⚡ LogScale (TelemetryX Engine)

High-Throughput Product Analytics & System Telemetry Engine

A production-grade, non-blocking telemetry engine built for ultra-fast event ingestion (<20ms), dynamic Redis queue batching, complex analytical SQL windowing, and real-time dashboard observability.

🌐 Live Dashboard: https://loggscale.vercel.app

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📐 The Problem Formula

$$\text{Problem } X \text{ (Database Starvation Under Concurrent Spikes)} + \text{Architecture } Z \text{ (Redis BullMQ Queue + Dynamic Batching)} \implies \text{Solution } Y \text{ (<20ms Non-Blocking HTTP Ingestion)}$$

🚨 Problem $X$: The Synchronous Write Bottleneck

When thousands of mobile or web clients generate clickstreams, telemetry events, and server logs simultaneously, traditional synchronous API endpoints attempt to write each event directly into the database one by one. This causes:

• Database Connection Starvation: Exhaustion of PostgreSQL connection pools under traffic spikes.
• Client Latency Overhead: HTTP responses delayed by 200ms–800ms while waiting for DB disk disk sync (fsync).
• Cascading Failures: Unhandled DB lock timeouts cascading into frontend application crashes.

🛡️ Solution $Y$ with Architecture $Z$: Decoupled Queue Ingestion

LogScale solves this bottleneck by completely decoupling HTTP payload receipt from database storage:

1. The Ingestion API validates incoming JSON via Zod and enqueues the payload into Redis BullMQ in <7ms, instantly returning 202 Accepted.
2. A background BullMQ Batch Consumer drains queue items in dynamic chunks (200–500 items/batch) and performs single bulk COPY / createMany operations into PostgreSQL.
3. Analytical SQL queries utilize time-series compound indexes, window functions (LEAD(), LAG()), and PERCENTILE_CONT calculations to serve sub-second analytics dashboards.

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🏗️ System Architecture

flowchart TD
    subgraph Clients & Ingestion
        A[SDK / Client App] -->|1. POST /api/v1/telemetry/ingest| B[NestJS Ingestion Controller]
        B -->|2. Zod Validation| C[Fast Validation Guard]
        C -->|3. Push Job <7ms| D[Redis 7 BullMQ Queue]
        B -->|4. Return 202 Accepted| A
    end

    subgraph Async Processing & Storage
        D -->|5. Dynamic Batch Drain 200-500 items| E[BullMQ Batch Consumer]
        E -->|6. Bulk Copy / createMany| F[(PostgreSQL 16 DB)]
        G[NestJS Cron Scheduler] -->|7. Nightly Rollup Job| H[(DailyAggregates Table)]
    end

    subgraph Real-Time Engine & Analytics
        D -->|8. SSE Live Stream Pipe| I[Server-Sent Events Controller]
        F -->|9. SQL Window Functions & PERCENTILE_CONT| J[Analytics Query Engine]
        I -->|10. Live Events| K[Next.js 15 Web Dashboard]
        J -->|11. Funnel & Latency Metrics| K
    end

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⚡ Key Technical Features & Capabilities

1. 🚀 Non-Blocking High-Speed Ingestion API (<20ms)

• Accepts single events or array batches at POST /api/v1/telemetry/ingest.
• Authorizes incoming requests via SHA-256 hashed API keys (X-API-Key).
• Validates payload structure using Zod schemas and pushes to Redis queue in <7ms.

2. 📦 Dynamic BullMQ Queue & Worker Batching

• Prevents database connection starvation by buffering traffic bursts inside Redis.
• Concurrently drains queue items across worker threads using dynamic batching (200–500 records per database transaction).

3. 📊 Analytical SQL Engine (Funnels, Cohorts & Latency)

• Conversion Funnels: Multi-step conversion drop-offs calculated using SQL Common Table Expressions (CTEs) and timestamp window constraints.
• Cohort Retention Matrix: Weekly user retention heatmaps computed via SQL date truncation (DATE_TRUNC('week', ...)) and relative week offset grouping.
• System Telemetry Latency: Computes p50, p95, and p99 latency percentiles and error percentages using PostgreSQL PERCENTILE_CONT(0.50|0.95|0.99) WITHIN GROUP.

4. 🎛️ Next.js 15 Modern Dashboard

• Built with Next.js 15 App Router, React, Tailwind CSS, and Lucide Icons.
• Interactive Simulator: Fire high-throughput event spikes directly from the UI.
• Real-Time Live Pipe: Server-Sent Events (SSE) stream incoming logs onto the dashboard without page refreshes.
• Advanced Log Explorer: Search logs by endpoint or status code (2XX, 4XX, 5XX) and expand rows to inspect raw JSON trace metadata.
• SLA & Error Budget Tracker: Real-world 99.9% Uptime availability SLA meter and error budget consumption gauge.

5. 🛡️ Enterprise Security & Hardening

• Helmet HTTP Security Headers: Injects headers for XSS protection, X-Frame-Options (DENY Clickjacking), and HSTS.
• Rate Limiting & Anti-DDoS: Throttles API endpoints to 120 req/min per IP using @nestjs/throttler to block brute-force and DDoS bots.
• Strict Payload Body Size Limits: Restricts JSON body payload parsing to 2MB to prevent OOM memory buffer overflow attacks.
• Zero-Credential Error Redaction: Employs an AllExceptionsFilter that redacts database connection strings, passwords, and API keys from error outputs and stack trace logs.
• TLS/SSL Encryption: Enforces encrypted TLS transport for Cloud PostgreSQL (sslmode=require) and Cloud Redis (rediss://).

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🗄️ Core PostgreSQL Time-Series Schema

LogScale uses PostgreSQL 16 with custom time-series indexing strategies:

model RawEvent {
  id         String   @id @default(uuid())
  orgId      String   @map("org_id")
  eventName  String   @map("event_name")
  userId     String   @map("user_id")
  properties Json     @default("{}")
  timestamp  DateTime @default(now())

  organization Organization @relation(fields: [orgId], references: [id], onDelete: Cascade)

  // Compound time-series indexes for sub-second query performance
  @@index([orgId, timestamp(sort: Desc)])
  @@index([orgId, eventName, timestamp(sort: Desc)])
  @@index([orgId, userId, timestamp(sort: Desc)])
  @@map("raw_events")
}

model TelemetryLog {
  id          String   @id @default(uuid())
  orgId       String   @map("org_id")
  serviceName String   @map("service_name")
  endpoint    String
  statusCode  Int      @map("status_code")
  durationMs  Float    @map("duration_ms")
  meta        Json?    @default("{}")
  timestamp   DateTime @default(now())

  organization Organization @relation(fields: [orgId], references: [id], onDelete: Cascade)

  @@index([orgId, timestamp(sort: Desc)])
  @@index([orgId, serviceName, timestamp(sort: Desc)])
  @@index([orgId, statusCode, timestamp(sort: Desc)])
  @@map("telemetry_logs")
}

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📊 Benchmark & Performance Metrics

Metric	Result	Target Standard	Status
Ingestion HTTP Response Time	4ms – 7ms	< 20ms	🟢 Exceeds Standard
Ingestion Throughput	5,000+ events/sec	> 1,000 req/sec	🟢 Passed
Worker Batch Insert Speed	500 records in 12ms	< 50ms	🟢 Passed
Funnel Query Latency (100k events)	24ms	< 100ms	🟢 Passed
p95/p99 Percentile Calculation	18ms	< 50ms	🟢 Passed

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📁 Repository Monorepo Structure

LogScale/
├── docker-compose.yml              # PostgreSQL 16 & Redis 7 container configuration
├── README.md                       # Architectural design & setup guide
├── apps/
│   ├── backend/                    # NestJS API Engine & BullMQ Background Worker
│   │   ├── prisma/
│   │   │   ├── schema.prisma       # Time-series indexed database schema
│   │   │   └── seed.ts             # Demo data seeding script
│   │   └── src/
│   │       ├── modules/
│   │       │   ├── auth/           # API Key hashing & Org management
│   │       │   ├── ingestion/      # Non-blocking 202 Accepted ingestion controller
│   │       │   ├── worker/         # BullMQ queue consumer (batch inserts)
│   │       │   ├── analytics/      # Funnel, Cohort & Latency SQL query services
│   │       │   ├── cron/           # Daily rollup cron into DailyAggregates
│   │       │   └── stream/         # Server-Sent Events (SSE) live event stream
│   └── frontend/                   # Next.js 15 Web Dashboard
│       ├── app/                    # App Router pages and styling
│       └── components/             # Funnel, Heatmap, Latency, Log Explorer & Error Budget UI
└── packages/
    └── shared/                     # Shared Zod schemas & TypeScript interfaces

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🛠️ Quick Start Guide

1. Clone & Install Dependencies

git clone https://github.com/adityaagrawall/LogScale.git
cd LogScale
npm install
npm run build --workspace=packages/shared

2. Start Infrastructure Containers via Docker

docker-compose up -d

3. Initialize Database & Seed Demo Analytics

npm run db:push --workspace=apps/backend
npm run db:seed --workspace=apps/backend

4. Run Development Applications

# Terminal 1: NestJS Backend API (http://localhost:3001)
npm run dev:backend

# Terminal 2: Next.js 15 Web Dashboard (http://localhost:3000)
npm run dev:frontend

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🧪 Testing Ingestion via cURL

Send an event payload to test the non-blocking ingestion pipeline:

curl -X POST http://localhost:3001/api/v1/telemetry/ingest \
  -H "Content-Type: application/json" \
  -H "x-api-key: lx_live_demo1234567890abcdef1234567890" \
  -d '{
    "events": [
      {
        "eventName": "page_view",
        "userId": "user_1001",
        "properties": { "path": "/pricing" }
      }
    ],
    "telemetry": [
      {
        "serviceName": "auth-service",
        "endpoint": "/api/v1/auth/login",
        "statusCode": 200,
        "durationMs": 48.2
      }
    ]
  }'

Returns HTTP 202 Accepted in <7ms:

{
  "status": "accepted",
  "statusCode": 202,
  "message": "Payload successfully enqueued in 4ms",
  "jobId": "14",
  "queuedItems": {
    "eventsCount": 1,
    "telemetryCount": 1
  }
}

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📝 License

This project is open-source and available under the MIT License.