RocksDB Contribution Guide

Before Contribution

Before contributing to RocksDB, please make sure that you are able to sign CLA. Your change will not be merged unless you have proper CLA signed. See https://code.facebook.com/cla for more information.

Basic Development Workflow

As most open-source projects in github, RocksDB contributors work on their fork, and send pull requests to RocksDB’s facebook repo. After a reviewer approves the pull request, a RocksDB team member at Facebook will merge it.

How to Run Unit Tests

Build Systems

RocksDB uses gtest. The makefile used for GNU make has some supports to help developers run all unit tests in parallel, which will be introduced below. If you use cmake, you might need find your way to run all the unit tests (you are welcome to contribute build system to make it easier).

Run Unit Tests In Parallel

In order to run unit tests in parallel, first install GNU parallel on your host, and run run

make all check [-j] 

You can specify number of parallel tests to run using environment variable J=1, for example:

make J=64 all check -j

If you switch between release and debug build, normal or lite build, or compiler or compiler options, call make clean first. So here is a safe routine to run all tests:

make clean
make J=64 all check -j

Debug Single Unit Test Failures

RocksDB uses gtest. You can running specific unit test by running the test binary contains it. If you use GNU make, the test binary will be just under your checkpoint. For example, test DBBasicTest.OpenWhenOpen is in binary db_basic_test, so just run

./db_basic_test

will run all tests in the binary.

gtest provides some useful command line parameters, and you can see them by calling --help:

./db_basic_test --help

Here are some frequently used ones:

Run subset of tests using --gtest_filter. If you only want to run DBBasicTest.OpenWhenOpen, call

./db_basic_test --gtest_filter=“*DBBasicTest.OpenWhenOpen*”

By default, the test DB created by tests are cleared up even if test fails. You can try to preserve it by using --gtest_throw_on_failure. If you want to stop the debugger when assert fails, specify --gtest_break_on_failure. KEEP_DB=1 environment variable is another way to preserve the test DB from being deleted at the end of a unit-test run, irrespective of whether the test fails or not:

KEEP_DB=1 ./db_basic_test --gtest_filter=DBBasicTest.Open

By default, the temporary test files will be under /tmp/rocksdbtest-/ (except when running in parallel they are under /dev/shm). You can override the location by using environment variable TEST_TMPDIR. For example:

TEST_TMPDIR=/dev/shm/my_dir ./db_basic_test

Java Unit Tests

Sometimes we need to run Java tests too. Run

make jclean rocksdbjava jtest

You can put -j but sometimes it causes problem. Try to remove -j if you see problems.

Some other build flavors

For more complicated code changes, we ask contributors to run more build flavors before sending the code review. The makefile for GNU make has better pre-defined support for it, although it can be manually done in CMake too.

To build with AddressSanitizer (ASAN), set environment variable COMPILE_WITH_ASAN:

COMPILE_WITH_ASAN=1 make all check -j

To build with ThreadSanitizer (TSAN), set environment variable COMPILE_WITH_TSAN:

COMPILE_WITH_TSAN=1 make all check -j

To run all valgrind tests:

make valgrind_test -j

To run _UndefinedBehaviorSanitizer (UBSAN), set environment variable COMPILE_WITH_UBSAN:

COMPILE_WITH_UBSAN=1 make all check -j

To run llvm's analyzer, run

make analyze

Code Style

RocksDB follows Google C++ Style: https://google.github.io/styleguide/cppguide.html . We limit each line to 80 characters.

Some formatting can be done by a formatter by running

build_tools/format-diff.sh

or simply make format if you use GNU make. If you lack of dependencies to run it, the script will print out instructions for you to install them.

Requirements Before Sending a Pull Request

HISTORY.md

Consider updating HISTORY.md to mention your change, especially if it's a bug fix, public API change or an awesome new feature.

Pull Request Summary

We recommend a "Test Plan:" section is included in the pull request summary, which introduces what testing is done to validate the quality and performance of the change.

Add Unit Tests

Almost all code changes need to go with changes in unit tests for validation. For new features, new unit tests or tests scenarios need to be added even if it has been validated manually. This is to make sure future contributors can rerun the tests to validate their changes don't cause problem with the feature.

Simple Changes

Pull requests for simple changes can be sent after running all unit tests with any build favor and see all tests pass. If any public interface is changed, or Java code involved, Java tests also need to be run.

Complex Changes

If the change is complicated enough, ASAN, TSAN and valgrind need to be run on your local environment before sending the pull request. If you run ASAN with higher version of llvm with covers almost all the functionality of valgrind, valgrind tests can be skipped. It may be hard for developers who use Windows. Just try to use the best equivalence tools available in your environment.

Changes with Higher Risk or Some Unknowns

For changes with higher risks, other than running all tests with multiple flavors, a crash test cycle needs to be executed and see no failure. If crash test doesn't cover the new feature, consider to add it there. To run all crash test, run

make crash_test -j

If you can't use GNU make, you can manually build db_stress binary, and run script:

  python -u tools/db_crashtest.py whitebox
  python -u tools/db_crashtest.py blackbox
  python -u tools/db_crashtest.py --simple whitebox
  python -u tools/db_crashtest.py --simple blackbox
  python -u tools/db_crashtest.py --cf_consistency blackbox
  python -u tools/db_crashtest.py --cf_consistency whitebox 

Performance Improvement Changes

For changes that might impact performance, we suggest normal benchmarks are run to make sure there is no regression. Depending the actual performance, you may choose to run against a database backed by disks, or memory-backed file systems. Explain in the pull request summary why the performance environment is chosen, if it is not obvious. If the change is to improve performance, bring at least one benchmark test case that favors the improvement and show the improvements.