This post has been republished via RSS; it originally appeared at: Microsoft Tech Community - Latest Blogs - .
IOPs is Overrated, yeah, I said it.
How many compute, storage area networks, hard drive vendors and storage services have posted their IOPs capabilities in marketing and didn’t include the throughput (MPBs)? Why when someone sends me IOPs for an Oracle database do I thank them kindly and ask for throughput?
Thank you for asking…
IO requests for Oracle can be exceptionally efficient depending on the type of workload. In this blog post, I’m going to take three, real examples of Oracle workloads and show how different the ratio is between IOPs and MBPs using the AWR report. Now there is a significant difference from what we produce for sizing and a raw AWR report, but I’m going to use the AWR data, as this is something anyone working with Oracle will recognize. The examples here are from different versions of Oracle, single instance vs. Exadata, but hopefully will explain why I am not a fan of IOPs for proving out a workload size.
Example #1
IOPS: 7736 per second
MBPs: 153 per second
Example #2
IOPS: 8327 per second
MBPs: 344 per second
Example #3
IOPS: 26215 per second
MBPs: 13008 per second
Interesting ratio of IO requests vs. throughput:
|
Source |
IO Requests Reads |
MBPs Reads |
Ratio |
|
Example #1 |
7736 |
153 |
51:1 |
|
Example #2 |
8327 |
344 |
24:1 |
|
Example #3 |
26215 |
12872 |
2:1 |
If you based the storage solution for these Oracle workloads based off the IOPS, you could make drastic mistakes on both compute as well as storage.
For Example #1, we see similar ratios set in compute for max limits on Azure compute for limits on IO:
|
|
Memory: GiB |
Temp storage (SSD) GiB |
Max data disks |
Max uncached disk throughput: IOPS/MBps |
Max burst uncached disk throughput: IOPS/MBps1 |
Max NICs |
Expected network bandwidth (Mbps) |
|
|
Standard_E2s_v44 |
2 |
16 |
Remote Storage Only |
4 |
3200/48 |
4000/200 |
2 |
5000 |
|
Standard_E4s_v4 |
4 |
32 |
Remote Storage Only |
8 |
6400/96 |
8000/200 |
2 |
10000 |
|
Standard_E8s_v4 |
8 |
64 |
Remote Storage Only |
16 |
12800/192 |
16000/400 |
4 |
12500 |
|
Standard_E16s_v4 |
16 |
128 |
Remote Storage Only |
32 |
25600/384 |
32000/800 |
8 |
12500 |
|
Standard_E20s_v4 |
20 |
160 |
Remote Storage Only |
32 |
32000/480 |
40000/1000 |
8 |
10000 |
|
Standard_E32s_v4 |
32 |
256 |
Remote Storage Only |
32 |
51200/768 |
64000/1600 |
8 |
16000 |
|
Standard_E48s_v4 |
48 |
384 |
Remote Storage Only |
32 |
76800/1152 |
80000/2000 |
8 |
24000 |
This is a smaller workload and we could easily go to the Standard E8s_v4 would be covering the average workload from this peak AWR that was submitted for sizing. If the vCPU and memory meets the requirements, then the IO peaks also do.
Using Example #2, where the ratio has more than halved, although the IO Requests on reads hasn’t changed that much, the MBPs (throughput) has more than doubled. We could meet the workload when bursting is available, but we really don’t want to count or pay for this and would need to size up. This is a clear case of why we lean on throughput vs. IOPs.
Example #3 is a very large Oracle workload coming from Exadata. There is considerable offloading, (smart scans) and along with flash cache scanning. With this, the IO requests are incredibly efficient, to the point that the requests vs. throughput is a ratio of 2:1. This is a workload that can only rely on network attached storage to meet its needs and would require some optimizing. There’s a reason the documentation shows both IOPs and throughput (MBPs). Make sure when you’re assessing workloads, especially Oracle, include the throughput. It may surprise you.