The Ultimate Open-source Load Testing Tools for Developers
As websites and web applications
grow more complex and feature-rich performance is an important issue for both
developers and users alike. There are studies that show that faster websites
lead to increased engagement of users, higher sales, and more visitors, it's
crucial to take note of how quickly you can get your site to your visitors and
have it in their browser.
The most common term used to
describe this field of study is web performance optimization and over the last
couple of years, a variety of best practices, methods, and techniques have been
created to enhance the experience of browsing. A lot of these strategies focus
on reducing the download size of websites by optimizing JavaScript and
restricting the number of HTTP requests that a webpage requires.
We'll discuss the other
aspect of web performance: how quickly will your server be able to respond to
user requests? We'll go over the general landscape of load tests, go through a
strategy to determine the server's highest response time, and then examine some
open-source load-testing software.
Sun Technologies is a world-class
load testing service provider for many organizations across the globe. Our team
of experts analyses the overall behavior of web-based, client-server
applications thoroughly to identify the real-world capacity of the applications
under varying loads as well as measuring and validate the apps for a better
user experience by fixing errors in the early stages.
What is Load Testing in Software Testing?
Load
Testing is a kind of Performance Testing that determines the performance of a
software application or product under real-world load conditions. The purpose
of load testing is to determine the behavior of the software when multiple
users are using it simultaneously. It's the reaction to the system as measured
under different conditions of load. The load test is performed for extreme and
normal load conditions.
Types of Load Testing
Before
you put any algorithm or software into the decision-making process it is
important to make certain that it's working. To ensure that your tools work,
they should require a variety of tests and measurements, with one of the most
important is load tests. There are many kinds of load testing and each has
specific purposes.
The
general rule is that load testing is a way of measuring the performance of your
system in various situations. Since data science is an expansive, ever-changing
field, knowing these parameters is crucial. To help you understand there are
five different types of load testing and the times you should be using these
types of tests and when you should apply them.
Stress Testing
Stress tests are probably the most well-known type that load tests. They
are the process of pushing your system to its limits to find out if they fail,
whether the failure is due to data corruption, leaks in memory, or another
issue. It is possible to do this by increasing any factor from concurrent users
and data throughput, up to the highest levels. Stress testing will reveal the
upper limits of your system are, and also what causes the first break.
Spike Testing
Spike testing is similar
to stress testing however, it is focused on short periods in volume. This is
why it has the term. In these tests, you increase the throughput to determine
the response of your system. It's not necessary to find the breaking point,
however, you're evaluating whether your system is able to withstand the sudden
increase in the number of measurements.
There are many algorithms
that do not need the test of spikes, however, yours could be based on the
intended purpose. Systems that might encounter spikes in real-life situations
such as Black Friday for an e-commerce site, absolutely require these tests. If
you don't have them, they may create a flawed system and not even realize it.
Volume Testing
Volume
testing is also known as flood testing, it involves feeding large amounts of
data to your system and seeing what it does. If you're developing the solution
to any large data application, testing in volume is essential.
Testing
in volume is a great way of uncovering bottlenecks within your system. It also
reveals its capabilities, such as stress testing. These findings can aid any
algorithm working with a lot of data but is vital in the case of big data. If
you do not test it in the volume of an application that uses big data then it
might not stand against real-world usage.
Load Banking Testing
The
data centers hosting your apps may benefit from load tests too. Tests on load
banks for generators use the device known as load bank that draws different
levels of power from it. It will be able to determine the way it performs in
different levels of energy consumption which your center may test it under.
The
regular testing of your load banks can take just 30 minutes. It is vital for
the security of the continued operation of your data center. These tests will
reveal whether your generator is prone to failure under certain conditions so
that it can be repaired or replaced prior to compromising your data. It's easy
for people to ignore load bank tests because it's not directly applicable to
your application, however it could be an important tool.
Soak Testing
The majority of load tests uncover issues that can be discovered within
the first few hours after the test. The system might have to run longer than
that, and certain issues will only manifest over time. This is the point where
soak testing comes into play.
The process of testing for soak often referred to as endurance
testing, assesses the system's performance over a long time. It can reveal
whether performance decreases with time, in the event of bottlenecks occurring
after a few days of usage, and even more. If you are planning to operate your
system for months, weeks, or even all day long it is recommended to test it in
a soak.
Soak testing generally involves using testing software to simulate these
lengthy durations in a shorter time. For instance, you can do 30 days of
testing in just two days which will save you time.
Load Testing Methodology
·
Initiate with a
solid strategy
·
Increase the
size of your load testing
·
Adopt complete
automation
·
Make your
procedure simpler
Benefits of Load Testing
·
Reduces the
risk of failure
·
Improves
scalability
·
Customer
satisfaction is higher
·
Reduces the
chance of a system outage
·
Recognize
inefficient code
10 Best Load Testing Tools
1.
LoadUI Pro
2.
WebLOAD
3.
Neoload
4.
HP Loadrunner
5.
LoadNinja
6.
StresStimulus
7.
LoadView
8.
Apache JMeter
9.
ReadyAPI Performance
10.
K6
Best Practices for Performing Load Testing
· Always test the
stability of your application prior to starting a load test. The application
should be declared to be functionally stable with the help of the functional
testing team. All major flaws must be addressed and verified before the build
is copied into Load Test environment.
·
Check you are
in a Load Test environment replicable or is similar to the actual environment,
such as the amount of servers used, Load balancers, server configurations, and
firewalls.
·
Make sure that
your test results are unique, and you have all test data copied into the load
environment prior to running the load test.
·
Create the test
scenarios so to replicate the real-time user's actions in production.
·
Create the
workload based on the user load as well as business flows. In case of an older
application check if there is an opportunity to talk to the management team
about the business flow and user load.
·
Gather all
important metrics such as Response Time, Hits per second, throughput CPU,
Memory, Running Vusers and Network.
Conclusion
Before
you begin a load test, it is important to choose the correct workload. Find the
footprint of the virtual user so that you can prepare the proper amount of load
generators, and the distribution of virtual users between them. You must create
the data that virtual users will utilize, and you'll need to set up the
hardware, environment servers, or whatever that you are testing regardless of
the test tool itself.
This is
the case for any test of performance that is conducted with the help of tools
such as JMeter K6, loadNinja, and others. Keep in mind that large-scale load
testing isn't an easy task. It is essential to be able to have all participants
available to assist and review the results as the test runs and to identify
optimization steps.
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