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.
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Co-authored-by: कारतोफ्फेलस्क्रिप्ट™ <aditya@netroy.in>
# Motivation
In Queue mode, finished executions would cause the main instance to
always pull all execution data from the database, unflatten it and then
use it to send out event log events and telemetry events, as well as
required returns to Respond to Webhook nodes etc.
This could cause OOM errors when the data was large, since it had to be
fully unpacked and transformed on the main instance’s side, using up a
lot of memory (and time).
This PR attempts to limit this behaviour to only happen in those
required cases where the data has to be forwarded to some waiting
webhook, for example.
# Changes
Execution data is only required in cases, where the active execution has
a `postExecutePromise` attached to it. These usually forward the data to
some other endpoint (e.g. a listening webhook connection).
By adding a helper `getPostExecutePromiseCount()`, we can decide that in
cases where there is nothing listening at all, there is no reason to
pull the data on the main instance.
Previously, there would always be postExecutePromises because the
telemetry events were called. Now, these have been moved into the
workers, which have been given the various InternalHooks calls to their
hook function arrays, so they themselves issue these telemetry and event
calls.
This results in all event log messages to now be logged on the worker’s
event log, as well as the worker’s eventbus being the one to send out
the events to destinations. The main event log does…pretty much nothing.
We are not logging executions on the main event log any more, because
this would require all events to be replicated 1:1 from the workers to
the main instance(s) (this IS possible and implemented, see the worker’s
`replicateToRedisEventLogFunction` - but it is not enabled to reduce the
amount of traffic over redis).
Partial events in the main log could confuse the recovery process and
would result in, ironically, the recovery corrupting the execution data
by considering them crashed.
# Refactor
I have also used the opportunity to reduce duplicate code and move some
of the hook functionality into
`packages/cli/src/executionLifecycleHooks/shared/sharedHookFunctions.ts`
in preparation for a future full refactor of the hooks
