Raymond Raymond | Spark & PySpark

Write and Read Parquet Files in Spark/Scala

event 2018-03-17 visibility 32,370 comment 2 insights toc
insights Stats

In this page, I’m going to demonstrate how to write and read parquet files in Spark/Scala by using Spark SQLContext class.


What is parquet format?

Go the following project site to understand more about parquet.




If you have not installed Spark, follow this page to setup:

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

Hadoop (Optional)

In this example, I am going to read CSV files in HDFS. You can setup your local Hadoop instance via the same above link.

Alternatively, you can change the file path to a local file.

IntelliJ IDEA

I am using IntelliJ to write the Scala script. You can also use Scala shell to test instead of using IDE. Scala SDK is also required. In my case, I am using the Scala SDK distributed as part of my Spark.


JDK is required to run Scala in JVM.

Read and Write parquet files

In this example, I am using Spark SQLContext object to read and write parquet files.


import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{DataFrame, SQLContext}
object ParquetTest {
def main(args: Array[String]) = {
// Two threads local[2]
val conf: SparkConf = new SparkConf().setMaster("local[2]").setAppName("ParquetTest")
val sc: SparkContext = new SparkContext(conf)
val sqlContext: SQLContext = new SQLContext(sc)
writeParquet(sc, sqlContext)
def writeParquet(sc: SparkContext, sqlContext: SQLContext) = {
// Read file as RDD
val rdd = sqlContext.read.format("csv").option("header", "true").load("hdfs://")
// Convert rdd to data frame using toDF; the following import is required to use toDF function.
val df: DataFrame = rdd.toDF()
// Write file to parquet
def readParquet(sqlContext: SQLContext) = {
// read back parquet to DF
val newDataDF = sqlContext.read.parquet("Sales.parquet")
// show contents

Before you run the code

Make sure IntelliJ project has all the required SDKs and libraries setup. In my case

  • JDK is using 1.8 JDK installed in my C drive.
  • Scala SDK: version 2.11.8 as part of my Spark installation (spark-2.2.1-bin-hadoop2.7)
  • Jars: all libraries in my Spark jar folder (for Spark libraries used in the sample code).


Run the code in IntelliJ

The following is the screenshot for the output:


What was created?

In the example code, a local folder Sales.parquet is created:


Run the code in Zeppelin

You can also run the same code in Zeppelin. If you don’t have a Zeppelin instance to play with, you can follow the same link in the Prerequisites section to setup.

More from Kontext
comment Comments
Raymond Raymond #213 access_time 7 years ago more_vert


Yes, you can.

For example, the following code is used to read parquet files from a Hadoop cluster.

def readParquet(sqlContext: SQLContext) = {
// read back parquet to DF
val newDataDF = sqlContext.read.parquet("hdfs://hdp-master:19000/user/hadoop/sqoop_test/blogs")
// show contents

The cluster was setup by following this post:

Configure Hadoop 3.1.0 in a Multi Node Cluster

Of source the hdp-master:19000 needs to be accessible from the server that running the Spark/Scala code.

At the moment, my HDFS is set as readable for all servers/users in the LAN. In a production environment, you may need to manage the permissions too.

Furthermore, you can also run Spark apps in a Spark Cluster instead of in stand-alone or local machine.  I will cover more about this in my future post.


person Ansh access_time 7 years ago

Can we connect and read remotely located HDFS Parquet file? by using above code

hide_source Anonymous #123 access_time 7 years ago more_vert

Can we connect and read remotely located HDFS Parquet file? by using above code

Please log in or register to comment.

account_circle Log in person_add Register

Log in with external accounts