⚡ Bolt: optimize dataset validation and fix telemetry NameError#171
⚡ Bolt: optimize dataset validation and fix telemetry NameError#171heidi-dang wants to merge 1 commit intofeat/bootstrap-scaffoldfrom
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Optimized `scripts/02_validate_clean.py` for faster dataset validation: - Replaced regex-based whitespace removal in `fuzzy_hash` with a string `split/join` approach, yielding a ~82% speedup in this hot-path. - Pre-compiled secret detection regex patterns to reduce overhead during large-scale scanning, providing a measurable (~4%) gain per check. Fixed a critical regression in `heidi_engine/telemetry.py`: - Removed a redundant and broken secondary cache check in `get_state` that referenced an undefined `target_run_id`, causing a `NameError`. - Ensured the primary, thread-safe `StateCache` remains functional and efficient. Verified correctness with `pytest tests/` (21/21 passed) and performance with dedicated benchmarking.
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the performance of dataset validation processes and addresses a critical bug in the telemetry system. By optimizing string manipulation and regex usage in Highlights
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Code Review
This pull request introduces some great performance optimizations to the dataset validation script and fixes a bug in the telemetry module.
The move to pre-compiled regexes and using string splitting instead of re.sub for whitespace removal in 02_validate_clean.py are solid improvements. The fix in telemetry.py correctly removes a redundant and broken cache check.
I have one suggestion regarding the secret detection logic to improve its consistency and robustness, which I've detailed in a specific comment. Overall, these are valuable changes.
| SECRET_CHECK_FIELDS = ["instruction", "input", "output", "response", "completion"] | ||
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| # BOLT OPTIMIZATION: Pre-compiled secret patterns for faster detection (~4% gain) | ||
| _COMPILED_SECRET_PATTERNS = [(re.compile(p), t) for p, t in SECRET_PATTERNS] |
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While pre-compiling regexes is a good optimization, there's an inconsistency with how secrets are handled in heidi_engine/telemetry.py that could pose a security risk.
In telemetry.py, all secret patterns are matched case-insensitively. However, in this file, case-insensitivity is only applied if the pattern string contains (?i). Several patterns, such as those for ghp_, glpat-, and sk- tokens, are missing this and will be matched case-sensitively.
This discrepancy could result in a secret being missed by this validation script but redacted in the logs, allowing it to remain in the dataset.
To ensure consistent and more robust secret detection across the project, I recommend compiling all patterns with re.IGNORECASE.
Additionally, I noticed the SECRET_PATTERNS lists themselves differ between this file and telemetry.py. It would be beneficial to consolidate them into a single source of truth to prevent such inconsistencies in the future.
| _COMPILED_SECRET_PATTERNS = [(re.compile(p), t) for p, t in SECRET_PATTERNS] | |
| _COMPILED_SECRET_PATTERNS = [(re.compile(p, re.IGNORECASE), t) for p, t in SECRET_PATTERNS] |
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Optimized
scripts/02_validate_clean.pyfor faster dataset validation by replacing regex-based whitespace removal with string operations (~82% gain) and pre-compiling secret detection patterns. Also fixed aNameErrorinheidi_engine/telemetry.pycaused by a broken redundant cache check, while preserving the functional primary cache. All tests pass.PR created automatically by Jules for task 1865705048273226241 started by @heidi-dang