Hi Karsten, implemented Stem counterparts of these (see attached). On one hand the code is delightfully simple, but on the other measurements I got were quite a bit slower. Curious to see what you get when running at the same place you took your measurements.
Cheers! -Damian
On Thu, Jan 7, 2016 at 8:02 AM, Karsten Loesing karsten@torproject.org wrote:
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On 03/01/16 21:25, Damian Johnson wrote:
Nice! Few questions...
- Where are your metrics-lib scripts used for the benchmarks?
Should be easy for me to write stem counterparts once I know what we're running. I'll later be including our demo scripts with the benchmarks later so if possible comments would be nice so they're good examples for newcomers to our libraries.
I'm planning to clean up this code before committing it to a real repository, but here's the unclean version in a pastebin:
- Which exact tarballs are you parsing? It would be useful if we
ran all our benchmarks on the same host with the same data.
I'm using tarballs from CollecTor, except for microdescriptors which I'm processing as described below.
Agreed about running this on the same host in the future.
- Please take note somewhere of the metric-lib commit id used
since I'll want to include that later when we add the results to our site.
Good idea.
For now, I think I'll wait for you to write similar benchmarks for Stem to learn whether I need to write any more for metrics-lib. And then I'll clean up things more on my side and commit them somewhere more serious than pastebin.
Sorry I didn't get to this for the task exchange. Been focusing on Nyx so quite a few things have fallen off my radar.
Sure, no worries at all. Very much looking forward to your results!
All the best, Karsten
Cheers! -Damian
On Sun, Jan 3, 2016 at 9:56 AM, Karsten Loesing karsten@torproject.org wrote: Hi Damian,
I'm digging out this old thread, because I think it's still relevant.
I started writing some performance evaluations for metrics-lib and got some early results. All examples read a monthly tarball from CollecTor and do something trivial with each contained descriptor that requires parsing them. Here are the average processing times by type:
server-descriptors-2015-11.tar.xz: 0.334261 ms server-descriptors-2015-11.tar: 0.285430 ms extra-infos-2015-11.tar.xz: 0.274610 ms extra-infos-2015-11.tar: 0.215500 ms consensuses-2015-11.tar.xz: 255.760446 ms consensuses-2015-11.tar: 246.713092 ms microdescs-2015-11.tar.xz[*]: 0.099397 ms microdescs-2015-11.tar[*]: 0.066566 ms
[*] The microdescs* tarballs contain microdesc consensuses and microdescriptors, but I only cared about the latter; what I did is extract tarballs, delete microdesc consensuses, and re-create and re-compress tarballs
These evaluations were all run on a Core i7 with 2GHz using an SSD as storage.
Any surprises in these results so far?
Would you want to move forward with the comparison and also include Stem? (And, Philipp, would you want to include Zoossh?)
All the best, Karsten
On 01/10/15 09:28, Karsten Loesing wrote:
Hello Philipp and iwakeh, hello list,
Damian and I sat down yesterday at the dev meeting to talk about doing a comparison of the various descriptor-parsing libraries with respect to capabilities, run-time performance, memory usage, etc.
We put together a list of things we'd like to compare and tests we'd like to run that we thought we'd want to share with you. Damian and I will both be working on these for metrics-lib for a short while and then switch to Stem. Please feel free to join us in these effort. The result is supposed to live on Stem's home page unless somebody comes up with a better place.
Thanks!
All the best, Damian and Karsten
On 30/09/15 10:57, Karsten Loesing wrote:
- capabilities - supported descriptor types - all the ones
on CollecTor's formats.html - hidden service descriptors (have an agreed @type for that) - getting/producing descriptors - reading from file/directory - reading from tarballs - reading from CollecTor's .xz-compressed tarballs
- fetching from CollecTor - downloading from directories
(authorities or mirrors) - generating (for unit test) - recognizing @type annotation - inferencing from file name - keeping reading history - user documentation - validation (format, crypto, successful sanitization) - packages available - how much usage by (large) applications
- performance (CPU time, memory overhead) - compression:
.xz-compressed tarballs/decompressed tarballs/plain-text - descriptor type: consensus, server descriptor, extra-info descriptor, microdescriptors - validation: on or off (allows lazy loading)
- tests by descriptor type - @type server-descriptor 1.0
- Stem's "List Outdated Relays" - average advertised
bandwidth - fraction of relays that can exit to port 80 - @type extra-info 1.0 - sum of all written and read bytes from write-history/read-history - number of countries from which v3 requests were received - @type network-status-consensus-3 - average number of relays with Exit flag - @type network-status-vote-3 - Stem's "Votes by Bandwidth Authorities" - @type dir-key-certificate-3 - @type network-status-microdesc-consensus-3 1.0 - @type microdescriptor 1.0 - look at single microdesc cons and microdescs, compile list of extended families - fraction of relays that can exit to port 80 - @type network-status-2 1.0 - @type directory 1.0 - @type bridge-network-status - @type bridge-server-descriptor - @type bridge-server-descriptor 1.0 - @type bridge-extra-info 1.3
- @type bridge-pool-assignment - @type tordnsel 1.0 - @type
torperf 1.0
- action items - get in touch with Dererk for packaging
metrics-lib for Debian
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