ClickHouse gets lazier (and faster): Introducing lazy materialization

Summary

Imagine if you could skip packing your bags for a trip because you find out at the airport you’re not going. That’s what ClickHouse is doing with data now. ClickHouse is one of the fastest analytical databases available, and much of that speed comes from avoiding unnecessary work. The less data it scans and processes, the faster queries run. Now it pushes this idea even further with a new optimization: lazy materialization, which delays reading column data until it’s actually needed. This seemingly "lazy" behavior turns out to be extremely effective in real-world workloads, especially for Top N queries that sort large datasets and apply LIMIT clauses, a common pattern in observability and general analytics. In these scenarios, lazy materialization can dramatically accelerate performance, often by orders of magnitude. Spoiler alert: We’ll show you how a ClickHouse query went from 219 seconds to just 139 milliseconds—a 1,576× speedup—without changing a single line of SQL. Same query. Same table. Same machine. The only thing that changed? When ClickHouse reads the data. In this post, we’ll walk through how lazy materialization works and how it fits into ClickHouse’s broader I/O optimization stack. To give a complete picture, we’ll also briefly demonstrate the other key building blocks of I/O efficiency in ClickHouse, highlighting not just what lazy materialization does, but how it differs from and complements the techniques already in place. We’ll begin by describing the core I/O-saving techniques ClickHouse already uses, then run a real-world query through them, layer by layer, until lazy materialization kicks in and changes everything. Over the years, ClickHouse has introduced a series of layered optimizations to aggressively reduce I/O. These techniques form the foundation of its speed and efficiency: Columnar storage allows skipping entire columns that aren’t needed for a query and also enables high compression by grouping similar values together, minimizing I/O du...