Metadata-Version: 2.4
Name: llmtrafficlens
Version: 0.5.0
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.

*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`).

## Try it on a public trace

No data of your own needed:

```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, with cache size priced as GLM-5.2 KV
at bf16:

| 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) |

This workload gains almost nothing below 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.

Cache size is 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.

## Methodology and validation

Metric definitions and their sources, the per-model KV byte accounting,
and what has been checked are in **[METHODOLOGY.md](METHODOLOGY.md)**. In
short: our block matching reproduces NVIDIA AIPerf's `analyze-trace` to
5e-15 on the same trace, and the official `kvcache-simulator`'s ceiling
once its warm-up window is applied. Not yet verified: an export actually
replaying through AIPerf or SGLang.

## 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.

## 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 record token counts and cost per request, not prefix structure;
trace analyzers require an already-hashed trace. This tool covers the step
between: raw gateway log to hashed trace.

## License

Apache-2.0
