Heuristic:Pola rs Polars Use Spawn Not Fork Multiprocessing
| Knowledge Sources | |
|---|---|
| Domains | Debugging, Python_Performance |
| Last Updated | 2026-02-09 10:00 GMT |
Overview
Always use `spawn` (not `fork`) as the Python multiprocessing start method when using Polars to avoid deadlocks caused by copying mutex state from a multithreaded parent process.
Description
Polars is inherently multithreaded, using Rayon thread pools for parallel execution. Python's `fork` multiprocessing method copies the entire parent process state including mutex locks. When a multithreaded process is forked, child processes inherit mutexes in their acquired state, causing deadlocks when those locks are never released. The `spawn` method creates a fresh Python interpreter without inheriting lock state, avoiding this class of bugs entirely. On Unix systems (Linux, BSD), `fork` is the default, which is why this issue commonly surprises users.
Usage
Apply this heuristic any time you use Python's `multiprocessing` module (or libraries built on it like `concurrent.futures.ProcessPoolExecutor`) alongside Polars. Set `multiprocessing.set_start_method("spawn")` before creating any process pools. Also consider whether multiprocessing is even necessary, since Polars already uses all CPU cores internally.
The Insight (Rule of Thumb)
- Action: Call `multiprocessing.set_start_method("spawn")` at the start of your program, or use `mp.get_context("spawn")` when creating process pools.
- Value: Prevents deadlocks that can occur randomly depending on which mutexes happen to be held at fork time. These bugs are notoriously difficult to debug because small code changes can make them appear or disappear.
- Trade-off: `spawn` is slower to create processes than `fork` (creates a fresh Python interpreter), and requires all arguments to be pickleable. Code must be importable (not in global scope or Jupyter notebooks). However, this overhead is negligible for tasks that justify multiprocessing.
Reasoning
The Polars documentation dedicates an entire page to this issue, stating: "Polars is multithreaded as to provide strong performance out-of-the-box. Thus, it cannot be combined with fork." The POSIX standard specifies that after `fork()`, "the new process shall contain a replica of the calling thread and its entire address space, possibly including the states of mutexes." When Polars holds a file lock during `pl.read_parquet`, forking copies that lock in acquired state to all children, causing them to hang indefinitely.
Additionally, the documentation warns against using multiprocessing at all in most Polars use cases: "Polars has been built from the start to use all your CPU cores. It is very unlikely that the multiprocessing module can improve your code performance."