Core Analysis¶

Problem Focus: The platform exposes plugin APIs and PSI/indexing infrastructure so a new language can gain completion, navigation and refactoring, but implementation requires significant technical investment.

Key Technical Steps (summary)¶

1. Syntax and PSI: Define grammar or plug an existing parser and build PSI node model.
2. Register file types/highlighting: Use extension points for file types, syntax highlighting and comment support.
3. Indexing and parsing: Implement indexers to enable fast search and find-usages.
4. Editor services: Implement completion, navigation, refactorings and inspections.
5. Build/test integration: Add support for common build systems, run configurations and write integration tests.

Advantages and Constraints¶

• Advantages: Reuse mature platform refactorings, debugger integration and UI components; incremental indexing helps responsiveness in large repos.
• Constraints: Requires deep PSI/API knowledge and JVM (Kotlin/Java) skills; watch index size, performance and cross-version compatibility.

Important Notice: Prefer platform indexing and PSI APIs over custom scans to get performance and consistency benefits.

Summary: A high-value route to enterprise-grade language support, but budget time for development, testing and performance tuning.

Core Analysis¶

Problem Focus: The platform is resource-intensive; in low-spec machines or small CI containers you must apply constraints to reduce overhead, but some limits are unavoidable.

Technical Analysis¶

• Hard requirements: Indexing, PSI and the Swing UI incur steady memory and disk costs; recommended minimum is ~8GB RAM.
• Possible optimizations: Shallow clones (--depth 1), building only required modules, mounting host .m2 cache in Docker, disabling parallel module compilation and lowering heap settings.

Practical Recommendations¶

1. Modular builds: Compile/test only needed modules and use incremental build scripts.
2. CI optimizations: Use prebuilt images, reuse caches, and map UID to avoid permission issues and repeated downloads.
3. Consider remote development: Run the heavy IDE/indexing on a stronger host or CI and access remotely (e.g., JetBrains Gateway) to offload local resources.

Important Notice: Even with optimizations, interactive features (indexing, live inspections) may lag or cause OOME on low-end machines.

Summary: Shallow cloning, selective builds and cache reuse help, but for full interactive experience use higher-spec hosts or remote development.

Core Analysis¶

Problem Focus: Source builds are commonly blocked by environment mismatch, insufficient resources, and large repository/permission issues, causing build failures or long runtimes.

Technical Analysis¶

• Environment dependencies: You must use JetBrains Runtime 21 (non-JCEF); an incorrect JBR causes runtime/compile issues.
• Resource constraints: README recommends at least 8GB RAM; packaging and tests consume more disk and memory.
• Repo/permission concerns: Full history is large; Android modules are fetched via ./getPlugins.sh; CI/Docker require UID and Maven cache handling.

Practical Recommendations¶

1. Follow README precisely: Configure JBR, MAVEN_REPOSITORY, and Windows core.longpaths.
2. Use shallow clones or Docker builds: git clone --depth 1 or Docker with mounted host .m2 cache and UID mapping.
3. Tune build params: Toggle parallel module compilation and adjust heap (user-local heap) per available RAM.

Important Notice: Full test suites and packaging still require significant resources; reuse images and caches in CI.

Summary: Preparing the environment per docs, using shallow clones/Docker and caching dramatically reduces build cost and failures.