Production postmortemThe case of 99.99% percentile

time to read 2 min | 326 words

imageA customer called us with a problem, for the most part, RavenDB was very well behaved, but they were worried about the 99.99% percentile of request duration. While the 99.9% was excellent (around a few milliseconds), the 99.99% was measured in seconds.

Usually, this is a particularly slow request that happens, and we can handle that by figuring out what is slow on that particular request and fix it. Common causes for such an issue is a request that returns a lot of unnecessary data, such as large full documents, when it needs just a few fields.

In this case, however, our metrics told us that the problem was pretty widespread. There wasn’t a particular slow request, rather, at what appeared to be random times, certain requests would be slow. We also realized that it wasn’t a particular request that was slow, but all requests within a given time period.

That hinted quite strongly at the problem, it was very likely that this is a GC issue that caused a long pause. We still had to do some work to rule out other factors, such as I/O / noisy neighbors, but we narrowed down on GC as being the root cause.

The problem in this case was that the customer was running multiple databases on the same RavenDB process. And each of them was fairly large. The total amount of memory that the RavenDB process was using was around 60GB of managed memory. At that level, anything that would cause a GC can cause a significant pause and impact operations.

The solution in this case was to break that into multiple separate processes, one for each database. In this manner, we didn’t have a single managed heap that the GC had to traverse, each heap was much smaller, and the GC pause times were both greatly reduced and spaced much further apart.

More posts in "Production postmortem" series:

  1. (07 Aug 2017) 30% boost with a single line change
  2. (04 Aug 2017) The case of 99.99% percentile
  3. (02 Aug 2017) The lightly loaded trashing server
  4. (23 Aug 2016) The insidious cost of managed memory
  5. (05 Feb 2016) A null reference in our abstraction
  6. (27 Jan 2016) The Razor Suicide
  7. (13 Nov 2015) The case of the “it is slow on that machine (only)”
  8. (21 Oct 2015) The case of the slow index rebuild
  9. (22 Sep 2015) The case of the Unicode Poo
  10. (03 Sep 2015) The industry at large
  11. (01 Sep 2015) The case of the lying configuration file
  12. (31 Aug 2015) The case of the memory eater and high load
  13. (14 Aug 2015) The case of the man in the middle
  14. (05 Aug 2015) Reading the errors
  15. (29 Jul 2015) The evil licensing code
  16. (23 Jul 2015) The case of the native memory leak
  17. (16 Jul 2015) The case of the intransigent new database
  18. (13 Jul 2015) The case of the hung over server
  19. (09 Jul 2015) The case of the infected cluster