bench.py:
- the proxy gets one CPU, the client four, the origin three (8 CPUs), so
the proxy is what runs out first
- CPU time is the busy time of a role's CPUs from /proc/stat, softirqs
included: with IRQ time accounting the cgroup's own figure leaves out
much of the network work
- every run records which side set its pace (proxy, client, origin), by
the busy share of the role's CPUs and of its busiest thread
- a 50 KB keep-alive scenario with 2000 clients
- haproxy with splice: haproxy::splice-auto and haproxy::splice
- wrk timeouts counted apart from errors; tools found by absolute path;
leftovers of an interrupted run are cleaned up
report.py turns runs into Markdown tables: throughput, the proxy's CPU
per gigabyte or per request, its peak memory, splice and copying proxies
in groups of their own, the best of each group marked.
RESULTS.md has the comparison of 0.9.4, 1.0.1, devel, nginx and haproxy,
with and without splice, on an Apple M4 and an Intel Skylake virtual
machine, and how it was measured; data/ has the measurements.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
`proxy -g8000,3,10`
First parameter is average read size we want to keep, second parameter is
minimal number of packets in the same direction to apply algorythm,
last value is delay added after polling and prior to reading data.
An example above adds 10 millisecond delay before reading data if average
polling size is below 8000 bytes and 3 read operations are made in the same
direction. It's specially usefule with splice. `logdump 1 1` is useful
to see how grace delays work, choose delay value to avoid filling the read
pipe/buffer (typically 64K) but keep the request sizes close to chosen average
on large file upload/download.
