llmtrafficlens 0.6.1

Profile LLM gateway logs for prefix-cache reuse potential, and export anonymized replayable traces

llmtrafficlens

Analyze an LLM gateway's request log for prefix-cache reuse, then export the log as an anonymized trace that standard benchmark tools can replay.

On the public Mooncake conversation trace, which the Try it section below reproduces verbatim:

$ llmtrafficlens profile conversation_trace.jsonl --format mooncake -o report
12,031 requests · unknown
input p50 6,909 tok · output p50 350 tok · stream 0% · 3.4 req/s

hit rate by cache size
      16K tokens    3.3%
      64K tokens    4.2%
     256K tokens    4.3%
       1M tokens    5.6%
       4M tokens   18.5%
      16M tokens   34.2%
       unbounded   37.4%  <- ceiling

session identifiers  0.0% of requests  (affinity routing cannot reach this reuse)
top prefix by volume 512 tok x 12,031 reqs = 5.3% of reusable volume
top prefix by count  512 tok x 12,031 reqs = 5.3% of reusable volume

wrote report.json, report.html

report.html carries the full leaderboards and distributions; report.json is the same content for scripts.

Two commands:

• profile — how much a prefix cache could save on this traffic, and how much cache memory that takes.
• export — the same log as a Mooncake- or bailian-format trace, with all text removed.

Background

An engine serves a request in two phases: prefill reads the whole prompt at once, then decode emits output tokens one by one. Prefill cost grows with prompt length, and it is pure recomputation whenever two requests begin with the same text — the same system prompt, the same few-shot examples, the same attached document.

A prefix cache avoids that: the intermediate state a prompt produces (its KV cache) stays in memory, and the next request starting with the same text reuses it instead of recomputing. Whether that pays off depends on three things, which are exactly what this tool reports:

• The traffic. If requests share no leading text, nothing can be reused. This is a property of your workload, not of your setup.
• Cache memory. KV state is bulky, so a cache holds a bounded number of tokens and evicts the rest. More memory, more hits — with diminishing returns you can measure instead of guess.
• Routing. Across several workers, a request only hits if it reaches the worker that already holds its prefix. Sending it there is what prefix-aware routing does; the alternative, session affinity, only works when requests carry a session identifier.

Reuse is tracked in fixed-size blocks (16 tokens by default) because engines cache at block granularity, not per character.

Install

pip install llmtrafficlens

No runtime dependencies. Python ≥ 3.10.

profile

llmtrafficlens profile gateway.csv -o report

Input. A CSV with a request_json column holding the OpenAI-format request body. These columns are used when present: response_json, model_name, status_code, and a timestamp column (timestamp, ts, created_at, ...; epoch or ISO-8601, or set --ts-column). Mooncake and qwen-bailian traces are accepted as input too (--format mooncake|bailian).

Output. A summary on stdout (shown above), plus report.html and report.json containing:

• hit rate at each cache size, ending with the unbounded case — the ceiling;
• top prefixes by reuse count, and separately by reusable token volume;
• share of requests carrying a session identifier;
• input/output token distributions, streaming ratio, model mix, QPS.

How to read it.

Ceiling — the share of input tokens that could be served from cache if memory were unlimited and every request reached the right worker. It bounds everything else: at a few percent, no amount of engineering makes prefix caching worthwhile on this traffic.

Capacity curve — the hit rate at each cache size, so you can see what the ceiling costs. Where it flattens is the point past which buying memory stops helping.

Session-identifier share — whether the cheap option is enough. When most requests carry an identifier, pinning each session to a worker captures the reuse. When none do (a common case for API traffic), the reuse sits in prefixes shared between unrelated requests, and only prefix-aware routing reaches it.

The two leaderboards — which prefixes to keep resident. They rank differently, and the token-volume one is what determines savings: a short prefix reused very often contributes almost no reusable volume, while a long prefix reused a few dozen times can account for most of it.

export

llmtrafficlens export gateway.csv --to mooncake -o trace.jsonl   # or --to bailian

A benchmark written by hand, with equally popular prompt groups, reports a higher hit rate than production reaches, because every group stays warm. Replaying the real log avoids that, and the export can be shared, because each request is reduced to one line of structure:

{"timestamp": 1753340000123, "input_length": 1994, "output_length": 117,
 "hash_ids": [4251731047194047120, 8125214104179782736, ...]}

hash_ids is a salted chained block hash: two requests share their first N hashes exactly when they share their first N blocks. Replay tools generate one synthetic block per hash, so the prefix-sharing structure is preserved while the content is not real.

# replay at the recorded pace
aiperf profile --custom-dataset-type mooncake_trace --input-file trace.jsonl --fixed-schedule ...
# replay at 2x the pace, for rate sweeps
aiperf profile --custom-dataset-type mooncake_trace --input-file trace.jsonl --synthesis-speedup-ratio 2.0 ...
# or SGLang
python -m sglang.bench_serving --dataset-name mooncake --dataset-path trace.jsonl ...

--to bailian adds chat_id, parent_chat_id and turn for AIPerf's bailian_trace mode; use it when the log carries session identifiers. Tell the consumer which block size the export used — AIPerf defaults to 512 for mooncake and 16 for bailian (--prompt-input-tokens-block-size).

Try it on a public trace

No data of your own needed:

curl -LO https://raw.githubusercontent.com/kvcache-ai/Mooncake/main/FAST25-release/traces/conversation_trace.jsonl
llmtrafficlens profile conversation_trace.jsonl --format mooncake -o mooncake-report

That prints the summary at the top of this README. Reading the curve: this workload gains almost nothing below 1M tokens, and most of its ceiling needs a cache past 16M — which for GLM-5.2 at bf16 (93 KiB/token) is 1.5 TiB, more than device memory alone holds, and the case tiered KV stores (host memory, SSD) are built for.

Cache size is reported in tokens because bytes are model-specific: 93 KiB/token for GLM-5.2, 68.6 for Kimi K2.5, 128 for a 32-layer fp16 GQA model. Some architectures also carry a DSA indexer cost on top of attention. Look yours up with kvcache-simulator list-models.

The repository also contains a synthetic sample log (examples/, not shipped in the pip package): 360 requests, three shared system prompts with skewed popularity, sparse session identifiers, real timestamps. It reports a 66.4% ceiling, and it shows the two leaderboards disagreeing — 16 tokens × 180 requests is 2.3% of the reusable volume, while 2400 tokens × 30 requests is 55.6% of it.

Privacy

Processing is local. Reports contain aggregates only. An export contains lengths, timestamps, anonymized session identifiers and salted block hashes; prompts cannot be reconstructed from it. The hash key is written to .ltl-salt on first run — keep it unchanged so runs stay comparable, and treat it as a secret.

FAQ

How is the hit rate computed?

Requests are replayed in timestamp order against an LRU block cache. Unbounded capacity gives the "ideal hit rate" of KVCache in the Wild (ATC'25), which is also the Mooncake simulator's infinite-capacity ceiling; bounded capacities give that simulator's capacity curve. LRU is the default eviction policy in both vLLM (FreeKVCacheBlockQueue) and SGLang (--radix-eviction-policy lru), though SGLang evicts radix-tree leaves rather than flat blocks.

Prefixes are identified by chained per-block hashing (hash(parent, block), 16-token blocks; 16 is the smallest block vLLM's FlashAttention backend supports and the granularity it aligns to), following the qwen-bailian trace convention.

What does the curve assume?

One global cache, i.e. a single worker or perfect routing. With N workers behind hash routing each sees a partition, so read it as fleet-level guidance rather than per-worker sizing. An engine's KV pool also holds in-flight requests; only the remainder is available for reuse.

The ceiling covers sharing between requests only. Intra-session reuse is not measurable without session identifiers in the log.

How do I convert token counts to gigabytes?

Per-token cost follows the architecture. GLM-5.2 caches two things:

component	derivation	per token
MLA latent	(512 + 64) x 2 bytes x 78 layers	87.75 KiB
DSA indexer	128 x 2 bytes x 21 full-indexer layers	5.25 KiB
		93 KiB

Only 21 of the 78 layers run a full indexer, the rest reuse the previous one's selection. Both terms match the kvcache-simulator catalog, which reports 95,232 bytes per token for this model.

Do not scale that figure to another model. Only some architectures carry a DSA indexer, and the attention term differs as well: Kimi K2.5 is 68.6 KiB per token, a 32-layer fp16 GQA model 128 KiB. Look yours up with kvcache-simulator list-models.

Has this been checked against other tools?

Yes, twice, both on the public Mooncake conversation trace with 512-token blocks, and both pinned by tests in tests/test_core.py.

NVIDIA AIPerf v0.11.0. aiperf analyze-trace reports cache_hit_rate: 0.38425808746366197, which is the unweighted mean of per-request hit-block ratios. Computing that same definition from our own block matching yields 0.3842580874636671 — a difference of 5e-15, so the two implementations agree on every request's prefix match.

kvcache.ai kvcache-simulator. With GLM-5.2 accounting it reports a 39.8% ceiling and an LRU curve of 4.5% at 30 blocks, 5.5% at 1,937, 17.9% at 7,729 and 34.6% at 30,918 — the same shape as ours at comparable block counts.

Then why is your ceiling lower than theirs?

Different aggregation, not disagreement.

Against AIPerf: we divide total hit tokens by total input tokens, which weights a 126,000-token request above a 900-token one, while AIPerf averages per-request ratios. Token weighting is the right choice for sizing a cache, since it is tokens that occupy it.

Against the simulator: it measures over the last 50% of requests, treating the first half as warm-up, while we measure the whole log. Applying its window to our replay gives 0.3983 against its 0.398. Ours is what the log as a whole would have achieved from a cold cache; theirs is the steady state after the cache fills. Expect our figure to sit a couple of points lower on traces with many first-time prefixes.

How exact are the token counts?

Characters divided by four, unless the log carries usage, in which case exact counts are used and the report says so. CJK-heavy text tokenizes closer to 1-2 characters per token, so absolute counts run low while ratios remain usable.

What has not been verified?

• Replay round-trip. We have not fed an export through AIPerf or SGLang and confirmed it replays. The formats match their documented schemas, which is not the same thing.
• Exact tokenization. Approximate mode hashes character chunks, so reported structure is a lower bound on what a real tokenizer would match: formatting differences that tokenize identically read as distinct prefixes here.
• Landscape claims. The statement below that no tool goes from a raw gateway log to a hashed trace comes from a survey of vendor documentation in July 2026. That is an absence of evidence, not a vendor denial.

Why not just use AIPerf or the Mooncake simulator?

Use them — for what they do. We do not replay traffic, control request rate, or search deployment configurations; AIPerf, SGLang bench_serving and simulators such as Vidur do, and all of them take a trace as input. Observability platforms record token counts and cost per request, not prefix structure. Trace analyzers, including both tools above, require an already-hashed trace. This tool covers the step between: raw gateway log to hashed trace.

License

Apache-2.0