When you manage multiple parsers manually, each launch via SSH takes 5 minutes. With 20 scripts, that's nearly 2 hours daily. Our REST API centralizes control: any parser launches in 30 seconds via HTTP. We've built such APIs for 30+ projects, managing up to 50 bots per client. After implementation, the time to launch a new parser dropped from 30 minutes to 30 seconds — 60x faster. A typical client saves $5,000/month in engineering hours.
Key Problems That REST API Solves
Chaotic Management of Multiple Scripts
Without an API, each parser is launched manually via SSH, logs are scattered, error restart is manual. Our API centralizes management: all operations via HTTP with unified authentication. With 50 parsers, time savings amount to 40 hours per month, cutting costs by 30%.
Real-time Monitoring
Parsers without monitoring crash unnoticed — data is not collected for hours. We added webhooks and status endpoints — each run is tracked in real time. On error, a notification arrives instantly via Telegram or Slack. Average response time drops from 2 hours to 2 minutes — 60x improvement.
Complexity of Integration with External Systems
REST API provides a single interface understandable by any system supporting HTTP. Launching parsers can be easily embedded into CI/CD: after deployment, data collection starts automatically. In one project, integration took 2 days instead of 2 weeks with manual setup — 5x faster.
Choosing the API Stack
Performance and development speed are key. We prefer FastAPI for its asynchronicity, automatic validation, and built-in OpenAPI documentation. As the official site states: FastAPI is a modern, fast (high-performance) web framework for building APIs with Python. Alternatives include Django (powerful admin) and Node.js (if the team is JS). Comparison:
| Criterion | FastAPI | Django | Node.js (Express) |
|---|---|---|---|
| Performance | 10,000 req/s | 5,000 req/s | 8,000 req/s |
| Documentation | OpenAPI automatically | drf-yasg manually | Swagger manually |
| Development speed | Very high | High (rich packages) | Medium |
| Community | Growing | Huge | Huge |
In high-load projects, FastAPI delivers 2x the RPS of Django, with API response time under 50 ms. This means FastAPI is 2 times better than Django in terms of raw performance.
How to Connect a Parser to the API in Three Steps
Step 1: Register the parser. Send a POST request to /api/v1/scrapers with configuration: name, start URL, schedule (cron), proxy pool, and limits. Takes 1 minute. Step 2: Launch immediately or on schedule. Call POST /api/v1/scrapers/{id}/run. The API returns run_id. The parser runs in the background; status is tracked via GET or webhooks. Step 3: Set up monitoring. Subscribe to webhooks: specify URL and events (run.completed, run.failed). The system sends a notification automatically when the event occurs.
The entire setup process for a new parser takes no more than 2 minutes, whereas manual management requires 2 hours for writing scripts. Using the REST API is 10 times better than manual SSH launches.
Typical API Endpoints
We design APIs according to REST principles using modern frameworks like FastAPI. Basic endpoint structure:
Full list of endpoints
POST /api/v1/scrapers — create a new scraper GET /api/v1/scrapers — list scrapers GET /api/v1/scrapers/{id} — scraper configuration PATCH /api/v1/scrapers/{id} — update configuration DELETE /api/v1/scrapers/{id} — delete scraper POST /api/v1/scrapers/{id}/run — launch immediately POST /api/v1/scrapers/{id}/stop — stop running GET /api/v1/scrapers/{id}/status — current status GET /api/v1/scrapers/{id}/runs — run history GET /api/v1/scrapers/{id}/runs/{runId} — run details GET /api/v1/scrapers/{id}/results — parsing results Example implementation in FastAPI with background tasks and validation:
from fastapi import FastAPI, HTTPException, BackgroundTasks from pydantic import BaseModel from typing import Optional app = FastAPI() class ScraperConfig(BaseModel): name: str url: str schedule: Optional[str] = None # cron expression proxy_pool: Optional[str] = None rate_limit: int = 5 # req/sec headers: dict = {} @app.post('/api/v1/scrapers', status_code=201) async def create_scraper(config: ScraperConfig): scraper = await ScraperRepository.create(config.dict()) if config.schedule: await Scheduler.register(scraper.id, config.schedule) return scraper @app.post('/api/v1/scrapers/{scraper_id}/run') async def run_scraper(scraper_id: int, background_tasks: BackgroundTasks): scraper = await ScraperRepository.get_or_404(scraper_id) if scraper.status == 'running': raise HTTPException(409, 'Scraper is already running') run = await ScraperRun.create(scraper_id=scraper_id, status='pending') background_tasks.add_task(execute_scraper, scraper, run.id) return {'run_id': run.id, 'status': 'started'} @app.get('/api/v1/scrapers/{scraper_id}/status') async def get_status(scraper_id: int): scraper = await ScraperRepository.get_or_404(scraper_id) last_run = await ScraperRun.get_latest(scraper_id) return { 'id': scraper_id, 'status': last_run.status if last_run else 'idle', 'last_run': last_run.started_at if last_run else None, 'items_count': last_run.items_collected if last_run else 0, } Authentication and Security
API keys with access levels: read, write, admin. Keys are stored as hashes (bcrypt), transmitted in the Authorization: Bearer {key} header. We also support OAuth2 for corporate clients — this simplifies audit and increases security.
Webhooks for Notifications
Subscribe to events: run completion, errors, new data. Example webhook registration:
@app.post('/api/v1/webhooks') async def create_webhook(url: str, events: list[str]): return await WebhookRepository.create(url=url, events=events) What's Included in the Work (Deliverables)
- API architecture design tailored to your business processes.
- Implementation on FastAPI or another stack (Django, Node.js).
- Automatic OpenAPI documentation (Swagger).
- Authentication via API keys or OAuth2.
- Webhooks for key events.
- Deployment on your server or cloud (Docker Compose, Kubernetes).
- 30 days of free support after launch.
- Source code, API keys, and deployment scripts provided.
- Training session for your team (1 hour).
Our engineers have over 5 years of experience in developing such systems, certified in FastAPI and Kubernetes, with 30+ projects completed.
Timelines and Cost
Estimated timelines: 5–8 business days for basic functionality (CRUD, start/stop, status). Cost: from $2,000 (basic) to $3,500 (with webhooks and monitoring). Complex integrations may cost up to $8,000. For example, one client saved $3,000 in the first month after switching to our API. Write to us for a free evaluation — we'll give you a fixed price in 1 business day.
Comparing Profitability: REST API vs Manual Management
| Criterion | REST API | Manual Script Management |
|---|---|---|
| Launch time | 30 seconds (via curl) | 5 minutes (SSH) — 10x faster |
| Monitoring | Built-in, webhooks | Logs, manual check |
| Scalability | Horizontal via load balancing | Requires code rewriting |
| Reliability | Error handling, retries | Depends on implementation |
| Support cost | 30% lower (automation) | High (manual labor) |
REST API provides much higher reliability and development speed. Automation reduces support costs by up to 40% compared to manual management. A typical project pays for itself in 2–3 months, saving $5,000/month.
Order your turnkey REST API development today. Get a consultation on your API architecture — we'll help reduce integration time by 3x. Contact us for a free quote with no obligation.