Pytest Profiling & Optimization

Systematic workflow for diagnosing, fixing, and reporting on slow Python/pytest test suites. Measure first, optimize based on data, never guess.

Before Starting

1. •

   Read project conventions — Check AGENTS.md, CLAUDE.md, or pyproject.toml to understand:

   • •Which test directories exist (unit, integration, e2e)
   • •How to run tests (uv run pytest, pytest, make test, etc.)
   • •Test database setup and fixture patterns
2. •

   Ask the user:

   • •Which test directory or marker to profile (or profile the full suite)
   • •Where to write the report file
3. •

   Check for pyinstrument — If not installed, ask the user before adding it:

   bash

   uv pip list | grep pyinstrument
   # If missing, ask before running: uv add --dev pyinstrument
   

Phase 1: Measure

All steps are read-only. No code changes.

Step 1 — Baseline timing + slowest tests

uv run pytest <test_dir> --durations=20 -q

Record:

• •Total wall time
• •Number of tests (passed, failed, skipped)
• •Top 20 slowest phases (setup, call, teardown)

Note whether the slowest items are setup, call, or teardown — this determines where to dig.

Step 2 — Fixture setup/teardown chains

Pick 2-3 of the slowest tests and run:

uv run pytest <test_dir> -k "name_of_slow_test" -q --setup-show

This shows the full fixture dependency chain. Look for:

• •Function-scoped fixtures that could be session-scoped
• •Expensive fixtures called repeatedly (DB setup, app creation, crypto)
• •Long teardown sequences

Step 3 — CPU profiling with pyinstrument

uv run pyinstrument -r text -m pytest <test_dir> -k "name_of_slow_test" -q

The call tree shows exactly where CPU time is spent. Repeat for 2-3 of the slowest tests to find common patterns.

Common hotspots to look for:

• •bcrypt.hashpw / bcrypt.gensalt — password hashing (~200ms per call at default 12 rounds)
• •MetaData.create_all — SQLAlchemy table creation
• •TRUNCATE ... CASCADE — PostgreSQL lock acquisition
• •TestClient(app) — ASGI lifespan enter/exit
• •Module imports — heavy libraries loaded at import time (matplotlib, boto3, numpy)
• •RSA key generation — rsa.generate_private_key()
• •Network connections — Redis/Valkey, external services

Step 4 — Micro-benchmarks (optional)

If a specific operation is suspect, isolate and measure it:

uv run python -c "
import time
# ... setup code ...
times = []
for i in range(10):
    start = time.time()
    # ... the suspect operation ...
    times.append(time.time() - start)
print(f'avg: {sum(times)/len(times)*1000:.1f}ms')
"

This is useful for comparing alternatives (e.g., TRUNCATE vs DELETE, bcrypt rounds 12 vs 4).

Phase 2: Analyze

Summarize findings before touching any code:

1. •Where is time spent? — Categorize by fixture setup, test body, teardown
2. •Which fixtures are expensive? — List per-call cost and how many tests use them
3. •Is there a common bottleneck? — One or two things that dominate across all tests
4. •Estimate total impact — (per-call cost) × (number of calls) = total savings

Phase 3: Optimize

Design targeted fixes based on the profiling data. Apply one fix at a time and measure after each.

Common fixes

Slow bcrypt hashing

Patch bcrypt.gensalt to use minimum rounds (4) in tests via a session-scoped autouse fixture in the root tests/conftest.py:

@pytest.fixture(autouse=True, scope="session")
def _fast_bcrypt():
    original_gensalt = bcrypt.gensalt
    def fast_gensalt(rounds=4, prefix=b"2b"):
        return original_gensalt(rounds=4, prefix=prefix)
    with patch("bcrypt.gensalt", fast_gensalt):
        yield

Slow TRUNCATE teardown

Replace TRUNCATE ... CASCADE with DELETE FROM in reverse FK order. TRUNCATE acquires ACCESS EXCLUSIVE locks (~300ms even on empty tables). DELETE with row-level locks takes ~2ms for small row counts.

Expensive function-scoped fixtures

If a fixture is pure setup with no per-test state, consider widening its scope:

• •scope="module" — shared within one test file
• •scope="session" — shared across the entire run

Only do this if tests don't mutate the fixture's state.

Slow RSA key generation

Cache a test keypair as a session-scoped fixture instead of generating per-test.

Slow module imports

These are one-time costs and generally not worth optimizing. Note them in the report but don't act on them unless they dominate.

Measurement after each fix

After each optimization:

1. •Run the full suite: uv run pytest <test_dir> -q --tb=no
2. •Record the new wall time
3. •Calculate delta from previous state

Phase 4: Commit

Commit each optimization as a separate commit with measured timings in the message:

perf(tests): <what changed>

<Why it helps.>

<previous>s → <new>s (saved ~<delta>s)

Use conventional commits (perf: prefix for performance improvements).

Phase 5: Report

Write a report to the location chosen by the user. The report must include all of the following sections:

Report template

# Test Suite Performance: Analysis & Fixes

**Date:** YYYY-MM-DD
**Result:** <before>s → <after>s (<speedup>x faster)

## Baseline

- Test command: `<exact command>`
- Total tests: X passed, Y failed (pre-existing), Z skipped
- Wall time: Xs

## Methodology

1. `pytest --durations=20` — identify slowest test phases
2. `pytest --setup-show` — trace fixture setup chains
3. `pyinstrument` — CPU call trees on slowest tests

## Profiling Findings

| Component | Time | Scope | Notes |
|---|---|---|---|
| ... | ... | ... | ... |

## Bottleneck Breakdown (estimated)

- **<bottleneck 1>:** N calls × Xms = ~Ys (Z%)
- **<bottleneck 2>:** N calls × Xms = ~Ys (Z%)
- **Actual test work:** ~Ys (Z%)

## Fixes

### Fix N: <title>

**Commit:** `<commit message>`
**File:** `<path>`
**Time saved:** <before>s → <after>s (**-Xs**)

<Description of what changed and why.>

### Combined Result

| State | Wall time | Delta |
|---|---|---|
| Baseline | Xs | — |
| + fix 1 | Xs | -Xs |
| + fix 2 | Xs | -Xs |
| **Total saved** | | **-Xs (Nx)** |

## Remaining Slow Tests

| Test | Time | Reason |
|---|---|---|
| ... | ... | ... |

Commit the report as a separate docs: commit after all optimization commits.

Key Principles

• •Measure before optimizing — never guess where time is spent
• •One fix at a time — measure after each change to attribute savings accurately
• •Don't optimize genuine workload — raster I/O, large batch operations, etc. are expected costs
• •Test correctness first — verify the same tests pass/fail before and after each fix
• •Keep production code unchanged — all optimizations go in test infrastructure only