./configure.py generates the
build.ninja files used to build ninja. It accepts various flags to adjust build parameters.
The primary build target of interest is
ninja, but when hacking on Ninja your changes should be testable so it's more useful to build and run
ninja_test when developing.
Build in “debug” mode while developing (disables optimizations and builds way faster on Windows):
To use clang, set
Github pull requests are convenient for me to merge (I can just click a button and it‘s all handled server-side), but I’m also comfortable accepting pre-github git patches (via
Good pull requests have all of these attributes:
These are typically merged without hesitation. If a change is lacking any of the above I usually will ask you to fix it, though there are obvious exceptions (fixing typos in comments don't need tests).
I am very wary of changes that increase the complexity of Ninja (in particular, new build file syntax or command-line flags) or increase the maintenance burden of Ninja. Ninja is already successfully in use by hundreds of developers for large projects and it already achieves (most of) the goals I set out for it to do. It's probably best to discuss new feature ideas on the mailing list before I shoot down your patch.
Set your build command to
./ninja ninja_test && ./ninja_test --gtest_filter=MyTest.Name
now you can repeatedly run that while developing until the tests pass (I frequently set it as my compilation command in Emacs). Remember to build “all” before committing to verify the other source still works!
If you have a Chrome build handy, it's a good test case. Otherwise, the github downoads page has a copy of the Chrome build files (and depfiles). You can untar that, then run
and compare that against a baseline Ninja.
There's a script at
misc/measure.py that repeatedly runs a command like the above (to address variance) and summarizes its runtime. E.g.
path/to/misc/measure.py path/to/my/ninja chrome
For changing the depfile parser, you can also build
parser_perftest and run that directly on some representative input files.
Generally it's the Google C++ coding style, but in brief:
\ato refer to arguments.
CanonicalizePath(string* path, string* err), the arguments are hopefully obvious)
sudo apt-get install asciidoc --no-install-recommends ./ninja manual
sudo apt-get install doxygen ./ninja doxygen
While developing, it‘s helpful to copy
ninja.exe to another name like
n.exe; otherwise, rebuilds will be unable to write
ninja.exe because it’s locked while in use.
python configure.py --bootstrap
python configure.py --bootstrap
Setup on Ubuntu Lucid:
sudo apt-get install gcc-mingw32 wine
export CC=i586-mingw32msvc-cc CXX=i586-mingw32msvc-c++ AR=i586-mingw32msvc-ar
Setup on Ubuntu Precise:
sudo apt-get install gcc-mingw-w64-i686 g++-mingw-w64-i686 wine
export CC=i686-w64-mingw32-gcc CXX=i686-w64-mingw32-g++ AR=i686-w64-mingw32-ar
Setup on Arch:
sudo pacman -Sy.
sudo pacman -S mingw-w64-gcc wine
export CC=x86_64-w64-mingw32-cc CXX=x86_64-w64-mingw32-c++ AR=x86_64-w64-mingw32-ar
./configure.py --platform=mingw --host=linux
ninja.exeusing a Linux ninja binary:
./ninja.exe(implicitly runs through wine(!))
The trick is to install just the compilers, and not all of Visual Studio, by following these instructions.
Do a clean debug build with the right flags:
CFLAGS=-coverage LDFLAGS=-coverage ./configure.py --debug ninja -t clean ninja_test && ninja ninja_test
Run the test binary to generate
.gcno files in the build directory, then run gcov on the .o files to generate
.gcov files in the root directory:
./ninja_test gcov build/*.o
Look at the generated
.gcov files directly, or use your favorit gcov viewer.
Build with afl-clang++:
CXX=path/to/afl-1.20b/afl-clang++ ./configure.py ninja
Then run afl-fuzz like so:
afl-fuzz -i misc/afl-fuzz -o /tmp/afl-fuzz-out ./ninja -n -f @@
You can pass
-x misc/afl-fuzz-tokens to use the token dictionary. In my testing, that did not seem more effective though.
If you want to use asan (the
isysroot bit is only needed on OS X; if clang can't find C++ standard headers make sure your LLVM checkout includes a libc++ checkout and has libc++ installed in the build directory):
CFLAGS="-fsanitize=address -isysroot $(xcrun -show-sdk-path)" \ LDFLAGS=-fsanitize=address CXX=path/to/afl-1.20b/afl-clang++ \ ./configure.py AFL_CXX=path/to/clang++ ninja
Make sure ninja can find the asan runtime:
DYLD_LIBRARY_PATH=path/to//lib/clang/3.7.0/lib/darwin/ \ afl-fuzz -i misc/afl-fuzz -o /tmp/afl-fuzz-out ./ninja -n -f @@