A customer called with a major issue, on a customer machine, a particular operation took too long. In fact, it takes quite a bit more than too long. Instead of the few milliseconds or (at worst, seconds), the customer saw a value in the many minutes.
At first, we narrowed it down to an extreme load on the indexing engine. The customer had a small set of documents that were referenced using LoadDocument by large number of other documents. That meant that whenever those documents were updated, we would need to reindex all the referencing documents.
In their case, that was in the tens to hundreds of thousands of referencing documents in some cases. So an update to a single document could force re-indexing of quarter million documents. Except… that this wasn’t actually the case. What drove everyone crazy was that here was a reasonable, truthful and correct answer. And on one machine the exact same thing took 20 – 30 seconds, and on the customer machine the process took 20 minutes.
The customer also assured us that those documents that everyone referenced are very rarely, if at all, touched or modified, so that shouldn’t be the issue.
The machine with the problem was significantly more powerful from the one without the problem. This issue also started to occur recently, out of the blue. Tracing the resource utilization in the system showed moderate CPU usage, low I/O and memory consumption and nothing much really going on. We looked at the debug logs, and we couldn’t really figure out what it was doing. There were very large gaps in the log where nothing seems to be happening. % Time in GC was low, so that ruled out a long GC pause that would explain the gap in the logs.
This is in version 2.5, which predates all of our introspection efforts, so figuring out what was going on was pretty hard. I’ll have another post talking about that in this context later.
Eventually we gained access to the machine and was able to reproduce this, and take a few mini dumps along the way. Looking at the stack traces, we found this:
And now it all became clear. Suggestions in RavenDB is a really cool feature, which allows you to ask RavenDB to figure out what the user actually meant to ask. It is also extremely CPU intensive during indexing, which is really visible when you try to pump large number of documents through it. And it is a single threaded process.
Except that the customer wasn’t using Suggestions in their application…
So, what happened, in order to hit this issue the following things all needed to happen:
- Suggestions to be enabled on the relevant index/indexes. Check – while the customer wasn’t currently using it, they were using it in the past, and unfortunately that stuck.
- A very large number of documents need to be indexed. Check – that happened when they updated one of the commonly referenced documents.
- A commonly referenced document needed to be modified. Check – that happens when they started work for next year, which modified those rarely touched documents.
Now, why didn’t it manifest itself on the other machines? Simple, on those machines, they used the current version of the application, which didn’t use suggestions. On the machines that were problematic, they upgraded to the new version, so even though they weren’t using suggestions, that was still in affect, and still running.
According to a cursory check, those suggestions has been running there for over 6 months, and no one noticed, because you needed the confluence of all three aspects to actually get this issue.
Removing the suggestions once we know they were there was very easy, and the problem was resolved.
More posts in "Production postmortem" series:
- (03 Oct 2022) Do you trust this server?
- (15 Sep 2022) The missed indexing reference
- (05 Aug 2022) The allocating query
- (22 Jul 2022) Efficiency all the way to Out of Memory error
- (18 Jul 2022) Broken networks and compressed streams
- (13 Jul 2022) Your math is wrong, recursion doesn’t work this way
- (12 Jul 2022) The data corruption in the node.js stack
- (11 Jul 2022) Out of memory on a clear sky
- (29 Apr 2022) Deduplicating replication speed
- (25 Apr 2022) The network latency and the I/O spikes
- (22 Apr 2022) The encrypted database that was too big to replicate
- (20 Apr 2022) Misleading security and other production snafus
- (03 Jan 2022) An error on the first act will lead to data corruption on the second act…
- (13 Dec 2021) The memory leak that only happened on Linux
- (17 Sep 2021) The Guinness record for page faults & high CPU
- (07 Jan 2021) The file system limitation
- (23 Mar 2020) high CPU when there is little work to be done
- (21 Feb 2020) The self signed certificate that couldn’t
- (31 Jan 2020) The slow slowdown of large systems
- (07 Jun 2019) Printer out of paper and the RavenDB hang
- (18 Feb 2019) This data corruption bug requires 3 simultaneous race conditions
- (25 Dec 2018) Handled errors and the curse of recursive error handling
- (23 Nov 2018) The ARM is killing me
- (22 Feb 2018) The unavailable Linux server
- (06 Dec 2017) data corruption, a view from INSIDE the sausage
- (01 Dec 2017) The random high CPU
- (07 Aug 2017) 30% boost with a single line change
- (04 Aug 2017) The case of 99.99% percentile
- (02 Aug 2017) The lightly loaded trashing server
- (23 Aug 2016) The insidious cost of managed memory
- (05 Feb 2016) A null reference in our abstraction
- (27 Jan 2016) The Razor Suicide
- (13 Nov 2015) The case of the “it is slow on that machine (only)”
- (21 Oct 2015) The case of the slow index rebuild
- (22 Sep 2015) The case of the Unicode Poo
- (03 Sep 2015) The industry at large
- (01 Sep 2015) The case of the lying configuration file
- (31 Aug 2015) The case of the memory eater and high load
- (14 Aug 2015) The case of the man in the middle
- (05 Aug 2015) Reading the errors
- (29 Jul 2015) The evil licensing code
- (23 Jul 2015) The case of the native memory leak
- (16 Jul 2015) The case of the intransigent new database
- (13 Jul 2015) The case of the hung over server
- (09 Jul 2015) The case of the infected cluster