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How to write single CSV file using spark?
Apache Spark by default writes CSV file output in multiple parts-*.CSV, inside a directory. Reason is simple it creates multiple files because each partition is saved individually. Apache Spark is built for distributed processing and multiple files are expected. However, you can overcome this situation by several methods. In previous posts, we have just read the data files (flat file, json), created rdd, dataframes using spark sql, but we haven't written file back to disk or any storage system. In this Apache Spark tutorial - you will learn how to write files back to disk.
Main menu: Spark Scala Tutorial
For this blog, I am creating Scala Object - textfileWriter in same project - txtReader folder where we created textfileReader.
Source File
I am using the same source file squid.txt file (with duplicate records) which I created in previous blog. However, in practical scenario source could be anything - relational database, hdfs file system, message queue etc. Practically, It will be never the case, i.e. reading and writing same file. This is just for demo purpose.
1286536309.586 921 192.168.0.68 TCP_MISS/200 507 POST http://rcv-srv37.inplay.tubemogul.co...eiver/services - DIRECT/174.129.41.128 application/xml
1286536309.608 829 192.168.0.68 TCP_MISS/200 507 POST http://rcv-srv37.inplay.tubemogul.co...eiver/services - DIRECT/174.129.41.128 application/xml
1286536309.660 785 192.168.0.68 TCP_MISS/200 507 POST http://rcv-srv37.inplay.tubemogul.co...eiver/services - DIRECT/174.129.41.128 application/xml
1286536309.684 808 192.168.0.68 TCP_MISS/200 507 POST http://rcv-srv37.inplay.tubemogul.co...eiver/services - DIRECT/174.129.41.128 application/xml
1286536309.775 195 192.168.0.227 TCP_MISS/200 4120 GET http://i4.ytimg.com/vi/gTHZnIAzmdY/default.jpg - DIRECT/209.85.153.118 image/jpeg
1286536309.795 215 192.168.0.227 TCP_MISS/200 5331 GET http://i2.ytimg.com/vi/-jBxVLD4fzg/default.jpg - DIRECT/209.85.153.118 image/jpeg
1286536309.815 234 192.168.0.227 TCP_MISS/200 5261 GET http://i1.ytimg.com/vi/dCjp28ps4qY/default.jpg - DIRECT/209.85.153.118 image/jpeg
Sample Code
Open jsonfileReader.scala and copy-paste the code written below.
I have written separate blog to explain what are basic terminologies used in Spark like rdd, SparkContext, SQLContext, various transformations and actions etc. You can go through these for basic understanding.
However, I have explained little bit in comments above each line of code what it actually does. For list of spark functions you can refer this.
You can make this code much simpler but my aim is to teach as well. Hence I have intentionally introduced header structure, SQL context, string rdd etc. However, if you are familiar with these, you can just focus on writing dataframe part highlighted in blue.
package com.dataneb.spark
// Each library has its significance, I have commented in below code how its being used
import org.apache.spark._
import org.apache.spark.sql._
import org.apache.log4j._
import org.apache.spark.sql.types.{StructType, StructField, StringType}
import org.apache.spark.sql.Row
object textfileWriter {
// Reducing the error level to just "ERROR" messages
// It uses library org.apache.log4j._
// You can apply other logging levels like ALL, DEBUG, ERROR, INFO, FATAL, OFF etc
Logger.getLogger("org").setLevel(Level.ERROR)
// Defining Spark configuration to define application name and the local resources to use
// It uses library org.apache.spark._
val conf = new SparkConf().setAppName("textfileWriter")
conf.setMaster("local")
// Using above configuration to define our SparkContext
val sc = new SparkContext(conf)
// Defining SQL context to run Spark SQL
// It uses library org.apache.spark.sql._
val sqlContext = new SQLContext(sc)
// Main function where all operations will occur
def main (args:Array[String]): Unit = {
// Reading the text file
val squidString = sc.textFile("/Users/Rajput/Documents/testdata/squid.txt")
// Defining the data-frame header structure
val squidHeader = "time duration client_add result_code bytes req_method url user hierarchy_code type"
// Defining schema from header which we defined above
// It uses library org.apache.spark.sql.types.{StructType, StructField, StringType}
val schema = StructType(squidHeader.split(" ").map(fieldName => StructField(fieldName,StringType, true)))
// Converting String RDD to Row RDD for 10 attributes
val rowRDD = squidString.map(_.split(" ")).map(x => Row(x(0), x(1), x(2), x(3), x(4), x(5) , x(6) , x(7) , x(8), x(9)))
// Creating dataframe based on Row RDD and schema
val squidDF = sqlContext.createDataFrame(rowRDD, schema)
// Writing dataframe to a file with overwrite mode, header and single partition.
squidDF
.repartition(1)
.write
.mode ("overwrite")
.format("com.databricks.spark.csv")
.option("header", "true")
.save("targetfile.csv")
sc.stop()
}
}
Run the code!
Output
There are several other methods to write these files.
Method 1
This is what we did above. If expected dataframe size is small you can either use repartition or coalesce to create single file output as /filename.csv/part-00000.
Scala> dataframe
.repartition(1)
.write
.mode ("overwrite")
.format("com.databricks.spark.csv")
.option("header", "true")
.save("filename.csv")
Repartition(1) will shuffle the data to write everything in one particular partition thus writer cost will be high and it might take long time if file size is huge.
Method 2
Coalesce will require lot of memory, if your file size is huge as you will run out of memory.
Scala> dataframe
.coalesce(1)
.write
.mode ("overwrite")
.format("com.databricks.spark.csv")
.option("header", "true")
.save("filename.csv")
Coalesce() vs repartition()
Coalesce and repartition both shuffles the data to increase or decrease the partition, but repartition is more costlier operation as it performs full shuffle. For example,
scala> val distData = sc.parallelize(1 to 16, 4)
distData: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[128] at parallelize at <console>:24
// current partition size
scala> distData.partitions.size
res63: Int = 4
// checking data across each partition
scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 0) iter else Iterator()).collect
res64: Array[Int] = Array(1, 2, 3, 4)
scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 1) iter else Iterator()).collect
res65: Array[Int] = Array(5, 6, 7, 8)
scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 2) iter else Iterator()).collect
res66: Array[Int] = Array(9, 10, 11, 12)
scala> distData.mapPartitionsWithIndex((index, iter) => if (index == 3) iter else Iterator()).collect
res67: Array[Int] = Array(13, 14, 15, 16)
// decreasing partitions to 2
scala> val coalData = distData.coalesce(2)
coalData: org.apache.spark.rdd.RDD[Int] = CoalescedRDD[133] at coalesce at <console>:25
// see how shuffling occurred. Instead of moving all data it just moved 2 partitions.
scala> coalData.mapPartitionsWithIndex((index, iter) => if (index == 0) iter else Iterator()).collect
res68: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8)
scala> coalData.mapPartitionsWithIndex((index, iter) => if (index == 1) iter else Iterator()).collect
res69: Array[Int] = Array(9, 10, 11, 12, 13, 14, 15, 16)
repartition()
Notice how repartition() will re-shuffle everything to create new partitions as compared to previous RDDs - distData and coalData. Hence repartition is more costlier operation as compared to coalesce.
scala> val repartData = distData.repartition(2)
repartData: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[139] at repartition at <console>:25
// checking data across each partition
scala> repartData.mapPartitionsWithIndex((index, iter) => if (index == 0) iter else Iterator()).collect
res70: Array[Int] = Array(1, 3, 6, 8, 9, 11, 13, 15)
scala> repartData.mapPartitionsWithIndex((index, iter) => if (index == 1) iter else Iterator()).collect
res71: Array[Int] = Array(2, 4, 5, 7, 10, 12, 14, 16)
Method 3
Let the file create on various partitions and later merge the files with separate Shell Script. This method will be fast depending upon your hard disk write speed.
#!/bin/bash
echo "ColName1, ColName2, ColName3, ... , ColNameX" > filename.csv
for i in /spark/output/*.CSV ; do
echo "FileNumber $i"
cat $i >> filename.csv
rm $i
done
echo "Done"
Method 4
If you are using Hadoop file system to store output files. You can leverage HDFS to merge files by using getmerge utility.
Input your source directory with all partition files and destination output file, it concatenates all the files in source into destination local file. You can also set -nl to add a newline character at the end of each file. Further, -skip-empty-file can be used to avoid unwanted newline characters in case of empty files.
Syntax : hadoop fs -getmerge [-nl] [-skip-empty-file]
hadoop fs -getmerge -nl /spark/source /spark/filename.csv
hadoop fs -getmerge /spark/source/file1.csv /spark/source/file2.txt filename.csv
Method 5
Use FileUtil.copyMerge() to merge all the files.
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs._
def merge(srcPath: String, dstPath: String): Unit = {
val hadoopConfig = new Configuration()
val hdfs = FileSystem.get(hadoopConfig)
FileUtil.copyMerge(hdfs, new Path(srcPath), hdfs, new Path(dstPath), true, hadoopConfig, null) }
val newData = << Your dataframe >>
val outputfile = "/spark/outputs/subject"
var filename = "sampleFile"
var outputFileName = outputfile + "/temp_" + filename
var mergedFileName = outputfile + "/merged_" + filename
var mergeFindGlob = outputFileName
newData.write
.format("com.databricks.spark.csv")
.option("header", "true")
.mode("overwrite")
.save(outputFileName)
merge(mergeFindGlob, mergedFileName )
If you have any question, please don't forget to write in comments section below. Thank you!
Navigation menu
1. Apache Spark and Scala Installation
2. Getting Familiar with Scala IDE
3. Spark data structure basics
4. Spark Shell
5. Reading data files in Spark
6. Writing data files in Spark
7. Spark streaming
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