The Elasticsearch search engine and database has evolved from a wrapper for Apache Lucene to a full-featured, distributed document data store, becoming the most popular enterprise search engine in the industry. Its adoption is often tied to specific use cases such as primary data storage, analytics, and log collection, where monitoring its health and performance is critical to ensure availability of metric data. Elasticsearch deployments can be simple or complex, with many elements producing metrics that must be monitored in real-time, including health and status metrics like cluster status, nodes, shards unassigned, indexing documents, HTTP connections, query response time, error metrics such as throttling and downtime. Monitoring these metrics is essential to ensure the health and performance of Elasticsearch clusters, and tools like New Relic provide a way to collect and send performance metrics from the cluster to a platform for quick discovery of problems.