can JMeter distributed test handle such loads? or should we fire individual tests on each server and use a backend listener to store the details.
If both of them are not the optimal way, what is the best way to build load test infrastructure to handle big loads?
There are no limitations for the throughput (number of requests per second) on JMeter side, the question whether you can conduct the required load or no mainly depends on the hardware you can allocate.
Given you have powerful enough machine and follow JMeter Best Practices you can even create such a load using single instance, however it's a good idea to check resources usage like CPU, RAM, Network and Disk IO, etc. using i.e. JMeter PerfMon Plugin. The idea is that JMeter must have enough headroom to operate as if it will not be able to send requests fast enough due to i.e. high CPU usage the perceived load will be less even if the system under test can handle more and you will get false negative results.
The answer to the question whether you need to use the Backend Listener mainly depends on the following criteria:
do you need the possibility to observe the test results in the real time while the test is running
do you need to store the results in some database instead of the .jtl results files
Solr queries support a parameter debug=results that explains the relevance of each returned document.
I'm currently considering to activate this for all our queries in a production environment to simplify handling of support requests about unexpected search results. I understand that this slightly increases the network load but could not find any mention if it also degrades performance in any noticable way.
My guess would be that all the information provided has to be collected at some point anyway so the performance impact should be neglible. There's a few CPU cycles to be expected to build the JSON strings with the debug information for the query result, but that's about it.
So both the impact on network load and performance would be within a single digit percent range.
Is this reasoning correct?
What are some of the things that determine how many web requests a single machine can handle? In general what's an average number (requests per second) that most machines should be able to handle? For example, I see some answers that say 2k requests/s can easily be handled. How about 5k? 10k? etc.
I'm basically trying to do my best at estimating how many machines I'd need to scale to some high throughput, before I dive into load testing.
Yes, That is possible through performance modelling but the answers will have 5-10% error margin.
If you know exact size of web request then probably you can find your nw limit and thus this gives you maximum possible request acceptance limit. some exploratory test with sample test you can get the cpu time required by each request roughly(in terms of response time or thoughput). thus you can extrapolate the results for higher number of requests using many theorems examples, little's law. Using this theorem you can find maximum no of users (request here) can be supported on a give hw for a give acceptable response time.
but this all is done after tuning your application to expected level otherwise you will end up with lot of hw because of lack of tuning.
I want to disable SPARQL query caching of Fuseki server. Can I disable it? And how to do ? I'm considering the following ways:
Using command line argument - It looks unprepared
Using settings file (*.ttl) - I couldn't find notation to disable caching
Edit server code - Basically I won't do it :(
Please tell how can I disable caching.
What caching are you talking about?
As discussed in JENA-388 the current default behaviour is actually to add headers that disable caching so there is not any HTTP level caching.
If you are using the TDB backend then there are caches used to improve query performance and those are not configurable AFAIK. Also even if you could do it turning them off would likely drastically worsen performance so would not be a good idea.
Edit
The --mem option uses a pure in-memory dataset so there is no caching. Be aware that this will actually be much slower than using TDB as you scale up your data and is only faster at small dataset sizes.
If you are looking to benchmark then there are much better ways to eliminate the effect of caches than turning them off since disabling caches (even when you can) won't give you realistic performance numbers. There are several real world ways to eliminate cache effects:
Run warmups - either some fixed number or until you see the system reach a steady state.
Eliminate outliers in your statistics, discard the best and worst N results and compute your statistics over the remainder
Use query parameterisation, use a query template and substitute different constants into it each time thus ensuring you aren't issuing an identical query each time. Query plan caching may still come into effect but as Jena doesn't do this anyway it won't matter for your tests.
You may want to take a look at my 2012 SemTech talk Practical SPARQL Benchmarking and the associated SPARQL Query Benchmarker tool. We've been working on a heavily revised version of the tool lately which has a lot of new features such as support for query parameterisation.
I want to do load testing for 10 million users for my site. The site is a Java based web-app. My approach is to create a Jmeter test plan for all the links and then take a report for the 10 million users. Then use jvisualVM to do profiling and check if there are any bottlenecks.
Is there any better way to do this? Is there any existing demo for doing this? I am doing this for the first time, so any assistance will be very helpful.
You are on the correct path, but your load limit is of with a high factor.
Why I'm saying this is cause your site probably will need more machine to handle 10Milj Concurrent users. A process alone would probably struggle to handle concurrent 32K TCP-streams. Also do some math of the bandwidth it would take to actually handle 10Milj users.
Now I do not know what kind of service you thinking of providing on your site, but when thinking of that JVisualVM slows down processing by a factor 10 (or more for method tracing), you would not actually measure the "real world" if you got JMeter and JVisualVM to work at the same time.
JVisualVM is more useful when you run on lower loads.
To create a good measurement first make sure your have a good baseline.
Make a test with 10 concurrent users, connect up JVisuamVM and let it run for a while, not down all interesting values.
After you have your baseline, then you can start adding more load.
Add 10times the load (ea: 100 users), look at the changes in JVisualVM. Continue this until it becomes obvious that JVisualVM slows you down, for every time to add extra load, make sure you have written down the numbers your are interested in. Plot down the numbers in a graph.
Now... Interpolate the graph (by hand) for the number of users you want. This works for memory usage, disc access etc, but not for used CPU time, cause JVisualVM will eat CPU and give you invalid numbers on that (especially if you have method tracing turned on).
If you really want to go as high as 10Milj users, I would not trust JMeter either, I would write a little test program of my own that performs the test you want. This would be okey, since the the setting up the site to handle 10Milj will also take time, so spending a little extra time of the test tools are not a waste.
Just because you have 10 million users in the database, doesn't mean that you need to load test using that many users. Think about it - is your site really going to have 10 million simultaneous users? For web applications, a ratio of 1:100 registered users is common i.e. you are unlikely to have more than 100K users at any moment.
Can JMeter handle that kind of load? I doubt it. Please try faban instead. It is very light-weight and can support thousands of users on a single VM. You also have much better flexibility in creating your workload and can also automate monitoring of your entire test infrastructure.
Now to the analysis part. You didn't say what server you were using. Any Java appserver will provide sufficient monitoring support. Commercial servers provide nice GUI tools while Tomcat provides extensive monitoring via JMX. You may want to start here before getting down to the JVM level.
For the JVM, you really don't want to use VisualVM while running such a large performance test. Besides to support such a load, I assume you are using multiple appserver/JVM instances. The major performance issue is usually GC, so use the JVM options to collect and log GC information. You will have to post-process the data.
This is a non-trivial exercise - good luck!
There are two types of load testing - bottleneck identification and throughput. The question leads me to believe this is about bottlenecks, so number of users is a something of a red herring, instead the goal being for a given configuration finding areas that can be improved to increase concurrency.
Application bottlenecks usually fall into three categories: database, memory leak, or slow algorithm. Finding them involves putting the application in question under stress (i.e. load) for an extended period of time - at least an hour, perhaps up to several days. Jmeter is a good tool for this purpose. One of the things to consider is running the same test with cookie handling enabled (i.e. Jmeter retains cookies and sends with each subsequent request) and disabled - sometimes you get very different results and this is important because the latter is effectively a simulation of what some crawlers do to your site. Details for bottleneck detection follow:
Database
Tables without indices or SQL statements involving multiple joins are frequent app bottlenecks. Every database server I've dealt with, MySQL, SQL Server, and Oracle has some way of logging or identifying slow running SQL statements. MySQL has the slow query log, whereas SQL Server has dynamic management views that track the slowest running SQL. Once you've got your hands on the slow statements use explain plan to see what the database engine is trying to do, use any features that suggest indices, and consider other strategies - such as denormalization - if those two options do not solve the bottleneck.
Memory Leak
Turn on verbose garbage collection logging and a JMX monitoring port. Then use jConsole, which provides much better graphs, to observe trends. In particular leaks usually show up as filling the Old Gen or Perm Gen spaces. Leaks are a bottleneck with the JVM spends increasing amounts of time attempting garbage collection unsuccessfully until an OOM Error is thrown.
Perm Gen implies the need to increase the space as a command line parameter to the JVM. While Old Gen implies a leak where you should stop the load test, generate a heap dump, and then use Eclipse Memory Analysis Tool to identify the leak.
Slow Algorithm
This is more difficult to track down. The most frequent offenders are synchronization, inter process communication (e.g. RMI, web services), and disk I/O. Another common issue is code using nested loops (look mom O(n^2) performance!).
Best way I've found to find these issues absent some deeper knowledge is generating stack traces. These will tell what all threads are doing at a given point in time. What you're looking for are BLOCKED threads or several threads all accessing the same code. This usually points at some slowness within the codebase.
I blogged, the way I proceeded with the performance test:
Make sure that the server (hardware can be as per the staging/production requirements) has no other installations that can affect the performance.
For setting up the users in DB, a procedure can be used and can be called as a part of jmeter test plan.
Install jmeter on a separate machine, so that jmeter won't affect the performance.
Create a test plan in jmeter (as shown in the figure 1) for all the uri's, with response checking and timer based requests.
Take the initial benchmark, using jmeter.
Check for the low performance uri's. These are the points to expect for bottlenecks.
Try different options for performance improvement, but focus on only one bottleneck at a time.
Try any one fix from step 6 and then take an benchmark. If there is any improvement commit the changes and repeat from step 5. Otherwise revert and try for any other options from step 6.
The next step would be to use load balancing, hardware scaling, clustering, etc. This may include some physical setup and hardware/software cost. Give the results with the scalability options.
For detailed explanation: http://www.daemonthread.com/2011/06/site-performance-tuning-using-jmeter.html
I started using JMeter plugins.
This allows me to gather application metrics available over JMX to use in my Load Test.