Async Endpoints: When and Why They Matter

Current Synchronous Limitations

Your current /api/scrape endpoint is synchronous—the client waits for the entire scraping to complete:

POST /api/scrape
// Client waits... (5-30 seconds)
// Returns: { success: true, data: {...} }

Problems with this approach:

⏰ Timeout issues: Long-running scrapes (30+ seconds) cause HTTP timeouts
🔒 Resource blocking: Client connection stays open, wasting server resources
📱 Poor UX: User sees "loading..." for 30+ seconds
🚫 Browser pool exhaustion: Limited concurrent requests due to connection limits

Async Endpoints: Key Benefits

1. Immediate Response + Background Processing

Step 1: Submit job

POST /api/scrape-async
Response: { "success": true, "jobId": "abc123", "status": "queued" }

Step 2: Check status

GET /api/jobs/abc123
Response: { "jobId": "abc123", "status": "processing", "progress": "40%" }

Step 3: Get results

GET /api/jobs/abc123
Response: { "jobId": "abc123", "status": "completed", "data": {...} }

2. Critical Use Cases Where Async Is Essential

A. Batch/Bulk Scraping

Scrape 100 product pages—would take 30+ minutes synchronously.

POST /api/scrape-batch
{
  "urls": ["url1", "url2", ...], // 100 URLs
  "selectors": {...}
}
Response: { "batchId": "batch456", "estimatedTime": "25 minutes" }

B. Complex Multi-Step Scraping

POST /api/scrape-flow
{
  "steps": [
    { "action": "login", "credentials": {...} },
    { "action": "search", "query": "laptops" },
    { "action": "paginate", "pages": 10 },
    { "action": "extract", "selectors": {...} }
  ]
}

C. Scheduled/Recurring Scraping

POST /api/scrape-schedule
{
  "url": "https://prices.com/product/123",
  "schedule": "daily",
  "selectors": { "price": ".price" }
}

When Sync vs Async Makes Sense

Use Case	Recommended	Why
Single page, simple selectors	Sync	Fast (<10s), immediate results needed
Price monitoring dashboard	Sync	Real-time data for user interaction
Bulk product catalog	Async	Long-running (minutes/hours)
Scheduled monitoring	Async	Background processing
User-initiated reports	Async	Better UX with progress updates
API integrations	Both	Sync for real-time, async for batch

Implementation Patterns

Pattern 1: Hybrid Approach (Recommended)

POST /api/scrape?async=true     // Optional async flag
POST /api/scrape                // Default sync behavior

Pattern 2: Separate Endpoints

POST /api/scrape                // Sync (current)
POST /api/scrape-async          // Async with job queue
GET  /api/jobs/:jobId           // Job status/results

Pattern 3: WebSocket Updates

POST /api/scrape-async
// WebSocket: { jobId: "abc", status: "processing", progress: 60% }
// WebSocket: { jobId: "abc", status: "completed", data: {...} }

For Your MVP: Recommendation

Start with sync (what you have), add async later when you need:

Keep sync for now—works great for single-page scraping
Add async when you encounter:
- Scraping taking >30 seconds consistently
- Users requesting batch processing
- Timeout errors becoming frequent
- Need for scheduling/automation

Simple async implementation:

Use Redis/database for job queue
Background worker processes jobs
REST endpoints for job status/results

Bottom line:
Async endpoints become important when scraping duration exceeds user patience (~10-15 seconds) or when you need batch/scheduled processing. For your current single-page use case, sync is perfectly fine.

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