Understanding your graphs part 2 - Processes
In part 1 of our ‘Understanding your graphs’ mini-series, we talked about GitHub Enterprise System Health graphs. We’ll now look at GitHub Enterprise Processes graphs.
The processes graph section looks deeper into the major individual services which make up the GitHub Enterprise appliance. Looking at these services individually can show how usage trends impact system resources over time.
- Process counts will fluctuate with usage trends.
longpollprocess will often have the highest average, with more peaks and valleys reflecting daily web UI usage trends.
resqueprocess groups normally consume the highest amount of memory, followed by
git-daemonprocesses are most influenced by user activity.
- Elements such the size of repositories interacted with and frequency of requests. Because of this, these process graphs can have peaks and valleys.
- CPU time consumed by processes accessing hardware directly, via trusted lower level operating system functions.
unicornprocess normally consumes the most CPU time.
- Often has lower values than the following CPU (Application) graph.
- CPU time consumed by processes via Linux kernel interfaces.
- The majority of GitHub Enterprise service CPU time occurs here.
- On busy systems,
unicornconsumes the most CPU time, followed by
I/O operations (Read IOPS)
babeldread Input/Output Operations Per Second ( IOPS ) values are influenced by Git fetch / pull activity.
unicornread IOPS are influenced by web application or API
resqueread IOPS are from background jobs, such as regular repository maintenance and repacking.
I/O operations (Write IOPS)
git-daemonwrite IOPS reflect Git push activity and is most often the largest consumer of write IOPS.
resquebackground jobs, such as search indexing, and repository repacking are also be a large consumer of write IOPS
Storage traffic (Read)
- Read throughput trends are a counterpart to Read IOPS. These values used together can help determine if storage system read performance is as expected.
- User activity such as fetching, and retrieving API data will result in read activity.
Storage traffic (Written)
- Write throughput trends are a counterpart to Write IOPS. These values used together can help determine if storage system read performance is as expected.
- Pushes, background repacks, and API
POSToperations are often the largest influencers of this graph.
Continue the conversation
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