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Cache writes are not atomic (embeddings + structured outputs) → an interrupted/concurrent write poisons future reads

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
55/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Quiet
Tech stack
numpy, python
Domain
backend

Research direction

Start with the embedding read/write paths in src/autointent/_wrappers/embedder/sentence_transformers.py and the structured-output paths in src/autointent/_dump_tools/unit_dumpers.py and src/autointent/generation/_cache.py. Reproduce the truncated .npy and missing model_dump.json cases, then verify that writes are atomic and corrupt or incomplete entries are removed and recomputed instead of raising.

Written by the indexing model from the issue text.

Description

Summary

Both file-based caches write entries in place with no temp-file + atomic rename, and the read paths don't defend against partial/truncated entries. If a writer is interrupted (crash, OOM, kill) or a reader in another process observes a file mid-write, the entry is left partial and every subsequent read of that key raises instead of recomputing — the bad entry poisons the cache until it is deleted by hand. This matters for parallel Optuna workers and the HTTP/MCP server, which can hit the same cache concurrently.

Where (on dev)

Embeddings — src/autointent/_wrappers/embedder/sentence_transformers.py:

  • write: np.save(embeddings_path, ...) straight to the final path (lines ~228–231)
  • read: if embeddings_path.exists(): np.load(embeddings_path) with no try/except (lines ~183–185)

Structured outputs — src/autointent/_dump_tools/unit_dumpers.py, PydanticModelDumper.dump (lines 158–165) does mkdir then writes class_info.json and model_dump.json as two separate, non-atomic steps. The read path StructuredOutputCache._load_from_disk (src/autointent/generation/_cache.py) only catches ValidationError / ImportError, so a missing model_dump.json raises an uncaught FileNotFoundError.

Reproduce (no network)

Embeddings — a truncated .npy makes the next embed() raise ValueError (not a miss), permanently:

# after one successful embed(utts), truncate the cached .npy to half its bytes:
raw = embeddings_path.read_bytes()
embeddings_path.write_bytes(raw[: len(raw) // 2])
embedder.embed(utts)   # -> raises ValueError; never recomputes

Structured — an entry directory missing model_dump.json (interrupted between the two writes) makes get() raise FileNotFoundError:

cache.set(msgs, Out, params, Out(label="x"))
(entry_dir / "model_dump.json").unlink()   # simulate a crash between the two file writes
StructuredOutputCache(use_cache=True).get(msgs, Out, params)   # -> raises FileNotFoundError

Both were reproduced in a benchmark: each cache raises on the next read and does not auto-recover.

Suggested fix

  1. Atomic writes: write to a temp path and os.replace() (atomic on POSIX) for both np.save and the structured dump (write into a temp directory, then rename it into place).
  2. Self-healing reads: wrap np.load / PydanticModelDumper.load so a corrupt/partial entry is deleted and treated as a miss (recompute) instead of raising. For the structured cache, also catch OSError / FileNotFoundError, not just ValidationError / ImportError.

Related: #326 (directory-aware deletion is needed for the cleanup path).

Severity

Medium-High under concurrency (parallel trials / long-running server).

How it was found

Robustness scenario of a benchmark of AutoIntent 0.3.1's caches.

Dominant language
Python
Stars
53
Forks
16
No merged PRs in 30d

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First steps

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  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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