ClickHouse addresses the challenges of row-level updates in columnar databases by treating updates as insert operations, utilizing engines like ReplacingMergeTree, CoalescingMergeTree, and CollapsingMergeTree. These engines leverage ClickHouse's high insert throughput and background merging processes to handle updates and deletes efficiently without modifying existing rows, thereby maintaining performance. This approach allows the database to manage high-ingest and rapidly changing workloads typical in IoT, e-commerce, finance, gaming, and CRM/HR applications. By transforming updates into fast inserts, ClickHouse circumvents the traditional update bottlenecks found in column stores, where each column is stored separately. The series explores the mechanics of these purpose-built engines, their role in fast SQL-style updates through patch parts, and benchmarks their performance against systems like PostgreSQL, demonstrating substantial speed advantages, especially in bulk update scenarios. The ReplacingMergeTree engine, for instance, retains the latest version of a row during merges, while CoalescingMergeTree supports partial updates, and CollapsingMergeTree allows for deletes by inserting canceling rows. Though efficient, these methods require users to understand ClickHouse’s merging logic and engine-specific behaviors, prompting the development of SQL-style updates in Part 2 of the series, which aim to offer familiar syntax without sacrificing speed.