Performance Test Best Practise

An old colleague asked if there are any standards identifying best practise in performance testing. I could not think of any but it started me thinking about what is best practise. Here are my thoughts on some areas of best practise in Performance Testing. They are NOT in any order of importance and the list is NOT exhaustive.

1.) Have a defined process and constantly refine it.
Before you start you should have a process defined and you should make sure you review this process to add improvements. The process needs to be flexible in order to accomodate different types of projects, from benchmarking a core application through to making sure an e-commerce site can handle the Christmas rush.

2.) Define the Goals up front.
This seems obvious, but you need to understand why are you testing and what the performance goals of the system under test are. (Note I use the word goals not requirements). Here, the move to ITIL may help where service design packages developed early on should include the performance requirements.

3.) Let Risk guide you.
The performance risk and consequences of failure should guide the type and amount of performance testing you do. Don’t just test what is easy to test.

4.) Don’t be afraid to say no.
If you are given responsibility for signing off on the performance of the system, you are the expert. If, subsequently, you are not given enough time or the correct tools then be prepared to say that you cannot test the system adequately. Remember that the caveats you place in your final report may never make it into the summary presented to the management board!

5.) Get the workload right.
If you don’t test the system with the correct workload it won’t matter if everything else is perfect – the results will be wrong. This means you need to understand user behaviours and their frequency. Don’t forget to include error scenarios as well.

6.) Develop Quality Scripts.
Make sure your scripts emulate user behaviour as much as possible and remember that users make mistakes, leave processes early and have comfort breaks! Also, make sure your script check what is returned to the user is what is expected.

7.) Select an appropriate test environment.
Is best practise using a production-sized test environment? Not sure, but make sure your test environment is sized and up to the job involved. Make sure you can collect the necessary data about the performance of that environment during the load test.

8.) Run your performance tests for long enough and often enough.
Make sure your tests are repeatable and that the results they produce are statistically valid.

9.) Participation.
Get all the people that need to be involved in the performance test working togther. Unless you are superhuman and multi-skilled you will need DBAs, administrators, developers, Project Managers, etc., to assist in the test. Remember, for the best results get these stakeholders involved in the process early on.

10.) Remember, people want results not data.
Don’t just present the canned report from the performance test tool; you need to analyse the results and present the key facts of the load test. And remember that different people will want different results from the load test – a manager will want to know if it passed whereas the DBA will want to know if the SGA is sized correctly, for example.

Small vs Large Scale Performance Test Environments

I have just added to the website a presentation that looks at sizing and extrapolation techniques for people considering building a small scale performance test environment instead of a large full scale performance test environment. In the paper several approaches are considered.
Factoring – This is where the architecture is easily scaled and therefore the performance test can be undertaken on a subset of the hardware.

Dimensioning – The architecture has known bottlenecks that drive the performance such as a central DB. The performance test environment must contain the bottleneck component but other components may not need to be representative of a full sized environment.

Modelling – This examines the use of modelling to take results from a small scale environment and predict the results for a larger scale environment.

Flipping – This looks at creating test environment that can be have the correct amount of resources allocated to them for a “full scale” performance test for example during off hours and then revert to a smaller scale performance test environment at other times.

Full Scale – Finally the advantages and disadvantages of a full scale performance test environment are discussed.

Finally the caveat for these techniques is that for any testing on a small scale performance test environment does not guarantee that all performance problems will be discovered due to application/scalability constrains that may only appear in the full sized environment!

You can download the presentation from here.

Calculating Concurrency from Performance Test Results

So you are on a performance test engagement and your boss asks how many people concurrently executing certain transactions like buying a book or doing a search. He wants is a measure of active concurrency – how many people are doing certain transaction. This should not be confused with Passive concurrency like how many people are logged in. Before we go anyfuther lets clarify that in this example a transaction is a request to the test system and a response back it does not include any think time. Now before you start getting out the virtual terminal server and incrementing counters at the start of the transaction and decrementing counters at the end. There is an easier way.

You can work this all out from your performance test results, without the need for code. Using a mathematical formula (it’s very simple so don’t panic) called Littles Law. Littles Law was first used to analyse the performance of telephone exchanges in 1969 by John Little.
Little’s law allows us to relate the mean number of items in the system in our case concurrent users with the mean time in the system (response time) as follows:

Number of Items in the system = Arrival Rate x Response Time

There is one rule to remember before you use little law you must make sure the system is balanced. That is the arrival rate into the system is the same at the exit rate.

I will begin with a none computer example the “Black Horse Pub” has a mean arrival rate of 5 customers per hour that stay for on average half an hour. Using Little’s law we can calculate the mean number of customers in the pub as Arrival Rate x Response Time = 5 x 0.5 = 2.5 customers.

To apply little law to a performance test we must first make sure that we are taking measurements from when the system under test is balanced. Remember a balanced system the rate of work entering the system matches the rate of work leaving the system. This for a typical load testing tool is after the ramp up period and the number of virtual users remains constant and response times have stabilised and the transaction per second graph is level. To capture this period of time in LoadRunner for example you would need to select the time period in the Summary report filter or under the Tools -> Options.
So record the average response time for the transaction of interest and the number of times per second the transaction is executed.

So from the example above the response time is 43.613 seconds. The arrival rate is the number of transactions executed divided by the duration. The duration for this example was a 10 minute period as can be confirm by the LoadRunner summary below.

This gives you an arrival rate of 2.005 calculated by taking the count 1203 divided by the duration 600.

So the concurrent number of users waiting for a search to return is 87.44
There you go from your performance test results you can easily calculate the concurrency for a particular transaction.