LAPIS-SILO

Sequence Indexing engine for Large Order of genomic data

License

Original genome indexing logic with roaring bitmaps by Prof. Neumann: https://db.in.tum.de/~neumann/gi/

Building

Use ./build_with_conan.py to build SILO. ./build_with_conan.py --help shows all available options.

We took the approach to scan directories for .cpp files to include instead of listing them manually in the CMakeLists.txt. This has the advantage that we don't need to maintain a list of files in the CMakeLists.txt.

It has the disadvantage that after a successful build on local machines, CMake can't detect whether files were added or deleted. This requires a clean build. You can either delete the build/ directory manually, or you execute ./build_with_conan.py --clean.

Since in any approach, a developer has to remember to either trigger a clean build or to adapt the CMakeLists.txt, we decided for the approach with less maintenance effort, since it will automatically work in GitHub Actions.

Running SILO locally with CLion

.run contains run configurations for CLion that are ready to use. They assume that you configured CMake in CLion to use ./build as build directory. CLion should be able to detect those files automatically.

With Conan

We use Conan to install dependencies for local development. See Dockerfile for how to set up Conan and its requirements. This has been tested on Ubuntu 22.04 and is not guaranteed to work on other systems.

The Conan profile (myProfile) on your system might differ: Create a new profile ~/.conan2/profiles/myProfile

conan profile detect

Copy conanprofile.example to conanprofile and insert the values of os, os_build, arch and arch_build from myProfile.

Build silo in ./build. This build will load and build the required libraries to ~/.conan2/data/ (can not be set by hand).

./build_with_conan.py

Executables are located in build/ upon a successful build.

With Docker:

(for CI and release)

Build docker container

docker build . --tag=silo

The Docker images are built in such a way that they can be used for both, preprocessing and running the api, with minimal configuration. The images contain default configuration so that a user only needs to mount data to the correct locations.

Configuration Files

For SILO, there are three different configuration files:

• DatabaseConfig described in file database_config.h
• PreprocessingConfig used when started with --preprocessing and described in file preprocessing_config.h
• RuntimeConfig used when started with --api and described in file runtime_config.h

The database config contains the schema of the database and is always required when preprocessing data. The database config will be saved together with the output of the preprocessing and is therefore not required when starting SILO as an API. An example of a configuration file can be seen in testBaseData/exampleDataset/database_config.yaml.

By default, the config files are expected to be YAML files in the current working directory in snake_case (database_config.yaml, preprocessing_config.yaml, runtime_config.yaml), but their location can be overridden using the options --databaseConfig=X, --preprocessingConfig=X, and --runtimeConfig=X.

Preprocessing and Runtime configurations contain default values for all fields and are thus only optional. Their parameters can also be provided as command-line arguments in snake_case and as environment variables prefixed with SILO_ in capital SNAKE_CASE. (e.g. SILO_INPUT_DIRECTORY).

The precendence is CLI argument > Environment Variable > Configuration File > Default Value

Run The Preprocessing

The preprocessing acts as a program that takes an input directory that contains the to-be-processed data and an output directory where the processed data will be stored. Both need to be mounted to the container. SILO also expects a database config and a preprocessing config that need to be mounted to the default locations.

docker run \
  -v your/input/directory:/preprocessing/input \
  -v your/preprocessing/output:/preprocessing/output \
  -v your/preprocessing_config.yaml:/app/preprocessing_config.yaml \
  -v your/database_config.yaml:/app/database_config.yaml \
  silo --preprocessing

Both config files can also be provided in custom locations:

silo --preprocessing --preprocessingConfig=./custom/preprocessing_config.yaml --databaseConfig=./custom/database_config.yaml

The Docker image contains a default preprocessing config that sets defaults specific for running SILO in Docker. Apart from that, there are default values if neither user-provided nor default config specify fields. The user-provided preprocessing config can be used to overwrite the default values. For a full reference, see

• testBaseData/test_preprocessing_config_with_overridden_defaults.yaml

Run docker container (api)

After preprocessing the data, the api can be started with the following command:

docker run 
  -p 8081:8081
  -v your/preprocessing/output:/data
  silo --api

The directory where SILO expects the preprocessing output can be overwritten via silo --api --dataDirectory=/custom/data/directory or in a corresponding configuration file.

Notes On Building The Image

Building Docker images locally relies on the local Docker cache. Docker will cache layers, and it will cache the dependencies built by Conan via cache mounts.

However, cache mounts don't work in GitHub Actions (docker/build-push-action#716), so there we only rely on Docker's layer cache via Docker's gha cache backend.

Creating A Release

This project uses Release Please to generate releases. On every commit on the main branch, it will update a Pull Request with a changelog. When the PR is merged, the release will be created. Creating a release means:

• A new Git tag is created.
• The Docker images of SILO are tagged with the new version.
  • Suppose the created version is 2.4.5, then it creates the tags 2, 2.4 and 2.4.5 on the current latest image.

The changelog and the version number are determined by the commit messages. Therefore, commit messages should follow the Conventional Commits specification. Also refer to the Release Please documentation for more information on how to write commit messages or see Conventional Commits below.

Testing

Unit Tests

For testing, we use the framework gtest and gmock for mocking. Tests are built using the same script as the production code: ./build_with_conan.

We use the convention, that each tested source file has its own test file, ending with *.test.cpp. The test file is placed in the same folder as the source file. If the function under test is described in a header file, the test file is located in the corresponding source folder.

To run all tests, run

build/silo_test

For linting we use clang-tidy. The config is stored in .clang-tidy.

When pushing to GitHub, a separate Docker image will be built, which runs the formatter. (This is a workaround, because building with clang-tidy under alpine was not possible yet.)

Functional End-To-End Tests

End-to-end tests are located in /endToEndTests. Those tests are used to verify the overall functionality of the SILO queries. To execute the tests:

• have a running SILO instance with preprocessd data e.g. via
  • SILO_IMAGE=ghcr.io/genspectrum/lapis-silo docker compose -f docker-compose-for-tests-preprocessing.yml up
  • SILO_IMAGE=ghcr.io/genspectrum/lapis-silo docker compose -f docker-compose-for-tests-api.yml up -d wait
• cd endToEndTests
• npm install
• SILO_URL=localhost:8081 npm run test

Logging

We use spdlog for logging. The log level can be controlled via the environment variable SPDLOG_LEVEL:

• Start SILO with SPDLOG_LEVEL=off to turn off logging.
• Start SILO with SPDLOG_LEVEL=debug to log at debug level.

SILO will log to ./logs/silo_<date>.log and to stdout.

We decided to use the macros provided by spdlog rather than the functions, because this lets us disable log statements at compile time by adjusting add_compile_definitions(SPDLOG_ACTIVE_LEVEL=SPDLOG_LEVEL_TRACE) to the desired log level via CMake. This might be desirable for benchmarking SILO. However, the default should be SPDLOG_LEVEL_TRACE to give the maintainer the possibility to adjust the log level to a log level that they prefer, without the need to recompile SILO.

Code Style Guidelines

Naming

We mainly follow the styleguide provided by google, with a few additions. The naming is enforced by clang-tidy. Please refer to .clang-tidy for more details on naming inside the code. Clang-tidy can not detect filenames. We decided to use snake_case for filenames.

Includes

The includes are sorted in the following order:

1. Corresponding header file (for source files)
2. System includes
3. External includes
4. Internal includes

Internal includes are marked by double quotes. External includes are marked by angle brackets.

Conventional Commits

We follow the conventional commits guidelines for commit messages. This will allow to automatically generate a changelog.

We use commitlint to enforce the commit message format. To use it locally, run npm install.

The last commit message can be checked with

npm run commitlint:last-commit

To check commit messages of a branch to the commit where it branches off from main, run

npm run commitlint:merge-base

Testing The Generated Changelog

To test the generated changelog, run

npm run release-please-dry-run -- --token=<GitHub PAT> --target-branch=<name of the upstream branch>

where

• <GitHub PAT> is a GitHub Personal Access Token. It doesn't need any permissions.
• <name of the upstream branch> is the name of the branch for which the changelog should be generated.

NOTE: This command does not respect local changes. It will pull the commit messages from the remote repository.