loadrunner | sane JavaScript loader and build tool | Build Tool library

Yes, that would work.

Note 1: you can use a forever loop instead of a during one as you're interrupting with maxDuration anyway.

Note 2: if you're setting the same httpProtocol on each scenario, you can define it on the setUp just like you did for maxDuration.

Note 3: you can use maxDuration(5.minutes) instead of maxDuration(300 seconds).

The problem was due to having public methods in the test class that were not annotated with @Test. The solution I used was adding @Ignore and @Test annotations to the method. ie:

@Ignore
@Test
fun someUnImplementedTest() {
}

Adding only @Test works as well, but it will run the test and mark it as passed allowing you to forget that it's not yet implemented.

Given you send the same request you should get the same response no matter what tool is being used for sending the request.

If you're getting different responses or not getting a response either you're sending not the same request or there is a difference in the tools configuration.

1. The most possible reason could be proxy, by default Loadrunner respects underlying operating system proxy settings and in JMeter you need to configure the upstream proxy connection manually, see Using JMeter behind a proxy article for more details

2. Another reason could be incorrect request configuration, i.e. protocol/host/port/path mismatch

3. And last but not the least, maybe your connect/response timeouts are too low, try ramping them up, the relevant setting lives under "Advanced" tab of the HTTP Request sampler (or even better go for HTTP Request Defaults)

Pull that data from the HTTP request log, which will show the download request, and, if the w3c time-taken value is included in the log, the time required to fulfill the download.

You can process the log at the end of the test for the response time data. If you need to, you cam import a set of datapoints into analysis for representation with the rest of your data. You might want to consider a normalized value for your download, instead of a raw response time. I imagine that the files are of different sizes, so naturally they will have different download times. However, if you divide download bytes with time (in seconds), then you will have a normalized measurement of bytes per second which then allows you to compare one download to the next for consistent operation.

Also, keep in mind that since you are downloading a file, writing to a local disk, for (presumably) multiple users on a host, you will face the risk of turning your local file system into a bottleneck. You can see this same effect if you turn up logging on all users to the highest level and run your test. The wait for lock and wait for write, plus the actual writing of data, becomes a drag anchor to the performance of your virtual user. This is why the recommended log level is "log on error" or send the error to the output window of the controller via lr_output_message() or lr_vuser_status_message(). Consider a control load generator of the same hardware definition as the others with only a single virtual user of this type on it. If the control group and global group degrade together then you have an app issue. If your control user does not degrade, but your other users do, then you have a test bed induced influence on your results.

These are all issues independent of the tool you are using for the test.

Ask for the logs for a month.

• Find the stats for session duration, then count the number of distinct IP's blocked by session duration.
• Once you have the high volume hour, count the number of page instances. Business processes will typically have a termination page which is distinct and allows you to understand how many times a particular action takes place, such as request new password, update profile, business process 1, etc...

With this you will have a measurement of users and actions. You will want your stakeholder to take ownership of this data. As Quality assurance, we should not own both the requirement and the test against it. We should own one, but not both. If your client will not own the requirement, cascading it down to rest of the organization, assume you will be left out in the cold with a result they do not like....i.e., defects that need to be addressed before deployment to production.

Now comes your largest challenge, which is something that needs to be fixed with a process issue by your client.....You are about to test using requirements that no other part of the organization, architecture, development, platform engineering, had when they built the solution. Even if your requirements are a perfect recovery, plus some amount for growth, any defects you find will be challenged aggressively.

Your test will not match any assumptions or requirements used by any other portion of the organization.

And, in a sense, these other orgs will be correct in aggressively challenging your results. It really isn't fair to hold their designed solution to a set of requirements which were not in place when they made decisions which impacted scalability and response times for the system. You would be wise to call this out with your clients before the first execution of any performance test.

You can buy yourself some time. If the client does have a demand for a particular response time, such as an adoption of the Google RAIL model, then you can implement a gate before accepting any code for multi-user performance testing that the code SHALL BE compliant for a single user. It is not going to get any faster for two or more users. Implementing this hard gate will solve about 80% of your performance issues, for the changes required to bring code into compliance for a single user most often will have benefits on the multi-user front.

You can buy yourself some time in a second way as well. Take a look at their current site using tools such as Google Lighthouse and GTMetrix. Most of us are creatures of habit, that includes architect, developers, and ops personnel. We design, build, deploy to patterns we know and are comfortable with....usually the same ones over and over again until we are forced to make a change. It is highly likely that the performance antipatterns pulled from Lighthouse and GTMetrix will be carried forward into a future release unless they are called out for mitigation. Begin citing defects directly off of these tools before you even run a performance test. You will need management support, but you might consider not even accepting a build for multi-user performance testing until GTMetrix scores at least a B across the board and Lighthouse a score of 90 or better.

This should leave edge cases when you do get to multi-user performance testing, such as too early allocation of a resource, holding onto resources too long, too large of a resource allocation, hitting something too often, lock contention on a shared resource. An architectural review might pick up on these, where someone might say, "we are pre-allocating this because.....," or "Marketing says we need to hold the cart for 30 minutes before de-allocation," or "...." Well, you get the idea.

Don't forget to have the database profiler running while functional testing is going on. You are likely to pick up a few missing indexes or high cost queries here which should be addressed before multi-user performance testing as well.

You are probably wondering why am I pointing out all of these things before your performance test takes place. Darnit, you are hired to engage in a performance test! The test you are about to conduct is very high risk politically. Even if it finds something ugly, because the other parts of the organization did not benefit from the requirements, the result is likely to be rejected until the issue shows up in production. By shifting the focus to objective measures even before you need to run two users in anger together there are many avenues to finding and fixing performance issues which are far less politically volatile. Food for thought.

Solved this issue by opening Dcom Config for 32-bit Applications:

• Go to Start | Run
• Type MMC -32 and click Ok
• Go to File | Select Add/Remove Snap-in option
• Add Component Services option in Management Console application
• Then follow the path to Dcom Config

And Boom! i found wlrun.LrEngine under Dcom.

• And further gave appropriate permissions to it (Right click >> properties >> security >> Click customize - edit and add permissions)

I guess the editor is unable to know which kind of object spark is while you are defining your class. Just because you named the class argument spark, it does not necessarily mean that your code is going to handle a SparkSession object.

This is an inherent "issue" (many quotes) of dynamic languages. Function arguments don't have types outside the runtime. When you are defining a class with your editor, you are definitely not in runtime.

For anyone using Python >3.5, I strongly recommend using type annotations. These annotations help to improve code documentation and can be checked statically with tools such as mypy.

For example, in the code above, I would recommend something like this:

1. You don't need to convert PKCS12 keystore into a JKS keystore, JMeter can deal with both types, moreover it's recommended to use PKCS12 as JKS is a proprietary format. You just need to "tell" JMeter to use PKCS12 format via system.properties file

OK, Let's look at the example code for lrd_server_attach() and then your code

First, the example