leakyspanner 0.1.2

Non-intrusive FastAPI memory leak profiler with web UI

🔧 LeakySpanner

Zero-interference memory leak profiler for FastAPI.
Pure spectator mode — watches everything, touches nothing.

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The Problem

FastAPI + Python async apps can silently grow to 4–5 GB RAM at peak load.
Finding where is painful because the leak could be anywhere — images held in memory,
uncleaned async tasks, connection pools, Gemini API response objects.

The Solution

LeakySpanner attaches to your FastAPI app as a pure spectator:

• ✅ Watches every request
• ✅ Samples memory in a background thread every N seconds
• ✅ Tracks memory delta per endpoint
• ✅ Uses tracemalloc to find top allocation sites
• ✅ Writes a clean log file you can grep through
• ❌ Never blocks a request
• ❌ Never adds latency
• ❌ Never modifies request/response
• ❌ If LeakySpanner crashes — your app keeps running

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Install

pip install leakyspanner

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Usage

from fastapi import FastAPI
from leakyspanner import leakyspanner

app = FastAPI()

# One line — that's it
leakyspanner(app)

With options:

leakyspanner(
    app,
    enabled=True,               # set False in tests
    log_path="./leak_logs",     # where to write logs
    level="deep",               # basic | mid | deep
    interval_seconds=5.0,       # memory snapshot every 5s
    leak_threshold_mb=50.0,     # warn if RSS grows 50MB above baseline
)

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Levels

Level	What it tracks
basic	Endpoint-level memory delta per request
mid	Endpoint deltas + tracemalloc top allocation stats (default)
deep	Everything + per-snapshot top allocation traces with file/line

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Log Output

LeakySpanner writes to ./leakyspanner_logs/leakyspanner_YYYYMMDD_HHMMSS.log

Snapshot log (every 5s):

[14:32:05] RSS=312.4MB  VMS=890.1MB  delta=+12.1MB  GC=(142, 8, 1)
[14:32:10] RSS=318.7MB  VMS=890.1MB  delta=+18.4MB  GC=(198, 8, 1)

Endpoint delta log (when delta > 1MB):

  [14:32:07] ENDPOINT POST /ocr/process  mem_delta=+6.23MB  status=200
  [14:32:08] ENDPOINT POST /ocr/process  mem_delta=+5.91MB  status=200

Warning (when growth > threshold):

  ⚠️  [14:35:00] MEMORY GROWTH ALERT: +87.3 MB above baseline
                 (baseline=312.4 MB, current=399.7 MB)

Session summary (on shutdown):

======================================================================
  LEAKYSPANNER SESSION SUMMARY
======================================================================
  Peak RSS    : 4821.3 MB
  Final RSS   : 4103.7 MB
  Baseline RSS: 312.4 MB
  Net growth  : +3791.3 MB

  ENDPOINT MEMORY DELTAS (sorted by avg):
    POST   /ocr/process                        calls= 412  avg=+8.73MB  max=+41.20MB
    GET    /health                             calls=1204  avg=+0.00MB  max= +0.01MB
======================================================================

Deep level — tracemalloc traces:

  ── top allocations ──
    42.3KB × 18  /app/services/gemini.py:87
    38.1KB × 412 /app/services/ocr.py:134
    21.7KB × 1   /usr/lib/python3.11/ssl.py:1092

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Reading the Logs

Once you have the log, look for:

1. Endpoint with highest avg delta → that's your primary suspect
2. Steadily rising RSS snapshots that never drops → classic leak
3. tracemalloc lines pointing to specific file:line → that's the smoking gun
4. GC counts growing without dropping → reference cycles not being collected

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Real-world Example (OCR App)

app = FastAPI()

leakyspanner(
    app,
    level="deep",
    log_path="/var/log/myapp/leaks",
    leak_threshold_mb=200.0,     # alert at 200MB growth
    interval_seconds=10.0,       # sample every 10s in prod
)

@app.post("/ocr/process")
async def process(file_id: str):
    # LeakySpanner watches this — you change nothing
    results = await asyncio.gather(
        call_gemini_1(file_id),
        call_gemini_2(file_id),
        call_qr_service(file_id),
        fetch_from_db(file_id),
    )
    return results

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License

MIT