Pitfalls of In‑Place Editing on Memory‑Mapped NumPy Arrays Across Processes

Memory-mapped NumPy arrays enable large-scale data handling with shared disk-backed storage, but performing in-place edits across multiple processes introduces serious risks.

Main Pitfalls

• Data corruption: No built-in locking means overlapping writes can destroy values.
• Race conditions: Processes may read outdated or partial values.
• Cross-platform issues: Behavior may vary between OS and file systems.

Solution: Use locks (e.g., from `multiprocessing`) or write to a queue and serialize writes back to disk.

Written by: ScriptDataInsights

Labels: Python, NumPy, memory mapping, multiprocessing, data corruption