Cloud platforms offer unique advantages for search infrastructure. This article explores architecture patterns for building cloud-native search services on AWS. We cover using EC2 instances with local NVMe storage for low-latency index access, Application Load Balancers for query distribution, and Auto Scaling Groups for demand-based capacity. Data pipeline patterns include using Kinesis for real-time document ingestion, S3 for index snapshots, and Lambda for async document processing. Cost optimization strategies cover reserved instances for baseline capacity, spot instances for batch indexing, and S3 Intelligent-Tiering for backup storage. Monitoring and observability use CloudWatch custom metrics, X-Ray for distributed tracing, and SNS alerts for SLA breaches. The article includes a complete Terraform configuration for deploying a production Solr cluster.
Tag: scalability
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Web Crawling at Scale: Architecture and Optimization Strategies
Building a web crawler that can process millions of pages requires careful architectural decisions. This article covers the multi-worker architecture pattern where a coordinator distributes URLs from a priority queue while workers fetch and process pages concurrently. Key optimization strategies include DNS caching, connection pooling, adaptive politeness delays, and incremental crawling using ETag and Last-Modified headers. We examine content extraction using techniques like DOM-based article extraction, boilerplate removal, and structured data parsing from JSON-LD and microdata. Error handling patterns cover retry strategies, circuit breakers for unresponsive hosts, and graceful degradation when facing rate limits or CAPTCHAs. The article includes real benchmarks showing how these optimizations reduced crawl time from 48 hours to 6 hours for a 500,000-page site.