Sizing Hot-Warm Architectures for Logging and Metrics in the Elasticsearch Service on Elastic Cloud
Blog post from Elastic
Elasticsearch Service on Elastic Cloud provides a flexible and efficient solution for handling logging and metrics workloads, offering various hardware choices and deployment templates. Users can opt for different architecture types such as uniform or hot-warm, with each having distinct features and suitability based on storage speed and use-case requirements. The hot-warm architecture uses 'hot' nodes for recent, frequently accessed data with fast SSD storage, and 'warm' nodes for long-term storage using slower, cost-effective storage options. Efficient data management involves optimizing index mappings, maintaining large shard sizes, and compressing JSON data to reduce disk usage. The service is available on AWS, GCP, and now Azure, with node configurations tailored for performance and storage needs. Sizing an Elasticsearch cluster involves estimating data volume, considering storage and query requirements, and choosing appropriate node types, with recommendations for master and coordinating nodes to enhance resilience and performance. Elastic Cloud facilitates easy setup and management, offering a free trial for users to explore its capabilities.
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