DevLog 012: Investigating export crash

Published on 23 August 2023.

I have managed to edit some footage using my own video editor. When I tried to export it, it took forever and eventually crashed. In this DevLog, we will investigate why that might be.

How export works

When we press the export button, the following code is run:

class Project:


    def export(self):
        path = "export.mp4"
        producer = self.split_into_sections().to_mlt_producer(
            cache=ExportSourceLoader(profile=self.profile, project=self)
        def work(progress):
            consumer = mlt.Consumer(self.profile, "avformat")
            consumer.set("target", path)
            while consumer.is_stopped() == 0:
            f"Exporting {path}",
            lambda result: None,

It creates an MLT producer with the real clips, and not the proxy clips. The work function is called in a thread, and this code does the actual export:

consumer = mlt.Consumer(self.profile, "avformat")
consumer.set("target", path)
while consumer.is_stopped() == 0:

As I remember, this is the code that takes forever and eventually crash. I also think its memory consumption steadily increase.

Way forward

There is not much Python code in here. Just the loop that queries the consumer. So my guess is that something in MLT consumes memory and eventually crashes. We had a similar problem, I think, before when we created proxies using MLT in this way. On the other hand, it seems unlikely that MLT would crash when exporting a “small” project.

What I want to try today is to export my project as an MLT XML file and try to render it using melt. It should do roughly the same thing as my Python code, but will avoid using the Python binding for MLT.

If there is something wrong with MLT, which I doubt, the export will fail here as well. If not, well, then I don’t know what is wrong, but we can at least rule out MLT (core).

The test

We have this code that enables us to export MLT XML:

if sys.argv[1:2] == ["--export-melt"]:
    path = sys.argv[2]
    print(f"Exporting {path}")
    project = Project.load(args=sys.argv[3:])
    consumer = mlt.Consumer(project.profile, "xml")
    consumer.set("resource", path)
    while consumer.is_stopped() == 0:

However, it creates the preview MLT producer which uses the proxy clips.

Since this is just a test, not intended to be committed, I modify this code to instead create an MLT producer with the real clips.

from rlvideolib.domain.project import ExportSourceLoader
producer = project.split_into_sections().to_mlt_producer(
    cache=ExportSourceLoader(profile=project.profile, project=project)

Now we can export the XML like this:

$ rlvideo --export-melt test.xml devlog-009.rlvideo 
Exporting test.xml

I verify that the XML file has references to the real clips. It does. Perfect!

We can now do the equivalent export with this command:

mlt-melt test.xml -consumer avformat target=export.mp4

And now, it’s just to wait and see what happens.

A few minutes later

The memory consumption seems to be quite stable. Unless there is a memory leak, this is what I expect. If the memory consumption keeps increasing for every frame that is exported, that would mean that you can only export longer videos by getting more memory. That does not seem right.

I should probably also verify that the export in the application keeps increasing memory consumption. If it does, then there might be a memory leak in the Python binding for MLT. Or I might use the binding incorrectly.

Using threads (which is used in the export) has also been problematic. I’ve experienced that the Python threads interfere with the MLT threads. I’m don’t understand the problem fully, it’s just a feeling. So that might be something to look into. Try the export with threading disabled.

A few hours later

I might have mistaken. The memory consumption seems to keep increasing. However, the export finish without crashing and the final result looks fine.


It seems that MLT consumes more and more memory the longer the exported video. To confirm this, I should probably do some more precise measures. Maybe using something like psrecord? However, memory consumption might not be problematic in itself. Perhaps it allocates more memory to speed things up, but will not allocated more than what is available. Perhaps the crash that I experienced before was not related to memory.

We have learned something today, and this knowledge will make us better prepared for the future.

Here are a few things I think of as possible next steps in this area:

We’ll see if we work on any of these the next time or something else.

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