This is a fun series to write, but I’m running out of topics where I can speak about the details at a high level without getting into nitty gritty details that will make no sense to anyone but database geeks. If you have any suggestions for additional topics, I would love to hear about them.
This post, however, is about another aspect of running a database engine. It is all about knowing what is actually going on in the system. A typical web application has very little state (maybe some caches, but that is pretty much about it) and can be fairly easily restarted if you run into some issue (memory leak, fragmentation, etc) to recover most problems while you investigate exactly what is going on. A surprising number of production systems actually have this feature that they just restart on a regular basis, for example. IIS will restart a web application every 29 hours, for example, and I have seen production deployment of serious software where the team was just unaware of that. It did manage to reduce a lot of the complexity, because the application never got around to live long enough to actually carry around that much garbage.
A database tend to be different. A database engine lives for a very long time, typically weeks, months or years, and it is pretty bad when it goes down, it isn’t a single node in the farm that is temporarily missing or slow while it is filling the cache, this is the entire system being down without anything that you can do about it (note, I’m talking about single node systems here, distributed systems has high availability systems that I’m ignoring at the moment). That tend to give you a very different perspective on how you work.
For example, if you are using are using Cassandra, it (at least used to) have an issue with memory fragmentation over time. It would still have a lot of available memory, but certain access pattern would chop that off into smaller and smaller slices, until just managing the memory at the JVM level caused issues. In practice, this can cause very long GC pauses (multiple minutes). And before you think that this is an situation unique to managed databases, Redis is known to suffer from fragmentation as well, which can lead to higher memory usage (and even kill the process, eventually) for pretty much the same reason.
Databases can’t really afford to use common allocation patterns (so no malloc / free or the equivalent) because they tend to hold on to memory for a lot longer, and their memory consumption is almost always dictated by the client. In other words, saving increasing large record will likely cause memory fragmentation, which I can then utilize further by doing another round of memory allocations, slightly larger than the round before (forcing even more fragmentation, etc). Most databases use dedicated allocators (typically some from of arena allocators) with limits that allows them to have better control of that and mitigate that issue. (For example, by blowing the entire arena on failure and allocating a new one, which doesn’t have any fragmentation).
But you actually need to build this kind of thing directly into the engine, and you need to also account for that. When you have a customer calling with “why is the memory usage going up”, you need to have some way to inspect this and figure out what to do about that. Note that we aren’t talking about memory leaks, we are talking about when everything works properly, just not in the ideal manner.
Memory is just one aspect of that, if one that is easy to look at. Other things that you need to watch for is anything that has a linear cost proportional to your runtime. For example, if you have a large LRU cache, you need to make sure that after a couple of months of running, pruning that cache isn’t going to be an O(N) job running every 5 minutes, never finding anything to prune, but costing a lot of CPU time. The number of file handles is also a good indication of a problem in some cases, some databases engines have a lot of files open (typically LSM ones), and they can accumulate over time until the server is running out of those.
Part of the job of the database engine is to consider not only what is going on now, but how to deal with (sometimes literally) abusive clients that try to do very strange things, and how to manage to handle them. In one particular case, a customer was using a feature that was designed to have a maximum of a few dozen entries in a particular query to pass 70,000+ entries. The amazing thing that this worked, but as you can imagine, all sort of assumptions internal to the that features were very viciously violated, requiring us to consider whatever to have a hard limit on this feature, so it is within its design specs or try to see if we can redesign the entire thing so it can handle this kind of load.
And the most “fun” is when those sort of bugs are only present after a couple of weeks of harsh production systems running. So even when you know what is causing this, actually reproducing the scenario (you need memory fragmented in a certain way, and a certain number of cache entries, and the application requesting a certain load factor) can be incredibly hard.
More posts in "The Guts n’ Glory of Database Internals" series:
- (08 Aug 2016) Early lock release
- (05 Aug 2016) Merging transactions
- (03 Aug 2016) Log shipping and point in time recovery
- (02 Aug 2016) What goes inside the transaction journal
- (18 Jul 2016) What the disk can do for you
- (15 Jul 2016) The curse of old age…
- (14 Jul 2016) Backup, restore and the environment…
- (11 Jul 2016) The communication protocol
- (08 Jul 2016) The enemy of thy database is…
- (07 Jul 2016) Writing to a data file
- (06 Jul 2016) Getting durable, faster
- (01 Jul 2016) Durability in the real world
- (30 Jun 2016) Understanding durability with hard disks
- (29 Jun 2016) Managing concurrency
- (28 Jun 2016) Managing records
- (16 Jun 2016) Seeing the forest for the trees
- (14 Jun 2016) B+Tree
- (09 Jun 2016) The LSM option
- (08 Jun 2016) Searching information and file format
- (07 Jun 2016) Persisting information