Based on the knowledge sources, the error you're experiencing (

exit code -9

) indicates that your process is being killed due to memory issues. This is a common problem when running Elementary's

edr report

command with limited resources. ## Solutions to Reduce Memory Usage You've already implemented several good practices, but here are additional options to further reduce memory usage: ### 1. Reduce

--days-back

Further While you're currently using

--days-back 7

, you can reduce this to 1-3 days. According to [multiple user reports](https://slack.com/archives/C02CTC89LAX/p1680534551722499), setting

--days-back

to 3 or even 1 significantly reduces memory consumption. ### 2. Lower

--executions-limit

Your current

--executions-limit 30

is already quite low, but you could try reducing it further to 5-10 if needed. Users have reported success with values as low as [5](https://slack.com/archives/C02CTC89LAX/p1674656787251049) or [100-120](https://slack.com/archives/C02CTC89LAX/p1701791821617429). ### 3. Increase Container Memory The most effective solution reported by users is to increase the memory allocation for your Airflow workers. According to the knowledge sources: - Users have successfully resolved this issue by increasing memory from [0.5GB to 2GB](https://slack.com/archives/C02CTC89LAX/p1703686732161589) - Another user increased from [600MB to 1024MB](https://slack.com/archives/C02CTC89LAX/p1678963756105439) - Some users needed [4-8GB](https://github.com/elementary-data/elementary/issues/1495) depending on their data volume ### 4. Regular Schema Cleanup As mentioned in [this GitHub issue](https://github.com/elementary-data/elementary/issues/1495), you can periodically full-refresh the Elementary schema to prevent data accumulation:

bash dbt run -t prod --select elementary --vars '{"elementary_full_refresh": "true"}'

## Recommended Command Try this modified command with more aggressive limits:

bash edr report --exclude-elementary-models true --env production --profile-target default --executions-limit 10 --days-back 1 --disable-passed-test-metrics true --profiles-dir /tmp/production

If this still fails, the most reliable solution is to increase the memory allocation for your Airflow workers/pods, as this is fundamentally a [resource constraint issue](https://slack.com/archives/C02CTC89LAX/p1729498047922669).