Motivation

discussed: https://lists.apache.org/thread/33hdp3hm705mzgrltv7o3468wvwbjsr3
closed: #56879

Insights

trying to apply gc.freeze / unfreeze cycle

First, to apply it in the same way as implemented in LocalExecutor, I perform gc.freeze and gc.unfreeze immediately before and after forking:

os.register_at_fork(before=gc.freeze, after_in_parent=gc.unfreeze)

However, after applying this, memory inspection revealed excessive memory leaks.

This is the existing (v3.1.5) memory graph pattern.

Looking at the graph shape, you can see heap memory dropping at specific intervals, which appears to be a typical pattern of old gc, so I inferred there might be a connection.
I believe objects that should be cleaned up when old gc (generation 2 gc) occurs are frozen and thus escape gc, continuing to accumulate. As shown below, if you forcibly collect gc before freezing or reduce the generation 2 gc threshold to an extreme low, memory doesn't increase:

def freeze():
	gc.collect()
	gc.freeze()
os.register_at_fork(before=freeze, after_in_parent=gc.unfreeze)

or

gc.set_threshold(700, 10, 1)

However, I judged that forcibly changing the gc flow would have very significant side effects, so I didn't apply this cycle.

apply it before parsing start

Instead, I inferred that simply freezing existing objects would be sufficient to help prevent COW.
There was a debate in the Python community about gc.freeze, and the main points are as follows:
https://discuss.python.org/t/it-seems-to-me-that-gc-freeze-is-pointless-and-the-documentation-misleading/71775

• Even with gc.freeze applied, COW occurs when the ref count of actual objects changes.
• Therefore, even if frozen, COW occurs for objects actually used in the fork process.
• However, the COW prevention effect for unused objects is clear.

Since Airflow loads the same modules for all components and much of it goes unused, I judged that simply freezing these would be sufficient to prevent COW, and I froze objects created before the dag parsing loop runs.

Performance

I deployed both the existing 3.1.5 version image and an image with gc.freeze applied to k8s. I deployed the same plugins and dags to the dag-processor. The parsing stats are as follows (dag name is masked):

Bundle       File Path                                 PID  Current Duration      # DAGs    # Errors  Last Duration    Last Run At
-----------  --------------------------------------  -----  ------------------  --------  ----------  ---------------  -------------------
dags-folder  **/dynamic_dags_**_****.py              54374  7.01s                     47           0  27.83s           2026-01-14T08:21:15
dags-folder  **/dynamic_dags_**_*******.py                                            54           0  20.90s           2026-01-14T08:21:41
dags-folder  **/dynamic_dags_**.py                   54325  8.04s                     38           0  19.06s           2026-01-14T08:21:13
dags-folder  **/dynamic_dags_**_*******_**_*.py                                       13           0  18.43s           2026-01-14T08:21:35
dags-folder  **/dynamic_dags_**_*******_**_*.py                                        5           0  4.64s            2026-01-14T08:21:19

After monitoring memory usage for about two days, the results are as follows (x axis is time with KST):

I confirmed that the overall average memory usage is lower with gc.freeze, and the memory peak is also lower in the applied version. This difference can be attributed to improved memory usage due to COW prevention in the fork process when dag file parsing time is long. Looking broadly, both show a slight upward trend in memory usage, which I judge is ultimately a problem that needs to be resolved.

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