Message-ID: <6df8863e77b970b466dbfc9a3a5c2bcec3199f48.camel@aquilenet.fr>
Subject: Alpine Linux general performances
From: =?ISO-8859-1?Q?=C9loi?= Rivard <eloi.rivard@aquilenet.fr>
To: ~alpine/users@lists.alpinelinux.org
Date: Tue, 02 Nov 2021 10:19:14 +0100
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Hi.

My company have a cluster of LXC/LXD hypervisors that run Alpine and Debian
containers, mostly for classical webservices (nginx + uwsgi + python apps +
ZEO/ZODB database). We made some investigations when we felt a "general
slowness" on our Alpine containers. With some testing we could exonerate most of
the i/o factors. In the end we encountered a significative difference on CPU-
heavy operations, like for instance the compilation of Python, between Debian
and Alpine containers.

I made a simple experiment: compile Python 3.10.0 on a fresh Alpine container,
and then again on a fresh Debian container. I made this twice, once on my
personal computer (Ryzen 7 1800x 16 cores - 16Gb of RAM), and once on our
production hypervisor (Xeon(R) E5-2609 0 @ 2.40GHz 8 cores - 64Gb of RAM). Here
are the results for the 'make' command (produced with 'time'):

# hypervisor

   # alpine
   real     5m13
   user     4m40
   sys      0m33

   # debian
   real     3m01
   user     2m47
   sys      0m13

# my personal computer

   # alpine
   real    3m50
   user    3m27
   sys     0m20

   # debian
   real    2m17
   user    2m07
   sys     0m07

In both cases Alpine containers take 66% more time to compile Python, compared
to Debian. We also compiled python directly on one of the hypervisors, and we
observed results really close to the Debian container. [1]
I wonder if this experiment has correlations with the "general slowness" we felt
on our production Alpine containers.

Some people wrote some interesting things [2] about the memory allocation in
musl being slower than on glibc. This is quite technical and I am not
sure what to think of this as memory allocation is not really in my field of
competence. In the end I am not even sure bad memory allocation performances are
the cause for a slow Python compilation, or that "slowness feeling" I mentioned.

So I wanted to ask here what do you think of all these.

- Is this situation well-know?
- Is the musl memory allocation a good lead to explain performance differences?
- Can memory allocation explain the whole thing?
- Is my experiment flawed because of some other factors?

Éloi

[1]
https://discuss.linuxcontainers.org/t/performance-problem-container-slower-than-host-x1-2/12291/4
[2]
https://www.linkedin.com/pulse/testing-alternative-c-memory-allocators-pt-2-musl-mystery-gomes/