Based on #7065 | Story: https://linear.app/n8n/issue/PAY-771
n8n on filesystem mode marks binary data to delete on manual execution
deletion, on unsaved execution completion, and on every execution
pruning cycle. We later prune binary data in a separate cycle via these
marker files, based on the configured TTL. In the context of introducing
an S3 client to manage binary data, the filesystem mode's mark-and-prune
setup is too tightly coupled to the general binary data management
client interface.
This PR...
- Ensures the deletion of an execution causes the deletion of any binary
data associated to it. This does away with the need for binary data TTL
and simplifies the filesystem mode's mark-and-prune setup.
- Refactors all execution deletions (including pruning) to cause soft
deletions, hard-deletes soft-deleted executions based on the existing
pruning config, and adjusts execution endpoints to filter out
soft-deleted executions. This reduces DB load, and keeps binary data
around long enough for users to access it when building workflows with
unsaved executions.
- Moves all execution pruning work from an execution lifecycle hook to
`execution.repository.ts`. This keeps related logic in a single place.
- Removes all marking logic from the binary data manager. This
simplifies the interface that the S3 client will meet.
- Adds basic sanity-check tests to pruning logic and execution deletion.
Out of scope:
- Improving existing pruning logic.
- Improving existing execution repository logic.
- Adjusting dir structure for filesystem mode.
---------
Co-authored-by: कारतोफ्फेलस्क्रिप्ट™ <aditya@netroy.in>
- For a saved execution, we write to disk binary data and metadata.
These two are only ever deleted via `POST /executions/delete`. No marker
file, so untouched by pruning.
- For an unsaved execution, we write to disk binary data, binary data
metadata, and a marker file at `/meta`. We later delete all three during
pruning.
- The third flow is legacy. Currently, if the execution is unsaved, we
actually store it in the DB while running the workflow and immediately
after the workflow is finished during the `onWorkflowPostExecute()` hook
we delete that execution, so the second flow applies. But formerly, we
did not store unsaved executions in the DB ("ephemeral executions") and
so we needed to write a marker file at `/persistMeta` so that, if the
ephemeral execution crashed after the step where binary data was stored,
we had a way to later delete its associated dangling binary data via a
second pruning cycle, and if the ephemeral execution succeeded, then we
immediately cleaned up the marker file at `/persistMeta` during the
`onWorkflowPostExecute()` hook.
This creation and cleanup at `/persistMeta` is still happening, but this
third flow no longer has a purpose, as we now store unsaved executions
in the DB and delete them immediately after. Hence the third flow can be
removed.
fix(core): Reduce memory consumption on BinaryDataManager.init
When there are a few thousand binary data file to delete, the `deleteMarkedFiles` and `deleteMarkedPersistedFiles` methods need a lot of memory to process these files, irrespective of if these files have any data or not.
* use streams to upload files to google drive
* use streams to download files from google drive
* use resumable uploads api for google drive
* avoid dangling promises, and reduce memory usage in error logging
* Mark binary data to be deleted when pruning executions
* eslint
* make pruneExecutionData async
Co-authored-by: कारतोफ्फेलस्क्रिप्ट™ <aditya@netroy.in>
* 📘 Clear all `@ts-ignore` comments from workflow package
* 👕 Default to error with package-level overrides
* refactor(core): clear all `@ts-ignore` comments from core package (#4473)
👕 Clear all `@ts-ignore` comments from core package
* ✏️ Update comment
