How to bundle many files in S3 using Spark

Question

I have 20 million files in S3 spanning roughly 8000 days.

The files are organized by timestamps in UTC, like this: s3://mybucket/path/txt/YYYY/MM/DD/filename.txt.gz. Each file is UTF-8 text containing between 0 (empty) and 100KB of text (95th percentile, although there are a few files that are up to several MBs).

Using Spark and Scala (I'm new to both and want to learn), I would like to save "daily bundles" (8000 of them), each containing whatever number of files were found for that day. Ideally I would like to store the original filenames as well as their content. The output should reside in S3 as well and be compressed, in some format that is suitable for input in further Spark steps and experiments.

One idea was to store bundles as a bunch of JSON objects (one per line and '\n'-separated), e.g.

{id:"doc0001", meta:{x:"blah", y:"foo", ...}, content:"some long string here"}
{id:"doc0002", meta:{x:"foo", y:"bar", ...}, content: "another long string"}

Alternatively, I could try the Hadoop SequenceFile, but again I'm not sure how to set that up elegantly.

Using the Spark shell for example, I saw that it was very easy to read the files, for example:

val textFile = sc.textFile("s3n://mybucket/path/txt/1996/04/09/*.txt.gz")
// or even
val textFile = sc.textFile("s3n://mybucket/path/txt/*/*/*/*.txt.gz")
// which will take for ever

But how do I "intercept" the reader to provide the file name?

Or perhaps I should get an RDD of all the files, split by day, and in a reduce step write out K=filename, V=fileContent?

Answer 1

You can use this

First You can get a Buffer/List of S3 Paths :

import scala.collection.JavaConverters._
import java.util.ArrayList
import com.amazonaws.services.s3.AmazonS3Client
import com.amazonaws.services.s3.model.ObjectListing
import com.amazonaws.services.s3.model.S3ObjectSummary
import com.amazonaws.services.s3.model.ListObjectsRequest

def listFiles(s3_bucket:String, base_prefix : String) = {
    var files = new ArrayList[String]

    //S3 Client and List Object Request
    var s3Client = new AmazonS3Client();
    var objectListing: ObjectListing = null;
    var listObjectsRequest = new ListObjectsRequest();

    //Your S3 Bucket
    listObjectsRequest.setBucketName(s3_bucket)

    //Your Folder path or Prefix
    listObjectsRequest.setPrefix(base_prefix)

    //Adding s3:// to the paths and adding to a list
    do {
      objectListing = s3Client.listObjects(listObjectsRequest);
      for (objectSummary <- objectListing.getObjectSummaries().asScala) {
        files.add("s3://" + s3_bucket + "/" + objectSummary.getKey());
      }
      listObjectsRequest.setMarker(objectListing.getNextMarker());
    } while (objectListing.isTruncated());

    //Removing Base Directory Name
    files.remove(0)

    //Creating a Scala List for same
    files.asScala
  }

Now Pass this List object to the following piece of code, note : sc is an object of SQLContext

var df: DataFrame = null;
  for (file <- files) {
    val fileDf= sc.textFile(file)
    if (df!= null) {
      df= df.unionAll(fileDf)
    } else {
      df= fileDf
    }
  }

Now you got a final Unified RDD i.e. df

Optional, And You can also repartition it in a single BigRDD

val files = sc.textFile(filename, 1).repartition(1)

Repartitioning always works :D