rtags

Pros of CMake

• Widely adopted and supported across multiple platforms and IDEs
• Extensive documentation and large community for support
• Powerful scripting capabilities for complex build configurations

Cons of CMake

• Steeper learning curve, especially for complex projects
• Can be verbose and require more boilerplate code
• Less focused on code indexing and refactoring capabilities

Code Comparison

CMake example:

cmake_minimum_required(VERSION 3.10)
project(MyProject)
add_executable(MyApp main.cpp)
target_link_libraries(MyApp PRIVATE MyLibrary)

RTags doesn't have a direct code equivalent, as it's primarily a C/C++ indexer and source code navigation tool. It uses compilation databases generated by build systems like CMake.

Key Differences

• Purpose: CMake is a build system generator, while RTags is a code indexer and navigation tool
• Scope: CMake manages the entire build process, RTags focuses on code analysis and navigation
• Integration: CMake integrates with various build tools and IDEs, RTags works alongside existing build systems

Use Cases

• CMake: Ideal for cross-platform projects requiring flexible build configurations
• RTags: Best for developers seeking advanced code navigation and refactoring in C/C++ projects

Both tools can be complementary in a C/C++ development workflow, with CMake generating the build system and compilation database, which RTags can then use for indexing and analysis.

Pros of llvm-project

• Comprehensive toolchain: Includes compiler, debugger, and runtime libraries
• Widely adopted and supported by major tech companies
• Extensive documentation and community resources

Cons of llvm-project

• Steeper learning curve due to its complexity
• Larger codebase, which may be overwhelming for small projects
• Requires more system resources to build and run

Code Comparison

rtags:

class Project : public std::enable_shared_from_this<Project>
{
public:
    std::shared_ptr<IndexDataMessage> createIndexDataMessage() const;
};

llvm-project:

class Module {
public:
  Module(StringRef Name, LLVMContext& C);
  const GlobalVariable *getNamedGlobal(StringRef Name) const;
};

Summary

rtags is a lightweight C/C++ indexer focused on code navigation and refactoring, while llvm-project is a comprehensive compiler infrastructure project. rtags is more suitable for quick setup and integration with editors, whereas llvm-project offers a complete toolchain for building compilers and related tools. The code snippets show rtags' focus on project management and indexing, while llvm-project demonstrates its emphasis on module-level operations within a compiler framework.

Pros of cquery

• Faster indexing and query response times
• Lower memory usage, especially for large projects
• Built-in support for cross-compilation and multiple compilation databases

Cons of cquery

• Less mature and potentially less stable than RTags
• Smaller community and fewer integrations with editors/IDEs
• Limited support for non-C++ languages compared to RTags

Code Comparison

cquery uses libclang for parsing, while RTags uses its own custom parser. This can lead to differences in how they handle certain C++ constructs.

cquery:

void cquery_parse(const char* filename) {
    CXIndex index = clang_createIndex(0, 0);
    CXTranslationUnit unit = clang_parseTranslationUnit(index, filename, nullptr, 0, nullptr, 0, CXTranslationUnit_None);
    // ... process the translation unit
}

RTags:

void rtags_parse(const char* filename) {
    Source source(filename);
    Parser parser;
    parser.parse(source);
    // ... process the parsed AST
}

Both projects aim to provide efficient code indexing and querying for C++ projects, but they take different approaches in implementation and feature sets. cquery focuses on performance and resource efficiency, while RTags offers broader language support and integration options.

Pros of ccls

• Faster indexing and lower memory usage
• Better support for C++20 features
• More actively maintained with frequent updates

Cons of ccls

• Less mature and potentially less stable than RTags
• Fewer integration options with text editors and IDEs
• May have a steeper learning curve for configuration

Code Comparison

RTags:

class RTagsClangThread : public Thread {
public:
    RTagsClangThread(const std::shared_ptr<Project> &project,
                     const Source &source,
                     const std::shared_ptr<Connection> &connection);
};

ccls:

struct Project {
  std::unordered_map<std::string, std::vector<std::string>> folder2compilers;
  std::vector<Directory> extra_flags;
  std::string project_dir;
};

Both projects aim to provide C/C++ language servers, but they differ in their implementation and focus. RTags has been around longer and offers more integrations, while ccls is newer, faster, and more actively developed. The code snippets show different approaches to project management, with RTags using a thread-based model and ccls utilizing a more data-oriented structure.

Pros of Bear

• Language-agnostic: Works with any compiler or build system
• Simpler setup: Generates compilation database without complex configuration
• Lightweight: Focuses solely on generating compilation database

Cons of Bear

• Limited functionality: Only generates compilation database, no indexing or code navigation
• Requires manual integration: Must be run separately for each build
• Less real-time: Doesn't provide live updates as code changes

Code Comparison

Bear usage:

bear -- make

RTags usage:

rc -J .
rdm &
rc -w

Bear generates a compile_commands.json file, while RTags creates an in-memory index. Bear is simpler to use but provides less functionality. RTags offers more advanced features like code navigation and refactoring, but requires more setup and ongoing management.

Bear is best for projects needing a quick compilation database, while RTags is suited for developers wanting comprehensive C/C++ code analysis and navigation tools integrated into their workflow.