How to Optimize Trino Performance with Different Storage Layers

Trino, an in-memory query engine, relies heavily on the underlying storage layer to optimize performance, with latency and throughput being key factors. Latency refers to the time taken to deliver data to the user, while throughput is the volume of data processed over time. To enhance Trino's performance, users should consider different storage layers like AWS S3, Azure ADLS, and HDFS for high throughput, and memory or NoSQL/document systems for low latency. Various use cases demonstrate how customers have optimized Trino queries: by moving small datasets to PostgreSQL for real-time applications, copying Oracle tables to Hadoop for frequent queries, and using memory or distributed storage for temporary tables in ETL processes. Implementing Starburst Enterprise with Trino requires a holistic approach to meet SLAs and improve user experience by leveraging appropriate storage solutions.