Highlight transitioned from using OpenSearch to Clickhouse for its search backend to improve performance and reduce costs. Originally, Highlight used OpenSearch to handle metadata searches across sessions and errors, but as customer data grew, performance declined due to increased search times and memory dependency. Clickhouse, being a column-oriented database, allows more efficient querying by loading only necessary data columns, which is advantageous for complex searches that can't benefit from indexing. The shift to Clickhouse also enabled the use of materialized views to enhance field autocomplete suggestions by tracking field usage frequency. To maintain data consistency between Postgres, the primary data store, and Clickhouse, Highlight implemented a system using Kafka, ensuring eventual consistency despite potential update delays. This migration resulted in significantly faster search query times, reduced operational costs, and simplified architecture by phasing out the expensive OpenSearch cluster.