hedge-python 0.1.0

Adaptive hedged request library for Python. Learns per-host latency via DDSketch, fires backup requests at estimated p90, caps hedge rate with token bucket.

hedge-python

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Python port of bhope/hedge — adaptive hedged requests for tail-latency optimisation.

hedge-python learns per-host latency distributions with DDSketch, races a backup request when the primary exceeds its estimated p90, and caps the hedge rate with a token bucket to prevent load amplification during outages. Zero configuration required. First-class support for httpx, aiohttp, and gRPC (unary + server-streaming).

Inspired by Dean & Barroso, The Tail at Scale (CACM 2013).

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Why hedging?

A small fraction of slow responses dominates user-perceived latency. Hedging fires a duplicate request after the primary blows past its expected deadline — whichever finishes first wins, the other is cancelled.

Result on a benchmark with 5% straggler requests (10× slower):

Framework	Configuration	p50	p90	p95	p99	p999	Overhead
httpx	No hedging	5.8	10.3	12.2	51.3	78.3	0.0%
httpx	Adaptive (hedge)	6.2	10.5	12.1	18.8	22.2	7.0%
aiohttp	No hedging	6.3	10.7	13.0	52.4	79.0	0.0%
aiohttp	Adaptive (hedge)	6.5	11.3	13.8	20.5	25.1	4.6%
grpc	No hedging	6.5	10.8	12.7	59.9	82.0	0.0%
grpc	Adaptive (hedge)	6.9	11.6	13.7	20.4	23.5	5.6%

Across all three frameworks, p99 latency drops by 60–66% at the cost of ~5–7% extra backend traffic. Reproduce with make bench-multi && make bench-plot.

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Quick Start

# Install with your preferred framework
pip install hedge-python[httpx]
pip install hedge-python[aiohttp]
pip install hedge-python[grpc]
pip install hedge-python[all]   # all frameworks

httpx

import asyncio
import httpx
from hedge import HedgeConfig
from hedge.transport import HedgedHttpxTransport

async def main():
    transport = HedgedHttpxTransport(config=HedgeConfig())
    async with httpx.AsyncClient(transport=transport) as client:
        resp = await client.get("https://api.example.com/data")
        print(resp.status_code)

asyncio.run(main())

aiohttp

import asyncio
from hedge import HedgeConfig
from hedge.transport import HedgedAiohttpSession

async def main():
    async with HedgedAiohttpSession(config=HedgeConfig()) as session:
        resp = await session.get("https://api.example.com/data")
        data = await resp.json()
        print(data)

asyncio.run(main())

gRPC (Unary)

import grpc.aio
from hedge import HedgeConfig
from hedge.interceptor import HedgedUnaryInterceptor

async def make_channel():
    return grpc.aio.insecure_channel(
        "localhost:50051",
        interceptors=[HedgedUnaryInterceptor(config=HedgeConfig(estimated_rps=500))],
    )

gRPC (Server Streaming — LLM inference, log tailing, …)

import grpc.aio
from hedge import HedgeConfig
from hedge.interceptor import HedgedServerStreamInterceptor

async def make_channel():
    return grpc.aio.insecure_channel(
        "localhost:50051",
        interceptors=[HedgedServerStreamInterceptor(config=HedgeConfig())],
    )

For server streaming, the hedge signal is time-to-first-message (TTFM): if the primary stream doesn't yield its first chunk within the estimated p90, a backup stream is started. Whichever yields first wins and continues streaming; the loser is cancelled at the wire level.

Runnable examples for each framework live in examples/ — the gRPC ones are fully self-contained (they spin up a local server with simulated stragglers so you can see hedging in action without any external dependency). See examples/README.md for the index.

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How It Works

1. DDSketch quantile estimator

Each target host gets a WindowedSketch — a pair of DDSketches that rotate every 30 seconds. DDSketch uses logarithmic bucket mapping to provide relative-error guarantees: any quantile estimate is within ±1% of the true value, regardless of the underlying distribution.

2. Adaptive trigger

On each request, the transport queries the sketch for the configured percentile (default p90). If the primary hasn't responded by that deadline, a backup request is fired. Whichever response arrives first is returned; the loser is cancelled (including the underlying gRPC Call for streams).

              ┌─ primary  ─────────── ✓ (fast) ──→ return
request ──────┤
              └─ hedge fires after p90 ─── ✗ (cancelled)

3. Token bucket budget

Hedges are rate-limited by a token bucket that refills at estimated_rps × budget_percent / 100 tokens per second. During genuine outages the bucket drains and hedging stops automatically — preventing the load-doubling spiral that would deepen the incident.

gRPC implementation note

The gRPC intercept_unary_unary continuation returns a Call object almost immediately; the real RTT is spent in the subsequent await call. We wrap both steps in a single asyncio task so the hedge timer reflects true end-to-end RPC latency. Cancelling a loser invokes call.cancel() first (notifying the server) then task.cancel() (cleaning up the coroutine).

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Configuration

All knobs live on HedgeConfig:

Parameter	Type	Default	Description
percentile	float	0.90	Sketch quantile used as hedge trigger
max_hedges	int	1	Maximum concurrent hedge requests per call
budget_percent	float	10.0	Max hedge rate as percent of total traffic
estimated_rps	float	100.0	Expected requests per second; sets token bucket capacity
min_delay	float	0.001	Floor on the hedge delay in seconds
warmup_requests	int	20	Number of initial requests using fixed delay
warmup_delay	float	0.01	Fixed hedge delay during warmup in seconds
window_duration	float	30.0	Sketch window rotation interval in seconds
stats	Stats | None	None	Inject a custom Stats for observability

Tip — estimated_rps: pick a value close to your real RPS so the token bucket capacity (rps × budget_percent / 100) is meaningful. If unsure, start at the default 100.0 and watch hedge_rate / budget_exhausted in the stats snapshot.

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Observability

from hedge import HedgeConfig, Stats
from hedge.transport import HedgedHttpxTransport

stats = Stats()
transport = HedgedHttpxTransport(config=HedgeConfig(stats=stats))

# ... after running some traffic ...
snap = stats.snapshot()
print(f"total={snap.total_requests} hedged={snap.hedged_requests}")
print(f"hedge_wins={snap.hedge_wins} primary_wins={snap.primary_wins}")
print(f"budget_exhausted={snap.budget_exhausted}")
print(f"hedge_rate={stats.hedge_rate():.2%}")

Stats is fully thread-safe and can be shared across multiple transports/interceptors to aggregate metrics.

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Benchmarks & charts

Two benchmark suites ship with the project:

Command	What it does	Output
make bench-compare	httpx only: No hedging vs Static 10ms vs Static 50ms vs Adaptive	benchmark/results.csv
make bench-multi	httpx vs aiohttp vs gRPC, No hedging vs Adaptive	benchmark/results_multi.csv
make bench-plot	Render both CSVs into charts	eval.png, eval_multi_framework.png

Each suite runs 500 requests against a simulated lognormal latency (mean=5ms, stddev=2ms) with 5% straggler probability (10× spike).

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Development

# Install uv (if not already)
curl -LsSf https://astral.sh/uv/install.sh | sh

make install            # install all extras with uv
make lint               # ruff check
make typecheck          # mypy
make test               # all tests
make test-unit          # unit tests only
make test-integration   # integration tests (requires httpx / aiohttp / grpcio)
make coverage           # coverage report (current: 96%)
make bench-multi        # multi-framework benchmark
make bench-plot         # render charts
make ci                 # lint + typecheck + test + coverage

Testing

• Unit tests (tests/unit/): DDSketch, token bucket, scheduler, stats, options, lazy import shims, gRPC interceptor branches (with fake continuations).
• Integration tests (tests/integration/): real httpx transport, real aiohttp session, real local gRPC server with .proto + generated pb2.
• Benchmarks (tests/benchmark/): DDSketch microbench, token bucket microbench, four-config comparison, three-framework comparison.

Current coverage: 97% (122 tests, ~7 seconds).

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Project Structure

hedge-python/
├── src/hedge/
│   ├── __init__.py          # Public API
│   ├── _options.py          # HedgeConfig dataclass
│   ├── _stats.py            # Thread-safe Stats + StatsSnapshot
│   ├── sketch/
│   │   ├── _ddsketch.py     # DDSketch quantile estimator
│   │   └── _windowed.py     # Sliding-window DDSketch pair
│   ├── budget/
│   │   └── _token_bucket.py # Token bucket rate limiter
│   ├── transport/
│   │   ├── _base.py         # Shared HedgeScheduler logic
│   │   ├── _httpx.py        # httpx AsyncBaseTransport adapter
│   │   └── _aiohttp.py      # aiohttp session wrapper
│   └── interceptor/
│       └── _grpc.py         # gRPC unary + server-stream interceptors
├── tests/
│   ├── unit/                # 7 unit-test files
│   ├── integration/
│   │   ├── proto/           # .proto + generated pb2 / pb2_grpc
│   │   ├── test_httpx_transport.py
│   │   ├── test_aiohttp_session.py
│   │   └── test_grpc_interceptor.py
│   └── benchmark/
│       ├── test_bench_ddsketch.py
│       ├── test_bench_token_bucket.py
│       ├── test_bench_hedge_comparison.py    # httpx 4-config
│       └── test_bench_multi_framework.py     # 3-framework comparison
├── benchmark/
│   ├── plot.py              # CSV → matplotlib charts
│   ├── results.csv          # produced by bench-compare
│   └── results_multi.csv    # produced by bench-multi
├── eval.png                 # single-framework chart
├── eval_multi_framework.png # cross-framework chart
├── pyproject.toml
├── Makefile
└── .github/workflows/ci.yml

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References

• Jeffrey Dean and Luiz André Barroso. "The Tail at Scale." Communications of the ACM, 56(2):74–80, February 2013.
• Charles Masson, Jee E. Rim, and Homin K. Lee. "DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees." Proceedings of the VLDB Endowment, 12(12):2195–2205, 2019.

Changelog

See CHANGELOG.md for the full release history.

License

hedge-python is released under the MIT License.