ClickHouse ® vs Aurora MySQL: Performance Guide

As datasets grow, analytical queries that were once quick to execute can become sluggish or even fail, leading to competition for resources between transactional and analytical workloads in databases like MySQL or Aurora. The guide compares Aurora MySQL, a transactional database optimized for Online Transaction Processing (OLTP) with row-based storage, and ClickHouse®, an analytical database designed for Online Analytical Processing (OLAP) using a columnar storage model. Aurora excels in handling frequent, small transactions with fast point lookups, while ClickHouse® is suited for aggregating large datasets, offering significant performance improvements for complex queries and concurrent analytical operations. Aurora's architecture separates compute and storage, with automatic scaling and low-latency read replicas, but struggles with large-scale aggregations due to inefficient row-based storage. Conversely, ClickHouse® leverages columnar storage to reduce I/O and improve compression, supporting massive parallel processing and distributed queries for rapid data aggregation. Cost differences become apparent as data scales, with ClickHouse® offering more cost-effective storage due to high compression rates. The document explores scenarios for using both databases in tandem, such as real-time dashboards and tiered storage strategies, and discusses migration paths, including Change Data Capture (CDC) and federated queries, to balance transactional consistency with analytical efficiency.