Aligning IO on a hard disk RAID – the Benchmarks

In the first part of this article I have showed how I align IO, now I want to share results of the benchmark that I have been running to see how much benefit can we get from a proper IO alignment on a 4-disk RAID1+0 with 64k stripe element. I haven’t been running any benchmarks in a while so be careful with my results and forgiving to my mistakes :)

The environment

Here is the summary of the system I have been running this on (for brevity I have removed some irrelevant information):

# Aspersa System Summary Report ##############################
    Platform | Linux
     Release | Ubuntu 10.04.2 LTS (lucid)
      Kernel | 2.6.32-31-server
Architecture | CPU = 64-bit, OS = 64-bit
# Processor ##################################################
  Processors | physical = 2, cores = 12, virtual = 24, hyperthreading = yes
      Speeds | 24x1600.000
      Models | 24xIntel(R) Xeon(R) CPU X5650 @ 2.67GHz
      Caches | 24x12288 KB
# Memory #####################################################
       Total | 23.59G
  Locator   Size     Speed             Form Factor   Type          Type Detail
  ========= ======== ================= ============= ============= ===========
  DIMM_A1   4096 MB  1333 MHz (0.8 ns) DIMM          {OUT OF SPEC} Other
# Disk Schedulers And Queue Size #############################
         sda | [deadline] 128
# RAID Controller ############################################
  Controller | LSI Logic MegaRAID SAS
       Model | MegaRAID SAS 8704EM2, PCIE interface, 8 ports
       Cache | 128MB Memory, BBU Present
         BBU | 100% Charged, Temperature 34C, isSOHGood=

  VirtualDev Size      RAID Level Disks SpnDpth Stripe Status  Cache
  ========== ========= ========== ===== ======= ====== ======= =========
  0(no name) 1.088 TB  1 (1-0-0)      2     2-2     64 Optimal WT, RA

  PhysiclDev Type State   Errors Vendor  Model        Size
  ========== ==== ======= ====== ======= ============ ===========
  Hard Disk  SAS  Online   0/0/0 SEAGATE ST3600057SS  558.911
  Hard Disk  SAS  Online   0/0/0 SEAGATE ST3600057SS  558.911
  Hard Disk  SAS  Online   0/0/0 SEAGATE ST3600057SS  558.911
  Hard Disk  SAS  Online   0/0/0 SEAGATE ST3600057SS  558.911

It says controller cache is set to write-through (WT), though in fact for every benchmark I have repeated it with (a) write-through and (b) write-back to see if write-back cache would minimize the effects of misalignment.

File system of choice was XFS. Barriers and physical disk cache was disabled. The tool I used was sysbench 0.4.10 that came with this Ubuntu system. I have run every fileio benchmark and an IO bound read-write oltp benchmark in autocommit mode.

File IO benchmark

For the FileIO benchmark, I used 64 files – 1GB, 4GB and 16GB total in size with 1, 4 and 8 threads. The operations were done in 16kB units to mimic InnoDB pages. There were couple interesting surprised I faced:

1. After I got (what I thought was) the best configuration, I added LVM on top of that and the performance improved another 20-40%. It took me a while to figure it out, but here’s what happened – for XFS file system on a raw partition I was using full partition size which was slightly over 1TB in size. When I added LVM on top however, I made the logical volume slightly below 1TB. Investigating this I found that 32-bit xfs inodes (which are used by default) have to live in the first terabyte of the device which seems to have affected the performance here (IMO that’s because of where first data extents were placed in this case). When I have mounted the partition with inode64 option however, the effect disappeared and performance without LVM was slightly better than with LVM as expected. I had to redo all of the benchmarks to get the numbers right.

2. I was running vmstat during one of the tests and my eye caught the spike in OS buffers during “prepare” phase of sysbench. I found out that sysbench would not honor –file-extra-flags during “prepare” phase and instead of having files created using direct IO they were buffered in OS cache and so writes to files were serialized until they were fully overwritten and that way flushed from OS buffers. Buffers would be flushed within first few seconds so the effects of this were marginal. Alexey Kopytov fixed this in the sysbench trunk immediately, though I didn’t want to recompile sysbench on this system so I’ve used Domas’ uncache after prepare to make sure caches were clean.

OLTP benchmark

As the goal was to compare performance with different IO alignment, not different MySQL configurations, I didn’t try out different MySQL versions or settings. Moreover, I have been running these benchmarks for a customer so I just used the setting that they would have used anyway. One thing I did change was – I have significantly reduced InnoDB buffer pool to make sure the benchmark is IO bound.

That said, benchmark was running on a Percona Server 5.0.92-87 with the following my.cnf configuration:

innodb_file_per_table = true
innodb_data_file_path = ibdata1:10M:autoextend
innodb_flush_log_at_trx_commit = 2
innodb_flush_method = O_DIRECT
innodb_log_buffer_size = 8M
innodb_buffer_pool_size = 128M
innodb_log_file_size = 64M
innodb_log_files_in_group = 2
innodb_read_io_threads = 8
innodb_write_io_threads = 8
innodb_io_capacity = 200
port = 3306
back_log = 50
max_connections = 2500
max_connect_errors = 10
table_cache = 2048
max_allowed_packet = 16M
binlog_cache_size = 16M
max_heap_table_size = 64M
thread_cache_size = 32
query_cache_size = 0
tmp_table_size = 64M
key_buffer_size = 8M
bulk_insert_buffer_size = 8M
myisam_sort_buffer_size = 8M
myisam_max_sort_file_size = 10G
myisam_repair_threads = 1

Amount of rows used was 20M, transactions were not used (autocommit), number of threads – 1, 4, 8, 16 and 32.

Benchmark scenarios

Here’s the different settings that I have ran the same benchmark on. As I mentioned earlier, each of those were run twice – first with RAID controller cache set to Write-Through and then to Write-Back.

1. Baseline – misalignment on the partition table, no LVM and no alignment settings in the file system. This is what you would often get on RHEL5, Ubuntu 8.04 or similar “older” systems if you wouldn’t do anything with respect to IO alignment.

2. Misalignment on the partition table, but proper alignment options on the file system. This is what we get when file system tries to balance writes but is not aware that it is not aligned to the beginning of the stripe element.

3. 1M alignment in partition table but no options on the file system. You should get this on RHEL6, Ubuntu 10.04 and similar systems if you wouldn’t do anything with respect to IO alignment yourself. In this case offset is correct, but file system is unaware how to align files properly.

4. Partition table and file system properly aligned; sunit/swidth set during mkfs. No LVM at this point.

5. Partition table aligned properly; sunit/swidth set during mounting but not during mkfs. This is your best option if you have a proper alignment in partition table but you did not set alignment options in xfs when creating it and you don’t want or can’t format the file system. One thing to note however – files that were written before this was set may still be unaligned, though xfs defragmentation may be able to fix that (not verified).

6. Added LVM on top of aligned partition table, used proper file system alignment.

Benchmark results

I had a hard time thinking how it would be best to present results so it’s not too stuffed and actually interesting. I decided that instead of preparing charts for each benchmark, I’ll just describe few less interesting numbers first, then I’ll show graphs for more interesting results. Let me know if you thought this was a bad idea :)

File IO benchmark results

Sequential read results are expectedly the least interesting. Read-ahead kicked in immediately giving ~9’600 iops (~150MB/s) at 1 thread, 14500 iops (~230MB/s) at 4 threads and ~16300 iops (~250MB/s) at 8 threads. Neither IO alignment nor file size made any difference. Adding LVM here reduced single-thread performance by 5-10%.

Sequential write results were a bit more interesting. With WT (write-through) cache enabled, performance was really poor whatsoever and there was virtually no difference whether it was 1 thread, 4 or 8 threads. Different file sizes made no difference too. Write-back cache gave an incredible performance boost – up to 33x in single-threaded workload. File system IO alignment seems to have made a difference – up to 15% with write-back cache enabled. Here’s 1GB seqwr with WT cache:

1GB seqwr WT cache

Here’s same test with WB cache:

1GB seqwr WB cache

And just to show you the difference between sequential writes with WT cache and WB cache:

1GB seqwr WT vs WB

Random read. This is probably the most interesting number for OLTP workload which is usually light on writes (especially if there’s a BBU protected Write-Back cache) and heavy on random reads. Regardless of the file size, the difference between aligned and misaligned reads was the same and, WT -vs- WB cache of course showed no difference at all. Here are the results:

16GB rndrd

As you can see IO alignment makes a difference here and improves performance up to 15% in case of 8 threads running concurrently. Because the customer was running a database which was way bigger than 16G, I’ve repeated the random read (and write) benchmark with 8 threads and total size of 256G. While the number of operations per second was slightly lower, the difference was still 15% — 909 iops unaligned -vs- 1049 aligned.

Random write. This is an important metric for write intensive workloads where there’s a lot of data being modified, inserts are done to random positions (not consecutive PK causing page splits) etc. Benchmark results are fairly consistent regardless of file size, let’s look at them. First, results with WT cache:

16 rndwr WT cache

And here’s with WB cache:

16 rndwr WB cache

Apparently proper IO alignment in this case gives up to 23% improvement when WB cache is used. With WT cache enabled, single thread performance improvement is marginal however WB cache brings single thread random write performance close to what 8 threads can do, and IO alignment gives extra 23% in this case.

I mentioned I did single test on a larger files (same test I did for random reads) i.e. 8 thread random write benchmark on files totaling to 256GB. With WB cache enabled, I got 919 iops unaligned and 1127 iops aligned i.e. the improvement is still 23%.

OLTP benchmark results

From this benchmark, I only have two graphs to show you. First one is with RAID controller set to WT cache:

sysbench OLTP 20M rows, WT cache

The second is with WB cache:

sysbench OLTP 20M rows, WB cache

I couldn’t figure out what exactly happened with setting #3 when WB cache was disabled, what I do know though is that, based on IO stats I was gathering during the benchmarks, the reason was in fact lower number of IO operations and higher response time – so it seems in this case misaligned IO had some collateral effects in a mixed read/write environment. Note that the benchmarks were all scripted and oltp benchmarks would automatically start after file tests so if there was an error in the setting, it would have reflected across all other benchmarks for the same setting.


For the two workloads that are most relevant to databases – random reads and random writes – IO alignment on a 4-disk RAID10 with standard 64k stripe element size makes a significant difference. When I launched the system that I was benchmarking, I could clearly see the difference in production as I had another machine running sideways with the same hardware, but with a misaligned IO. Here’s diskstats from the two shards running side by side:

  #ts device    rd_s rd_avkb rd_mb_s rd_mrg rd_cnc   rd_rt    wr_s wr_avkb wr_mb_s wr_mrg wr_cnc   wr_rt busy in_prg
{540} dm-0     447.1    34.0     7.4     0%    2.4     5.4    23.4    49.6     0.6     0%    0.0     0.6  85%      0

  #ts device    rd_s rd_avkb rd_mb_s rd_mrg rd_cnc   rd_rt    wr_s wr_avkb wr_mb_s wr_mrg wr_cnc   wr_rt busy in_prg
{925} dm-0     462.1    34.1     7.7     0%    3.8     8.2    12.1    87.0     0.5     0%    0.0     0.7  93%      0

While number of operations from the OS perspective is very similar, due to high concurrency response time in the first case is significantly better.

It would be interesting however to run similar benchmarks on a larger RAID5 system where it should make even bigger difference on writes. Another interesting setting might be a [mirrored] RAID0 with many more stripes as not having proper file system alignment should have really interesting effects. Large stripe on the other hand should somewhat reduce the effects of misalignment, though it would definitely be interesting to run benchmarks and verify that. If you have some numbers to share, please leave a comment. Next, I plan to look at IO alignment on Flash cards to see what benefits we can get there from proper alignment.

You can find scripts and plain data here on our public wiki.


Aligning IO on a hard disk RAID – the Theory

Now that flash storage is becoming more popular, IO alignment question keeps popping up more often than it used to when all we had were rotating hard disk drives. I think the reason is very simple – when systems only had one bearing hard disk drive (HDD) as in RAID1 or one disk drive at all, you couldn’t really have misaligned IO because HDDs operate in 512-byte sectors and that’s also the smallest amount of disk IO that systems can do. NAND flash on the other hand can have a page size of 512-bytes, 2kbytes or 4kbytes (and often you don’t know what size it is really) so the IO alignment question becomes more relevant.

It was and still is, however, relevant with HDD RAID storage – technology we have been using for many years – when there’s striping like in RAID0, 5, 6 or any variation of them (5+0, 1+0, 1+0+0 etc.). While IO inside the RAID is perfectly aligned to disk sectors (again due to the fact operations are done in multiples of 512-bytes), outside of the RAID you want to align IO to a stripe element as you may otherwise end up reading or writing to more disks than you would like to. I decided to do some benchmarks on a hard disk array and see when this matters and whether it matters at all.

In this article I will however focus on the process of alignment, if you’re curious about benchmark results, here they are.

What is IO alignment

I would like to start with some background on IO alignment. So what is IO alignment and how does a misaligned IO look like? Here is one example of it:


In this case the RAID controller is using 32KB stripe unit and that can fit in 2 standard InnoDB pages (16KB in size) as long as they are aligned properly. In first case when reading or writing a single InnoDB page RAID will only read or write to a single disk because of the alignment to a stripe unit. In the second example however every other page spans two disks so there is going to be twice as many operations to read or write these pages which could mean more waiting in some cases and more work for the RAID controller for that same operation. In practice stripes by default are bigger in size – I would often see see 64KB (mdadm default chunk size) or 128KB stripe unit size so in these cases there would be fewer pages spanning multiple disks so the effects of misalignment would be less significant.

Here’s another example of misalignment, described in SGI xfs training slides:


D stands for the disk here so the RAID has 4 bearing disks (spindles) and if there’s a misalignment on the file system, you can see how RAID ends up doing 5 IO operations – two to D4 and one on each of the other three disks instead of just doing one IO to each of the disks. In this case even if this is the single IO request from OS, it’s guaranteed to be slower both for reading and writing.

So, how do we avoid misalignment? Well, we must ensure alignment on each layer of the stack. Here’s how a typical stack looks like:


Let’s talk about each of them briefly:

InnoDB page

You don’t need to do anything to align InnoDB pages – file system takes care of it (assuming you configure the file system correctly). I would however mention couple things about InnoDB storage: first – in Percona Server you can now customize page size and it may be good idea to check that page size is no bigger than stripe element; second – logs are actually written in 512 byte units (in Percona Server 5.1 and 5.5 you can customize this) while I will be talking here about InnoDB data pages which are 16KB in size.

File system

File system plays very important role here – it maps files logical address to physical address (at a certain level) so when writing a file, file system decides how to distribute writes properly so they make the best use of the underlying storage, it also makes sure file starts in a proper position with respect to stripe size. The size of logical IO units also is up to the file system.

The goal is to write and read as little as possible. If you gonna be writing small (say 500 byte) files mostly, it’s best to use 512-byte blocks, for bigger files 4k may make more sense (you can’t use blocks bigger than 4k (page size) on Linux unless you are using HugePages). Some file systems let you set stripe width and stripe unit size so they can do a proper alignment based on that. Mind however that different file systems (and different versions of them) might be using different units for these options so you should refer to a manual on your system to be sure you’re doing the right thing.

Say we have 6-disk RAID5 (so 5 bearing disks) with 64k stripe unit size and 4k file system block size, here’s how we would create the file system:

xfs      - mkfs.xfs -b 4k -d su=64k,sw=5 /dev/ice (alternatively you can specify sunit=X,swidth=Y as options when mounting the device)
ext2/3/4 - mke2fs -b 4096 -E stride=16,stripe-width=80 /dev/ice (some older versions of ext2/3 do not support stripe-with)

You should be all set with the file system alignment at this point. Let’s get down one more level:


If you are using LVM, you want to make sure it does not introduce misalignment. On the other hand it can be used to fix it if it was misaligned on the partition table. On the system that I have been benchmarking, defaults worked out just fine because I was using a rather small 64k stripe element. Have I used 128k or 256k RAID stripe elements, I would have ended up with LVM physical extent starting somewhere in the middle of the stripe which would in turn screw up file system alignment.

You can only set alignment options early in the process when using pvcreate to initialize disk for LVM use, the two options you are interested in are –dataalignment and –dataalignmentoffset. If you have set the offset correctly when creating partitions (see below), you don’t need to use –dataalignmentoffset, otherwise with this option you can shift the beginning of data area to the start of next stripe element. –dataalignment should be set to the size of the stripe element – that way the start of a Physical Extent will always align to the start of the stripe element.

In addition to setting correct options for pvcreate it is also a good idea to use appropriate Volume Group Physical Extent Size for vgcreate – I think default 4MB should be good enough for most cases, when changing however, I would try to not make it smaller than a stripe element size.

To give you a bit more interesting alignment example, let’s assume we have a RAID with 256k stripe element size and a misalignment in partition table – partition /dev/sdb1 starts 11 sectors ahead of the stripe element start (reminder: 1 sector = 512 bytes). Now we want to get to the beginning of next stripe element i.e. 256th kbyte so we need to offset the start by 501 sectors and set proper alignment:

pvcreate --dataalignmentoffset 501s --dataalignment 256k /dev/sdb1

You can check where physical extents will start (or check your current setup) using pvs -o +pe_start. Now let’s move down one more level.

Partition table

This is the most frustrating part of the IO alignment and I think the reason people get frustrated with it is that by default fdisk is using “cylinders” as units instead of sectors. Moreover, on some “older” systems like RHEL5 it would actually align to “cylinders” and leave first “cylinder” blank. This comes from older times when disks were really small and they were actually physical disks. Drive geometry displayed here is not real- this RAID does not really have 255 heads and 63 sectors per track:

db2# fdisk -l

Disk /dev/sda: 1198.0 GB, 1197998080000 bytes
255 heads, 63 sectors/track, 145648 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes
   Device Boot      Start         End      Blocks   Id  System
/dev/sda1   *           1        1216     9764864   83  Linux
/dev/sda2            1216        1738     4194304   82  Linux swap / Solaris
Partition 2 does not end on cylinder boundary.
/dev/sda3            1738      145649  1155959808   83  Linux
Partition 3 does not end on cylinder boundary.

So it makes a lot more sense to use sectors with fdisk these days which you can get with -u when invoking it or with “u” when working in the interactive mode:

db2# fdisk -ul

Disk /dev/sda: 1198.0 GB, 1197998080000 bytes
255 heads, 63 sectors/track, 145648 cylinders, total 2339840000 sectors
Units = sectors of 1 * 512 = 512 bytes
   Device Boot      Start         End      Blocks   Id  System
/dev/sda1   *        2048    19531775     9764864   83  Linux
/dev/sda2        19531776    27920383     4194304   82  Linux swap / Solaris
Partition 2 does not end on cylinder boundary.
/dev/sda3        27920384  2339839999  1155959808   83  Linux
Partition 3 does not end on cylinder boundary.

The rest of the task is easy – you just have to make sure that Start sector divides by number of sectors in a stripe element without a remainder. Let’s check if /dev/sda3 aligns to 1MB stripe element. 1MB is 2048 sectors, dividing 27920384 by 2048 we get 13633 so it does align to 1MB boundary.

Recent systems like RHEL6 (not verified) and Ubuntu 10.04 (verified) would by default align to 1MB if storage does not support IO alignment hints which is good enough for most cases, however here’s what I got on Ubuntu 8.04 using defaults (you would get the same on RHEL5 and many other systems):

db1# fdisk -ul

Disk /dev/sda: 1197.9 GB, 1197998080000 bytes
255 heads, 63 sectors/track, 145648 cylinders, total 2339840000 sectors
Units = sectors of 1 * 512 = 512 bytes
Disk identifier: 0x00091218

   Device Boot      Start         End      Blocks   Id  System
/dev/sda1   *          63    19535039     9767488+  83  Linux
/dev/sda2        19535040    27342629     3903795   82  Linux swap / Solaris
/dev/sda3        27342630  2339835119  1156246245   8e  Linux LVM

sda1 and sda3 do not even align to 1k. sda2 aligns up to 32k but the RAID controller actually has 64k stripe so all IO on this system is unaligned (unless compensated by LVM, see above). So on such a system, when creating file systems with fdisk, don’t use the default value for a start sector, instead use the next number that divides by the number of sectors in a stripe element without a reminder and make sure you’re using sectors as units to simplify the math.

Besides DOS partition table which you would typically work with using fdisk (or cfdisk, or sfdisk), there’s also a more modern – GUID partition table (GPT). The tool for the task of working with GPT is typically parted. If you are already running GPT on your system and want to check if it’s aligned, here’s a command for you:

db2# parted /dev/sda unit s print
Model: LSI MegaRAID 8704EM2 (scsi)
Disk /dev/sda: 2339840000s
Sector size (logical/physical): 512B/512B
Partition Table: msdos

Number  Start      End          Size         Type     File system     Flags
 1      2048s      19531775s    19529728s    primary  ext4            boot
 2      19531776s  27920383s    8388608s     primary  linux-swap(v1)
 3      27920384s  2339839999s  2311919616s  primary

This is the same output we saw from fdisk earlier. Again you want to look at Start sector and make sure it divides by the size of stripe element without a reminder.

Lastly, if this is not a system boot disk you are working on, you may not need partition table at all – you can just use the whole raw /dev/sdb and either format it with mkfs directly or add it as an LVM physical volume. This let’s you avoid any mistakes when working on partition table.

RAID stripe

Further down below on the storage stack there’s a group of RAID stripe units (elements) sometimes referred to as a stripe though most of the tools refer to it as a stripe width. RAID level, number of disks and the size of a stripe element set the stripe width size. In case of RAID1 and JBOD there’s no striping, with RAID0 number of bearing disks is actual number of disks (N), with RAID1+0 (RAID10) it’s N/2, with RAID5 – N-1 (single parity), with RAID6 – N-2 (double parity). You want to know that when setting parameters for file system but when RAID is configured, there’s nothing more you can do about it – you just need to know these.

Stripe unit size is the amount of data that will be written to single disk before skipping to next disk in the array. This is also one of the options you usually have to decide on very early when configuring RAID.

Disk sectors

Most if not all hard disk drives available on the market these days use 512-byte sectors so most of the time if not always you don’t care about alignment at this level and nor do RAID controllers as they also operate in 512-bytes internally. This however gets more complicated with SSD drives which often operate in 4kbyte units, though this is surely a topic for another research.


While it may seem there are many moving parts between the database and actual disks, it’s not really all that difficult to get a proper alignment if you’re careful when configuring all of the layers. Not always however you have a fully transparent system – for example in the cloud you don’t really know if the data is properly aligned underneath: you don’t know if you should be using an offset, what stripe size and how many stripe elements. It’s easy to check if you’re aligned – run a benchmark with an offset and compare to a base, but it’s much harder to figure out proper alignment options if you are not aligned.

Now it may be interesting to see what are real life effects of misalignment, my benchmark results are in the second part.

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