Journey of Migrating Millions of Queries on The Cloud

TL;DR

This paper discusses the challenges and solutions involved in migrating millions of distributed SQL queries to a new cloud-based query engine, ensuring correctness and performance through customer-specific benchmarking and simulation.

Contribution

It introduces a system for migrating large-scale queries that includes customer-specific benchmarking, query set minimization, and performance regression detection.

Findings

Effective query set minimization improves testing efficiency.

Customer-specific benchmarks help detect incompatibilities.

Simulation results aid in identifying performance regressions.

Abstract

Treasure Data is processing millions of distributed SQL queries every day on the cloud. Upgrading the query engine service at this scale is challenging because we need to migrate all of the production queries of the customers to a new version while preserving the correctness and performance of the data processing pipelines. To ensure the quality of the query engines, we utilize our query logs to build customer-specific benchmarks and replay these queries with real customer data in a secure pre-production environment. To simulate millions of queries, we need effective minimization of test query sets and better reporting of the simulation results to proactively find incompatible changes and performance regression of the new version. This paper describes the overall design of our system and shares various challenges in maintaining the quality of the query engine service on the cloud.