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🚀 Hotel Stream-ETL

License: MIT Node.js TypeScript GitHub stars GitHub issues PRs Welcome PostgreSQL

Zero-disk ETL pipeline for streaming massive datasets directly from compressed sources to PostgreSQL via AWS S3

Stream 40GB+ compressed data without ever writing decompressed files to disk. Built for scale, designed for cost-efficiency.

🎯 The Problem We Solve

Traditional ETL pipelines for large datasets:

Download (2.7GB) → Decompress to Disk (40GB) → Upload to S3 → Insert to DB
❌ Disk Space Exhausted
❌ Network Saturated
❌ Slow & Expensive

Hotel Stream-ETL:

Download → Decompress (Memory Only) → S3 → PostgreSQL
✅ Zero Intermediate Disk Writes
✅ 196+ Records/Sec
✅ <500MB Memory
✅ Production-Ready

⚡ Quick Stats

Metric Value
Test Dataset 40GB (2.7GB compressed)
Memory Used <500MB
Insert Rate 196 records/sec
Total Time 1,049 seconds (17.5 min)
Success Rate 100%
Disk Space Required 0 bytes*

*Database storage only

📊 How It Works

┌────────────────────────┐
│  Compressed Source     │
│  (2.7GB zstd file)     │
└───────────┬────────────┘
            │ Stream download
            ▼
┌────────────────────────┐
│  S3 Multipart Upload   │
│  (No disk intermediate)│
└───────────┬────────────┘
            │ Decompress + Stream
            ▼
┌────────────────────────┐
│  PostgreSQL Insert     │
│  (Batch UPSERT)        │
│  196 records/sec       │
└────────────────────────┘

🚀 Features

  • ✅ Pure Streaming - No decompressed files on disk
  • ✅ High Throughput - 196+ records/sec insert rate
  • ✅ Memory Efficient - Peak usage <500MB
  • ✅ Type Safe - Full TypeScript, strict mode
  • ✅ S3 Integration - AWS SDK v3, multipart uploads
  • ✅ Connection Pooling - Optimized PostgreSQL access
  • ✅ Real-time Monitoring - CLI dashboard with stats
  • ✅ Error Handling - Retry logic with backoff
  • ✅ SSL/TLS Support - Secure database connections
  • ✅ Caching - Skip downloads for recent files
  • ✅ Production Ready - Comprehensive logging

📦 Installation

Prerequisites

  • Node.js 18+
  • PostgreSQL 13+
  • AWS Account (S3 access)

Setup

# Clone repository
git clone https://github.com/yourusername/stream-etl.git
cd stream-etl

# Install dependencies
npm install

# Configure environment
cp .env.example .env
# Edit .env with your credentials

# Build
npm run build

# Run
npm run pipeline

# Monitor (in another terminal)
npm run monitor

🔧 Configuration

Create .env file:

# Data Source
KEY_ID=your_api_key_id
API_KEY=your_api_key

# AWS S3
AWS_REGION=us-east-1
S3_BUCKET=your-bucket-name
AWS_ACCESS_KEY_ID=***
AWS_SECRET_ACCESS_KEY=***

# PostgreSQL
DB_HOST=your-db-host
DB_PORT=5432
DB_NAME=your_database
DB_USER=your_user
DB_PASSWORD=***

📖 Documentation

💻 Usage

Full Pipeline

npm run pipeline

Downloads → Decompresses → Uploads to S3 → Inserts to PostgreSQL

Monitor Dashboard

npm run monitor

Real-time statistics and progress

Build Only

npm run build

Type Check

npm run type-check

🏗️ Tech Stack

Component Technology
Language TypeScript 5.0
Runtime Node.js 18+
Cloud AWS S3
Database PostgreSQL 13+
Streaming Node.js Transform streams
Compression zstd

📝 Real-World Example

╔══════════════════════════════════════════════════════════╗
║     Stream-ETL: Hotel Data Sync Pipeline                ║
╚══════════════════════════════════════════════════════════╝

Step 0: Testing database connection...
✓ PostgreSQL connection successful

Step 1: Checking for recent file...
✓ Found recent S3 file (0.0 hours old)

Step 2: (Skipped - using cached)

Step 3: Streaming from S3 and inserting...
✓ Processed 530 batches (106,000 records)

Performance Summary:
  Insert time: 542.18s
  Insert rate: 196 records/sec
  Success rate: 100% (0 failed)
  Total time: 1589.88s

🎯 When to Use

✅ Good for:

  • One-time or infrequent data syncs
  • Fixed-size datasets (GBs to TBs)
  • Constrained disk environments
  • Node.js infrastructure
  • Budget-conscious operations

❌ Consider alternatives for:

  • Complex multi-step transformations → Apache Spark
  • Mission-critical recurring ETL → Apache Airflow
  • Multi-source orchestration → AWS Glue
  • Real-time streaming → Kafka/Flink

🐛 Troubleshooting

No records inserted?

SELECT COUNT(*) FROM hotels;

Connection timeout?

# Verify database connectivity
telnet your-db-host 5432

S3 upload stalled?

# Check S3 bucket access
aws s3 ls s3://your-bucket/

See Troubleshooting Guide for more.

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for:

  • Code standards
  • Development setup
  • Pull request process
  • Reporting issues

Quick start:

git checkout -b feature/your-feature
npm run build
npm run test
git push origin feature/your-feature
# Open PR

📄 License

MIT License - see LICENSE for details

📞 Support

⭐ Recognition

If Stream-ETL helped you, please give us a star!

GitHub stars

📚 Articles & Case Studies

Made with ❤️ for data engineers who care about performance

3. Configure AWS S3

Set up S3 credentials in .env:

AWS_REGION=us-east-1
S3_BUCKET=your-bucket-name
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key

4. Configure WorldOTA API

Add your WorldOTA API credentials in .env:

KEY_ID=your_api_key_id
API_KEY=your_api_key

Complete .env Example

# WorldOTA API
KEY_ID=your_key_id
API_KEY=your_api_key

# AWS S3
AWS_REGION=us-east-1
S3_BUCKET=hotel-dumps
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key

# PostgreSQL
DB_HOST=localhost
DB_PORT=5432
DB_NAME=hotel_sync
DB_USER=postgres
DB_PASSWORD=your_password

Usage

Run the Complete Pipeline

npm run build
npm start

Or with TypeScript directly:

npx ts-node src/pipelines/usage.ts

Usage in Code

import HotelSyncPipeline from "./pipelines/hotelSyncPipeline";

const pipeline = new HotelSyncPipeline({
  keyId: process.env.KEY_ID!,
  apiKey: process.env.API_KEY!,
  s3Config: {
    region: process.env.AWS_REGION!,
    bucket: process.env.S3_BUCKET!,
    accessKeyId: process.env.AWS_ACCESS_KEY_ID!,
    secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY!,
  },
  postgresConfig: {
    host: process.env.DB_HOST!,
    port: parseInt(process.env.DB_PORT || "5432"),
    database: process.env.DB_NAME!,
    user: process.env.DB_USER!,
    password: process.env.DB_PASSWORD!,
  },
  batchSize: 200,
  keepLocalFiles: false,
});

const stats = await pipeline.run();
console.log(stats);

Service Architecture

HotelDumpService

Handles downloading and decompressing hotel dumps from WorldOTA.

const service = new HotelDumpService({
  keyId: "your_key",
  apiKey: "your_api_key",
  downloadDir: "./downloads",
  maxRetries: 3,
});

const { records, stats } = await service.processHotelDump();

PostgresService

Manages PostgreSQL connections and inserts.

const db = new PostgresService({
  host: "localhost",
  port: 5432,
  database: "hotel_sync",
  user: "postgres",
  password: "password",
});

await db.testConnection();
await db.createHotelsTable();
await db.insertHotels(records, { upsert: true });

S3StreamService

Handles S3 operations and streaming.

// Decompress directly to S3
await decompressStreamToS3(compressedPath, s3Config, "hotels.jsonl");

// Stream from S3 with batching
await streamHotelsFromS3(s3Config, "hotels.jsonl", async (batch) => {
  console.log(`Processing batch of ${batch.length}`);
});

HotelSyncPipeline

Orchestrates the complete workflow.

const pipeline = new HotelSyncPipeline(config);
const stats = await pipeline.run();

Database Schema

The pipeline creates a hotels table with the following structure:

CREATE TABLE hotels (
  id SERIAL PRIMARY KEY,
  hotel_id VARCHAR(255) UNIQUE NOT NULL,
  name VARCHAR(500),
  description TEXT,
  country VARCHAR(255),
  state VARCHAR(255),
  city VARCHAR(255),
  zip_code VARCHAR(20),
  address VARCHAR(500),
  latitude DECIMAL(10, 8),
  longitude DECIMAL(11, 8),
  star_rating DECIMAL(2, 1),
  phone VARCHAR(20),
  fax VARCHAR(20),
  website VARCHAR(500),
  email VARCHAR(255),
  check_in_time VARCHAR(10),
  check_out_time VARCHAR(10),
  images TEXT[],
  amenities TEXT[],
  languages TEXT[],
  raw_data JSONB,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Indexes are created on:

  • hotel_id (unique)
  • city
  • country
  • star_rating
  • updated_at

Performance Tips

  1. Batch Size: Adjust batchSize based on memory and network:

    • Small machines: 50-100
    • Normal machines: 200-500
    • Large machines: 1000+

  2. Database Connection Pool: Increase maxConnections for faster inserts (default 20)

  3. AWS Region: Use the same region as your database if possible to reduce latency

  4. Disk Space: Ensure at least 5GB free space for download/decompression

Monitoring & Debugging

The pipeline provides detailed logs:

Step 0: Testing database connection...
✓ PostgreSQL connection successful: 2024-02-05 12:00:00
✓ Hotels table ready

Step 1: Fetching and downloading dump file...
Downloading dump file...
  Progress: 45.2%
✓ Dump file downloaded: ./downloads/hotel_dump_1770163443318.jsonl.zst (2345.67MB)

Step 2: Decompressing and uploading to S3...
Decompressing and uploading to S3...
  Upload progress: 78.3%
✓ File decompressed and uploaded to S3: s3://bucket/hotel_dump_1770163443318.jsonl

Step 3: Streaming from S3 and inserting into PostgreSQL...
  Inserted: 1000 records
  Inserted: 2000 records
✓ Streamed 50000 hotel records from S3

Performance Summary:
  Download:        120.45s
  Decompress+S3:   45.20s
  DB Insert:       180.30s
  Total:           345.95s

Data Summary:
  Total Records:   50000
  Successful:      49850
  Failed:          150
  Success Rate:    99.70%

Troubleshooting

Connection Refused

  • Check PostgreSQL is running: psql -h localhost
  • Verify credentials in .env

S3 Upload Failed

  • Verify AWS credentials
  • Check bucket exists and is accessible
  • Ensure region is correct

Out of Memory

  • Reduce batchSize
  • Check available system memory
  • Close other applications

Slow Database Inserts

  • Increase maxConnections in PostgreSQL config
  • Check database disk space
  • Verify network latency to database

License

MIT

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Streaming ETL pipeline for PostgreSQL with AWS S3 (TypeScript, Node.js, zero intermediate storage)

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