Production postmortemThe insidious cost of managed memory
time to read 3 min | 542 words

A customer reported that under memory constrained system, a certain operation is taking all the memory and swapping hard. On a machine with just a bit more memory, the operation completed very quickly. It didn’t take long to figure out what was going on, we were reading too much, and we started swapping, and everything went to hell after that.

The problem is that we have code that is there specifically to prevent that, it is there to check that the size that we load from the disk isn’t too big, and that we aren’t doing something foolish. But something broke here.

Here is a sample document, it is simple JSON (without indentation), and it isn’t terribly large:

image

The problem happens when we convert it to a .NET object:

image

Yep, when we de-serialized it, it takes close to 13 times more space than the text format.

For fun, let us take the following JSON:

image

This generates a string whose size is less than 1KB.

But when parsing it:

image

The reason, by the way? It is the structure of the document.

The reason, by the way:

image

So each two bytes for object creation in JSON ( the {} ) are holding, we are allocating 116 bytes. No wonder this blows up so quickly.

This behavior is utterly dependent on the structure of the document, by the way, and is very hard to protect against, because you don’t really have a way of seeing how much you allocated.

We resolved it by not only watching the size of the documents that we are reading, but the amount of free memory available on the machine (aborting if it gets too low), but that is a really awkward way of doing that.  I’m pretty sure that this is also something that you can use to attack a server, forcing it to allocate a lot of memory by sending very little data to it.

I opened an issue on the CoreCLR about this, and we’ll see if there is something that can be done.

In RavenDB 4.0, we resolved that entirely by using the blittable format, and we have one-to-one mapping between the size of the document on disk and the allocated size (actually, since we map, there is not even allocation of the data, we just access it directly Smile).

Tweet Share Share 2 comments
Tags:

More posts in "Production postmortem" series:

  1. (07 Apr 2025) The race condition in the interlock
  2. (12 Dec 2023) The Spawn of Denial of Service
  3. (24 Jul 2023) The dog ate my request
  4. (03 Jul 2023) ENOMEM when trying to free memory
  5. (27 Jan 2023) The server ate all my memory
  6. (23 Jan 2023) The big server that couldn’t handle the load
  7. (16 Jan 2023) The heisenbug server
  8. (03 Oct 2022) Do you trust this server?
  9. (15 Sep 2022) The missed indexing reference
  10. (05 Aug 2022) The allocating query
  11. (22 Jul 2022) Efficiency all the way to Out of Memory error
  12. (18 Jul 2022) Broken networks and compressed streams
  13. (13 Jul 2022) Your math is wrong, recursion doesn’t work this way
  14. (12 Jul 2022) The data corruption in the node.js stack
  15. (11 Jul 2022) Out of memory on a clear sky
  16. (29 Apr 2022) Deduplicating replication speed
  17. (25 Apr 2022) The network latency and the I/O spikes
  18. (22 Apr 2022) The encrypted database that was too big to replicate
  19. (20 Apr 2022) Misleading security and other production snafus
  20. (03 Jan 2022) An error on the first act will lead to data corruption on the second act…
  21. (13 Dec 2021) The memory leak that only happened on Linux
  22. (17 Sep 2021) The Guinness record for page faults & high CPU
  23. (07 Jan 2021) The file system limitation
  24. (23 Mar 2020) high CPU when there is little work to be done
  25. (21 Feb 2020) The self signed certificate that couldn’t
  26. (31 Jan 2020) The slow slowdown of large systems
  27. (07 Jun 2019) Printer out of paper and the RavenDB hang
  28. (18 Feb 2019) This data corruption bug requires 3 simultaneous race conditions
  29. (25 Dec 2018) Handled errors and the curse of recursive error handling
  30. (23 Nov 2018) The ARM is killing me
  31. (22 Feb 2018) The unavailable Linux server
  32. (06 Dec 2017) data corruption, a view from INSIDE the sausage
  33. (01 Dec 2017) The random high CPU
  34. (07 Aug 2017) 30% boost with a single line change
  35. (04 Aug 2017) The case of 99.99% percentile
  36. (02 Aug 2017) The lightly loaded trashing server
  37. (23 Aug 2016) The insidious cost of managed memory
  38. (05 Feb 2016) A null reference in our abstraction
  39. (27 Jan 2016) The Razor Suicide
  40. (13 Nov 2015) The case of the “it is slow on that machine (only)”
  41. (21 Oct 2015) The case of the slow index rebuild
  42. (22 Sep 2015) The case of the Unicode Poo
  43. (03 Sep 2015) The industry at large
  44. (01 Sep 2015) The case of the lying configuration file
  45. (31 Aug 2015) The case of the memory eater and high load
  46. (14 Aug 2015) The case of the man in the middle
  47. (05 Aug 2015) Reading the errors
  48. (29 Jul 2015) The evil licensing code
  49. (23 Jul 2015) The case of the native memory leak
  50. (16 Jul 2015) The case of the intransigent new database
  51. (13 Jul 2015) The case of the hung over server
  52. (09 Jul 2015) The case of the infected cluster