Comparing the Performance of Frameworks for JVM Backend Services

von | 25. August 2022 | Architektur, Editor's Choice, Software Engineering, Tools & Frameworks

Senior Developer

Motivation

What is the performance of a backend service? Every application is developed to serve a certain purpose which in most cases consists of processing tasks. These tasks can be of various types, like processing data or serving network requests. Based on that we can define the performance of an application as the amount of workload it can handle in a given time interval depending on the consumed or needed resources.

During the development of distributed applications, their performance is always a crucial part. Because if an application is not performing as intended, it is also not fulfilling the purpose it was developed for.

But what to do when an application does not deliver the required performance? What if an application was developed to process a certain amount of tasks each second, but it simply does not? Or what if the response time of our application under certain load grows so high that it results in bad user experience or even request timeouts? If you have encountered such a situation before, some of you may have discussed the following questions:

1) Can we deploy more instances of our application?

Sometimes this is possible! Especially applications that are developed for cloud infrastructure can be easily scaled up and down so that the sum of deployed services has exactly the desired performance. But multiple instances also require the multiple of resources which can be expensive or sometimes even unavailable. Additionally this approach is not improving the performance of the application itself and thus can only be an intermediate solution.

2) Are we able to improve the existing processing logic?

For example, is there any potential for parallelization, for caching mechanisms, and so on? The refactoring of existing logic should be an ongoing process during development of any software. This also includes the continuous search for performance issues. Each issue you fix and each improvement you make should be a future-oriented step and one of the best practices when building a long term solution that does not suffer from performance problems. But having to search for possible improvements with limited time in a complex system can be a hard, cumbersome and sometimes even impossible task.

3) Should we have chosen a different framework 2 years ago, when we started implementing this application?

Maybe!

In this article, I want to focus on the third question. How relevant is the choice of a framework when trying to achieve a certain performance? How big are the differences between currently commonly used frameworks?

Test setup

The overall setup of this test can be found here.

Let’s think about a possible setup which we can use to compare the performance of different frameworks.

Applications of distributed systems are often part of complex service structures. It is very common that a service uses a database, calls other services that are under our control (Internal Dependency), but also uses endpoints from third parties which we do not have control over and which can be very slow (External Dependency). We always have to keep in mind that when measuring the performance of a deployed application we are also measuring all systems the application relies on.

Trying to compare the performance of frameworks in such a system would be very hard, because there are simply too many parameters. In order to eliminate any side effects and achieve comparability, we will reduce the test setup to the smallest possible setup that still represents the built-in networking capabilities, used threading models and load handling capabilities of the tested frameworks. For this, we will reduce the amount of dependencies to a single external dependency.

The test setup is now simple enough, but we also have to make sure that the test is meaningful by assuring that we are really measuring the performance of the service under test. This performance shall not be influenced by our test setup:

  • The service shall not be influenced by the system it runs on
    If multiple services are deployed in parallel, make sure they are not influencing each other. Repeating certain parts of the test within an isolated deployment shows that this is not the case. A performance test consumes a lot computational power and memory. The system resources have to be monitored during the test to assure the system or computer is not overloaded.

  • The service shall not be limited by the performance of the mock service
    Overrating the capabilities of mock services is a known pitfall for performance testing. To overcome this we will do a little technical adjustment and raise the count of instances of the mocked service to 3. A load test against the mock service setup assures that it does not influence the performance of the service under test significantly.

The functionality of the service under test consists of a call to the mocked third party dependency. When called, the mock service will wait for one second and then respond with a random number. The service under test will forward this answer.
All services will be started inside their own docker container. We will use Gatling as a load testing client which is run directly on the same machine and will call the service under test with a certain amount of requests per second. Apart from the described functional endpoint that will be used in the test, each service provides a further endpoint that provides service metrics for a Prometheus client.

Candidates

I did a survey among my valued colleagues at Senacor to find out which JVM frameworks they have encountered in productive systems. To this list I added the JavaScript Runtime Node.js to have a little comparison to the outside of the JVM world. The focus lies on JVM frameworks, due to the simple fact that I myself mainly worked in projects that used JVM backend services.

We made our test setup as simple as possible to have fewer parameters and achieve comparability. We are creating our services under exactly the same principle and will only implement the most simple application serving our needs without any performance relevant adjustments or improvements. In general, I tried to keep the code as similar to the ‘getting started’ tutorials of the framework websites as possible to test exactly the impression of the service the framework creators are conveying.

Spring

Spring is one of the most commonly used JVM frameworks, initially released 2002 in an open source license. It is available in a blocking servlet stack and a non-blocking reactive stack. We will test both of them separately.

KTOR

KTOR is a framework for asynchronous client and server applications that promises to be lightweight, flexible, simple and fun. The first release was announced in 2018 by Jetbrains. It is written completely in Kotlin and built upon its coroutines. Coroutines are conceptually similar to threads, but much more lightweight. Ktor offers the possibility to choose the underlying http engine. We will test it with a Netty and with a CIO engine. CIO stands for “coroutine based I/O” and is a web engine based on Kotlin and coroutines.

Vert.x

Vert.x is developed by Eclipse and was published in 2011. It promises to be flexible, resource efficient and enable writing non-blocking code without unnecessary complexity. Like Node or Spring Reactive, Vert.x implements the Reactor pattern with an interesting addition: Instead of a single event loop, Vert.x uses multiple and call this “Multi-Reactor”. Vert.x is described as “a toolkit, not a framework”, which underlines its flexibility on the one hand, but also indicates it has to be configured to a certain degree. Indeed, Vert.x was the only service where I had to do a little performance influencing adjustment to make it comparable to the other services: Set the number of verticles and set the max connection count of the http client.

Micronaut

Micronaut is a framework for light weight and modular applications. It keeps the startup time and memory footprint low among under methods by avoiding reflection and “ahead of time compilation”. It was developed by the Micronaut Foundation. Their blog also contains an entry about a performance comparison of Micronaut vs. SpringBoot.

Micronaut offers great support and instructions on generating native images. Though, we won’t use native images in this comparison, since it would also be possible to generate those for the other frameworks. This is a topic for a different blog entry 🙂 ( like this about the theory of native images or this about how to build native images in a CI Pipeline) .

Node

In contrast to all other JVM based frameworks we will also test a Node.js server which is an asynchronous and event driven JavaScript runtime. Node implements the Reactor pattern, which means that it uses an “event loop” to achieve its asynchronous behavior. The special part is that node is executing all computations in a single thread. However, there is a pool of worker threads that handle time-consuming I/O tasks. See here for more details.

Test Execution and Results

We will test our candidates in three disciplines:

  1. How many requests per second can they process without getting unresponsive?
  2. Given a constant request load, are they able to interact with very slow third party systems?
  3. Given a constant request load, how much of the available resources are the deployed containers consuming?

1. Requests per second

When serving requests that originate from human interactions, there will always be fluctuation in the request rate depending on the daytime. For example, if you are processing transaction data, you will probably have a peak in your requests rates during lunchtime, because many people are using their credit cards to purchase food during that specific timeframe. How good are our frameworks at withstanding such peaks in the request rate?

To test this behavior, the Gatling client calls each service one after another starting with 100 users over three minutes. If the rate of successful calls is greater than 90% the test will be repeated with user count raised by 100. If at least 10% of the calls fail we consider the service as not proper functional and are not starting a further test with a higher user count. For details please see the script that executes the load test and the gatling test itself.

The focus of this test are the capabilities of the frameworks to handle and process incoming requests in parallel. How many requests can the frameworks accept per second?

Result

Average response time 100 200 300 400 500 600 700 800 900 1000 1100
Vertx 1s 1s 1s 1s 1s 1s 1s 3.1s 4.1s 7.46s X
Ktor-Netty 1s 1s 1s 1s 1s 1s 1.3s 5.7s X
Ktor-CIO 1s 1s 1s 1s 1s 1.1s X
Micronaut 1s 1s 1s 1s 1s 1.4s X
Node 1s 1s 1s 1s 2.9 X
Spring-Reactive 1s 1s 1s 1s 2.1s X
Spring 1s 1s X

Observations & Conclusions

When a service is able to handle a certain user count, the response time is near to the configured delay of the mockservice (i.e. one second). Before a service becomes unresponsive it can be observed that the response times are increasing drastically and the rate of successfully handled requests drops.

The Spring Service is the only one working with a blocking threading model. Each incoming call is processed by a dedicated JVM thread, which can nicely be observed in the Grafana dashboard.

The underlying Tomcat http engine works with a fixed count of 200 threads. With this given, it is logical that it can serve 200 requests per second. So why not simply raising the count of threads? On the one hand, we want to compare the frameworks without any performance relevant adjustments. When working with such a service you have to decide what the maximal thread count shall be before deployment. The service is not scaling automatically when the request count gets higher than initially expected. On the other hand, raising the thread count is also in general not the perfect solution for performance problems, because maintaining threads always costs resources. More details about this will be provided in the third test.

For all other services it is clearly visible that they are working with a non-blocking threading model since the requests rate is much higher than the count of active threads would allow it to be. This seems to work a bit better for micronaut and ktor than for spring reactive and node. Vertx is the clear winner of this test. At 700 req/sec it still responds nearly as fast as the mocked service. With higher request counts the response times of Vertx get noticeably higher, but it stays responsive up to impressing 1000 req/sec.

2. Very slow third party systems

Sometimes, third party systems are quite unreliable. It may occur that they are not available at all, or they get very, very slow. Which framework is best equipped to deal with this sort of problem?

For this test we will adjust our mock services to have a larger delay than in the other tests. Starting at 1 second and rising increasingly up to 8 seconds. For each step, the Gatling client will call the services for three minutes with 100 req / sec.

The focus of this test is task handling. Over three minutes more requests are reaching the service than can be processed. How good are the frameworks able to handle this piling of tasks?

Result

Average response time 1s 2s 4s 6s 8s
Vertx 1s 2s 4s 6s 8s
Ktor-Netty 1s 2s 4s 6s 8s
Ktor-CIO 1s 2s 4s 6s 8s
Micronaut 1s 2s 4s 6s 8s
Node 1s 2s 4s 6s 8s
Spring-Reactive 1s 2s 4s 15s X
Spring 1s 2s X

Observations & Conclusions

Again the result for the Spring service is very logical. With 100 requests per second, each taking 2 seconds to be processed all 200 threads will always be blocked. When raising the delay any further, the service will not be able to respond to all clients. Interestingly Spring Reactive as an asynchronous framework should be able to handle many tasks in parallel, but also got noticeably slower and in the end unresponsive. All other frameworks did not exert a noticeable influence at all.

3. Resource consumption

The resource consumption is a critical part of each service. When deploying to a cloud environment the consumed resources can be directly translated into costs. When working with on premise infrastructure it can occur that the available resources for the service are limited and you have to make sure that the service is able to work within these boundaries.

To test this, the Gatling client will call each service one after another with a certain rate of calls per second over three minutes. We are using cadvisor which exposes metrics about active containers as prometheus metrics, to compare the resource consumption of the different containers. Before each test we will restart all containers, so that the resource metrics are not influenced by earlier executions. Please note: For this very test we raised the thread count of the spring service to 500. All values are means over multiple test runs.

Result

CPU Time 200 req/sec 400 req/sec 500 req/sec
Vertx 32.6 s 43.4 s 1.14 min
Ktor-Netty 1.22 min 1.82 min 2.52 min
Ktor-CIO 1.33 min 2.89 min 2.72 min
Micronaut 1.33 min 1.74 min 2.53 min
Node 57.3 s 2.02 min 2.87 min
Spring-Reactive 1.17 min 1.51 min 2.62 min
Spring 1.20 min 1.82 min 2.38 min
Max memory usage 200 req/sec 400 req/sec 500 req/sec
Vertx 376 MiB 385 MiB 424 MiB
Ktor-Netty 405 MiB 434 MiB 510 MiB
Ktor-CIO 417 MiB 604 MiB 898 MiB
Micronaut 472 MiB 500 MiB 510 MiB
Node 93 MiB 113 MiB 391 MiB
Spring-Reactive 600 MiB 680 MiB 1.05 GiB
Spring 596 MiB 910 MiB 1.09 GiB

Observations & Conclusions

The Vertx service requires the fewest CPU computation time, while all other services require about the same. The CPU computation advantage of Vertx can only be attributed to efficient processing of the requests, since in general all services had to process the same logic.

Especially for lower request rates, the node service requires less memory than all the JVM frameworks. That is not surprising since the Java Runtime Environment is bigger than the Node Runtime. Depending on the scenario, this could be a big advantage. For example, with the same memory one could host more instances of node containers than of JVM containers.

Note: We have to be careful when comparing memory consumption of JVM services, because the JVM pre-allocates memory it does not actually use. Therefore, we are not measuring the memory allocated by the JVM, but the memory consumed by the whole container, which turned out to be a more meaningful metric.

For each request rate, spring-reactive and especially spring consume more memory than all other frameworks. At 500 req / sec each consumes nearly double the amount of the other frameworks with KTOR-CIO being an exception.

Resume

After all these numbers let us come back to our initial question. Many questions during the development of software can not be answered generally and are very dependent on the use case. Which framework to use and whether to use a non-blocking or a blocking one is surely one of them. But no worries, the question we want to answer is a different one: Are there differences between the performance of currently commonly used (JVM) frameworks? This question we can answer with a definitive: Yes!

Especially when you have strict requirements like

  • a high request load
  • slow third party dependencies
  • limited resources

the choice of a framework can make a big difference. A non-blocking framework is not the one golden solution for all these challenges, but surely some of them are easier to overcome when choosing one.

So which framework to choose?

Of course, the performance of a framework is not the only criteria when deciding which one fits best your situation or use case. For example, the skill set and the experience of the team should affect the decision as much as a technical requirement. However, in each scenario you should at least think about the performance requirements before committing to a framework.

When you know beforehand that the service will not have to face any of the performance related requirements listed above Spring MVC is still a pretty solid choice. Spring has a huge community and you will find help for any of you questions very fast.

If you are used to the Spring world and want to stick to it, but also want to use some benefits of asynchronous programming, Spring Reactive is definitely worth a try. Especially because you can simply reuse certain Spring modules, like SpringSecurity, that you may already have implemented for the blocking Spring stack, an own trial and comparison can be quickly achieved. How to avoid the complex syntax for asynchronous code that comes with Spring Reactive by using Kotlin Coroutines is described in the next chapter.

In case you really need a high performant JVM solution, Spring Reactive may not be enough. In that case you should take a look at Vertx, which performed excellent in our tests. As said before: Be aware, that Vertx is described as a toolkit, not as a framework. This means you will have to configure certain things on your own, that you are maybe used to get delivered in other frameworks.

Micronaut, Ktor and Node also produced pretty impressing results in our tests and can be seen to be on the middle ground between the other solutions. They are fully supported frameworks and have also proven that they are capable to result in high performant services.

One last word about asynchronous frameworks and programming

An often read counterargument against reactive programming and non-blocking frameworks is, that the code becomes hard to read, to maintain and to understand. And this may be true, when one tries to write stream-like instructions for the publisher schema of Spring-Reactive. At that point I want to emphasize an in my opinion big advantage of Kotlin and its coroutines: the asynchronous/non-blocking code looks nearly like synchronous/blocking code. You don’t have to learn or get used to completely new patterns, but can just write code that is easy to read and has the potential to deliver a highly performant result by being asynchronous. As stated above, Ktor is written completely in Kotlin and built upon coroutines. But all the other presented frameworks also have a support for Kotlin + coroutines. Here is a great article, if you want to understand how coroutines work “under the hood”.