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Mohamed Tahri
Mohamed Tahri

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Snapshot Testing in Python with pytest-verify — Part 2: Async Support

#python #pytest #snapshot #testing

In the previous article, we explored how pytest-verify makes snapshot testing effortless for structured data — JSON, YAML, XML, DataFrames, and more.

But what if your tests are asynchronous?
Modern Python apps rely heavily on async I/O — think of:

  • Async web frameworks like FastAPI, aiohttp, or Quart

  • Async database drivers (asyncpg, motor)

  • Message brokers and streaming systems

Until now, testing async functions meant juggling plugins and decorators.
Not anymore 🚀

Introducing Native Async Support

Starting from pytest-verify v1.2.0, you can snapshot async test functions directly — no wrappers, no extra ceremony.

Whether your test is async or simply returns a coroutine, @verify_snapshot now handles both seamlessly:

⚙️ Setup

1.Install the async extras:

pip install pytest-verify[async]
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2.Enable async mode in your pytest configuration (either in pytest.ini or pyproject.toml):

pyproject.toml:

[tool.pytest.ini_options] asyncio_mode = "auto"
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pytest.ini:

[pytest] asyncio_mode = auto
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Example 1 — Async REST API Snapshot

Let’s simulate a lightweight async API call:

import asyncio from pytest_verify import verify_snapshot async def get_user(): await asyncio.sleep(0.1) return {"id": 123, "name": "Mohamed", "country": "France"} @verify_snapshot() async def test_async_user_snapshot(): """Ensure async API output stays stable.""" user = await get_user() return user
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✅ On first run → baseline snapshot created.
✅ On next runs → automatic comparison with full diff view if anything changes.

Example 2 — Async Data Transformation

You can also snapshot results from async pipelines or background tasks:

from pytest_verify import verify_snapshot async def compute_metrics(): return {"accuracy": 99.94, "loss": 0.102} @verify_snapshot(abs_tol=0.1) async def test_async_data_pipeline(): """Verify numeric tolerance in async output.""" return await compute_metrics()
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✅ pytest-verify waits for the coroutine, serializes the returned dict, and applies your configured tolerances automatically — no extra setup.

Example 3 — Async Ignore Fields and Tolerances

You can combine async support with all snapshot options:

from pytest_verify import verify_snapshot import asyncio, random @verify_snapshot( ignore_fields=["$.meta.timestamp"], abs_tol_fields={"$.metrics.latency": 1.0} ) async def test_async_snapshot_with_ignores(): await asyncio.sleep(0.05) return { "meta": {"timestamp": "2025-10-26T09:00:00Z"}, "metrics": {"latency": random.uniform(99, 101)}, }
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✅ This test ignores the volatile timestamp field
✅ Allows ±1.0 drift for latency values
✅ Works perfectly under async execution

Wrapping Up — Async Testing Made Effortless

In modern Python, async is everywhere — your tests shouldn’t lag behind.
With pytest-verify >= v1.2.0, you can now:

  • Snapshot async APIs and coroutine results directly.

  • Use all features — ignore fields, tolerances, diff viewer — in async mode.

  • Keep your test suite consistent and declarative.

  • No extra plugins. No decorator juggling. Just pure, powerful snapshot testing.

💡 Final Thought

If your tests look like this 👇:

await some_async_func() assert ... assert ... assert ... assert ...
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You can now replace them with:

@verify_snapshot() async def test_async_output(): return await some_async_func()
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Cleaner, safer, and instantly snapshot-aware.

If you find this new async support useful, give pytest-verify a ⭐ on GitHub and
share your feedback!

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