commit 8ed728cbba5c8622e0138cea19a9edd9e256f145 Author: Damian Johnson atagar@torproject.org Date: Tue Jan 19 08:58:55 2016 -0800

Updating Stem's benchmarks

Changed our benchmark script by using parse_file() rather than the DescriptorReader. Didn't make a big difference, but did lower our runtime by about 5%. --- docs/tutorials/mirror_mirror_on_the_wall.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/tutorials/mirror_mirror_on_the_wall.rst b/docs/tutorials/mirror_mirror_on_the_wall.rst index 656d228..e5b423e 100644 --- a/docs/tutorials/mirror_mirror_on_the_wall.rst +++ b/docs/tutorials/mirror_mirror_on_the_wall.rst @@ -290,10 +290,10 @@ Bridge Pool Assignments :red:`No` :green:`Yes` :red:`No` Torperf :red:`No` :green:`Yes` :red:`No` Tordnsel :green:`Yes` :green:`Yes` :red:`No` **Benchmarks** -Server Descriptors :green:`0.63 ms` :green:`0.29 ms` :green:`0.46 ms` -Extrainfo Descriptors :green:`0.42 ms` :green:`0.22 ms` :red:`unsupported` -Microdescriptors :green:`0.34 ms` :green:`0.07 ms` :red:`unsupported` -Consensus :green:`876.09 ms` :green:`246.71 ms` :green:`83.00 ms` +Server Descriptors :green:`0.60 ms` :green:`0.29 ms` :green:`0.46 ms` +Extrainfo Descriptors :green:`0.40 ms` :green:`0.22 ms` :red:`unsupported` +Microdescriptors :green:`0.33 ms` :green:`0.07 ms` :red:`unsupported` +Consensus :green:`865.72 ms` :green:`246.71 ms` :green:`83.00 ms` Benchmarked With Commit :green:`c01a9cd` :green:`8767f3e` :green:`2380e55` Language Interpreter :green:`Python 3.5.1` :green:`Java 1.7.0` :green:`Go 1.5.2` =========================== ===================== =================== ============== @@ -304,7 +304,7 @@ Few things to note about these benchmarks... reading from tarballs.

* Your Python version makes a very large difference for Stem. For instance, - with Python 2.7 reading a consensus takes **1,393.10 ms** (almost twice as + with Python 2.7 reading a consensus takes **1,290.84 ms** (almost twice as long).

* Metrics-lib and Stem can both read from compressed tarballs at a small