Anything you zcat I zcat faster (under certain conditions)

Pleasant Surprises, or how I learned to stop worrying and benchmark the code

It all started with a flight to see my parents for christmas. During normal weeks I often find talks about programming that I wish to watch but can't find the time to, so when I fly, I download the videos to my phone and go through them. This has the side effect that every time I travel I come out of it with a strong desire to be a better programmer. In this case, the video that got to me was Plain Threads are the GOTO of todays computing. At my work, I frequently have to delve into thousands of gzipped logs each about 20MB compressed. In order to make sense of it all, I naively wrote a log parser that had some pretty bad design decisions that I had to fix.

Starting a full rewrite of my log parser, I had to first set up the decompressor. Once I had a decompressor implemented, I remembered the age old rule that performance is not always intuitive, so code should be benchmarked. During my benchmarking, I stumbled upon an interesting finding: I was beating all the standard tools.

The Benchmark Script

Upon discovering my unexpected performance, I decided that it was time to do extensive benchmarking to ensure it wasn't just a fluke. First I separated out the decompression code from the rest of the repository and put it up on github at I needed to create a command line interface to the decompressor so I decided to just treat all commandline args as paths to files. This is a slight deviation from zcat which supports four commandline flags:

Flag Action Reason Ignored
-f –force Forces decompression We're going to try decompressing regardless
-h –help Display a help screen and quit We have no command line flags so help isn't necessary
-L –license Display the gzip license and quit The code is distributed under the Unlicense
-V –version Version. Display the version number and compilation options then quit The project isn't versioned

The benchmark script contains two phases: Warm and cold cache. We test both to ensure that theres no corner cases that would lead to poor performance. For testing We have 5 tests:

  1. zcat
  2. GNU parallel + zcat
  3. pigz
  4. GNU parallel + pigz
  5. fzcat (The program this blog post is about)

I wrote the benchmark script in python to keep things simple. All it does it invoke the 5 tests with warm and cold caches using all the files, and files inside folders that are passed in as commandline arguments.

I also wrote a validation script that just does zcat and fzcat to ensure that the output of the two scripts is identical.


I ran the test against 189 files, each around 20MB in size when compressed. The full results are on the github page but there wasn't a significant difference between cold and warm caches so I'll use warm cache for the rest of this post:

Test Seconds
zcat 107.95278143882751
parallel zcat 42.61844491958618
pigz 55.39036321640015
parallel pigz 42.56399941444397
fzcat 28.799103498458862


fzcat will only be faster under certain conditions:

  1. You must be decompressing multiple files at once
  2. You must have a multicore machine
  3. You must have enough ram to fit 4 of the decompressed files entirely in memory

The 3rd restriction is to ensure that files are output to stdout in order. If you don't meet those requirements I recommend you use pigz for single files, or GNU Parallel + zcat for multiple files.


Those figures are all a practical person would need, but I can't let that go without proper investigation as to why we are getting these results. My theory as to why my performance was superior falls into the following:

  1. Using mmap instead of read leads to copying memory around less, so zcat and pigz probably are using read.
  2. zcat and pigz are processing a single file at a time, rather than buffered the decompressed output in ram like I am doing. This enables them to be used on machine without piles of ram.
  3. miniz is a superb implementation of zlib

Testing the theories

mmap vs read

In order to test this theory, I need to write a fork of the decompressor that uses read instead of mmap.

Type Time
read 27.672048330307007
mmap 27.314319849014282

This is interesting in that mmap didn't create as significant of an improvement as I expected. I must not be IO pegged. The code for this change (which won't be merged into master, we will keep mmap) is available at

single vs multiple files

I think its safe to assume zcat is processing one file at a time, but pigz is a special threaded implementation of zlib so first I am going to investigate the pigz source code to see what its doing. First I downloaded the source tarball from NOTE All code snippets from pigz that I include in this blog are under the pigz license. Upon inspection of the Makefile it looks like pigz.c is included in the final pigz binary so thats probably a good place to start looking. Inside main() there appears this block:

/* process command-line arguments, no options after "--" */
done = noop = 0;
for (n = 1; n < argc; n++)
    if (noop == 0 && strcmp(argv[n], "--") == 0) {
	noop = 1;
    else if (noop || option(argv[n])) { /* true if file name, process it */
	if (done == 1 && g.pipeout && !g.decode && !g.list && g.form > 1)
	    complain("warning: output will be concatenated zip files -- "
		     "will not be able to extract");
	process(strcmp(argv[n], "-") ? argv[n] : NULL);

This is looping through the arguments in order, and passing the args to process() which, would support that theory. Digging into process further we see a lot of setup and then these lines:

/* process ind to outd */
if (g.verbosity > 1)
    fprintf(stderr, "%s to %s ", g.inf, g.outf);
if (g.decode) {
    if (method == 8)
    else if (method == 257)

So pigz does indeed process each file in-order.

Since zcat is using read instead of mmap I will keep the decompressor from the above test and remove any threading so that it processes one file at a time.

Type Time
Single 64.25435829162598
Threaded 27.672048330307007

The code for this portion of the test is available at

miniz vs zlib

For this final test, we will use the code from the above test which is using read and only a single thread. This should be enough to compare the raw performance of miniz vs zlib by comparing our binary vs zcat.

Type Time
fzcat (modified for test) 64.25435829162598
zcat 109.0133900642395


So it seems that the benefit of mmap vs read isn't as significant as I expected. THe benefit theoretically could be more significant on a machine with multiple processes reading the same file but I'll leave that as an excercise for the reader.

miniz turned out to be significantly faster than zlib even when both are used in the same fashion (single threaded and read). Additionally, using the copious amounts of ram available to machines today allowed us to speed everything up even more with threading.


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