News Crawler Pipeline
Data PipelineAdvancedOrg criticalScheduled scraper pipeline with URL-frontier queuing, document storage, full-text indexing, and CDN-cached read API. Use for aggregators, content trackers, or data harvesting systems.
Recommended for: Web crawlers
Use Case
Web crawlers, content aggregation, search indexing
Best Fit Scenarios
- Web crawlers
- Content aggregation
- Search indexing
Stack Breakdown
Architecture Layers
Components by Category
async
backend
database
infra
Why This Topology Works
Scheduler distributes crawl jobs across workers via Redis queue. MongoDB stores raw content while Elasticsearch indexes for fast retrieval. CDN caches API responses.
Scaling Notes
Workers scale based on queue depth. MongoDB shards by domain for write distribution. Elasticsearch replicas handle search traffic.
Observability
Crawl success rate per domain. Queue processing latency. Elasticsearch indexing lag monitoring.
Typical Bottlenecks
- Queue lag, retry storms, and DLQ growth during incidents
- Service latency and timeout behavior on critical routes
- Write amplification and query contention on primary stores
Async Flow and Reliability
User-facing operations remain synchronous while long-running work moves through queues or streams. Workers consume jobs independently with retry and failure isolation, improving resilience under burst load.
Upgrade Path
Harden each domain with clear ownership, enforce SLO budgets, and adopt multi-region or active-passive failover where downtime costs are high.
Operating Envelope
Complexity is marked as Advanced with an intended scope of Org critical. Use this as a planning baseline before adapting the template to your reliability and team constraints.