You run agents on many servers and have them all load up the target servers.Ģ. In my opinion JMeter is by far the best generic benchmarking tool out there:ġ. Also those benchmark tools are not scalable, they typically cannot be ran properly over multiple machines and cannot be used with real data. Often things like the way hashing is setup are critical to the performance and it’s important that you are not abstracted away from those details. It might be way easier to get started but understanding how the client behaves (like the Cassandra driver or the Couchbase client) is key to making a correct assessment of the technology. I don’t recommend using the benchmarking tools that come with the database or technology in question. Find out virtualisation technologies used, bandwidth capacities and limits of your storage (for the instance types you want to start benchmarking). This is because a lot of these technologies rely on the client to do hashing and some of the processing.Ĥ. So if you test 5 cassandra nodes you need at least another 5 nodes to actually drive the load. For high performance applications you usually need to have at least 1:1 ratio of loaders to target nodes. Think about the architecture of the tests. You’ll have many sheets with test runs and such so it’s good to get organised from the start.ģ. I’ve found out that writing an excel table header right from the start with the actual cells that need to be filled in helps a lot. This part will take you at least a couple of days. You will need them later on the actual testing servers and also if somebody asks about versions and dependencies such as JVM version. Try to save the actual commands you used to install stuff (eg yum install something) in a ‘ notes’ file. Read a bit about the architecture and go through the getting started guide. If you haven’t done that already get your client code and experiment with it if you can. To be able to deduce the relationship between hardware configuration and the performance of the system (also called Scaling Profile) experiments must also vary the hardware configuration. Notice that at the input of the performance profile is also the hardware configuration. The more experiments we run the better the model. From all the experiments an empirical model can be deduced. This performance profile is derived from treating the system as a black box, feeding it repeated inputs and observing the output. It just has to provide enough information so that someone will be able to use it to estimate performance given a specific hardware configuration and a load level. It doesn’t have to be a mathematical model. A performance profile is a model of the production environment that you can use to estimate the performance given an load and a number of instances. Your goal is to find out what is the performance profile of a software+hardware setup. Our goal is to be able to estimate the performance of a system given a specific load and a hardware configuration before we have it actually in production. You typically run all the instances for just a few days and should be under $1k depending on your instances. You can draw conclusions based on that but you can also test with more if the tests seem inconclusive. We’ve seen orders of magnitude variances in the performance yielded by various cloud providers with the same price and configuration.īenchmarking a system is actually not that complicated.Ģ. The network latency is neglected and the storage layer is really just about SSD vs Spindle. Cloud marketing has made it so bad nowadays that people think all cores are about equal and all RAMs are the same. That can easily get you to $200k of wasted burned capital not to mention the stress of migrating data between incompatible data types, lost customers due to outages or bad UX. Just imagine spending 6 months developing around the wrong NoSQL technology with a team of 4 senior developers. It’s not the servers or the licensing for the technology, it’s the time involved in developing something around it. Option number 1 is often chosen although getting married to a bad technology as many people have found out the hard way is very expensive. There are two ways to get a feel of your environment’s performance profile: The vice-versa is also often true, senior developers go with proven technologies and their known pitfalls because they simply don’t think other technologies can be better. Typical developers are under immense pressure to meet a deadline and they very often just take gut decisions and choose technologies they haven’t worked with before because they sound good on paper. People make wrong design decisions all the time.
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