Case Study: ClearVoice achieves simplified scalable backend and unified search with Crate.io

ClearVoice Simplifies their Scalable Backend with CrateDB

ClearVoice needed a scalable, easy-to-maintain backend as it prepared for launch. Having used MySQL in development but expecting massive data growth, ClearVoice wanted to denormalize and distribute data without the operational overhead of sharded MySQL or Hadoop and was evaluating MongoDB plus Elasticsearch when it discovered CrateDB from vendor Crate.io; the SQLAlchemy compatibility and SQL interface made CrateDB an attractive fit.

Crate.io’s CrateDB was implemented and integrated with ClearVoice’s SQLAlchemy-based Python stack—migration took only a couple of days—and provided built-in full-text search so ClearVoice could replace a MongoDB+Elasticsearch architecture with one scalable package. The move simplified scaling and maintenance, lowered the developer learning curve through SQL, and today CrateDB powers ClearVoice’s search index holding 20 million records with expectations to grow into the billions.

ClearVoice

Jeff Nappi

Director of Engineering