Why is the alpine Docker image over 50% slower than the ubuntu image?

Question

I noticed that my Python application is much slower when running it on python:2-alpine3.6 than running it without Docker on Ubuntu. I came up with two small benchmark commands and there's a huge difference visible between the two operating systems, both when I'm running them on an Ubuntu server, and when I'm using Docker for Mac.

$ BENCHMARK="import timeit; print(timeit.timeit('import json; json.dumps(list(range(10000)))', number=5000))"
$ docker run python:2-alpine3.6 python -c $BENCHMARK
7.6094589233
$ docker run python:2-slim python -c $BENCHMARK
4.3410820961
$ docker run python:3-alpine3.6 python -c $BENCHMARK
7.0276606959
$ docker run python:3-slim python -c $BENCHMARK
5.6621271420

I also tried the following 'benchmark', which doesn't use Python:

$ docker run -ti ubuntu bash
root@6b633e9197cc:/# time $(i=0; while (( i < 9999999 )); do (( i ++ 
)); done)

real    0m39.053s
user    0m39.050s
sys     0m0.000s
$ docker run -ti alpine sh
/ # apk add --no-cache bash > /dev/null
/ # bash
bash-4.3# time $(i=0; while (( i < 9999999 )); do (( i ++ )); done)

real    1m4.277s
user    1m4.290s
sys     0m0.000s

What could be causing this difference?

Answer 1

I've run the same benchmark as you did, using just Python 3:

$ docker run python:3-alpine3.6 python --version
Python 3.6.2
$ docker run python:3-slim python --version
Python 3.6.2

resulting in more that 2 seconds difference:

$ docker run python:3-slim python -c "$BENCHMARK"
3.6475560404360294
$ docker run python:3-alpine3.6 python -c "$BENCHMARK"
5.834922112524509

Alpine is using different implementation of libc (base system library) from musl project , there are many differences between those libraries. As a result each library might perform better in certain use-case.

Here's a strace diff between those commads above. The output start to differ from line 269. Of course there are different addresses in memory but otherwise it's very similar. Most of the time is obviously spent waiting for python command to finish.

After installing strace into both containers we can obtain more interesting trace (I've reduced number of iterations in the benchmark to 10).

For example glibc is loading libraries in following manner (line 182):

openat(AT_FDCWD, "/usr/local/lib/python3.6", O_RDONLY|O_NONBLOCK|O_DIRECTORY|O_CLOEXEC) = 3
getdents(3, /* 205 entries */, 32768)   = 6824
getdents(3, /* 0 entries */, 32768)     = 0

same code in musl:

open("/usr/local/lib/python3.6", O_RDONLY|O_DIRECTORY|O_CLOEXEC) = 3
fcntl(3, F_SETFD, FD_CLOEXEC)           = 0
getdents64(3, /* 62 entries */, 2048)   = 2040
getdents64(3, /* 61 entries */, 2048)   = 2024
getdents64(3, /* 60 entries */, 2048)   = 2032
getdents64(3, /* 22 entries */, 2048)   = 728
getdents64(3, /* 0 entries */, 2048)    = 0

I'm not saying this is the key difference, but reducing number of I/O in core libraries might contribute to better performance. From the diff you can see that executing the very same Python code might lead to slightly different system calls. Probably the most important could be done in optimizing loop performance. I'm not qualified enough to judge whether the performance issue is caused by memory allocation or some other instruction.

• glibc with 10 iterations:

  write(1, "0.032388824969530106\n", 210.032388824969530106)
  

• musl with 10 iterations:

  write(1, "0.035214247182011604\n", 210.035214247182011604)
  

musl is slower by 0.0028254222124814987 seconds. As the difference grows with number of iterations, I'd assume the difference is in memory allocation of JSON objects.

If we reduce the benchmark to solely importing json we notice the difference is not that huge:

$ BENCHMARK="import timeit; print(timeit.timeit('import json;', number=5000))"
$ docker run python:3-slim python -c "$BENCHMARK"
0.03683806210756302
$ docker run python:3-alpine3.6 python -c "$BENCHMARK"
0.038280246779322624

Loading python libraries looks comparable. Generating list() produces bigger difference:

$ BENCHMARK="import timeit; print(timeit.timeit('list(range(10000))', number=5000))"
$ docker run python:3-slim python -c "$BENCHMARK"
0.5666235145181417
$ docker run python:3-alpine3.6 python -c "$BENCHMARK"
0.6885563563555479

Obviously the most expensive operation is json.dumps() which might point to differences in memory allocation between those libraries.

Looking again at the benchmark musl is really slightly slower in memory allocation:

                          musl  | glibc
-----------------------+--------+--------+
Tiny allocation & free |  0.005 | 0.002  |
-----------------------+--------+--------+
Big allocation & free  |  0.027 | 0.016  |
-----------------------+--------+--------+

I'm not sure what is meant by "big allocation" but musl is almost 2x slower which might become significant when you repeat such operation thousands or million times.