Cody what else being equal are you talking about? My point is that the difference ratio between compressed and uncompressed on SSD will be different from the ratio on shared network drive. I'm interested in seeing the actual difference. One of the biggest bottlenecks on a low-end server with virtual machines is the disk. Show 1 more comment. Active Oldest Votes. Helge Klein Helge Klein 8, 8 8 gold badges 45 45 silver badges 69 69 bronze badges. I'm not too impressed by the supported platforms, windows up to XP and no support for mac.
Is there an updated support matrix other than: iometer. Add a comment. Lonnie Best Lonnie Best 7, 10 10 gold badges 50 50 silver badges 85 85 bronze badges. Additional server oriented disk benchmarks: Diskspd fio vdbench.
Anon Anon 4, 2 2 gold badges 33 33 silver badges 50 50 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. This last section details the percentile latencies per operation type of storage performance from the minimum value to the maximum value.
It's up to you to decide the acceptable latency for that percentile. Eventually, you'll reach a point where it no longer makes sense to take the latency values seriously. However, you need to pay close attention to the parameters you set and whether they match your real scenario. These include verifying the health of the storage space, checking your resource usage so that another program doesn't interfere with the test, and preparing performance manager if you want to collect additional data.
Storage performance is a delicate thing. Meaning, there are many variables that can affect performance. And so, it's likely you may encounter a number that is inconsistent with your expectations.
The following highlights some of the variables that affect performance, although it's not a comprehensive list:. A node is known as a volume owner or the coordinator node a non-coordinator node would be the node that does not own a specific volume. Every standard volume is assigned a node and the other nodes can access this standard volume through network hops, which results in slower performance higher latency.
If not, you may need to manually change the CSV ownership. The main reason behind this approach is most likely because it's simple and fast. If your real-world goal is to test file copy performance, then this may be a perfectly valid reason to use this method. However, if your goal is to measure storage performance, we recommend to not use this method. The following short summary explains why using file copy to measure storage performance may not provide the results that you're looking for:.
File copies might not be optimized, There are two levels of parallelism that occur, one internal and the other external. Internally, if the file copy is headed for a remote target, the CopyFileEx engine does apply some parallelization. Externally, there are different ways of invoking the CopyFileEx engine. For example, copies from File Explorer are single threaded, but Robocopy is multi-threaded.
For these reasons, it's important to understand whether the implications of the test are what you are looking for. Every copy has two sides. When you simply copy and paste a file, you may be using two disks: the source disk and the destination disk.
If one is slower than the other, you essentially measure the performance of the slower disk. There are other cases where the communication between the source, destination, and the copy engine may affect the performance in unique ways. To learn more, see Using file copy to measure storage performance.
For a three-node, three-way mirrored situation, write operations always make a network hop because it needs to store data on all the drives across the three nodes. Therefore, write operations make a network hop regardless. However, if you use a different resiliency structure, this could change. From this example, you can clearly see in the results of the following figure that latency decreased, IOPS increased, and throughput increased when the coordinator node owns the CSV.
For these tests, focus on how far you can push the throughput while maintaining acceptable latencies. OLAP workloads focus on data retrieval and analysis, allowing users to perform complex queries to extract multidimensional data. Contrary to OLTP, these workloads are not storage latency sensitive. They emphasize queueing many operations without caring much about bandwidth. As a result, OLAP workloads often result in longer processing times.
For these tests, focus on the volume of data processed per second rather than the number of IOPS. Latency requirements are also less important, but this is subjective. This is because they each track their own sequential offset. Not an IT pro? Resources for IT Professionals. Sign in. United States English. Ask a question. Quick access. Search related threads. Remove From My Forums.
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