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How Fast is Spring?

Performance has always been one of the top priorities of the Spring Engineering team, and we are continually monitoring and responding to changes and to feedback. Some fairly intense and precise work has been done recently (in the last 2-3 years) and this article is here to help you to find the results of that work and to learn how to measure and improve performance in your own applications. The headline is that Spring Boot 2.1 and Spring 5.1 have some quite nice optimizations for startup time and heap usage. Here’s a graph made by measuring startup time for heap constrained apps:


As you squeeze the available heap, and application generally isn’t affected on startup, until it reaches a critical point. The characteristic "hockey stick" shape of the graphs shows the point at which garbage collection loses the fight and the app no longer starts up at all. We see from the graphs that it is quite possible to run a simple Netty app in 10MB heap with Spring Boot 2.1 (but not with 2.0). It is also a bit faster in 2.1 compared to 2.0.

Most of the detail here refers specifically to measurements and optimizations for startup time, but heap memory consumption is also something that is very relevant because constraints on heap size can lead to slower startup (see the graph above). There are other aspects of performance that we could (and will) focus on, particularly where annotations are being used for HTTP request mapping, for example; but those concerns will have to wait for another, separate write up.

When all things are considered, remember that Spring was designed ab initio to be lightweight, and actually does very little work unless you ask it to. There are many optional features, so you don’t have to use them. Here are some quick summary points:

  • Packaging: an exploded jar with the application’s own main is always faster

  • Server: there is no measureable difference between Tomcat, Jetty and Undertow

  • Netty is a bit faster on startup - you won’t notice in a large app

  • The more features you use, the more classes are loaded, and class loading takes about 80% of startup time

  • Functional bean definitions rule - you might see 40% faster start up in some apps

  • If you can’t use functional beans then manually choosing a set of Spring Boot autoconfigurations is usually faster than @EnableAutoConfiguration

  • A minimal Spring Boot app with an HTTP endpoint starts in <1sec and uses <10MB heap

Some links:

TL;DR How do I make my app go faster?

(Copied from here.) You are mostly going to have to drop features, so not all of these suggestions will be possible for all apps. Some are not so painful, and actually pretty natural in a container, e.g. if you are building a docker image it’s better to unpack the jar and put application classes in a different filesystem layer anyway.

  • Classpath exclusions from Spring Boot web starters:

    • Hibernate Validator

    • Jackson (but Spring Boot actuators depend on it). Use Gson if you need JSON rendering (only works with MVC out of the box).

    • Logback: use slf4j-jdk14 instead

  • Use the spring-context-indexer. It’s not going to add much, but every little helps.

  • Don’t use actuators if you can afford not to.

  • Use Spring Boot 2.1 and Spring 5.1.

  • Fix the location of the Spring Boot config file(s) with spring.config.location (command line argument or System property etc.). Example for testing in IDE: spring.config.location=file:./src/main/resources/

  • Switch off JMX if you don’t need it with spring.jmx.enabled=false

  • Make bean definitions lazy by default. There’s nothing in Spring Boot that does this generally, but you can set to defer the Spring Data initialization. There is also a LazyInitBeanFactoryPostProcessor in this project you can copy.

  • Unpack the fat jar and run with an explicit classpath.

  • Run the JVM with -noverify. Also consider -XX:TieredStopAtLevel=1 (that will slow down the JIT later at the expense of the saved startup time).

A more extreme approach is to choose all the @Configuration classes manually, instead of using @EnableAutoConfiguration. This is fiddly and error prone, and you lose some of the benefits of autoconfiguration, but once done should be stable for any given codebase. The main benefit of this approach is really leaving out beans that you are not using. It is a fallacy to assume that autoconfiguration is slower because it "scans the classpath". The candidate autoconfiguration classes are selected very quickly without loading any of the classes, so this aspect is covered very efficiently and has been optimized extremely aggressively. But you might find that the default autoconfiguration settings choose things that you don’t end up using.

The most extreme choice is to re-write all your application configuration using functional bean definitions. This includes all the Spring Boot autoconfiguration you are using, most of which can be re-used, but it’s still manual work to identify which classes to use and register all the bean definitions. If you try this approach you might see a 2x improvement in startup time. Look at the BuncApplication in the micro apps to see how to start Spring Boot without the @Configuration class processor.

Excluding netty-transport-native-epoll also boosts the startup time by 30ms or so (Linux only). This is a regression since Spring Boot 2.0, so once we understand it a bit better we can probably eliminate it.

Some Basic Benchmarks

Here is a subset of the static benchmarks from Each app is started with a new JVM (separate process) per application startup, and has an explicit classpath (not fat jar). The app is always the same, but with different levels of automatic (and in some cases manual) configuration. The "Score" is startup time in seconds, measured as the time from starting the JVM to seeing a marker in the logger output (at this point the app is up and accepting HTTP connections).

Benchmark   (sample) Mode  Cnt  Score   Error  Units Beans Classes
MainBenchmark  actr  avgt   10  1.316 ± 0.060   s/op 186   5666
MainBenchmark  jdbc  avgt   10  1.237 ± 0.050   s/op 147   5625
MainBenchmark  demo  avgt   10  1.056 ± 0.040   s/op 111   5266
MainBenchmark  slim  avgt   10  1.003 ± 0.011   s/op 105   5208
MainBenchmark  thin  avgt   10  0.855 ± 0.028   s/op 60    4892
MainBenchmark  lite  avgt   10  0.694 ± 0.015   s/op 30    4580
MainBenchmark  func  avgt   10  0.652 ± 0.017   s/op 25    4378
The host machine is "tower", i7, 3.4GHz, 32G RAM, SSD.
  • Actr: same as "demo" sample plus Actuator

  • Jdbc: same as "demo" sample plus JDBC

  • Demo: vanilla Spring Boot MVC app with one endpoint (no Actuator)

  • Slim: same thing but explicitly @Imports all configuration

  • Thin: reduce the @Imports down to a set of 4 that are needed for the endpoint

  • Lite: copy the imports from "thin" and make them into hard-coded, unconditional configuration

  • Func: extract the configuration methods from "lite" and register bits of it using the function bean API

Generally speaking, the more features are used, the more classes that are loaded, and also the more beans are created in the ApplicationContext. The correlation is actually very tight between startup time and number of classes loaded (much tighter than versus number of beans). Here’s a graph compiled from that data and extended with a range of other things, like JPA, bits of Spring Cloud, all the way up to the "kitchen sink" with everything on the classpath including Zuul and Sleuth:


The data for the graph can be scraped from the benchmark report if you run the "MainBenchmark" and the "StripBenchmark" in the static benchmarks (the table above is old data from a time when they were both in the same class). There are instructions about how to do that in the README.

Garbage Collection Pressure

While it is true, and measureable, that more classes loaded (i.e. more features) is directly correlated with slower startup time, there are some subtleties, and one of the most important and also the slipperiest to analyse is garbage collection (GC). Garbage collection can be a really big deal for long running applications, and we have all heard stories of long GC pauses in large applications (the bigger your heap the longer you are likely to wait). Custom GC strategies are big business and an important tool for tweaking long-running, especially large applications. On startup there are some other things happening, but those can be related to garbage collection as well, and many of the optimizations in Spring 5.1 and Spring Boot 2.1 were obtained by analysing those.

The main thing to look out for is tight loops with temporary objects being created and discarded. Some code in that pattern is unavoidable, and some is out of our control (e.g. it’s in the JDK itself), and all we can do in that case is try not to call it. But these hordes of temporary objects create pressure on garbage collection and swell the heap, even if they never actually make it onto the heap per se. You can often see the effect of the extra GC pressure as a spike in heap size, if you can catch it happening. Flame graphs from async-profiler are a better tool because they are allow more fine-grained sampling than most profiling tools, and because they are visually very striking.

Here’s an example flame graph from the HTTP sample app we have been benchmarking, with Spring Boot 2.0 and with Spring Boot 2.1:


Spring Boot 2.0

Spring Boot 2.1

The red/brown GC flame on the right is noticeably smaller in Spring Boot 2.1. This is a sign of less GC pressure as a result of a change in the bean factory internals. The Spring Framework issue behind one of the main changes is here if you want to look at the details.

Recognizing that GC pressure is an issue is one thing (and async-profiler is the best tool we have found), but locating its source is something of an art. The best tool we have found for that is Flight Recorder (or Java Mission Control) which is part of the OpenJDK release, although it used to be only in the Oracle distribution. The problem with Flight Recorder is that the sampling rate is not really high enough to capture enough data on startup, so you have to try and build tight loops that do something you are interested in, or suspect might be contributing to the problem, and analyse those over a longer period (a few seconds or more). This leads to additional insight, but no real data on whether a "real" application will benefit from changing the hotspot. Much of the code in the spring-boot-allocations project is this kind of code: main methods that run tight loops focusing on suspected hotspots that can then be analyzed with Flight Controller.

WebFlux and Micro Apps

We might expect some variations between apps using a Servlet container and those using the newer reactive runtime from Netty introduced in Spring 5.0. The benchmark figures above are using Tomcat. There are some similar measurements in a different subdirectory of the same repo. Here are the results from the flux benchmarks:

Benchmark            (sample)  Mode  Cnt  Score   Error  Units Classes
MainBenchmark.main       demo    ss   10  1.081 ± 0.075   s/op 5779
MainBenchmark.main       jlog    ss   10  0.933 ± 0.065   s/op 4367
MiniBenchmark.boot       demo    ss   10  0.579 ± 0.041   s/op 4138
MiniBenchmark.boot       jlog    ss   10  0.486 ± 0.020   s/op 2974       demo    ss   10  0.538 ± 0.009   s/op 3138       jlog    ss   10  0.420 ± 0.011   s/op 2351
MiniBenchmark.micro      demo    ss   10  0.288 ± 0.006   s/op 2112
MiniBenchmark.micro      jlog    ss   10  0.186 ± 0.006   s/op 1371

All the apps have a single HTTP endpoint, just like the apps in the static benchmarks (Tomcat, Servlet). All are a bit faster than Tomcat, but not much (maybe 10%). Note that the fastest one ("micro jlog") is up and running in less than 200ms. Spring is really not doing very much there, and all the cost is basically getting the classes loaded for the features needed by the app (an HTTP server).


  • The MainBenchmark.main(demo) is full Boot + Webflux + autoconfiguration.

  • The boot samples use Spring Boot but no autoconfiguration.

  • The jlog samples exclude logback as well as Hibernate Validator and Jackson.

  • The mini samples do not use Spring Boot (just @EnableWebFlux).

  • The micro samples do not use @EnableWebflux either, just a manual route registration.

The mini jlog sample runs in about 46MB memory (10 heap, 36 non-heap). The micro jlog sample runs in 38MB (8 heap, 30 non-heap). Non-heap is really what matters for these smaller apps. They are all included on the scatter plot above, so they are consistent with the general correlation between startup time and classes loaded.

Classpath Exclusions

Your mileage my vary, but consider excluding:

  • Jackson (spring-boot-starter-json): it’s not super expensive (maybe 50ms on startup), but Gson is faster, and also has a smaller footprint.

  • Logback (spring-boot-starter-logging): still the best, most flexible logging library, but all that flexibility comes with a cost.

  • Hibernate Validator (org.hibernate.validator:hibernate-validator): does a lot of work on startup, so if you are not using it, exclude it.

  • Actuators (spring-boot-starter-actuator): a really useful feature set, so hard to recommend removing it completely, but if you aren’t using it, don’t put it on the classpath.

Spring Tweaks

  • Use the spring-context-indexer. It’s a drop in on the classpath, so very easy to install. It only works on your application’s own @Component classes, and really only likely to be a very small boost to startup time for all but the largest (1000s beans) applications. But it is measureable.

  • Don’t use actuators if you can afford not to.

  • Use Spring Boot 2.1 and Spring 5.1. Both have small, but important optimizations, especially regarding garbage collection pressure on startup. This is what enables newer apps to start up with less heap.

  • Use explicit spring.config.location. Spring Boot looks in quite a lot of locations for (or .yml), so if you know exactly where it is, or might be at runtime, you can shave off a few percent.

  • Switch off JMX: spring.jmx.enabled=false. If you aren’t using it you don’t need to pay the cost of creating and registering the MBeans.

  • Make bean definitions lazy by default. There’s nothing in Spring Boot that does this, but there’s a LazyInitBeanFactoryPostProcessor in this project you can copy. It is just a BeanFactoryPostProcessor that switches all beans to lazy=true.

  • Spring Data has some lazy initialization features now (in Lovelace, or Spring Boot 2.1). In Spring Boot you can just set (or lazy for completely lazy) - for large apps with 100s of entities improves startup time by more than a factor of 10.

  • Use functional bean definitions instead of @Configuration. More detail later on this.

JVM Tweaks

Useful command line tweaks for startup time:

  • -noverify - pretty much harmless, has a big impact. Might not be permitted in a low trust environment.

  • -XX:TieredStopAtLevel=1 - potentially degrades performance later, after startup, since it restricts the JVM ability to optimize itself at runtime. Your mileage my vary but it will have a measureable impact on startup time.

  • - not really a thing any more, but older versions of Tomcat used to really need it. Might have a small effect on modern apps with or without Tomcat if anyone is using random numbers.

  • -XX:+AlwaysPreTouch - small but possibly measurable effect on startup.

  • Use an explicit classpath - i.e. explode the fat jar and use java -cp …​. Use the application’s native main class. More detail on this later.

Class Data Sharing

Class Data Sharing (CDS) was a commercial only feature of the Oracle JDK since version 7, but it has also available in OpenJ9 (the open source version of the IBM JVM) and now in OpenJDK since version 10. OpenJ9 has had CDS for a long time, and it is super easy to use in that platform. It was designed for optimizing memory usage, not startup time, but those two concerns are not unrelated.

You can run OpenJ9 in the same way as a regular OpenJDK JVM, but the CDS is switched on with different command line flags. It’s super convenient with OpenJ9 because all you need is -Xshareclasses. It’s probably also a good idea to increase the size of the cache, e.g. -Xscmx128m, and to hint that you want a fast startup with -Xquickstart. These flags are always on in the benchmarks if they detect the OpenJ9 or IBM JVM.

Benchmark results with OpenJ9 and CDS:

Benchmark            (sample)  Mode  Cnt  Score   Error  Units Classes
MainBenchmark.main       demo    ss   10  0.939 ± 0.027   s/op 5954
MainBenchmark.main       jlog    ss   10  0.709 ± 0.034   s/op 4536
MiniBenchmark.boot       demo    ss   10  0.505 ± 0.035   s/op 4314
MiniBenchmark.boot       jlog    ss   10  0.406 ± 0.085   s/op 3090       demo    ss   10  0.432 ± 0.019   s/op 3256       jlog    ss   10  0.340 ± 0.018   s/op 2427
MiniBenchmark.micro      demo    ss   10  0.204 ± 0.019   s/op 2238
MiniBenchmark.micro      jlog    ss   10  0.152 ± 0.045   s/op 1436

That is quite impressive in some cases (25% faster than without CDS for the fastest apps). Similar results can be achieved with OpenJDK: includes CDS (with a less convenient command line interface) since Java 10. Here’s a scatter plot of the smaller end of the classes loaded versus startup time relationship, with regular OpenJDK (no CDS) in red and OpenJ9 (with CDS) in blue:


Java 10 and 11 also have an experimental feature called Ahead of Time compilation (AOT) that lets you build a native image from a Java application. Potentially this is super fast on startup, and most apps that can successfully be converted are indeed very fast to start up (by a factor of 10 for the small apps in the benchmarks here). Many "real life" applications cannot yet be converted. AOT is implemented using Graal VM, which we will come back to later.

Lazy Subsystems

We mentioned lazy bean definitions and the idea of a LazyInitBeanFactoryPostProcessor being generally of interest above. The benefits are clear, especially for a Spring Boot application with lots of autoconfigured beasn that you never uese, but also limited because even if you don’t use them sometimes they needto be created to satisfy a dependency. Those limitations could possibly be addressed by another idea that is more of a research topic, and that is to break down application into modules and initialize each one separately on demand.

To do this you would need to be able to precisely identify a subsystem in your source code and mark it somehow. An example of such a subsystem would be the actuators in Spring Boot, which we can identify mainly by the package names of the auto configuration classes. There is a prototype in this project: Lazy Actuator. You can just add it to an existing project and it converts all the actuator endpoints into lazy beans which will only be instantiated when they are used, saving about 40% of the startup time in a micro application like the canonical one-endpoint HTTP sample app in the benchmarks above. E.g. (for Maven):


To make this kind of pattern more mainstream would probably take some changes in the core Spring programming model, to allow the subsystems to be identified and dealt with in special ways at runtime. It also increases the complexity of an application, which might not really be worth it in a lot of cases - one of the best features of Spring Boot is the simplicity of the application context (all beans are created equal). So this remains an area of active research.

Manual Configuration

As mentioned already, it can be beneficial, if a little fiddly, to manually select the @Configuration classes instead of relying on @EnableAutoConfiguration. For example, in a simple web application the most discardable autoconfigurations are

  • TaskExecutionAutoConfiguration and TaskSchedulingAutoConfiguration for background execution with @Scheduled and @Async.

  • JmxAutoConfiguration configures a lot of useful JMX instrumentation, but if you aren’t going to use it, it’s a waste.

  • RestTemplateAutoConfiguration and WebClientAutoConfiguration, both of which are on by default but you probably only use at most one of them.

Here’s a minimal Spring Boot application with Netty and one endpoint, and selecting all configuration classes manually:

@Import({ PropertyPlaceholderAutoConfiguration.class,
          HttpHandlerAutoConfiguration.class })
public class SampleApplication {

	public RouterFunction<?> userEndpoints() {
		return route(GET("/"), request -> ok().body(Mono.just("Hello"), String.class));

	public static void main(String[] args) {, args);

It will start in well under a second and in less than 10MB heap. The same app with @SpringBootApplication (including all autoconfiguration) will start approximately 20% slower. Here are some benchmarks:

Style Beans Classes Startup(ms)


















  • "auto": fully leaded @SpringBootApplication

  • "lazy": all bean definitions are lazy by default

  • "manual": manually selected autoconfigurations

  • "func": the same but converted to functional bean definitions

Functional Bean Definitions

Functional bean registration is a feature added to Spring 5.0, in the form of a few new methods in BeanDefinitionBuilder and some convenience methods in GenericApplicationContext. It allows for completely non-reflective creation of components by Spring, by attaching a Supplier to a BeanDefinition, instead of a Class.

The programming model is a little bit different than the most popular @Configuration style, but it still has the same goal: to extract configuration logic into separate resources, and allow the logic to be implemented in Java. If you had a configuration class like this:

public class SampleConfiguration {

    public Foo foo() {
        return new Foo();

    public Bar bar(Foo foo) {
        return new Bar(foo);


You could convert it to functional style like this:

public class SampleConfiguration
        implements ApplicationContextInitializer<GenericApplicationContext> {

    public Foo foo() {
        return new Foo();

    public Bar bar(Foo foo) {
        return new Bar(foo);

    public void initialize(GenericApplicationContext context) {
        context.registerBean(SampleConfiguration.class, () -> this);
                () -> context.getBean(SampleConfiguration.class).foo());
        context.registerBean(Bar.class, () -> context.getBean(SampleConfiguration.class)


There are multiple options for where to make these registerBean() method calls, but here we have chosen to show them wrapped in an ApplicationContextInitializer. The ApplicationContextInitializer is a core framework interface, but it has a special place in Spring Boot because a SpringApplication can be loaded up with initializers through its public API, or by declaring them in META-INF/spring.factories. The spring.factories approach is one that easily allows the application and its integration tests (using @SpringBootTest) to share the same configuration.

This programming model is not yet mainstream in Spring Boot applications, but it has been implemented in Spring Cloud Function and is also a basic building block in Spring Fu. Also the fastest full Spring Boot benchmark apps above ("bunc") are implemented this way. The main reason for this is that functional bean registration is the fastest way for Spring to create bean instances - it requires virtually no computation beyond instantiating a class and calling its constructors natively.

The other, non-functional types of BeanDefinition will always be slower, but that will not stop us from optimizing further and the gap will almost certainly narrow as Spring evolves.

The existing functional bean implementations in libraries and apps had to manually copy quite a bit of code from Spring Boot, and convert it to the functional style. For small applications this might be practical, but the more features from Spring Boot you use, the less convenient it will be. Recognizing this we have started work on various tools that could be used to automatically convert @Configuration to ApplicationContextInitializer code. You can do it at runtime with reflection, and this turns out to be surprisingly fast (proving that not all reflection is bad), or you could do it at compile time, which promises to be optimal in turns of start up time but is technically a little bit harder to implement.

The Future

Whatever the future brings, I think we can be certain that Spring will stay as lightweight as possible, and continue to improve performance, in terms of startup time, memory usage and also runtime CPU usage. The most promising lines of attack at present are the functional bean registrations, and probably some automated way to generate those from @Configuration, plus the work we are doing with the Graal team at Oracle to make GraalVM more generally usable for Spring Boot applications. There are still optimizations to be made in the core framework, as well as in Spring Boot probably. Keep an eye out on the Spring Blog for more new research and new releases, and more topical analysis of performance hotspots and tweaks you can make to avoid them.

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