If your storage system implements inline compression, performance results with small IO size random writes with time_based and runtime may be inflated with fio versions < 3.3 due to fio generating unexpectedly compressible data when using fio’s default data pattern. Although unintuitive, performance can often be increased by enabling compression especially if the bottleneck is on the storage media, replication or a combination of both.
fio 2.8.1 vs fio 3.33 data patterns
Therefore if you are comparing performance results generated using fio version < 3.3 and fio >=3.3 the random write performance on the same storage platform my appear reduced with more recent fio versions.
fio-3.3 was released in December 2017 but older fio versions are still in use particularly on distributions with long term (LTS) support. For instance Ubuntu 16, which is supported until 2026 ships with fio-2.2.10
This will create a 1GB file called fiofile on the F:\ Drive in Windows then read the file. Notice that the specification is “Driveletter” “Backslash” “Colon” “Filename”
In fio terms we are “escaping” the : which fio traditionally uses as a file separator.
Creating a mixed read/write workload with fio can be a bit confusing. Assume we want to create a fixed rate workload of 100 IOPS split 70:30 between reads and writes.
Don’t mix rwmixread and rate_iops
TL;DR
Specify the rate directly with rate_iops=<read-rate>,<write-rate> do not try to use rwmixread with rate_iops. For the example above use.
rate_iops=70,30
Additionally older versions of fio exhibit problems when using rate_poisson with rate_iops . fio version 3.7 that I was using did not exhibit the problem.
The parameters norandommap and randrepeat significantly change the way that repeated random IO workloads will be executed, and also can meaningfully change the results of an experiment due to the way that caching works on most storage system.
buffer_compress_percentage does what you’d expect and specifies how compressible the data is
refill_buffers Is required to make the above compress percentage do what you’d expect in the large. IOW, I want the entire file to be compressible by the buffer_compress_percentage amount
buffer_pattern This is a big one. Without setting this pattern, fio will use Null bytes to achieve compressibility, and Nutanix like many other storage vendors will suppress runs of Zero’s and so the data reduction will mostly be from zero suppression rather than from compression.
Much of this is well explained in the README for latest version of fio.
Also NOTE Older versions of fio do not support many of the fancy data creation flags, but will not alert you to the fact that fio is ignoring them. I spent quite a bit of time wondering why my data was not compressed, until I downloaded and compiled the latest fio.
If your underlying filesystem/devices have different response times (e.g. some devices are cached – or are on SSD) and others are on spinning disk, then the behavior of fio can be quite different depending on how the fio config file is specified. Typically there are two approaches
1) Have a single “job” that has multiple devices
2) Make each device a “job”
With a single job, the iodepth parameter will be the total iodepth for the job (not per device) . If multiple jobs are used (with one device per job) then the iodepth value is per device.
Option 1 (a single job) results in [roughly] equal IO across disks regardless of response time. This is like having a volume manager or RAID device, in that the overall oprate is limited by the slowest device.
For example, notice that even though the wait/response times are quite uneven (ranging from 0.8 ms to 1.5ms) the r/s rate is quite even. You will notice though the that queue size is very variable so as to achieve similar throughput in the face of uneven response times.
To get this sort of behavior use the following fio syntax. We allow fio to use up to 128 outstanding IO’s to distribute amongst the 8 “files” or in this case “devices”. In order to maintain the maximum throughput for the overall job, the devices with slower response times have more outstanding IO’s than the devices with faster response times.
The second option, give an uneven throughput because each device is linked to a separate job, and so is completely independent. The iodepth parameter is specific to each device, so every device has 16 outstanding IO’s. The throughput (r/s) is directly tied to the response time of the specific device that it is working on. So response times that are 10x faster generate throughput that is 10x faster. For simulating real workloads this is probably not what you want.
For instance when sizing workingset and cache, the disks that have better throughput may dominate the cache.
