Metadata-Version: 2.4
Name: llmtrafficlens
Version: 0.4.4
Summary: Profile LLM gateway logs for prefix-cache reuse potential, and export anonymized replayable traces
Project-URL: Homepage, https://github.com/GMISWE/llmtrafficlens
Author: Jason Zhu
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: benchmark,kv-cache,llm,mooncake,prefix-cache,trace,traffic-analysis,workload-characterization
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: System :: Benchmark
Requires-Python: >=3.10
Description-Content-Type: text/markdown

# 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.

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

```bash
pip install llmtrafficlens
```

No runtime dependencies. Python ≥ 3.10.

## profile

```bash
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.** `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. The next section converts token counts to gigabytes
for a real model.

*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

```bash
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:

```json
{"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.

```bash
# 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`).

We have not round-tripped an export through AIPerf or SGLang yet. The
formats match their documented schemas, but replay is unverified.

## Try it on a public trace

This needs no data of your own, and doubles as a correctness check:

```bash
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
```

The curve over those 12,031 requests. Cache size is how many tokens of KV
the engine can keep resident; the memory column prices that for
[GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) at bf16, 93 KiB per
token:

| cache size | as GLM-5.2 KV | hit rate |
|---|---|---|
| 16K tokens | 1.5 GiB | 3.3% |
| 64K | 5.8 GiB | 4.2% |
| 256K | 23 GiB | 4.3% |
| 1M | 93 GiB | 5.6% |
| 4M | 372 GiB | 18.5% |
| 16M | 1.5 TiB | 34.2% |
| unbounded | — | **37.4%** (ceiling) |

Read it as: this workload gains almost nothing until the cache passes 1M
tokens, and most of its ceiling needs KV storage past a terabyte — more
than device memory alone holds, which is the case tiered KV stores (host
memory, SSD) are built for.

The unit is tokens because bytes depend on the model, and on more than the
attention formula. GLM-5.2 caches two things per token:

| component | derivation | per token |
|---|---|---|
| MLA latent | `(512 + 64) × 2 bytes × 78 layers` | 87.75 KiB |
| DSA indexer | `128 × 2 bytes × 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. This matches the `kvcache-simulator` catalog, which
reports 95,232 bytes per token.

Do not carry 93 KiB to another model. Only some architectures have a DSA
indexer to account for, 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`. That per-model bookkeeping is why
this tool reports tokens and not bytes.

**Cross-checked against NVIDIA AIPerf.** `aiperf analyze-trace` (v0.11.0)
computes prefix reuse on the same file. Its per-request block hit ratios
reproduce ours to floating-point precision:

```bash
aiperf analyze-trace conversation_trace.jsonl --block-size 512
# cache_hit_rate: 0.38425808746366197
```

Our unbounded ceiling reads 37.4% rather than 38.4% because the two
aggregate differently, not because they disagree: AIPerf averages each
request's hit ratio, weighting a 900-token request the same as a
126,000-token one, while we divide total hit tokens by total input tokens.
Computing AIPerf's definition from our own block matching yields
0.3842580874636671, a difference of 5e-15. Token weighting is the right
choice for sizing a cache, since it is tokens that occupy it.

**Cross-checked against the official simulator.** The
[KVCache.AI hit-rate simulator](https://kvcache.ai/tools/kv-cache-hit-rate-simulator/)
ships as `pip install kvcache-simulator`. On the same trace 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.

Its ceiling is 39.8% against our 37.4% for one reason, which its output
states: 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. So the two agree; they answer
slightly different questions. Ours is what the log as a whole would have
achieved from a cold cache, which is the conservative reading; theirs is
the steady state after the cache fills, which is what a long-running
service sees. Expect our figure to sit a couple of points lower on traces
with many first-time prefixes.

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.

## Methodology

Each metric follows a published construction. Deviations are listed.

- **Hit rate** — 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)](https://arxiv.org/abs/2506.02634), which
  is also the Mooncake simulator's infinite-capacity ceiling; bounded
  capacities give that simulator's [capacity
  curve](https://kvcache.ai/blog/calculate-kvcache-cache-budge/). 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.
  - The simulation models one global cache, i.e. a single worker or
    perfect routing. With N workers behind hash routing, each sees a
    partition, so read the curve as fleet-level guidance.
  - An engine's KV pool also holds in-flight requests. Only the remainder
    is available for reuse.
- **Block hashing** — 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) follows the
  [qwen-bailian trace](https://github.com/alibaba-edu/qwen-bailian-usagetraces-anon)
  convention. Deviation: in approximate mode we hash fixed-size character
  chunks rather than tokenizer output, so two prompts match only if they
  are byte-identical.
- **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.
- **Coverage** — the ceiling covers sharing between requests only.
  Intra-session reuse is not measurable without session identifiers in the
  log. Without a timestamp column, row order is used instead: arrival
  statistics are omitted and the export is flagged as unfit for
  fixed-schedule replay.

## 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.

## Related tools and scope

We do not replay traffic, control request rate, or search deployment
configurations. AIPerf, SGLang `bench_serving` and simulators such as
Vidur do that, and all of them take a trace as input.

Observability platforms (Langfuse, Helicone, Datadog LLM Observability)
record token counts and cost per request, not prefix structure. Trace
analyzers such as the Mooncake simulator and `aiperf analyze-trace`
require an already-hashed trace. As of July 2026 we found no tool that
goes from a raw gateway log to a hashed trace, which is the step this
fills.

## License

Apache-2.0
