Tier 1 of plan §7.5: a radix tree of request prefixes that estimates which
replica has already prefilled one. Bytes, not tokens — needing the engine's
exact tokenizer is what makes tier 2 a separate project.
It went in inert, and the decision metric is what caught it: 144 lookups, zero
hits. The cause was a rule I had written and tested for. A node's label is the
first request that created it, so the second request for the same system
prompt diverges in its last few bytes — a new question after the same
preamble, the single most common shape there is. Requiring the whole label to
match threw away the 700 bytes they shared. What matters is the shared head,
because that is what the engine cached, and a partial label match now counts.
Two unit tests asserted the wrong rule and have been inverted.
With that fixed, measured end to end against engines whose caches are too
small for the working set: 22.5% cache hits without, 81.7% with, and the
breakdown shows the misses are exactly the first round, when the tree is
empty.
The scenario matters as much as the number. With a handful of prompts and a
large cache, scattering is free — every replica ends up holding everything —
and prefix routing can only add constraint. The first version of the test
measured that case and showed a loss, which was a true statement about a
situation the feature is not for.
Two things keep it from being the disaster prefix routing usually is. The
imbalance guard, which needs both an absolute and a relative gap before it
fires, because either alone misfires — absolute on an idle cluster, relative
on a busy one. And it is a scorer rather than a filter: affinity is a vote the
queue and inflight scorers can outvote, not a rule.
The tree records the replica the picker actually chose, not the one the scorer
proposed, and a node prefers the replica it has seen most rather than most
recently — a count is stable under a randomizing picker, where recency just
chases whatever the picker last did.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Inserted between quota checks and scheduling: admission answers when a request
may proceed, the scheduler still answers where it goes. Keeping them apart is
the point — an engine queue is FIFO and knows nothing about tenants, so once a
batch job is in it, no endpoint selection rescues the request behind it.
- saturation detector: one number per pool, the worse of queue depth and KV
occupancy, since either alone misleads.
- priority bands with per-band saturation ceilings, so the cluster degrades from
the bottom up.
- tenant round-robin within a band: one caller sending a thousand requests takes
one slot in the rotation, not a thousand.
- bounded queues with capacity (503) and TTL (504); cancelled requests leave the
queue instead of holding capacity.
- metrics for queue depth, wait time, rejections and pool saturation.
Exit criterion, measured in test/e2e: with 500 concurrent bronze requests
running continuously, interactive p99 in the platinum band stays at 1.04-1.24x
idle against a 1.5x budget. With admission off the same load pushes it to
55-60x. Both directions are asserted, so the test fails if admission regresses
and also if the load generator stops being a real load.
Two lags had to be closed, and they needed different answers. Engine metrics are
scraped and lag an interval; router inflight does not, but it counts running
requests, and treating those as queue makes a healthy engine look overloaded
every interval. What is actually queued is inflight minus batch capacity, and
capacity is not in the metric contract, so it is learned as the high-water mark
of observed running. Separately, between admission granting a request and the
router dispatching it the request is invisible everywhere, so a simultaneous
burst passed the check en masse; the controller now counts its own outstanding
leases against dispatched inflight.
One plan correction: a saturation ceiling of 1.0 was documented as "never held
back". It is not — it means "flow up to the healthy limit". A band that must
never wait needs a ceiling above 1. Plan §7.2.2 updated.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
llmgw server is real now. It fronts a static set of engine replicas with an
OpenAI-compatible API, scoring endpoints through the pipeline added in the
previous commit.
- proxy: SSE relayed event by event with a flush after each, cancellation
propagated upstream, and separate deadlines for dial, headers, first token,
inter-token idle and total. The idle watchdog is what distinguishes a wedged
engine from a slow one.
- router: dispatch with retry and model fallback. Retry only happens while
nothing has reached the client; after that, splicing two responses together
would be worse than the short stream the client already sees.
- config: YAML via koanf overlaid on defaults, with LLMGW_ env overrides.
Validation rejects duplicate endpoint ids and aliases that shadow a model,
because both misroute silently at request time rather than failing loudly.
- metrics: requests, duration, TTFT, tokens, plus per-scorer values and
per-filter drops on a separate admin listener with health and pprof.
Phase 1 exit criterion, measured in test/e2e: 1000 concurrent streams across
3 sim-engines complete at ~6800 req/s; every client disconnect frees the KV
blocks on every engine; router overhead p99 is about 0.9 ms against the plan's
2 ms budget.
The handlers live in internal/router rather than an internal/router/openai
package as the plan's tree sketched: they need the dispatch loop, and splitting
them apart would have meant an interface with exactly one implementation. Plan
§4 updated to match.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
ilkerandClaude Opus 5