amazon s3 - How to read multiple gzipped files from S3 into a single RDD?

Question

I have many gzipped files stored on S3 which are organized by project and hour per day, the pattern of the paths of the files is as:

s3://<bucket>/project1/20141201/logtype1/logtype1.0000.gz
s3://<bucket>/project1/20141201/logtype1/logtype1.0100.gz
....
s3://<bucket>/project1/20141201/logtype1/logtype1.2300.gz

Since the data should be analyzed on a daily basis, I have to download and decompress the files belongs to a specific day, then assemble the content as a single RDD.

There should be several ways can do this, but I would like to know the best practice for Spark.

Thanks in advance.

Answer 1

The underlying Hadoop API that Spark uses to access S3 allows you specify input files using a glob expression.

From the Spark docs:

All of Spark’s file-based input methods, including textFile, support running on directories, compressed files, and wildcards as well. For example, you can use textFile("/my/directory"), textFile("/my/directory/*.txt"), and textFile("/my/directory/*.gz").

So in your case you should be able to open all those files as a single RDD using something like this:

rdd = sc.textFile("s3://bucket/project1/20141201/logtype1/logtype1.*.gz")

Just for the record, you can also specify files using a comma-delimited list, and you can even mix that with the * and ? wildcards.

For example:

rdd = sc.textFile("s3://bucket/201412??/*/*.gz,s3://bucket/random-file.txt")

Briefly, what this does is:

• The * matches all strings, so in this case all gz files in all folders under 201412?? will be loaded.
• The ? matches a single character, so 201412?? will cover all days in December 2014 like 20141201, 20141202, and so forth.
• The , lets you just load separate files at once into the same RDD, like the random-file.txt in this case.

Some notes about the appropriate URL scheme for S3 paths:

• If you're running Spark on EMR, the correct URL scheme is s3://.
• If you're running open-source Spark (i.e. no proprietary Amazon libraries) built on Hadoop 2.7 or newer, s3a:// is the way to go.
• s3n:// has been deprecated on the open source side in favor of s3a://. You should only use s3n:// if you're running Spark on Hadoop 2.6 or older.