Tag Archives: HDD

Storage caching options in Linux 3.9 kernel

dm-cache is (albeit still classified “experimental”) is in the just released Linux 3.9 kernel. It deals with generic block devices and uses the device mapper framework. While there have been a few other similar tools flying around, since this one has been adopted into the kernel it looks like this will be the one that you’ll be seeing the most in to the future. It saves sysadmins the hassle of compiling extra stuff for a system.

A typical use is for an SSD to cache a HDD. Similar to a battery backed RAID controller, the objective is to insulate the application from latency caused by the mechanical device, the most laggy part of which is seek time (measured in milliseconds). Giventhe  relatively high storage capacity of an SSD (in the hundreds of GBs), this allows you to mostly disregard the mechanical latency for writes and that’s very useful for database systems such as MariaDB.

That covers writes (for the moment), but what about reads? Can MariaDB benefit from the read-caching? For the MyISAM storage engine, yes (as it relies on filesystem caching for speeding up row data access). For InnoDB, much less so. But let’s explore this, because it’s not quite a yes/no story – it depends. For typical systems with a correctly dimensioned system and InnoDB buffer pool, most of the active dataset will reside in RAM. For a system using a cached RAID controller that means that an actual disk read is not likely to be in the cache. With an SSD cache you might get lucky as it’s bigger – so stuff that has been read or written in some recent past may still be there. What we have found from testing with hdlatency (on actual client/hosting infra) is that SANs typically don’t have enough cache to pull that off – they too may have SSD caches now, but remember they get accessed by many more users with different data needs as well. The result of SSD filesystem caching for reads is actually similar to InnoDB tweaks that implement a secondary buffer pool on SSD storage, it creates a relatively large and cheap space for “lukewarm” pages (ones that haven’t been recently accessed).

So why does it depend? Because your active dataset might be too large, and/or your combined reads/writes are still more than the physical disks can handle. It’s very important to consider the latter: write caching insulates you from the seeks and allows an intermediate layer to re-order writes to optimise the head movement, but the writes still need to be done and thus ultimately you remain bound by an upper end physical limit. Insulation is not complete separation. If your active dataset is larger than RAM+SSD, then the reads also also need to be taken into account for seek capacity.

So right now you could say that at decent prices, if your active dataset is in the range of a few hundred GB to even a few TB, RAM with the optional addition of SSD caching can all work out nicely – what can still make it go sour is the rate of writes. Conclusion: this type of setup provides you with more headroom than a battery backed RAID controller, should you need that.

Separating reporting to distinct database servers (typically slaves, configured for relatively few connections and large queries) actually still helps quite a bit as it really changes what’s in the buffer pool and other caches. Or, differently put, looking at the access patterns of the different parts of your application is important – there are numerous variation on this basic pattern. It’s a form of functional sharding.

You’ll have noticed I didn’t mention any benchmarks when discussing all this (and most other topics). Many if not most benchmarks have artificial aspects to them, which makes them problematic when dealing with the real world. As shown above, applying background knowledge of the systems and structures, logic, and maths gets you a very long way (either independently or in consultation with us). It can get you through important decision processes quicker. Testing can still play an important part, but then it’s either part of or very close to your real world environment, not a lab activity. It will be specific to you. Don’t get trapped having to deliver on numbers from benchmarks.

Green HDs and RAID Arrays

Some so-called “Green” harddisks don’t like being in a RAID array. These are primarily SATA drives, and they gain their green credentials by being able reduce their RPM when not in use, as well as other aggressive power management trickery. That’s all cool and in a way desirable – we want our hardware to use less power whenever possible! – but the time it takes some drives to “wake up” again is longer than a RAID setup is willing to tolerate.

First of all, you may wonder why I bother with SATA disks at all for RAID. I’ve written about this before, but they simply deliver plenty for much less money. Higher RPM doesn’t necessarily help you for a db-related (random access) workload, and for tasks like backups which do have a lot of speed may not be a primary concern. SATA disks have a shorter command queue than SAS, so that means they might need to seek more – however a smart RAID controller would already arrange its I/O in such a way as to optimise that.

The particular application where I tripped over Green disks was a backup array using software RAID10. Yep, a cheap setup – the objective is to have lots of diskspace with resilience, and access speed is not a requirement.

Not all Green HDs are the same. Western Digital ones allow their settings to be changed, although that does need a DOS tool (just a bit of a pest using a USB stick with FreeDOS and the WD tool, but it’s doable), whereas Seagate has decided to restrict their Green models such that they don’t accept any APM commands and can’t change their configuration.

I’ve now replaced Seagates with (non-Green) Hitachi drives, and I’m told that Samsung disks are also ok.

So this is something to keep in mind when looking at SATA RAID arrays. I also think it might be a topic that the Linux software RAID code could address – if it were “Green HD aware” it could a) make sure that they don’t go to a state that is unacceptable, and b) be tolerant with their response time – this could be configurable. Obviously, some applications of RAID have higher demands than others, not all are the same.