Dealing with a large gzipped file in Spark

Spark cannot parallelize reading a single gzip file.

The best you can do split it in chunks that are gzipped.

However, Spark is really slow at reading gzip files. You can do this to speed it up:

file_names_rdd = sc.parallelize(list_of_files, 100)
lines_rdd = file_names_rdd.flatMap(lambda _: gzip.open(_).readlines())

Going through Python is twice has fast as reading the native Spark gzip reader.

If the file format is not splittable, then there's no way to avoid reading the file in its entirety on one core. In order to parallelize work, you have to know how to assign chunks of work to different computers. In the gzip case, suppose you divide it up into 128M chunks. The nth chunk depends on the n-1-th chunk's position information to know how to decompress, which depends on the n-2-nd chunk, and so on down to the first.

If you want to parallelize, you need to make this file splittable. One way is to unzip it and process it uncompressed, or you can unzip it, split it into several files (one file for each parallel task you want), and gzip each file.