Deploying an Elasticsearch cluster for logs and metrics involves careful consideration of benchmarking and sizing to ensure optimal performance and resource allocation. The process begins with determining the cluster's throughput capacity and assessing available resources while considering constraints such as hardware, company strategy, and service quality. Key components influencing performance include storage, memory, compute resources, and network capabilities. For effective cluster sizing, particularly in log and metric use cases, it is important to calculate data volume, retention periods, and the necessary number of data nodes, with provisions for failover capacity. Benchmarking is crucial to validate cluster performance under real-world conditions, using tools like Rally to test indexing and search capabilities and determine optimal configurations for bulk size and client numbers. Ultimately, understanding these metrics allows for informed adjustments to enhance indexing speed and overall cluster efficiency, ensuring the infrastructure meets both current and future demands.