Elasticsearch, originally a search engine, has evolved into a highly efficient, scalable datastore that can process and retrieve time series data, which are data points associated with specific timestamps. While specialized databases exist for time series data, Elasticsearch can handle it using queries and aggregations, particularly useful in applications like server monitoring. A case study using Metricbeat, a tool that ships data to Elasticsearch, illustrates how to query time series data, with documents containing time series data and metadata. The process involves using Elasticsearch queries to filter and aggregate data efficiently, such as employing date histogram aggregations for downsampling data to a coarser granularity, which is often necessary for visualization or further processing. For specific metrics like CPU usage and network traffic, Elasticsearch's aggregation capabilities allow for detailed analysis and calculations, including downsampling and deriving rates per time unit. This functionality is facilitated by Elasticsearch's support for queries and aggregations, which can be tailored to specific time intervals and conditions, demonstrating its flexibility and power in managing time series data within a unified framework.