create-release.yml is back to calling the chaos test through no mechanism at
all; it never called it. Also drops the E2E_REDIS_CHAOS opt-in gate itself:
the redis_chaos marker still exists for -m selection and is still excluded
from the per-PR selector by path (tests/e2e/(ui|claude_code|load)/), but the
test no longer needs an env var to run once its file is targeted.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Now that create-release.yml gates stable and RC releases on this test directly,
the weekly schedule is redundant: every release gets a run against its own
commit instead of whatever happened to be on the default branch that Saturday.
workflow_dispatch stays for manual runs.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Local runs measured 3.5 KB per request, so 10 KB keeps close to 3x headroom
while tightening from the earlier 12 KB.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Local runs measured p50 0.19s, p90 0.23s, p99 0.69s, so 2s/3s/5s left several
times that as slack. 1s/2s/3s keeps a comfortable margin while catching a
smaller regression than the looser ceilings would have.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Three local runs measured 1.33x-1.36x, so 2x is the tightest bound the data
supports and still catches a regression far smaller than 4x would. Noted in the
comment that this is the ceiling to loosen first if a weekly run trips it,
since core count shifts how much of baseline CPU is fixed per-request work.
Co-Authored-By: Claude Code <noreply@anthropic.com>
RSS moved 0.91x-1.40x across three identical local runs, so it stays loose at
2x rather than the arbitrary 1.5x carried over from the pre-padding-payload
calibration. CPU per request held steady at 1.33x-1.36x across the same runs,
so 4x replaces the looser 6x it inherited from stale numbers.
Co-Authored-By: Claude Code <noreply@anthropic.com>
A ratio against the healthy phase cannot bound either metric. Once the Redis
circuit breaker opens, a request skips Redis instead of waiting on its socket
timeout, so the chaos phase can measure cheaper than the baseline it is compared
against: local runs came in at 0.61x baseline p90 while a log-bytes ratio read
724x. Splitting Budget into RatioBudget and AbsoluteBudget lets RSS and CPU keep
the ratio they need, since both are machine-shaped, while latency and log volume
get the wall-clock ceiling a user actually cares about.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Pad the locust payload to tens of KB so per-request bookkeeping cost scales with
body size instead of hiding behind a 40-byte prompt. Turn on
use_redis_transaction_buffer in the chaos config and JSON_LOGS in the workflow so
the spend buffer, pod lock, and JSON-encoded breaker tracebacks are all part of
the measured chaos cost. Add a log-bytes-per-request budget alongside latency,
RSS, and CPU, reading the proxy's log file size at each phase split; its ceiling
is uncalibrated since no chaos run has measured it yet.
Co-Authored-By: Claude Code <noreply@anthropic.com>
The Anthropic Messages route reaches the same Redis touchpoints and cost-tracking
callback through its own request path, so a failure-path regression there would not
surface from chat completions alone. Each simulated user now picks one endpoint round
robin and stays on it, and the per-endpoint split is asserted and reported so a run
that silently drove only one route fails instead of passing.
Co-Authored-By: Claude Code <noreply@anthropic.com>
CLIENT PAUSE ALL for the length of the chaos phase instead of CLIENT PAUSE WRITE, so every Redis touchpoint on the request path times out rather than just the writes. The pause is sized to the phase because it freezes the control connection too; teardown's CLIENT UNPAUSE is a safety net for a phase that overran
Latency, RSS and CPU are now budgeted as chaos-over-baseline ratios (p50/p90/p99 for latency and RSS, CPU seconds per request once) through a small phase_budget module, replacing the machine-shaped absolutes. The Redis timeout rate is reported but no longer asserted
The final /metrics scrape waits for litellm_deployment_failure_responses_total to stop moving, since that counter is bumped from the async logging queue and lagged the load generator by thousands of increments. The model group carries a unique marker so a deployment left behind by an aborted run cannot absorb this run's retries
Co-Authored-By: Claude Code <noreply@anthropic.com>
The sequential version sent one request at a time, so a Redis outage never
reached the concurrency where the failed-tracking alert body actually grows.
This drives the proxy with locust against one model group of three mock
deployments, two failing at order 1 and one serving at order 2, so every
request spends its retries on the failing pair and lands on the serving
deployment through the order-based fallback. Two phases, a healthy baseline
and a CLIENT PAUSE WRITE window, and every request must succeed in both.
Latency, RSS and CPU are reported as p50/p90/p99 per phase rather than
asserted on: RSS and CPU come from psutil on the proxy's process tree, since
a multi-worker proxy serves /metrics from the prometheus multiprocess
collector and that drops the process collector's series. Thresholds stay open
until weekly runs give real baselines.
Co-Authored-By: Claude Code <noreply@anthropic.com>
