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 Jun 2019) Printer out of paper and the RavenDB hang
  2. (18 Feb 2019) This data corruption bug requires 3 simultaneous race conditions
  3. (25 Dec 2018) Handled errors and the curse of recursive error handling
  4. (23 Nov 2018) The ARM is killing me
  5. (22 Feb 2018) The unavailable Linux server
  6. (06 Dec 2017) data corruption, a view from INSIDE the sausage
  7. (01 Dec 2017) The random high CPU
  8. (07 Aug 2017) 30% boost with a single line change
  9. (04 Aug 2017) The case of 99.99% percentile
  10. (02 Aug 2017) The lightly loaded trashing server
  11. (23 Aug 2016) The insidious cost of managed memory
  12. (05 Feb 2016) A null reference in our abstraction
  13. (27 Jan 2016) The Razor Suicide
  14. (13 Nov 2015) The case of the “it is slow on that machine (only)”
  15. (21 Oct 2015) The case of the slow index rebuild
  16. (22 Sep 2015) The case of the Unicode Poo
  17. (03 Sep 2015) The industry at large
  18. (01 Sep 2015) The case of the lying configuration file
  19. (31 Aug 2015) The case of the memory eater and high load
  20. (14 Aug 2015) The case of the man in the middle
  21. (05 Aug 2015) Reading the errors
  22. (29 Jul 2015) The evil licensing code
  23. (23 Jul 2015) The case of the native memory leak
  24. (16 Jul 2015) The case of the intransigent new database
  25. (13 Jul 2015) The case of the hung over server
  26. (09 Jul 2015) The case of the infected cluster