68 lines
1.9 KiB
Markdown
68 lines
1.9 KiB
Markdown
# Optimize memory usage
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Funkwhale has a reasonable memory footprint. If you're running Funkwhale on a limited device, you can use these tweaks to reduce the footprint.
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## Reduce workers concurrency
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Funkwhale uses Celery to handle asynchronous tasks. By default, Celery spawns a worker per CPU core. This can lead to higher memory usage.
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You can set the number of workers using the `CELERYD_CONCURRENCY` variable in your `.env` file. For example, a value of `CELERYD_CONCURRENCY=1` spawns a single worker.
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```{note}
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Reducing the number of celery workers slows down the handling of asynchronous tasks. On larger instances, this can cause performance problems.
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```
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## Switch to solo pool execution
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Celery uses a `prefork` pool by default. This enables the server to process many tasks at the same time. You can switch to a `solo` pool which handles tasks one at a time. This reduces memory overhead but removes the ability to process tasks concurrently.
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::::{tab-set}
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:::{tab-item} Debian
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:sync: debian
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1. Open your `funkwhale-worker` unit file in an editor.
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```{code-block} sh
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sudo nano /etc/systemd/system/funkwhale-worker.service
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```
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2. Add the `--pool=solo` flag to the `ExecStart` line of your unit file.
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```{code-block} text
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ExecStart=/srv/funkwhale/venv/bin/celery \
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--app funkwhale_api.taskapp \
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--pool solo \
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worker \
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--loglevel INFO \
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--concurrency=${CELERYD_CONCURRENCY}
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```
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3. Restart the Celery service.
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```{code-block} sh
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sudo systemctl restart funkwhale-worker.service
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```
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:::
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:::{tab-item} Docker
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:sync: docker
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1. Add the `--pool=solo` flag to the `celerybeat` command in `docker-compose.yml`.
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```{code-block} yaml
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celerybeat:
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…
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command: celery -A --pool=solo funkwhale_api.taskapp beat --pidfile= -l INFO
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```
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2. Restart Celery.
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```{code-block} sh
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sudo docker compose restart celerybeat
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```
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:::
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::::
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