Production postmortemThe case of the intransigent new database

time to read 3 min | 480 words

A customer called us to tell that they had a problem with RavenDB. As part of their process for handling new customers, they would create a new database, setup indexes, and then direct all the queries for that customer to that database.

Unfortunately, this system that has worked so well in development died a horrible death in production. But, and this was strange, only for new customers, and only in the create new customer stage. The problem was:

  • The user would create a new database in RavenDB. This just create a db record, and its location on disk. It doesn’t actually initialize a database.
  • On the first request, we initialize the db, creating it if needed. The first request will wait until this happens, then proceed.
  • On their production systems, that first request (which they used to create the indexes they require) would time out with an error.

Somehow, the creation of a new database would take way too long.

The first thought we had was they are creating the database on a path of an already existing database, maybe a big one that had a long initialization period, or maybe one that required recovery. But the customer validated that they were creating the database on an empty folder.

We looked at the logs, and the logs just showed a bunch of time were there was no activity. In fact, we had a single method call to open the database that took over 15 seconds to run. Except that on a new database, this method just create a bunch of files to start things out and is ready really quickly.

That is the point that led us to suspect that the issue was environmental. Luckily, as the result of many such calls, RavenDB comes with a pretty basic I/O Test tool. I asked the customer to run this on their production system, and I got the following:image

And now everything was clear. They were running on an I/O constrained system (a cloud machine), and they were running into an interesting problem. When RavenDB creates a database, it pre-allocate some files for its transactional journal.

Those files are 64MB in size, and the total write for a new Esent RavenDB database with default configuration is just over 65MB. If your write throughput is less than 1MB/sec sustained, that will be problematic.

I let the customer know about the configuration option to take less space at startup (Esent RavenDB databases can go as low as 5MB, Voron RavenDB starts at 256Kb), but I also gave them a hearty recommendation to make sure that their I/O rates improved, because this isn’t going to be the only case where slow I/O will kill them.

More posts in "Production postmortem" series:

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