spark/dev

C++ Intellisense for Spark on Windows (VS Code)

Steps

1. Install the C++ extension
2. Install the CMake extension
3. Copy c_cpp_properties.json from this folder to $workspaceFolder/.vscode/
4. In the VS Code Command Palette, run CMake: Select Configure Preset, then select 'spark' from the dropdown

Notes

• Intellisense won't work for generated files/types until you build libspark

C++ Intellisense for Sparkd on Linux (VS Code)

1. Install the CMake extension
2. Install the Clangd extension
3. (Optional) Install the C++ extension (needed for debugging, but intellisense should work without it).
4. If the C++ extension is installed, disable its intellisense in your workspace settings (Clangd intellisense is better)

   "C_Cpp.intelliSenseEngine": "disabled"
   

5. In the VS Code Command Palette, run CMake: Select Configure Preset, then select the preset you wish to build from the dropdown
   • The Clang presets may work best with the Clangd extension
   • Intellisense won't work for generated files/types until you build libspark

Build Sparkd on Linux

Notes

• Sparkd is built using CMakePresets

Steps

1. Set up the Python virtual environment
   
   • Run commands from the repo root dir unless otherwise noted
   1. Create the virtual environment

      python3 -m venv .venv
      

   2. Activate the virtual environment

      . .venv/bin/activate
      

   3. Verify python in the venv is first in the path
      
      • Expected: output shows the absolute path to .venv/bin/python

      which python
      

   4. Update pip
      
      • The venv module initializes virtual environments with its own version of pip, rather than the system's version. Even if the latter is up-to-date, the former may not be.

      python -m pip install --upgrade pip
      

   5. Install the packages listed requirements.txt

      pip install -r requirements.txt
      

2. Build Sparkd
   
   • From the terminal

     tools/build_sparkd.sh
     

     • Run with --help to see available options
   • From VS Code
     1. Install the CMake extension
     2. In the VS Code Command Palette, run CMake: Select Configure Preset, then select the preset you wish to build from the dropdown
     3. Run CMake: Build in the command palette.

Build Spark Api Server on Windows

Dependencies

• CMake (version >= 3.23)
• Python (version >= 3.9)
• Visual Studio (version)
• Dotnet (sdk version >= 7.0.0)

References

• CMakePresets
• Visual Studio 17 2022 (CMake generator)
• Python Virtual Environment

Notes

• Run commands from the repo root dir unless otherwise noted

1. Set up the Python Virtual Environment

   # Create the virtual environment
   python3 -m venv .venv
   
   # Activate the virtual environment
   .venv\bin\activate.bat
   
   # Update pip
   python -m pip install --upgrade pip
   
   # Install packages
   pip install -r requirements.txt
   

2. Build
   • Run in python virtual environment

   # Configure
   cmake --preset spark-api-server-windows
   
   # Build debug
   cmake --build --preset spark-api-server-windows-debug
   dotnet build Spark.Api.Server.sln --configuration Debug
   
   # Build release
   cmake --build --preset spark-api-server-windows-release
   dotnet build Spark.Api.Server.sln --configuration Release
   

Build Spark Api Server on Linux

Dependencies

• CMake (version >= 3.23)
• Ninja Build
• Python (version >= 3.9)
• Dotnet (sdk version >= 7.0.0)

References

• CMakePresets
• Ninja Multi-Config (CMake generator)
• Python Virtual Environment

Prerequisites

• Open repository in a VS Code Devcontainer, or create your own docker container in which to build

Notes

• Run commands from the repo root dir unless otherwise noted

1. Set up the Python Virtual Environment

   # Create the virtual environment
   python3 -m venv .venv
   
   # Activate the virtual environment
   . .venv/bin/activate
   
   # Update pip
   python -m pip install --upgrade pip
   
   # Install packages
   pip install -r requirements.txt
   

2. Build
   • Run in python virtual environment

   # Configure
   cmake --preset spark-api-server-linux
   
   # Build debug
   cmake --build --preset spark-api-server-linux-debug
   dotnet build Spark.Api.Server.sln --configuration Debug
   
   # Build release
   cmake --build --preset spark-api-server-linux-release
   dotnet build Spark.Api.Server.sln --configuration Release