Static File Shootout: Apache RewriteRules vs. Flask

Erik Rose

7

Ever wonder just how much you gain by having Apache serve your static files? I had a particularly hairy set of RewriteRules to support this in my project and a fairly simple Python routine as an alternative, so I ran a few benchmarks to find out.

DXR is a code navigation and static analysis tool for the Firefox codebase. It consists of two parts:

  • A Flask app which runs under Apache via mod_wsgi
  • An offline build process which generates a syntax-highlighted version of every Firefox source file as HTML and lays them down on disk

These generated files are the ones served by RewriteRules in production:

However, for convenience during development, we also have a trivial Python routine to serve those files:

I pitted the RewriteRules against the Python controller on a local VM, so keep in mind that all the standard caveats of complex systems apply. That said, let’s see what happened!

Having heard complaints about NFS-based VirtualBox shared directories (where my generated files lived), I expected both solutions to be bottlenecked on IO. To my surprise, I saw a pronounced difference between them.

The RewriteRules serve static pages in an average of 6 ms at a concurrency of 10. This is a representative test run of ab. The tables at the bottom are the most important parts:

(py27)[15:16:12 ~/Checkouts/dxr]% ab -c 10 -n 1000 http://33.33.33.77/code/README.mkd
This is ApacheBench, Version 2.3 <$Revision: 655654 $>
Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
Licensed to The Apache Software Foundation, http://www.apache.org/

Benchmarking 33.33.33.77 (be patient)
Completed 100 requests
Completed 200 requests
Completed 300 requests
Completed 400 requests
Completed 500 requests
Completed 600 requests
Completed 700 requests
Completed 800 requests
Completed 900 requests
Completed 1000 requests
Finished 1000 requests

Server Software:        Apache/2.2.22
Server Hostname:        33.33.33.77
Server Port:            80

Document Path:          /code/README.mkd
Document Length:        7348 bytes

Concurrency Level:      10
Time taken for tests:   0.573 seconds
Complete requests:      1000
Failed requests:        0
Write errors:           0
Total transferred:      7635628 bytes
HTML transferred:       7355348 bytes
Requests per second:    1744.93 [#/sec] (mean)
Time per request:       5.731 [ms] (mean)
Time per request:       0.573 [ms] (mean, across all concurrent requests)
Transfer rate:          13011.36 [Kbytes/sec] received

Connection Times (ms)
              min  mean[+/-sd] median   max
Connect:        0    2   0.4      2       4
Processing:     1    4   4.9      4     124
Waiting:        1    4   3.1      4      99
Total:          2    6   4.9      5     124

Percentage of the requests served within a certain time (ms)
  50%      5
  66%      6
  75%      6
  80%      6
  90%      6
  95%      7
  98%      7
  99%      8
 100%    124 (longest request)

Routing the requests through Python instead drives the mean up to 14 ms:

 50%     14
 66%     15
 75%     16
 80%     17
 90%     19
 95%     21
 98%     23
 99%     25
100%     32 (longest request)

This is with WSGIDaemonProcess example.com processes=2 threads=2, which, after a little experimentation, I determined is close to optimal for my 4-CPU VM. It makes some intuitive sense: one thread for each logical core. The host box has 4 physical cores with hyperthreading, so there are plenty to go around.

Turning the concurrency down to 2 had surprising results: Python actually got slightly faster than Apache: 3 ms avg. This could be measurement noise.

 50%      3
 66%      3
 75%      4
 80%      4
 90%      4
 95%      5
 98%      7
 99%      8
100%    222 (longest request)

-c 4 yields 6 ms:

 50%      6
 66%      7
 75%      8
 80%      8
 90%     10
 95%     11
 98%     12
 99%     12
100%     14 (longest request)

And, more generally, there is a linear performance trailoff as concurrency increases:

There's a linear relationship between concurrent requests and mean response time.

This was a surprise, as I expected more of a cliff when I exceeded the complement of 4 WSGI threads.

When we keep our concurrency down, it turns out that Apache doesn’t necessarily run the RewriteRules any faster than Python executes browse(). However, at high concurrency, Apache does pull ahead of Python, presumably because it has more threads to go around. That will probably hold true in production, since raw Apache processes eat less RAM than WSGI children and will thus have their threads capped less stringently.

Is a gain of twenty-some milliseconds for likely concurrency levels worth the added complexity—and logic duplication—of the RewriteRules? I think not. To get an idea of what 20 ms feels like, the human audio wetware juuuust begins to recognize two adjacent sounds as distinct when they are 20 ms apart: any closer, and they blend into a continuous tone. (Some sources go as low as 12Hz.) There are some usability studies that estimate a 1% dropoff in conversion rate for every extra 100 ms a page takes to load, but no one bothers to measure very fast-loading pages, and I would expect to reach a “fast enough” plateau eventually. Even if the linear relationship were overturned on real hardware, real hardware should be faster, making the latency differences (within reasonable concurrencies) even less than 20 ms. The “go ahead and use Python” conclusion should hold.

Finally, it’s worth mentioning that I’m still serving what are classically in the category of “static resources”—CSS, JS, images, and the like—with Apache, because we can do so in one simple Alias directive. What’s to lose?

Obviously, this is a boisterously multidimensional search space, comprising virtualization, threads, hardware, and IO abstraction, and I had ab running on the same physical box, so take these exact results as a single data point. However, they do establish a nice ceiling that lets us stop worrying about wringing every last drop out of the web server.

7 responses

  1. karl wrote on ::

    Did you try with different type of resources? html, css, js, etc.

    and also studying the variability along different file sizes.

    Finally the last thing would be to see how it behaves and how much the performance is impacted in a test with different type of bandwidth. The latency of networks usually dwarfes the local performance issues.

  2. Erik Rose wrote on ::

    The resource type shouldn’t make a difference at this level. It’s just bytes; we aren’t involving a browser. The only relevant piece is the size of the resource, which in this case was 7348 bytes. Indeed, that is part of the multidimensional soup, as you point out.

    I wouldn’t expect network speed to play much into the results, since either Apache or Python should be able to keep up with any reasonable pipe. However, slow readers could plug up Python threads, reducing the pool for everybody else.

    That said, there are certainly many other variables to explore!

  3. Nils Maier wrote on ::

    May I offer a shameless plug and ask if you tried or considered https://tn123.org/mod_xsendfile/ already?

  4. Robert wrote on :

    ^ I was also going to say such a thing, but tell you to look at mod_wsgi’s sendfile options.

  5. Erik Rose wrote on ::

    I didn’t know about X-Sendfile when I did the benchmarking, but I almost used it by accident. Take a look at the code snippet where I use send_from_directory(). That calls send_file() under the covers, which “will use the most efficient method available and configured. By default it will try to use the WSGI server’s file_wrapper support. Alternatively you can set the application’s use_x_sendfile attribute to True to directly emit an X-Sendfile header. This however requires support of the underlying webserver for X-Sendfile.”

    So, I’m basically one attribute-set and an Apache module away from doing that. If I try it, I’ll update the results here. :-)

  6. Hanno Schlichting wrote on :

    If you have time for more experimentation, you could look at:

    - X-Sendfile (http://flask.pocoo.org/docs/api/#flask.Flask.use_x_sendfile)

    Seems to be supported by Flask, but needs an Apache module. This causes the Python app to just emit the headers of the response, including a special X-Sendfile one. The X-Sendfile header tells the Apache process the filesystem path of the file and lets it handle the actual sending of the data, including kernel-level sendfile support, so the file isn’t even loaded into Apache’s memory. Nginx implements the same idea (http://wiki.nginx.org/XSendfile).

    - FileWrapperExtension (http://code.google.com/p/modwsgi/wiki/FileWrapperExtension)

    This is a similar Python/WSGI specific idea of the above, but there seem to be more cases where it fails or needs extra configuration.

    - Use Python 3.2′s new GIL (http://docs.python.org/3/whatsnew/3.2.html#multi-threading)

    It seems mod_wgsi 3 supports Python 3. If the application doesn’t do much else, you could try running it under Python 3.2 to take advantage of the new GIL / thread scheduling. I’d expect this to give more uniform response times and help with the 90%+ times.
    If you are interested in the back story, there’s lots of information in Dave Beazley’s blog posts about the GIL (http://www.dabeaz.com/blog.html).

    - Use taskset to pin processes to CPU’s

    The taskset command allows one to pin a process to a specific CPU. If you have multiple processes and threads, the default scheduling often ends up trying to schedule one or more threads of the same Python process on different CPU cores. But the GIL often prevents them from actually executing in parallel. The net result for any given thread is, that it’s more or less randomly executed on all CPU cores, which destroys all the L1/L2 caches or even memory access times in NUMA systems.

    For the number of processes/threads, it’s indeed best to do actual experimentation and measurements. Most general advice out there suggests to oversubscribe and have twice as many threads as CPU cores. But that usually only holds true for applications with a mixed CPU and i/o load.

  7. Erik Rose wrote on ::

    The GIL thing in particular is really a shame. It would be interesting to run some benchmarks on real hardware with different numbers of threads per process, on both IO and CPU-bound jobs.