🚀 Scaling Pipeline Execution Search: Why We Moved Beyond MongoDB | Blog

Harness, a company focused on enhancing performance and user experience, faced challenges with its pipeline execution search system due to MongoDB's limitations in handling large volumes of data and complex queries. As the scale of operations grew, MongoDB struggled with indexing inefficiencies, complex aggregations, and slow query times, prompting the need for a more efficient search solution. The company transitioned to Elasticsearch, which offered improved full-text and fuzzy search capabilities, better aggregation performance, and effective index lifecycle management. This migration involved adopting a CQRS architecture, where writes were managed by MongoDB and reads were handled by Elasticsearch, optimizing performance and scalability. The new system allowed for faster search results, reduced query latency, and improved resource efficiency, making the infrastructure more robust for future growth.