[RFC]: build a developer dashboard for tracking repository build status

[pulkitgupta2]

Full name

Pulkit Gupta

University status

Yes

University name

Rose-Hulman Institute of Technology

University program

Computer Science

Expected graduation

2028

Short biography

I started programming at the age of 15 during the COVID-19 lockdown, guided purely by curiosity and a fascination with how software works. With no formal instruction, I spent countless hours watching tutorials, experimenting with code, and building projects from scratch sometimes coding for over 7 hours a day. Within a year, I had the confidence to take on real-world projects, and a part-time opportunity at a startup soon gave me a glimpse into professional development. That’s when I truly realized how vast the world of software engineering is.

Since then, I’ve developed and deployed production grade systems using a wide range of technologies including JavaScript, TypeScript, React, Node.js, PostgreSQL, Redis, and CI/CD pipelines. My work spans building high-performance web apps, scalable backend architectures, and tools used by thousands of users including AI-driven analytics platforms, trading simulators, and NGO coordination systems. I'm currently pursuing a B.S. in Computer Science at Rose-Hulman Institute of Technology.

I’m passionate about open source, systems design, and numerical computing. I enjoy exploring how code can create real impact at scale, and I’m excited to contribute to the open-source community through Google Summer of Code.

Timezone

Eastern Daylight Time

Contact details

email:pulkit.gupta713@gmail.com,github:pulkitgupta2

Platform

Mac

Editor

VSCode. I've used it since day one and never really felt the need to switch. It does everything I need, so I haven’t explored other editors much maybe I’m missing out, but VSCode just works for me.

Programming experience

Programming Experience

I've been programming professionally for several years and have built full-scale applications used by thousands of users across different domains like education, investing, and language learning.

At Multibhashi, I contributed to an AI-powered document translation platform that processed 5,000+ files monthly. I also worked on a Flutter-based language learning app built for the Government of India, which helped over 500,000 users learn regional languages. You can find the live app here: Ek Bharat Shreshtha Bharat – Bhasha Sangam App. Additionally, I developed a study material publishing tool that allowed bulk uploads of PDFs, quizzes, and videos—accessible on the frontend here: Multibhashi Classes.

At Dexter Ventures, I developed a deal-flow management system that syncs over 50,000+ emails monthly, using a Redis-backed queue and AI tagging to identify and prioritize startup leads. I also built a full-stack transaction visualization and tracking platform with Next.js, AdonisJS, and PostgreSQL, used by internal teams to manage a portfolio of 60+ startups. Since this was an internal tool, I can’t provide public access, but you can find screenshots and highlights on my portfolio: pulkit-gupta.com.

One of my most meaningful projects is Mauka, a full-stack platform I designed to connect 500+ volunteers with 50+ NGOs across India. It includes a web app and a mobile app that automate real-time matching based on skills and availability, significantly reducing manual coordination overhead. You can explore the platform here: volunteermauka.in.

I also developed InvestEd, a virtual trading simulator that enables high school students to learn about investing by trading real-time market data with virtual currency. It supported over 1,000 students and was used in a nationwide competition spanning 50+ schools. The platform is available here: edinvest.in.

JavaScript experience

I have over 4 years of experience with JavaScript, and nearly all the projects I've mentioned above—from AI-powered tools and trading simulators to large-scale dashboards—have been built primarily using JavaScript or its superset, TypeScript.

What I love most about JavaScript is how it adopts familiar constructs from languages like Java or C—such as braces for code blocks and structured syntax—but without the heavy boilerplate. When you're building scalable systems, it's important to follow solid practices like modularization, abstraction, and clean separation of concerns. JavaScript supports these patterns well, especially with features like object-oriented programming (classes, inheritance), closures, higher-order functions, and flexible module systems—without the complexity that languages like Java or C++ often introduce.

That said, these same features can quickly become pain points if not used carefully. My least favorite aspect of JavaScript is its lack of strict typing, which can make large codebases fragile and harder to maintain. That’s why I almost always use TypeScript—it brings the type safety and tooling support that JavaScript lacks by default.

On a deeper level, another challenge is how JavaScript handles equality comparisons (== vs ===) and type coercion. These quirks often lead to unexpected behavior and subtle bugs—something that can be especially risky in production-scale applications.

Node.js experience

I've used Node.js as my primary backend runtime for over 4 years not just because it's JavaScript based, but because it aligns well with how I think about building fast, event-driven systems. Beyond writing APIs, I've come to appreciate how flexible and unopinionated Node.js is. it gives me full control over how I architect projects.

Over time, I’ve developed my own structure for organizing Node.js codebases around modularity, domain-driven design, and clear separation of concerns (routing, services, data access, etc.). I’ve also integrated tools like Jest for testing, ESLint/Prettier for consistency, and built CI/CD pipelines to auto-deploy apps via GitHub Actions and PM2 on cloud servers.

I’ve worked with multiple frameworks on top of Node.js—like Express, AdonisJS, and NestJS—depending on the use case.

Node.js has also taught me a lot about performance bottlenecks—how to handle async concurrency, when to offload work to workers, and how to design for fault tolerance.

C/Fortran experience

I don't have any experience with Fortran.

As for C, I haven’t used it in any professional or real-world projects yet, but I’m currently taking a college course that goes in-depth into C programming and low-level systems concepts. Through this, I’ve built a solid foundation in areas like memory management, pointers, bitwise operations, and working directly with the stack/heap. I’m confident in my understanding of core C fundamentals and how low-level concepts tie into system performance and efficiency.

Interest in stdlib

One of the main reasons I chose stdlib as one of my GSoC applications is how strongly maintained and well-engineered it is. The level of testing, the consistency in design, and the high standards it sets for code quality are unlike anything I’ve seen in other open-source projects. It made me realize how important things like clean code, rigorous testing, documentation, and structure really are especially when you're building something at scale or for long-term use.

Ever since I started coding, I haven’t really had mentors. Most of what I’ve learned has come from exploration, mistakes, and curiosity. But at this point in my journey, I’m looking to learn from the best. That’s what excites me most about contributing to stdlib: the opportunity to work within a codebase that sets a high bar, and to grow by understanding how professionals think about architecture, maintainability, and precision.

Of course, what also makes stdlib stand out is not just how it's built but what it offers. The fact that it brings scientific computing, numerical methods, and advanced statistical tools into the JavaScript ecosystem is huge. It fills a major gap and opens the door to building serious tools like simulation engines, educational apps, and data analysis platforms right in the browser. That’s exactly the kind of impact I want to be part of.

Version control

Yes

Contributions to stdlib

I haven’t been able to make any significant contributions yet. So far, I’ve raised one main pull request, which is still pending review, along with a few others for minor documentation and formatting fixes. Here's the list of my contributions so far:

https://github.com/stdlib-js/stdlib/pulls?q=is%3Apr+author%3Apulkitgupta2+

stdlib showcase

stdlib Showcase

I created a Monte Carlo Simulation Playground to demonstrate practical use cases of the stdlib.

🔗 GitHub Repo
🔗 Demo

Summary of stdlib Usage:

• Random Number Generation
  Utilized @stdlib/random/base/uniform for robust and reproducible random sampling.

• Statistical Computation
  Used @stdlib/stats/mean and @stdlib/stats/variance to compute the average and variability of steps taken in the Gambler’s Ruin simulation, showcasing stdlib’s statistical analysis capabilities.

• Mathematical Functions
  Applied @stdlib/math/base/special/log1p to generate log-transformed insights of starting funds and goals, supporting future strategy extensions (e.g., Martingale or log-scale modeling).

Modules Implemented:

• Gambler’s Ruin – Simulates and visualizes the probability of success or bankruptcy over repeated bets.
• Pi Estimator – Estimates the value of π via random sampling inside a unit square.

Goals

Abstract

The stdlib project encompasses over 3,500 repositories orchestrated through a centralized repository. While orchestration largely works as intended, build failures do occur, and quickly detecting and resolving them is critical to prevent downstream breakages and ensure ecosystem integrity. This project aims to develop a comprehensive build status dashboard that will provide real-time visibility into repository build statuses across the stdlib ecosystem.

The dashboard will allow stdlib developers to quickly identify failing builds, access relevant error information, and navigate to associated resources. By implementing an efficient backend with a hybrid caching architecture and a responsive, information-dense frontend, the project will create a developer tool that significantly improves the monitoring and maintenance workflow for the stdlib ecosystem.

Goals

1. Build Status Visualization: Create a dashboard displaying real-time build status for all stdlib repositories
2. Failure Detection: Implement prioritized views to highlight failing builds that require attention
3. Repository Filtering: Support filtering repositories by build status, owner, and other attributes
4. Quick Navigation: Provide direct links to repository resources and build artifacts
5. Historical Analysis: Create visualizations for historical build data and success rate trends
6. Performance Optimization: Ensure the system remains responsive even with 3,500+ repositories
7. Developer Experience: Design an intuitive interface optimized for developer workflows

Backend

Possible Approach for Backend Implementation:

Approach 1: Full Pre-processing via Cron Job

This approach involves pre-generating all dashboard data through a nightly job and serving exclusively from static (JSON) files.

Advantages:

• Zero database load during operating hours
• Predictable performance for all API responses
• Simple API implementation (serve pre-built files)

Disadvantages:

• Processing 4,000+ repositories could become memory-intensive
• Cannot show updates until next rebuild

Approach 2 (Chosen Approach): Partial Pre-processing via Cron Job and Caching

This approach combines scheduled pre-processing of critical summary data with server-side caching for detailed information.

Advantages:

• Balanced database load distribution
• Fresher data than full pre-processing
• Adaptive to actual usage patterns

Disadvantages:

• More complex implementation than other approaches
• Requires careful cache invalidation logic
• Needs proper cache infrastructure (Redis)

Detailed Implementation Plan for Approach 2

1. Data Storage Strategy
   a. File System Cache

/cache 
├── summary.json # Overall statistics 
├── failing_repositories.json # List of failing repositories 
├── repository_index.json # Minimal data for all repositories 
└── timestamp.json # Last update timestamp

b. Redis Cache
For API query results with appropriate TTLs:

repos:{status}:page:{page}:size:{pageSize} # Repository listings (1 hour TTL) 
repo:{id} # Repository details (4 hour TTL) 
repo:{id}:workflow-runs # Workflow history (24 hour TTL)

2. SQL Queries

Overall Stats Query

SELECT 
    COUNT(*) AS total_repositories,
    SUM(CASE WHEN latest_status = 'success' THEN 1 ELSE 0 END) AS passing_count,
    SUM(CASE WHEN latest_status = 'failed' THEN 1 ELSE 0 END) AS failing_count,
    SUM(CASE WHEN latest_status = 'running' THEN 1 ELSE 0 END) AS running_count
FROM (
    SELECT 
        r.id, 
        COALESCE(wr.conclusion, wr.status) AS latest_status
    FROM stdlib_github.repository r
    LEFT JOIN LATERAL (
        SELECT 
            conclusion, 
            status
        FROM stdlib_github.workflow_run
        WHERE repository_id = r.id
        ORDER BY created_at DESC
        LIMIT 1
    ) wr ON true
) latest_status;

Failing Repositories Query

SELECT 
    r.id AS repository_id,
    r.owner,
    r.name,
    r.url,
    wr.id AS workflow_run_id,
    wr.name AS workflow_name,
    wr.status,
    wr.conclusion,
    wr.html_url AS workflow_url,
    wr.updated_at
FROM 
    stdlib_github.repository r
JOIN LATERAL (
    SELECT 
        id, name, status, conclusion, html_url, updated_at
    FROM stdlib_github.workflow_run
    WHERE repository_id = r.id
    ORDER BY created_at DESC
    LIMIT 1
) wr ON (wr.conclusion = 'failure' OR wr.status = 'failed')
ORDER BY wr.updated_at DESC;

Paginated Repository Query

SELECT 
    r.id AS repository_id,
    r.owner,
    r.name,
    r.url,
    wr.id AS workflow_run_id,
    wr.name AS workflow_name,
    wr.status,
    wr.conclusion,
    wr.html_url AS workflow_url,
    wr.updated_at,
    COALESCE(c.statements, 0) AS coverage_statements
FROM 
    stdlib_github.repository r
LEFT JOIN LATERAL (
    SELECT 
        id, name, status, conclusion, html_url, updated_at
    FROM stdlib_github.workflow_run
    WHERE repository_id = r.id
    ORDER BY created_at DESC
    LIMIT 1
) wr ON true
LEFT JOIN LATERAL (
    SELECT 
        statements
    FROM stdlib_github.coverage
    WHERE repository_id = r.id
    ORDER BY created_at DESC
    LIMIT 1
) c ON true
WHERE 
    1=1
    [filter_conditions]
ORDER BY 
    [sort_field] [sort_direction]
LIMIT $1 OFFSET $2;

3. API Endpoints Design

1. Dashboard Summary Endpoint

GET /api/dashboard/summary

Response Format:

{
  "total_repositories": 3527,
  "passing_count": 3210,
  "failing_count": 142,
  "running_count": 175,
  "last_updated": "2025-04-04T02:00:13.456Z"
}

2. Repository List Endpoint

GET /api/repositories?page=1&pageSize=50&status=failed&owner=stdlib-js&sort=updated_at&order=desc

Query Parameters:

• page: Page number (default: 1)
• pageSize: Results per page (default: 50, max: 100)
• status: Filter by status (optional: 'failed', 'success', 'running')
• owner: Filter by repository owner (optional)
• search: Text search on repository name (optional)
• sort: Sort field (default: 'updated_at', options: 'name', 'owner', 'updated_at', 'status')
• order: Sort direction (default: 'desc', options: 'asc', 'desc')

Response Format:

{
  "total": 142,
  "page": 1,
  "pageSize": 50,
  "totalPages": 3,
  "data": [
    {
      "repository_id": 12345,
      "owner": "stdlib-js",
      "name": "array-base",
      "url": "https://github.com/stdlib-js/array-base",
      "workflow_run_id": 98765,
      "workflow_name": "Test and Build",
      "status": "completed",
      "conclusion": "failure",
      "workflow_url": "https://github.com/stdlib-js/array-base/actions/runs/98765",
      "updated_at": "2025-04-03T18:42:31Z",
      "coverage_statements": 95.6
    },
    // More repositorie
  ]
}

3. Repository Detail Endpoint

GET /api/repository/:id

Response Format:

{
  "repository_id": 12345,
  "owner": "stdlib-js",
  "name": "array-base",
  "description": "Base utilities for stdlib arrays.",
  "url": "https://github.com/stdlib-js/array-base",
  "workflow_run_id": 98765,
  "workflow_name": "Test and Build",
  "status": "completed",
  "conclusion": "failure",
  "workflow_url": "https://github.com/stdlib-js/array-base/actions/runs/98765",
  "updated_at": "2025-04-03T18:42:31Z",
  "coverage": {
    "statements": 95.6,
    "lines": 94.8,
    "branches": 92.3,
    "functions": 97.1
  },
  "latest_tag": {
    "tag": "v0.4.2",
    "created_at": "2025-03-15T10:23:45Z"
  }
}

4. Repository Workflow Runs Endpoint

GET /api/repository/:id/workflow-runs?limit=10

Query Parameters:

• limit: Number of workflow runs to return (default: 10, max: 50)

Response Format:

{
  "repository_id": 12345,
  "workflow_runs": [
    {
      "id": 98765,
      "name": "Test and Build",
      "event": "push",
      "status": "completed",
      "conclusion": "failure",
      "run_number": 42,
      "html_url": "https://github.com/stdlib-js/array-base/actions/runs/98765",
      "created_at": "2025-04-03T18:30:00Z",
      "updated_at": "2025-04-03T18:42:31Z"
    },
    // More workflow runs...
  ]
}

5. Cache Rebuild Endpoint (Admin Only)

POST /api/admin/rebuild-cache

Response Format:

{
  "success": true,
  "message": "Cache rebuild initiated",
  "timestamp": "2025-04-05T14:23:45Z"
}

4. Database Optimization

To ensure optimal database performance:

1. Create efficient indexes:

-- Indexes for workflow_run table
CREATE INDEX IF NOT EXISTS idx_workflow_run_repo_date ON stdlib_github.workflow_run(repository_id, created_at DESC);
CREATE INDEX IF NOT EXISTS idx_workflow_status ON stdlib_github.workflow_run(status, conclusion);

-- Indexes for coverage table
CREATE INDEX IF NOT EXISTS idx_coverage_repo_date ON stdlib_github.coverage(repository_id, created_at DESC);

-- Indexes for repository table
CREATE INDEX IF NOT EXISTS idx_repository_owner ON stdlib_github.repository(owner);

2. Configure connection pooling:

const pool = new pg.Pool({
  host: process.env.DB_HOST,
  port: process.env.DB_PORT,
  user: process.env.DB_USER,
  password: process.env.DB_PASSWORD,
  database: process.env.DB_NAME,
  max: 20,                      // Maximum connections
  idleTimeoutMillis: 30000,     // How long a client is kept idle
  connectionTimeoutMillis: 2000 // Connection timeout
});

Monitoring and Maintenance

1. Cache Hit Rate: Monitor to optimize TTLs
2. Database Query Times: Track slow queries
3. Automated Testing: Ensure all endpoints handle large result sets
4. Log Analysis: Review logs for warning signs

Frontend

Frontend is fairly simple and should take less than 2 weeks to be built.

Technical Stack

Component	Technology	Rationale
Framework	React 18	Recommended in specs, offers component-based architecture ideal for dashboard interfaces
Styling	Tailwind CSS	Recommended in specs, provides utility-first approach for rapid UI development
Build Tool	ESBuild	Recommended in specs, offers extremely fast builds (~10-100x faster than alternatives)
State Management	React Context + Hooks	Lightweight state management without additional dependencies
Data Fetching	React Query	Offers caching, background refresh, and pagination support out of the box
Routing	React Router	Standard routing solution for React applications

Architecture Overview

The frontend will follow a modular architecture with clear separation of concerns:

src/
├── components/       # Reusable UI components
│   ├── common/       # Generic UI elements
│   ├── dashboard/    # Dashboard-specific components
│   ├── repository/   # Repository detail components
│   └── layouts/      # Page layout components
├── hooks/            # Custom React hooks
├── services/         # API service layer
├── contexts/         # React context providers
├── utils/            # Utility functions
├── pages/            # Route components
└── App.jsx           # Entry point

Testing

Testing Strategy

A comprehensive testing approach is essential to ensure the Stdlib Build Status Dashboard functions reliably across all components. This section outlines the testing methodologies for both frontend and backend components.

Backend Testing

1. Unit Testing

Framework: Jest

Focus Areas:

• Database query functions
• Data transformation utilities
• Caching mechanisms
• API endpoint handlers

Testing Approach:

• Mock the PostgreSQL database to test query functions
• Test cache read/write operations with mock Redis/filesystem
• Verify data transformation functions produce expected outputs
• Ensure error handling works correctly for edge cases

Example Test Cases:

• Verify query functions handle null/undefined values correctly
• Test cache key generation and TTL settings
• Ensure pagination logic works with various page sizes
• Validate filters are correctly applied to queries

2. Integration Testing

Framework: Jest + Supertest

Focus Areas:

• API endpoint behavior
• Database interaction
• Caching integration
• Error handling across components

Testing Approach:

• Use test database with controlled test data
• Test complete request/response cycles for each endpoint
• Verify caching behavior with actual Redis instance
• Test error propagation and handling

Example Test Cases:

• Verify repository filtering returns correct datasets
• Test cache hit/miss scenarios
• Ensure database connection errors are handled gracefully
• Validate pagination with real datasets

3. Load Testing

Framework: Artillery or k6

Focus Areas:

• API performance under load
• Database query performance
• Cache effectiveness
• Memory usage under stress

Testing Approach:

• Simulate concurrent users accessing dashboard
• Test with dataset representative of full repository count
• Measure response times under various loads
• Monitor memory usage during sustained activity

Example Test Cases:

• Verify response times remain acceptable with 50+ simultaneous users
• Test cache effectiveness with repeated similar queries
• Ensure stability when handling 3,500+ repositories
• Validate memory usage remains within acceptable limits

4. Database Performance Testing

Tools: pg_stat_statements, explain analyze

Focus Areas:

• Query execution time
• Index effectiveness
• Join performance
• Connection pool behavior

Testing Approach:

• Analyze query execution plans
• Measure query times with various dataset sizes
• Test index performance with large datasets
• Validate connection pool configuration

Example Test Cases:

• Verify indexes improve performance for frequent queries
• Test query performance with full repository dataset
• Ensure connection pool handles peak loads

Frontend Testing

1. Unit Testing

Framework: Jest + React Testing Library

Focus Areas:

• Individual component rendering
• State management
• Event handling
• Utility functions

Testing Approach:

• Test components in isolation
• Verify state changes trigger correct UI updates
• Test event handlers with simulated user interactions
• Validate utility functions with various inputs

Example Test Cases:

• Verify StatusBadge shows correct color for each status
• Test pagination component advances pages correctly
• Ensure filter components update state correctly
• Validate data formatting utilities

2. Component Testing

Framework: Jest + React Testing Library

Focus Areas:

• Component composition
• Parent-child interactions
• Context provider behavior
• Component lifecycle

Testing Approach:

• Test related components together
• Verify data flows correctly between components
• Test context updates propagate to consuming components
• Validate component mounting/unmounting behavior

Example Test Cases:

• Test repository table renders rows correctly
• Verify filter changes update displayed repositories
• Ensure error states display appropriate messages
• Test that loading states render correctly

3. Integration Testing

Framework: Cypress

Focus Areas:

• Complete user flows
• API interactions
• Navigation
• End-to-end functionality

Testing Approach:

• Simulate real user interactions
• Test complete workflows from landing to detail views
• Verify data display matches API responses
• Test navigation between different views

Example Test Cases:

• Navigate from dashboard to repository detail
• Filter repositories by status and verify results
• Search for specific repositories
• Test view toggling between table and card views

4. Accessibility Testing

Tools: jest-axe, Lighthouse

Focus Areas:

• Keyboard navigation
• Screen reader compatibility
• Color contrast
• ARIA attributes

Testing Approach:

• Automated accessibility checks
• Manual testing with keyboard navigation
• Verify screen reader announcements
• Test color contrast compliance

Example Test Cases:

• Ensure all interactive elements are keyboard accessible
• Verify focus states are visible
• Test that data tables have proper headers and relationships
• Validate form inputs have appropriate labels

5. Performance Testing

Tools: Lighthouse, React Profiler

Focus Areas:

• Initial load time
• Rendering performance
• Memory usage
• Bundle size

Testing Approach:

• Measure key performance metrics
• Profile component rendering
• Test with large datasets to identify bottlenecks
• Analyze bundle sizes and dependencies

Example Test Cases:

• Measure time to interactive on dashboard page
• Test rendering performance with 100+ repository rows
• Verify smooth scrolling in virtualized tables
• Validate code splitting reduces initial load time

Cross-Cutting Testing Concerns

1. Mocking Strategy

• API Responses: Use MSW (Mock Service Worker) to intercept and mock API requests
• Database: Use in-memory test database for backend tests
• Time-Based Functions: Mock Date object for predictable time-based tests

2. Test Data Management

• Create representative test datasets with various repository statuses
• Generate data factories for easy test data creation
• Maintain consistent test data across frontend and backend tests

3. Continuous Integration

• Run unit and integration tests on each commit
• Schedule regular load tests on development environment
• Include accessibility checks in CI pipeline
• Generate test coverage reports

4. Test Environment Management

• Maintain separate test database
• Use containerization for consistent test environments
• Configure environment variables for test scenarios

Testing Timeline

1. Setup Testing Infrastructure: During the initial backend and frontend setup
2. Unit Tests: Developed alongside each component implementation
3. Integration Tests: Added after basic functionality is complete
4. Performance and Load Testing: Conducted during the optimization phase
5. Accessibility Testing: Performed during the polishing phase

This comprehensive testing strategy ensures the Stdlib Build Status Dashboard is reliable, performant, and accessible while allowing for efficient development and maintenance.

Conclusion

This initial plan provides a solid foundation for the Stdlib Build Status Dashboard project, outlining both backend and frontend architectures, data processing strategies, and implementation approaches. However, it's important to note that this proposal will require significant refinement and adaptation as we delve deeper into the specific requirements and constraints of the stdlib ecosystem.

The backend implementation follows a hybrid approach that balances database load, data freshness, and system performance through strategic caching. The proposed architecture provides a scalable solution that can handle the current 3,500+ repositories with room to grow. However, the exact data retention policies, cache invalidation strategies, and query optimizations will need to be fine-tuned based on actual usage patterns and specific infrastructure constraints.

The frontend design emphasizes clarity, efficiency, and information density, with a focus on helping developers quickly identify and resolve build failures. While I have designed a comprehensive interface showing a wide range of metrics and visualizations, there are some discrepancies between what's shown in the frontend mockups and what's currently defined in the backend endpoints. These differences highlight areas where we'll need to make explicit decisions about which features to prioritize and implement.

Some specific areas requiring further clarification include:

• The extent of historical data to maintain and visualize
• Which repository metrics are most valuable to stdlib developers
• Specific error log formatting and display requirements
• Authentication and authorization needs for admin functions
• Integration with any existing notification systems

The proposed architecture allows for incremental development and feature expansion. The modular approach will enable me to start with core functionality (build status monitoring and basic filtering) and progressively add more advanced features (analytics, historical data visualization) as the project evolves.

Why this project?

I'm genuinely excited about the Stdlib Build Status Dashboard project because it solves a real problem that developers face daily. Working with over 3,500 repositories is mind-boggling to me, and creating a tool that helps developers quickly spot and fix build failures would make a tangible difference to their workflow.

What draws me particularly to this project is the opportunity to work with the stdlib team, who are known for their commitment to quality and best practices. I've been using JavaScript libraries for years, but I've always wanted to understand how large-scale, high-quality libraries are maintained behind the scenes. Learning how to build scalable systems with proper testing under the guidance of experienced mentors would be incredibly valuable for my growth as a developer.

I'm also excited by the technical challenges—balancing database performance with real-time updates, creating an intuitive UI that handles thousands of repositories, and implementing efficient caching strategies.

Qualifications

I believe I’m well-qualified to execute this proposal because it directly aligns with the kind of full-stack systems I’ve built in the past. I have over 4 years of experience with JavaScript, Node, and PostgreSQL, and have used them extensively in both professional and personal projects.

For example, at Dexter Ventures, I built dashboards to visualize deal-flow and transaction data using React, Next.js, and PostgreSQL, along with real-time components and filtering systems for internal teams. At Multibhashi, I worked on admin tools and analytics dashboards that handled thousands of users, using a similar stack.

I'm also comfortable working with backend tasks like database modeling, REST API design, auth systems, and CI/CD setup. I’ve deployed production apps with Docker, PM2, and Nginx, and understand how to track and report system health and activity metrics—similar to what this dashboard project aims to achieve.

Most importantly, I have a strong interest in writing clean, maintainable code. Contributing to stdlib is an opportunity for me to build something useful, while also learning from one of the most rigorously structured open-source projects out there.

Prior art

This project concept isn’t new, similar dashboards have been implemented in other ecosystems like Jenkins, NPM, GitHub Actions, and CircleCI.

Commitment

I do not have any other commitments at this time and will be fully available throughout the 12-week GSoC program. I plan to dedicate at least 40 hours per week to the project, and I’m excited to make the most of this opportunity by contributing consistently and with focus.

Schedule

12-Week Implementation Plan

Note: Given my extensive experience with this technology stack, there is a high likelihood that I will complete this project ahead of schedule. However, I have outlined a full 12-week plan assuming worst-case scenarios to account for any unforeseen challenges, detailed feedback iterations, or scope adjustments that may arise during the project. This conservative timeline ensures a high-quality, thoroughly tested deliverable regardless of any obstacles encountered.

Phase 1: Core Infrastructure (Weeks 1-3)

Week 1: Backend Foundation

• Set up Fastify server with configuration
• Implement database connection pool
• Create API route structure
• Develop core query functions with optimization
• Set up caching infrastructure (file system and Redis)
• Implement the nightly cron job for data pre-processing
• Deliverable: Working backend with caching infrastructure and basic queries

Week 2: API Development

• Implement repository list endpoint with filtering and pagination
• Create repository detail endpoint
• Develop workflow runs endpoint
• Add admin endpoints for cache management
• Implement error handling and logging
• Create initial test suite for backend
• Deliverable: Complete backend API with comprehensive documentation

Week 3: Frontend Structure & Core Components

• Set up React project with ESBuild and Tailwind CSS
• Implement application routing
• Create reusable UI components (cards, tables, badges)
• Set up React Query for data fetching with caching
• Develop main layout and navigation components
• Deliverable: Frontend application structure with working navigation

Phase 2: Core Functionality (Weeks 4-6)

Week 4: Dashboard Implementation

• Create dashboard overview with summary statistics
• Implement repository table with sorting and pagination
• Add status filtering controls
• Develop search functionality
• Create repository cards for alternative view
• Implement view toggle functionality
• Deliverable: Functioning dashboard with repository listing and filtering

Week 5: Repository Detail View

• Create repository detail page
• Implement workflow run history display
• Add build error log visualization
• Create coverage metrics display
• Implement metadata panels
• Add links to GitHub and artifacts
• Deliverable: Complete repository detail view with all metrics and navigation

Week 6: Integration & Refinement (Midterm)

• Integrate all frontend components with backend
• Implement comprehensive error handling for API requests
• Add loading states and fallbacks
• Optimize component rendering and data fetching
• Conduct testing for core functionality
• Fix any bugs or issues identified
• Deliverable: Fully functional MVP dashboard with integrated features

Phase 3: Advanced Features & Optimization (Weeks 7-9)

Week 7: Analytics Implementation

• Create backend endpoints for historical data
• Implement build trend charts
• Add coverage trend visualization
• Create repository activity displays
• Implement time-based filtering
• Deliverable: Working analytics features with visualizations

Week 8: Performance Optimization

• Implement virtualization for large data tables
• Add code splitting for improved load times
• Optimize database query performance
• Refine Redis caching strategy based on usage patterns
• Set up monitoring for backend performance
• Conduct load testing and address bottlenecks
• Deliverable: Optimized application handling 3,500+ repositories efficiently

Week 9: Advanced Features

• Implement saved filter presets
• Add keyboard shortcuts for navigation
• Create export functionality for data
• Implement advanced sorting options
• Add column customization for tables
• Develop enhanced filtering capabilities
• Deliverable: Advanced user experience features

Phase 4: Polish & Completion (Weeks 10-12)

Week 10: Testing & Quality Assurance

• Implement comprehensive test suite for backend
• Add frontend component tests
• Create end-to-end tests for critical workflows
• Perform accessibility testing and improvements
• Conduct cross-browser compatibility testing
• Deliverable: Well-tested application with high test coverage

Week 11: Documentation & Refinement

• Create user documentation
• Write developer documentation and API references
• Add inline code documentation
• Create setup and deployment guides
• Address feedback from mentors and community
• Deliverable: Comprehensive documentation and refined codebase

Week 12: Final Polish & Project Submission

• Perform final bug fixes and refinements
• Conduct final performance testing
• Prepare presentation and demonstration
• Create project summary and final report
• Package code for final submission
• Deliverable: Production-ready dashboard with complete documentation

Buffer & Extended Goals

If the implementation progresses ahead of schedule, the following extended goals could be considered:

Possible Extensions (Time Permitting)

• Notification System: Email notifications for critical build failures
• Advanced Metrics: Repository health scores and trend analysis
• User Preferences: Personalized dashboard views and settings
• Mobile Optimization: Enhanced responsive design for mobile monitoring
• Build Comparison: Side-by-side comparison of successful and failing builds

Related issues

No response

Checklist

• I have read and understood the Code of Conduct.
• I have read and understood the application materials found in this repository.
• I understand that plagiarism will not be tolerated, and I have authored this application in my own words.
• I have read and understood the patch requirement which is necessary for my application to be considered for acceptance.
• I have read and understood the stdlib showcase requirement which is necessary for my application to be considered for acceptance.
• The issue name begins with [RFC]: and succinctly describes your proposal.
• I understand that, in order to apply to be a GSoC contributor, I must submit my final application to https://summerofcode.withgoogle.com/ before the submission deadline.

[kgryte]

@pulkitgupta2 Thank you for opening this RFC. A few comments/questions:

1. Thank you for the detailed overview and sample PG queries.
2. How are you planning on securing the backend server in order to prevent malicious attacks where someone attempts to crash the server by repeatedly sending detailed data requests?