Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update heading level #6183

Merged
merged 1 commit into from
Oct 1, 2024
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions website/docs/faqs/Troubleshooting/job-memory-limits.md
Original file line number Diff line number Diff line change
Expand Up @@ -6,14 +6,14 @@ sidebar_label: 'Job failures due to exceeded memory limits'

If you're receiving a `This run exceeded your account's run memory limits` error in your failed job, it means that the job exceeded the [memory limits](/docs/deploy/job-scheduler#job-memory) set for your account. All dbt Cloud accounts have a pod memory of 600Mib and memory limits are on a per run basis. They're typically influenced by the amount of result data that dbt has to ingest and process, which is small but can become bloated unexpectedly by project design choices.

## Common reasons
### Common reasons

Some common reasons for higher memory usage are:

- dbt run/build: Macros that capture large result sets from run query may not all be necessary and may be memory inefficient.
- dbt docs generate: Source or model schemas with large numbers of tables (even if those tables aren't all used by dbt) cause the ingest of very large results for catalog queries.

## Resolution
### Resolution

There are various reasons why you could be experiencing this error but they are mostly the outcome of retrieving too much data back into dbt. For example, using the `run_query()` operations or similar macros, or even using database/schemas that have a lot of other non-dbt related tables/views. Try to reduce the amount of data / number of rows retrieved back into dbt by refactoring the SQL in your `run_query()` operation using `group`, `where`, or `limit` clauses. Additionally, you can also use a database/schema with fewer non-dbt related tables/views.

Expand All @@ -26,5 +26,5 @@ As an additional resource, check out [this example video](https://www.youtube.co

If you've tried the earlier suggestions and are still experiencing failed job runs with this error about hitting the memory limits of your account, please [reach out to support](mailto:[email protected]). We're happy to help!

## Additional resources
### Additional resources
- [Blog post on how we shaved 90 mins off](https://docs.getdbt.com/blog/how-we-shaved-90-minutes-off-model)
Loading