark.read.format("csv").option("header", "true").load("hdfs://0.0.0.0:19000/Sales.csv")
z.show(df)

image

Alternative method for converting RDD<String> to DataFrame

For previous Spark versions, you may need to convert RDD<String> to DataFrame using map functions.

%spark
import org.apache.spark.sql._
import org.apache.spark.sql.types._
import org.apache.spark.sql.SQLContext
//import spark.implicits._
import java.text.SimpleDateFormat
import java.util.Date

// Read file as RDD
val rdd=sc.textFile("hdfs://0.0.0.0:19000/Sales.csv")
val header = rdd.first()
val records = rdd.filter(row => row != header)

// create a data row
def row(line: List[String]): Row = { Row(line(0), line(1).toDouble) }

def dfSchema(columnNames: List[String]): StructType = {
  StructType(
      Seq(StructField("MonthOld", StringType, true),
      StructField("Amount", DoubleType, false))
      )
}
     
val headerColumns = header.split(",").to[List]    
val schema = dfSchema(headerColumns)
val data = records.map(_.split(",").to[List]).map(row)

//val df = spark.createDataFrame(data, schema)
//or
val df = new SQLContext(sc).createDataFrame(data, schema)
val df2 = df.withColumn("Month", from_unixtime(unix_timestamp($"MonthOld","dd/MM/yyyy"),"yyyy-MM-dd")).drop("MonthOld")

z.show(df2)

The result is similar to the previous one except the date format is also converted:

image

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