E-crawl
High-Throughput Distributed Web Crawler
Client Context & Objectives
A high-volume drop-shipping eCommerce company managing an active catalog of over 40,000 product SKUs sourced from external manufacturer partner websites.
The Core Technical Challenge
The client was experiencing severe order cancellation rates and unhappy customers because supplier websites frequently changed stock status without notifications. The existing scraping scripts took over 12 hours to run, failed mid-cycle, and caused expensive out-of-stock orders.
My Role & Architectural Approach
Role: Distributed Systems Architect & Backend Developer. Designed and implemented the crawler architecture, state management, retry logic, and monitoring.
Engineered a distributed crawling cluster utilizing Python, Celery task distribution, and PostgreSQL for state synchronization. Designed an intelligent scheduler with exponential backoff, checkpoint resumption from points of failure, and adaptive rate-limiting to avoid IP bans.
Verified Results & Business Impact
- Reduced crawl cycle time from 12+ hours to under 20 minutes for 40,000+ items—a 97% reduction in processing latency.
- Eliminated inventory mismatch cancellations, protecting merchant account ratings on Amazon and eBay.
- Built automated failure recovery and checkpointing, ensuring zero lost state during unexpected worker restarts.
- Architected for low operational cost using lightweight containerized workers on AWS.
Engineering Highlights
- Distributed worker architecture capable of crawling 40,000+ SKUs in <20 minutes
- Intelligent checkpoint resumption allowing jobs to continue from exact points of failure
- Automated anomaly detection and low-inventory alerting before stock reaches zero
- Comprehensive reporting dashboard with exportable product health metrics
- Optimized AWS infrastructure designed for minimal monthly cloud expenditure
System Interface & Workflow Gallery
Click any screenshot to expand.
