Metadata-Version: 2.4
Name: snailmail
Version: 0.3.0
Summary: A local HTTP server that serves a directory over Range with injectable latency and bandwidth limits, for benchmarking range / object-store / virtual-chunk reads under realistic network conditions.
Project-URL: Homepage, https://github.com/ianhi/snailmail
Project-URL: Repository, https://github.com/ianhi/snailmail
Project-URL: Issues, https://github.com/ianhi/snailmail/issues
Project-URL: Changelog, https://github.com/ianhi/snailmail/blob/main/CHANGELOG.md
Author-email: Ian Hunt-Isaak <ian@earthmover.io>
License-Expression: MIT
License-File: LICENSE
Keywords: bandwidth,benchmark,http,icechunk,latency,object-store,range-requests,zarr
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Internet :: WWW/HTTP :: HTTP Servers
Classifier: Topic :: System :: Benchmark
Requires-Python: >=3.10
Requires-Dist: aiohttp>=3.9
Requires-Dist: numpy>=1.24
Provides-Extra: s3
Requires-Dist: moto[s3,server]<6,>=5; extra == 's3'
Description-Content-Type: text/markdown

# snailmail

[![PyPI](https://img.shields.io/pypi/v/snailmail.svg)](https://pypi.org/project/snailmail/)
[![CI](https://github.com/ianhi/snailmail/actions/workflows/ci.yml/badge.svg)](https://github.com/ianhi/snailmail/actions/workflows/ci.yml)

A local HTTP server that serves a directory over HTTP Range, injecting per-request
latency and a bandwidth cap, and counts GETs and peak concurrency.

Use it to benchmark range-based readers — object stores, Zarr/Icechunk virtual
chunks, tiled image formats — under realistic network conditions, on your laptop,
with no cloud and no root.

## Why you'd want it

Local disk hides the cost that dominates remote reads: network round-trips.
A read pattern that finishes instantly against a warm page cache can take
minutes of serial round-trips against object storage. snailmail adds a
per-request latency draw and a shared bandwidth pipe so you can measure how a
reader behaves over the wire. `max_in_flight` tells you peak concurrency, which
wall-clock time alone cannot.

## Install

```bash
uv add snailmail        # or: pip install snailmail
```

## Use it in a benchmark

snailmail serves a directory. Every file under the root is reachable at its path
relative to the root, which matches the shape of an object store or Icechunk virtual
dataset (one object per file). Point your reader at `server.base` and have it fetch
keys like `chunks/0.0.0`.

A key is served iff its resolved real path is a file **inside** the root. Symlinks are
followed, but a symlink whose target escapes the root is not served (it 404s) and is
not listed by `files()` or counted in `n_files` — index and serving agree.

### Serving a single file

To benchmark one file, use `HTTPRangeServer.from_file(path)` — it serves that file
directly (reachable at its basename), with no directory, no temp dir, and **no copy**,
so a multi-hundred-MB fixture costs nothing to set up:

```python
from snailmail import HTTPRangeServer, LogNormal

with HTTPRangeServer.from_file("CMU-1.tiff", latency=LogNormal(mode_ms=40)) as server:
    open_and_read(server.url("CMU-1.tiff"))   # server.files() == ["CMU-1.tiff"]
    print(server.stats())
```

It's the same server with one key: `describe()`, `files()`, `url()`, and `stats()`
behave exactly as in directory mode. The file is streamed from disk via the same
machinery, and since only that one path is ever served, there's no traversal surface —
every other key 404s.

```python
from snailmail import HTTPRangeServer, LogNormal

with HTTPRangeServer("my_zarr_store/", latency=LogNormal(mode_ms=40), bandwidth_mbs=100) as server:
    server.reset_counts()
    open_and_read(server.base)         # your reader: obstore, icechunk, zarr, ...
    print(server.stats())
    # {'n_gets': 312, 'n_requests': 312, 'n_misses': 0, 'max_in_flight': 16,
    #  'total_bytes': .., 'methods': {'GET': 312}, 'paths': {..}}
```

`open_and_read` stands in for the reader you're benchmarking. It makes HTTP GETs
(with `Range` headers) against `server.base`; snailmail injects the latency, meters
the bytes through the bandwidth pipe, and streams the file from disk in response. A
direct request looks like this:

```python
import urllib.request

with HTTPRangeServer("my_zarr_store/") as server:
    req = urllib.request.Request(server.url("chunks/0.0.0"), headers={"Range": "bytes=0-1023"})
    first_kib = urllib.request.urlopen(req).read()
```

`server.url(key)` builds the URL for a key; `server.files()` lists the served keys.
`stats()` is a snapshot of request counters since the last `reset_counts()`:
`n_requests` counts every request, `n_gets` only the GETs, and `n_misses` the
requests for keys that don't exist (404, like an object store's NoSuchKey). Tune
between measurements with `set_latency(dist)`, `set_bandwidth_mbs(x)`, and
`reset_counts()`.

Latency is a pluggable distribution passed as `latency=`:

```python
from snailmail import LogNormal, Normal, Exponential, Fixed

LogNormal(mode_ms=45, sigma=0.5)   # unimodal hump with long right tail; fits object-store GET RTT
Normal(mean_ms=45, std_ms=10)      # symmetric, truncated at 0
Exponential(mean_ms=45)            # peak at 0; a poor model for GET RTT
Fixed(20)                          # deterministic
```

`latency=None` (the default) injects no latency.

## From the CLI

```bash
snailmail ./store --dist lognormal --mode-ms 45 --sigma 0.5
snailmail ./store --dist normal --mean-ms 45 --std-ms 10
snailmail ./store --dist exponential --mean-ms 45
snailmail ./store --dist fixed --value-ms 20
snailmail ./store --bandwidth-mbs 100 --port 8080 --json   # no latency; JSON address line
```

The argument is the directory to serve.

`--json` prints a single machine-readable line and flushes it before serving,
so a script can spawn snailmail, read the bound address from stdout, and proceed.

The CLI rejects a flag that doesn't belong to the chosen `--dist`. Omit `--dist`
for no injected latency.

## Object storage (Icechunk metadata)

The range server above models reading chunk **data**. But a tool like
[Icechunk](https://icechunk.io) also reads and writes **metadata** — config, refs,
snapshots, manifests — from an object store. Put that metadata on local disk and those
reads are *free*: once your data reads are tuned down to ~1 request, the metadata
round-trips that now dominate are invisible, and you can't compare against the cloud
honestly.

`ObjectStore` closes that gap. It's a real S3-compatible object store —
[moto](https://github.com/getmoto/moto) running in-process, so list/get/put/delete and
conditional writes all behave like S3 — wrapped in the **same** per-request latency and
bandwidth model as the range server (see [What it models](#what-it-models)). Metadata
operations pay realistic RTT, and it counts them, split by repo component, so you can read
off the metadata cost of an open or read separately from the data cost.

It's a store first: latency is **optional** wire shaping. Omit it and `ObjectStore()` is
just a plain local S3 store (still counted); add `latency=`/`bandwidth_mbs=` to shape the
wire. It needs the `s3` extra (which pulls in moto):

```bash
uv add 'snailmail[s3]'        # or: pip install 'snailmail[s3]'
```

Point Icechunk at it with `icechunk_storage()`, which returns a ready-wired
`icechunk.Storage` (path-style, plain HTTP, dummy credentials):

```python
import icechunk
from snailmail import ObjectStore, LogNormal

with ObjectStore(latency=LogNormal(mode_ms=45)) as store:
    repo = icechunk.Repository.open(store.icechunk_storage(prefix="my-repo"))
    read_an_array(repo)        # the reopen + read you're benchmarking

    print(store.stats())
    # {'n_requests': 6, 'n_misses': 2, 'metadata_requests': 4, 'data_requests': 0,
    #  'ops': {'GET': 6}, 'max_in_flight': 3, 'bytes_down': 2427, 'bytes_up': 0,
    #  'prefixes': {'config': 1, 'refs': 1, 'snapshots': 1, 'manifests': 1, 'other': 2},
    #  'prefix_bytes': {'config': 323, 'refs': 337, 'snapshots': 604, 'manifests': 355},
    #  'conditional_stripped': 0, 'conditional_rejected': 0}
```

`metadata_requests` (config/refs/snapshots/manifests/transactions) and `data_requests`
(chunks) split the cost the way a benchmark wants it; `prefixes` and `prefix_bytes` give
the per-component breakdown. As with the range server, tune between measurements with
`set_latency(dist)`, `set_bandwidth_mbs(x)`, and `reset_counts()`, and read the endpoint
from `store.endpoint_url` if you're driving it with another S3 client (e.g. `obstore` or
`boto3`). The store is in-process and ephemeral — objects live in memory (moto spools any
object over ~5 MB to a temp file) and vanish on exit. Per-request access logging is off by
default; pass `quiet=False` to see every S3 request on stderr.

### Emulating store quirks (conditional writes)

Real object stores differ in which S3 features they implement, and those differences
change how a tool like Icechunk must be configured. `ObjectStore` emulates such quirks via
a `StoreBehavior` — grouped so the API stays stable as more quirks are added.

The first quirk is **conditional writes** (`If-None-Match` / `If-Match`, which Icechunk
uses to make ref creation and commits atomic). Not every store implements them — JASMIN's,
for instance, rejects them. `StoreBehavior(conditional_writes=...)` models each behavior
locally, with no cloud credentials:

| `conditional_writes` | Models a store that… | A conditional write… |
|---|---|---|
| `"enforce"` *(default)* | supports them (real S3) | is honored (compare-and-swap) |
| `"reject"` | does **not** implement them (e.g. JASMIN) | is refused with `501 NotImplemented` |
| `"ignore"` | accepts but silently ignores them | overwrites unconditionally |

```python
from snailmail import ObjectStore, StoreBehavior

# Behaves like JASMIN: reject conditional writes with NotImplemented.
with ObjectStore(behavior=StoreBehavior(conditional_writes="reject")) as store:
    ...
    print(store.stats()["conditional_rejected"])   # count of writes refused
```

`"ignore"` is the quieter hazard — the write *succeeds* but loses its atomicity guarantee,
so it surfaces lost-update bugs; `stats()["conditional_stripped"]` counts those.

This makes otherwise creds-only failures reproducible on a laptop. `repros/icechunk_2228.py`
is a self-contained reproduction of [icechunk#2228](https://github.com/earth-mover/icechunk/issues/2228)
(conditional-op settings silently dropped under `spec_version=1`) — run it with
`uv run repros/icechunk_2228.py`, no JASMIN account required.

## What it models

**Latency** is a per-request draw from the chosen distribution. `lognormal` is
the recommended default: parameterise it by the PDF mode (`--mode-ms`) and shape
(`--sigma`). `normal`, `exponential`, and `fixed` are available for comparison.

**Bandwidth** is a single shared FIFO pipe (`--bandwidth-mbs`, MB/s = 1e6 bytes/s).
Per-request round-trips run in parallel, but response bytes serialize through the
pipe, so aggregate egress is capped and over-read costs real transfer time. Omit
for unlimited bandwidth.

HTTP correctness (206, `Content-Range`, suffix ranges, 416, conditional requests)
and on-disk streaming come from aiohttp's `web.FileResponse`. Files are never
loaded into RAM, so multi-gigabyte files work.

Missing keys return 404 and are counted in `n_misses`, matching object-store
NoSuchKey behavior.

## Notes

- Loopback only (binds `127.0.0.1`); nothing leaves the machine.
- Consumers must opt into plain HTTP: obstore `client_options={"allow_http": True}`,
  icechunk `http_store({"allow_http": "true"})`.
- The injected latency is added to the real (sub-millisecond, local-SSD)
  range-read time, so the modelled RTT is dominated by the configured value.
- For transport-accurate shaping on real packets, use `tc netem` (Linux) or
  `dnctl`/`pfctl` (macOS) in front of any file server. snailmail trades that
  for zero-setup, in-process instrumentation.

Contributing? See [AGENTS.md](AGENTS.md). MIT licensed.
