A Benchmark setup for Java buildsystems.

See Github pages at:

Build Status

The different buildsystems are installed locally on demand by the makefiles.


Also see my notes


# to run all buildsystems
$ make

# to run all buildsystems freshly (TODO: do something about bazel global cache)
$ rm -rf caches/bazel/bazel<version>/bazel_cache
$ make clean all

# to run for just selected buildsystems, e.g. maven vs. gradle:
$ make clean maven gradle

# to run for just selected buildsystems in particular versions
$ make clean maven3.3.3 gradle2.7

# to run with changed code:
# change file in build/<project>/source
$ touch build/<project>/source
$ make ...

The process is configured using variables that can be changed, the configs folder has a file setting defaults, and some example files for different kinds of builds.

It is possible to run a custom Benchmark configuration using:

# to run specific configuration
$ make clean all CONFIG=configs/


The whole setup is described here

Configuring Benchmarks

Custom configurations are loaded after the providing some convention over configuration. If present, a in the project root will be loaded after but before specific configuration, allowing to override permanent defaults with your defaults.


While Maven and Gradle are used by most Java projects in the wild, there are many alternatives to choose from. Comparing those is difficult. This project is a setup to run a buildprocess for java projects using multiple buildsystems.

My main goal was to find out which buildsystem feature pays off under which circumstances (and under which it is irrelevant). As secondary independent goal is to get a feel for how much difference there is in performance for “realistic” isolated projects (like open-source projects). A comparison for huge mono-repo situations was not that interesting to me.

A single benchmark is driven by GNU make. The Makefile creates a build folder, containing a folder structure for benchmarks. Subfolders follow the pattern <benchmarkname>/<buildsystemname>. Into those folders, Java source files and buildsystem specific files will be copied / generated. Then the buildsystem is invoked inside that folder and the time until completion is measured.


The builds should work for any source tree that follows these conventions:


Sample output: See




What influences performance?

JVM startup adds something like 3 seconds to the whole process. Several tools offer daemons to reduce this offset. Tools not written in JVM languages do not have this offset. A background process living on between builds can also cache other valuable data to make further runs startup faster. The startup time reduction however becomes only relevant at large scales.

Parallel task execution: On machines with multiple cores, it may be possible to reduce build time by utilizing more than one CPU. Even with just one CPU, multithreaded execution can have a performance bonus. However the build-time rarely is reduced by the number of CPUs. The overhead of finding out how to split tasks over several CPUs can eliminate benefits, and often there will be many dependencies that lead to tasks necessarily being build in sequence. Most buildtool will thus mostly offer to only build completely independent sub-modules in parallel. For single-module projects, no additional CPU is used then. Some tasks may even only work when not run in parallel, so using parallel features also increases maintenance effort.

Compiler speed may differ for different compilers. The scala compiler and clojure compiler seemed slower than javac for compiling java sources.

Incremental re-compilation, meaning compiling only files that are affected by a change, can drastically reduce build times.

Incremental build steps beyond compilation help (e.g. Maven can compile incrementally, but not test incrementally).

Incremental builds for sub-module filesets. Several buildsystems can (re-)build only those submodules that have change, but cannot only (re-)build only half a submodule. Being able to incrementally rebuild smaller parts can speed up builds in specific situations.

Incremental compilation that understand the language. Sbt as an example may analyze the changes to a changed file to decide whether depending classes need to be recompiled. So when the change only affect private methods, sbt can decide that dependencies need not be recompiled. However the benefit only becomes noticeable at large scales.

Caching influences incremental builds. Several buildsystems have a simple caching strategy in that they will not run a task if the output still exist. This will improve performance for repeated builds.

Some buildsystems can offer advanced (true) caching of build results, in that the cache is an independent storage that maintains multiple versions of build results over time. This can dramatically reduce build times in many more situations than simple caching described before.

For large projects with plenty of subprojects and subtasks, performance can be gained by caching in a fine-grained way and reusing more previous artifacts. The example and setup used in this benchmark may not be optimal for any given buildsystem. In particular, Pants has some online examples defining plenty of smaller library targets for individual Java files, which might improve caching performance when rebuilding after a single java file changed (not sure what other advantage it could have).

So which buildsystem should I use for Java projects?

It depends on what you need.

To choose, consider the following:

No really, which one should I use for Java projects?

Maven or Gradle are the default choice for most open-source Java projects and many businesses out there. Maven may still be more popular in the industry for stability, but Gradle has a stronger innovation drive. Both have hundreds of open-source plugins available, and both get special support from IDEs, Continuous integration servers, etc. My personal recommendation is for Gradle, see the buildsystem comparison document for more details.

Google’s Bazel is the open-sourced part of the Blaze system optimized for huge monorepo corporate ecosystems, where thousands of projects with interdependencies are continuously build and deployed. Buck and Pants are derived from Googles Bazel, Buck is used at Facebook, Pants at Twitter. If choosing among these 3, I would recommend Bazel because Google is behind it.

All three of them require more developer attention and effort, because they are rule based, and have no high-level abstraction of a project object model like Gradle or Maven. And being still fairly new as open-source projects at this time, they do not have the mature support from other open-source tools. As an example, buck only lately got a feature to automatically download dependencies from central Maven repositories. Pants still uses version numbers labelled 0.0.x, and the required format of the buildfiles may still change between versions. Since they have been used in a corporate setting where strict standards could be enforced, they are prone to detection of new bugs when being used in the wild by projects following a huge variety of conventions. Bazel and Buck offer huge performance benefits due to advanced caching of build results. Pants seems to attempt to improve build performance by optionally including many other third-party tools like nailgun and zinc, but the benefit was not visible in the benchmarks. Buck seems mainly targetted at building Java apps for Android.

Ant is still being used, but it’s unclear what advantages it offers. Maybe simplicity for creating many small unconventional tasks. Gant is built on top of ant and similar in purpose, but allowing to write in Groovy. Other buildsystems like make, rake or scons might be similar enough to ant, but they do not get further consideration here because they have little tradition for Java projects.

Leiningen and sbt are optimized (in usability) for Clojure and Scala respectively. If you only use Java, it probably does not pay off to use either of them, unless you wanted to learn / integrate those languages anyway.

Buildr seems to be mostly similar to Gradle but written in Ruby, which offers some advantages and disadvantages. Based on mailing list activity, it seems the project lost the interest of it’s userbase.

The Kotlin ecosystem also has a buildsystem called Kobalt, but it seemed not established enough to be considered in the benchmarks.

Name Target language Written in Since Support Caching Model
ant Java XML Java 2000 Apache None rules
maven Java (Scala, Ruby, C#) XML Java 2002 Apache None POM
gradle Java, Groovy (Scala, C++,) Groovy, Kotlin Java, Groovy 2007 Gradleware last build POM
buildr Java, Scala, Groovy Ruby Ruby 2010? Apache None POM
sbt Scala, Java Scala Scala 2010? ? None POM
leiningen Clojure, Java Clojure Clojure 2009? ? None POM
buck Java (Android) Python-ish Java, Python 2012 Facebook true cache rules
bazel C++, Java, Python, Go Python-ish C++, Java 2015 (2005?) Google true cache rules
pants Java, Scala, Python Python Python 2014 (2010) Twitter None rules

I get InstantiationExceptions with some buildsystem, what is going on?

java.lang.InstantiationException during tests is usually a sign that a TestRunner is trying to run a non-Testcase class (like abstract or util classes). Not all Buildsystems can cope well with that by default.

Why GNU make?

I chose GNU make for this project because it is omnipresent in linux and very close to shell scripting.

Why jinja2?

I needed some templating engine, and scripting in Python seemed the least effort. Jinja2 is popular and een around for a while. Mako and Genshi also seemed nice at a glance.

Why commons-math?

I chose to test against commons-math because it is reasonably large, well tested, and has no dependencies outside the JDK. Other libraries working okay are commons-text, commons-io, commons-imaging, guava.

The main problems I had with commons-math was that the naming for the Testcases is not consistent. The commons-math ant file lists those rules:

    <include name="**/*"/>
    <include name="**/*"/>
    <include name="**/*"/>
    <exclude name="**/*"/>

And even those do not cover all Testcases defined in the codebase.