Every table is now for one machine, ARM or Intel, with no averages of
the two, the copying proxies and those using splice() in one table:
bold the best of each group and * the best of the row for the HTTP proxy
and the relay, only the best of the row for CONNECT and SOCKS. The
tables for 13 October replace those of 11-12 October, whose columns for
splice by direction and 16 KB buffers are gone with them.
The HTTPS section is measured again with today's devel, with the TLS
streams and envoy. report.py takes the TLS streams and 1 MB answers for
bulk data.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
tls-term-* and tls-init-* measure TLS ended or begun by the proxy on a
TCP relay: 3proxy tcppm under ssl_serv/ssl_cli, nginx stream, haproxy
mode tcp and stunnel, with a certificate made for each run and an HTTPS
listener on the origin. nginx::halfclose turns on proxy_half_close.
bench/mix is the Go load generator and origin for the mix-* scenarios.
A run stops when the origin exits (e.g. killed by the OOM killer)
instead of recording failures for everything after it.
RESULTS.md gets the HTTPS and mix tables; data/ the full suite with 16
KB buffers and -s by default.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
RESULTS.md now leads with devel as of the automatic splice mode (lazy
pipes kept per connection): the HTTP proxy, CONNECT tunnels (against
squid and tinyproxy), SOCKSv5 tunnels (against dante) and TCP relays
(tcppm against nginx stream and haproxy mode tcp), each a copying and a
splice table, on the M4 and Skylake hosts; 16 KB buffers against 64 KB.
The earlier sessions follow.
data/: *-auto-http.csv (HTTP proxy) and *-tunnels.csv (CONNECT, SOCKS,
relay) for both machines.
README.md: which proxies take part in which scenarios, reworded.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
bench.py:
- 2000 clients posting 2 KB and getting 50 KB back, next to the GET one
- haproxy with splice-response and splice-request, like -s1 and -s2
- CONNECT and SOCKSv5 scenarios: wrk and iperf3 run under proxychains-ng,
so every connection asks the proxy for a tunnel; and the same loads
through a TCP relay to a fixed destination
- tinyproxy and squid (HTTP proxy, CONNECT), dante (SOCKS); each proxy
takes part only in the scenarios it supports
- 3proxy also gets its socks service and a tcppm to the web server
report.py: one throughput table for several machines (--machine), each
cell their medians and their average; bold for the best of a group, * for
the best of the row; --split puts the splice proxies in a table of their
own, --prefix picks scenarios.
RESULTS.md: the comparison as one table for both machines, splice by
direction (devel -s0/-s1/-s3 against haproxy's settings) and grace delay
with splice, with their data.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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>
