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: cloud
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Containerizing Search: Docker and Kubernetes for Solr Deployments
Container orchestration has transformed how we deploy and manage search infrastructure. This guide covers Docker best practices for Apache Solr, including image optimization, volume management for index persistence, and health check configuration. We then move to Kubernetes deployments using StatefulSets for Solr nodes, persistent volume claims for index storage, and horizontal pod autoscaling based on query load. Advanced topics include implementing rolling updates with zero downtime, configuring resource limits and requests for predictable performance, and setting up monitoring with Prometheus and Grafana. Production patterns cover multi-AZ deployments, backup strategies using Kubernetes CronJobs, and disaster recovery procedures.