Database usage in practice often involves heavy text processing. For example, in "observability" use cases, databases must extract, store, and search billions of log messages daily. Most databases, including many column-oriented OLAP databases, struggle with such massive amounts of text data. The only way to process text data at scale is by using specialized inverted indexes in databases.

This presentation explains how inverted indexes work and which (text) search patterns they support. Where appropriate, we describe our experience and the gotchas we encountered when adding an inverted index to ClickHouse, one of the most popular open-source databases for analytics.