Combining Polars with Python's multiprocessing

TLDR: if you find that using Python's built-in multiprocessing module together with Polars results in a Polars error about multiprocessing methods, you should make sure you are using spawn, not fork, as the starting method:

from multiprocessing import get_context

def my_fun(s):

with get_context("spawn").Pool() as pool:, ["input1", "input2", ...])

When not to use multiprocessing

Before we dive into the details, it is important to emphasize that Polars has been built from the start to use all your CPU cores. It does this by executing computations which can be done in parallel in separate threads. For example, requesting two expressions in a select statement can be done in parallel, with the results only being combined at the end. Another example is aggregating a value within groups using groupby().agg(<expr>), each group can be evaluated separately. It is very unlikely that the multiprocessing module can improve your code performance in these cases.

See the optimizations section for more optimizations.

When to use multiprocessing

Although Polars is multithreaded, other libraries may be single-threaded. When the other library is the bottleneck, and the problem at hand is parallelizable, it makes sense to use multiprocessing to speed up.

The problem with the default multiprocessing config


The Python multiprocessing documentation lists the three methods a process pool can be created:

  1. spawn
  2. fork
  3. forkserver

The description of fork is (as of 2022-10-15):

The parent process uses os.fork() to fork the Python interpreter. The child process, when it begins, is effectively identical to the parent process. All resources of the parent are inherited by the child process. Note that safely forking a multithreaded process is problematic.

Available on Unix only. The default on Unix.

The short summary is: Polars is multithreaded as to provide strong performance out-of-the-box. Thus, it cannot be combined with fork. If you are on Unix (Linux, BSD, etc), you are using fork, unless you explicitly override it.

The reason you may not have encountered this before is that pure Python code, and most Python libraries, are (mostly) single threaded. Alternatively, you are on Windows or MacOS, on which fork is not even available as a method (for MacOS it was up to Python 3.7).

Thus one should use spawn, or forkserver, instead. spawn is available on all platforms and the safest choice, and hence the recommended method.


The problem with fork is in the copying of the parent's process. Consider the example below, which is a slightly modified example posted on the Polars issue tracker:

import multiprocessing
import polars as pl

def test_sub_process(df: pl.DataFrame, job_id):
    df_filtered = df.filter(pl.col("a") > 0)
    print(f"Filtered (job_id: {job_id})", df_filtered, sep="\n")

def create_dataset():
    return pl.DataFrame({"a": [0, 2, 3, 4, 5], "b": [0, 4, 5, 56, 4]})

def setup():
    # some setup work
    df = create_dataset()

def main():
    test_df = pl.read_parquet("/tmp/test.parquet")

    for i in range(0, 5):
        proc = multiprocessing.get_context("spawn").Process(target=test_sub_process, args=(test_df, i))

        print(f"Executed sub process {i}")

if __name__ == "__main__":

Using fork as the method, instead of spawn, will cause a dead lock. Please note: Polars will not even start and raise the error on multiprocessing method being set wrong, but if the check would not be there, the deadlock would exist.

The fork method is equivalent to calling os.fork(), which is a system call as defined in the POSIX standard:

A process shall be created with a single thread. If a multi-threaded process calls fork(), the new process shall contain a replica of the calling thread and its entire address space, possibly including the states of mutexes and other resources. Consequently, to avoid errors, the child process may only execute async-signal-safe operations until such time as one of the exec functions is called.

In contrast, spawn will create a completely new fresh Python interpreter, and not inherit the state of mutexes.

So what happens in the code example? For reading the file with pl.read_parquet the file has to be locked. Then os.fork() is called, copying the state of the parent process, including mutexes. Thus all child processes will copy the file lock in an acquired state, leaving them hanging indefinitely waiting for the file lock to be released, which never happens.

What makes debugging these issues tricky is that fork can work. Change the example to not having the call to pl.read_parquet:

from .example1 import create_dataset, test_sub_process
import multiprocessing

def main():
    test_df = create_dataset()

    for i in range(0, 5):
        proc = multiprocessing.get_context("fork").Process(target=test_sub_process, args=(test_df, i))

        print(f"Executed sub process {i}")

if __name__ == "__main__":

This works fine. Therefore debugging these issues in larger code bases, i.e. not the small toy examples here, can be a real pain, as a seemingly unrelated change can break your multiprocessing code. In general, one should therefore never use the fork start method with multithreaded libraries unless there are very specific requirements that cannot be met otherwise.

Pro's and cons of fork

Based on the example, you may think, why is fork available in Python to start with?

First, probably because of historical reasons: spawn was added to Python in version 3.4, whilst fork has been part of Python from the 2.x series.

Second, there are several limitations for spawn and forkserver that do not apply to fork, in particular all arguments should be pickable. See the Python multiprocessing docs for more information.

Third, because it is faster to create new processes compared to spawn, as spawn is effectively fork + creating a brand new Python process without the locks by calling execv. Hence the warning in the Python docs that it is slower: there is more overhead to spawn. However, in almost all cases, one would like to use multiple processes to speed up computations that take multiple minutes or even hours, meaning the overhead is negligible in the grand scheme of things. And more importantly, it actually works in combination with multithreaded libraries.

Fourth, spawn starts a new process, and therefore it requires code to be importable, in contrast to fork. In particular, this means that when using spawn the relevant code should not be in the global scope, such as in Jupyter notebooks or in plain scripts. Hence in the examples above, we define functions where we spawn within, and run those functions from a __main__ clause. This is not an issue for typical projects, but in quick experimentation in notebooks it could fail.