Troubleshooting resource allocation problems

Troubleshooting common resource allocation problems on your appliance

We recommend using the monitor dashboard to stay informed on your appliance's resource health and make decisions on how to fix high usage issues, such as the ones outlined on this page.

For system-critical issues, and prior to making modifications to your appliance, we highly recommend contacting us by visiting GitHub Enterprise Support and including your support bundle. For more information, see "Providing data to GitHub Enterprise Support."

High CPU usage

Possible Causes

• CPU of your instance is under-provisioned for your workload.
• Upgrading to a new GitHub Enterprise Server releases often increases CPU and memory usage due to new features. Additionally, post-upgrade migration or reconciliation background jobs can temporarily degrade performance until they complete.
• Elevated requests against Git or API. Increased requests to Git or API can occur due to various factors, such as excessive repository cloning, CI/CD processes, or unintentional usage by API scripts or new workloads.
• Increased number of GitHub Actions jobs.
• Elevated amount of Git commands executed a large repository.

Recommendations

• Ensure CPU cores are provisioned appropriately.
• Set alert thresholds.
• After an upgrade, check whether background upgrade jobs have completed, by running ghe-check-background-upgrade-jobs.
• Use webhooks instead of pulling.
• Use API rate-limiting.
• Analyze Git usage by checking current operations and Git traffic.

High memory usage

Possible causes

• Memory of your instance is under-provisioned.
• Elevated requests against Git or API. Increased requests to Git or API can occur due to various factors, such as excessive repository cloning, CI/CD processes, or unintentional usage by API scripts or new workloads.
• Individual services exceeding their expected memory usage and running Out Of Memory (OOM).
• Increased background job processing.

Recommendations

• Memory of your instance is under-provisioned for your workload, data volume, given usage over time may exceed the minimum requirements.
• Within the Nomad graphs, identify services with out of memory trends which are often followed by free memory trends after they get restarted. For more information, see "Accessing the monitor dashboard."
• Check logs for processes going out of memory by running rg -z 'kernel: Out of memory: Killed process' /var/log/syslog* (for this, first log in to the administrative shell using SSH - see "Accessing the administrative shell (SSH).")
• Ensure the correct ratio of memory to CPU services is met (at least 6.5:1).
• Check the amount of tasks queued for background processing - see "Accessing the monitor dashboard."

Low disk space availability

Both storage volumes, the one mounted to the root filesystem path (/) and the other to the user filesystem path (/data/user) can cause problems to the stability of your instance if low disk space is available.

Keep in mind that the root storage volume is split into two equally-sized partitions. One of the partitions will be mounted as the root filesystem (/). The other partition is only mounted during upgrades and rollbacks of upgrades as /mnt/upgrade, to facilitate easier rollbacks if necessary. For more information, see "System overview."

Possible Causes

• Service failure causing increased amount of logs
• High disk usage through organic traffic

Recommendations

• Check disk usage of /var/log folder by running (sudo du -csh /var/log/*) or manually force a log rotation (sudo logrotate -f /etc/logrotate.conf).
• Check the disk for large files that have been deleted but still have open file handles (ghe-check-disk-usage).
• Increase disk storage capacity - see "Increasing storage capacity."

Higher than usual response times

Possible causes

• Elevated requests against Git or API. Increased requests to Git or API can occur due to various factors, such as excessive repository cloning, CI/CD processes, or unintentional usage by API scripts or new workloads.
• Slow database queries.
• Post upgrade ElasticSearch elevated service resource usage.
• Reaching IOPS quotas on disk and/or heavy IO contention.
• Saturated workers.
• Webhook delivery delays.

Recommendations

• Look for spikes or sustained numbers in the Disk pending operations: Number of operations queued graphs.
• Check the App request/response panel to see if only certain services are affected.
• After an upgrade, check whether background upgrade jobs have completed, by running ghe-check-background-upgrade-jobs.
• Check the database logs for slow queries in /var/log/github/exceptions.log (for this, first log in to the administrative shell using SSH - see "Accessing the administrative shell (SSH)"), for example by checking for Top 10 slow requests by URL: grep SlowRequest github-logs/exceptions.log | jq '.url' | sort | uniq -c | sort -rn | head.
• Check the Queued requests graph for certain workers and consider adjusting their active worker count.
• Increase the storage disks to ones with higher IOPS/throughput.
• Check the amount of tasks queued for background processing - see "Accessing the monitor dashboard."

Elevated error rates

Possible Causes

• Elevated requests against Git or API. Increased requests to Git or API can occur due to various factors, such as excessive repository cloning, CI/CD processes, or unintentional usage by API scripts or new workloads.
• Failing haproxy service or non-availability of individual services.
• Failed repository network maintenance over time.

Recommendations

• Check the App request/response panel to see if only certain services are affected.
• Check the haproxy logs and try to identify if bad actors may be cause.
• Check for failed repository network maintenance jobs (visit http(s)://[hostname]/stafftools/networks).