saveAsTextFile() to write the final RDD as single text file - Apache Spark

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I am working on a batch application using Apache Spark, i wanted to write the final RDD as text file, currently i am using saveAsTextFile("filePath") method available in RDD.

My text file contains the fields delimited with \u0001 delimiter. So in the model class toString() method i added all the fields seperated with \u0001 delimiter.

is this the correct way to handle this? or any other best approach available?

Also what if i iterate the RDD and write the file content using FileWriter class available in Java?

Please advise on this.

Regards, Shankar


To write as a single file there are a few options. If your writing to HDFS or a similar distributed store you can first coalesce your RDD down to a single partition (note your data must then fit on a single worker), or you could collect the data to the driver and then use a filewriter.

Save the RDD to Files, saveAsTextFile() method. This will write the data to simple text files where the .​toString() method is called on each RDD element and one element is written per line. as makes sense for your dataset. public static final int NUM_PARTITIONS = 2; outputDirectory = args[1]; JavaRDD<ApacheAccessLog> accessLogs = sc. saveAsTextFile(path) Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. saveAsSequenceFile(path) (Java and Scala)


public static boolean copyMerge(SparkConf sparkConf, JavaRDD rdd, String dstPath) throws IOException, URISyntaxException {
    Configuration hadoopConf = sparkConf.hadoopConfiguration();
    hadoopConf.set("fs.s3.awsAccessKeyId", awsAccessKey);
    hadoopConf.set("fs.s3.awsSecretAccessKey", awsSecretKey);
    String tempFolder = "s3://bucket/folder";
    rdd.saveAsTextFile(tempFolder);
    FileSystem hdfs = FileSystem.get(new URI(tempFolder), hadoopConfig);
    return FileUtil.copyMerge(hdfs, new Path(tempFolder), hdfs, new Path(dstPath), false, hadoopConfig, null);
}

This solution is for S3 or any HDFS system. Achieved in two steps:

  1. Save the RDD by saveAsTextFile, this generates multiple files in the folder.

  2. Run Hadoop "copyMerge".

Loading and Saving Your Data | Spark Tutorial, Interested in learning Apache Spark? When we load a single text file as an RDD, then each input line becomes an element in the RDD. Saving the text files: Spark consists of a function called saveAsTextFile(), which saves the path This is how Spark becomes able to write output from multiple codes. I am trying the word count problem in spark using python. But I am facing the problem when I try to save the output RDD in a text file using .saveAsTextFile command. Here is my code. Please help me


Instead of doing collect and collecting it to driver I would rather suggest to use coalesce which would be good in reducing memory problems

pyspark - creating directory when trying to RDD as, txt. 2. assuming data is less ( as you want to write to a single file ) perform a rdd.​collect() and write on to hdfs in  saveAsTextFile(path) Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. saveAsSequenceFile(path) (Java and Scala)


RDD Programming Guide - Spark 2.4.5 , To write applications in Scala, you will need to use a compatible Scala version (​e.g. 2.12. You must stop() the active SparkContext before creating a new one. Text file RDDs can be created using SparkContext 's textFile method. elements of the RDD using some function and returns the final result to the driver program  The iterator will consume as much memory as the largest partition in this RDD. Note: this results in multiple Spark jobs, and if the input RDD is the result of a wide transformation (e.g. join with different partitioners), to avoid recomputing the input RDD should be cached first.


RDD (Spark 1.1.1 JavaDoc), These operations are automatically available on any RDD of the right type (e.g. Return the Cartesian product of this RDD and another one, that is, the RDD of all Save this RDD as a text file, using string representations of elements. void, saveAsTextFile(String path, Class<? extends public final Partition[] partitions(). rdd.coalesce(1, shuffle = true).saveAsTextFile() is the best simple option as it will keep the processing of upstream tasks parallel and then only perform the shuffle to one node (rdd.repartition(1).saveAsTextFile() is an exact synonym). rdd.saveAsSingleTextFile() as provided bellow additionally allows one to store the rdd in a single file with


How to name file when saveAsTextFile in spark?, in spark? - apache-spark. When saving as a textfile in spark version 1.5.1 I use: rdd. it will treat that as a directory and then write one file per partition. saveAsTextFile(path) Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. saveAsSequenceFile(path)