Felix Barnsteiner discusses the use of Elasticsearch as a time series database in Grafana, highlighting its potential despite initial skepticism due to its traditional use for logs rather than metrics. He emphasizes the advantages of adopting a metrics 2.0 approach, which uses key-value pairs and tags, making the data model suitable for Elasticsearch's aggregation framework. The article addresses challenges such as adapting to the Lucene query format, handling templated queries, and avoiding common pitfalls like sawtooth-like graphs and incomplete data issues at graph boundaries. Barnsteiner advises caution in migrating from other time series databases like Graphite, as fundamental differences can complicate the transition. He also points out issues with using the sum function in Elasticsearch, proposing imperfect solutions and suggesting that similar challenges exist across different databases like InfluxDB. Overall, the discussion reflects on the complexities of effectively utilizing Elasticsearch within Grafana, encouraging feedback and further exploration of solutions.