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:

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

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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:

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

But when parsing it:

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

The reason, by the way:

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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).

More posts in "Production postmortem" series:

  1. (06 Dec 2017) data corruption, a view from INSIDE the sausage
  2. (01 Dec 2017) The random high CPU
  3. (07 Aug 2017) 30% boost with a single line change
  4. (04 Aug 2017) The case of 99.99% percentile
  5. (02 Aug 2017) The lightly loaded trashing server
  6. (23 Aug 2016) The insidious cost of managed memory
  7. (05 Feb 2016) A null reference in our abstraction
  8. (27 Jan 2016) The Razor Suicide
  9. (13 Nov 2015) The case of the “it is slow on that machine (only)”
  10. (21 Oct 2015) The case of the slow index rebuild
  11. (22 Sep 2015) The case of the Unicode Poo
  12. (03 Sep 2015) The industry at large
  13. (01 Sep 2015) The case of the lying configuration file
  14. (31 Aug 2015) The case of the memory eater and high load
  15. (14 Aug 2015) The case of the man in the middle
  16. (05 Aug 2015) Reading the errors
  17. (29 Jul 2015) The evil licensing code
  18. (23 Jul 2015) The case of the native memory leak
  19. (16 Jul 2015) The case of the intransigent new database
  20. (13 Jul 2015) The case of the hung over server
  21. (09 Jul 2015) The case of the infected cluster