Production postmortemThe allocating query

time to read 4 min | 636 words

A customer was experiencing large memory spikes in some cases, and we were looking into the allocation patterns of some of the queries that were involved. One of the things that popped up was a query that allocated just under 30GB of managed memory during its processing.

Let me repeat that, because it bears repeating. That query allocated 30(!) GB(!) during its execution. Now, that doesn’t mean that it was consuming 30 GB, it was just the allocations involved. Most of that memory was immediately discarded during the operation. But 30 GB of garbage to cleanup puts a lot of pressure on the system. We took a closer look at the offensive query. It looked something like this:

from index “Notifications/RoutingAndPriority”
where startsWith(Route, $routeKeyPrefix)
order by
Priority desc

That does not seem like a query that should be all that expensive. But details matter, so we dove into this. For this particular query, the routes are hierarchical structures that are unique for each message. Something like:

  • notifications/traffic/new-york-city/67a81019-941b-4d04-a0db-0559ed45343c
  • notifications/emergency/las-vegas/0a8e18fb-563b-4b6a-8e93-e10e08239656

And the queries that were generated were using the city & topic to filter the information that they were interested in.

The customer in question had a lot of notifications going on at all times. And each one of their Routes was unique. Internally, RavenDB uses Lucene (currently Smile ) to handle searches, and Lucene is using an inverse index to execute queries.

The usual way to think about is like this:

image

We have a list of terms (Brown, Green & Purple) and each of them has a list of the matching documents that contain the particular term.

The process of issuing a prefix query then is easy, scan all entries that match the prefix and return their results. This is indeed what Lucene is doing. However… while it is doing that, it will do something like this:

Pay close attention to what is actually happening here. There are two enumerators that we work with. One for the terms for the field and one for the documents for a specific term.

All of this is perfectly reasonable, but there is an issue. What happens when you have a lot of unique values? Well, then Lucene will have a lot of iterations of the loop. In this case, each term has just a single match, and Lucene is pretty good at optimizing search by specific term.

The actual problem is that Lucene allocates a string instance for each term. If we have 30 million notifications for New York’s traffic, that means that we’ll allocate 30 million strings during the processing of the query. We aren’t retaining these strings, mind. They’ll be cleaned up by the GC quickly enough, but that is an additional cost that we don’t actually want.

Luckily, in this case, there is a much simple solution. Given that the pattern of the route is known, we can skip the unique portion of the route. That means that in our index, we’ll do something similar to:

Route = doc.Route.Substring(0, doc.Route.LastIndexOf('/') + 1)

Once that is done, the number of unique matches there would be negligible. There would be no more allocations galore to observe and overall system performance is much improved.

We looked into whether there is something that we can do with Lucene to avoid this allocations issue, but it is endemic to the way the API works. The longer term plan is to fix that completely, of course. We are making great strides there already Smile.

In short, if you are doing startsWith() queries or similar, pay attention to the number of unique terms that you have to go through. A simple optimization on the index like the one above can bring quite a bit of dividends.

More posts in "Production postmortem" series:

  1. (12 Dec 2023) The Spawn of Denial of Service
  2. (24 Jul 2023) The dog ate my request
  3. (03 Jul 2023) ENOMEM when trying to free memory
  4. (27 Jan 2023) The server ate all my memory
  5. (23 Jan 2023) The big server that couldn’t handle the load
  6. (16 Jan 2023) The heisenbug server
  7. (03 Oct 2022) Do you trust this server?
  8. (15 Sep 2022) The missed indexing reference
  9. (05 Aug 2022) The allocating query
  10. (22 Jul 2022) Efficiency all the way to Out of Memory error
  11. (18 Jul 2022) Broken networks and compressed streams
  12. (13 Jul 2022) Your math is wrong, recursion doesn’t work this way
  13. (12 Jul 2022) The data corruption in the node.js stack
  14. (11 Jul 2022) Out of memory on a clear sky
  15. (29 Apr 2022) Deduplicating replication speed
  16. (25 Apr 2022) The network latency and the I/O spikes
  17. (22 Apr 2022) The encrypted database that was too big to replicate
  18. (20 Apr 2022) Misleading security and other production snafus
  19. (03 Jan 2022) An error on the first act will lead to data corruption on the second act…
  20. (13 Dec 2021) The memory leak that only happened on Linux
  21. (17 Sep 2021) The Guinness record for page faults & high CPU
  22. (07 Jan 2021) The file system limitation
  23. (23 Mar 2020) high CPU when there is little work to be done
  24. (21 Feb 2020) The self signed certificate that couldn’t
  25. (31 Jan 2020) The slow slowdown of large systems
  26. (07 Jun 2019) Printer out of paper and the RavenDB hang
  27. (18 Feb 2019) This data corruption bug requires 3 simultaneous race conditions
  28. (25 Dec 2018) Handled errors and the curse of recursive error handling
  29. (23 Nov 2018) The ARM is killing me
  30. (22 Feb 2018) The unavailable Linux server
  31. (06 Dec 2017) data corruption, a view from INSIDE the sausage
  32. (01 Dec 2017) The random high CPU
  33. (07 Aug 2017) 30% boost with a single line change
  34. (04 Aug 2017) The case of 99.99% percentile
  35. (02 Aug 2017) The lightly loaded trashing server
  36. (23 Aug 2016) The insidious cost of managed memory
  37. (05 Feb 2016) A null reference in our abstraction
  38. (27 Jan 2016) The Razor Suicide
  39. (13 Nov 2015) The case of the “it is slow on that machine (only)”
  40. (21 Oct 2015) The case of the slow index rebuild
  41. (22 Sep 2015) The case of the Unicode Poo
  42. (03 Sep 2015) The industry at large
  43. (01 Sep 2015) The case of the lying configuration file
  44. (31 Aug 2015) The case of the memory eater and high load
  45. (14 Aug 2015) The case of the man in the middle
  46. (05 Aug 2015) Reading the errors
  47. (29 Jul 2015) The evil licensing code
  48. (23 Jul 2015) The case of the native memory leak
  49. (16 Jul 2015) The case of the intransigent new database
  50. (13 Jul 2015) The case of the hung over server
  51. (09 Jul 2015) The case of the infected cluster