---
title: "Announcing General Availability of ClickHouse Full-text Search"
description: "ClickHouse has reached general availability for its native Full-text Search feature, designed to accelerate token-based text filtering across large datasets. The capability relies on deterministic inverted indexes rather than probabilistic Bloom filter skip indexes, mapping individual tokens directly to row numbers. It supports plain strings, string arrays, and map keys or values while allowing configurable pre-processing and tokenization using standard SQL expressions. The system does not implement relevance scoring or store positional data for ranking, focusing instead on rapid filtering paired with large-scale analytical aggregations. Testing shows significant reductions in scanned granules alongside substantial latency improvements, though text indexes require more storage than Bloom filters."
---

# Announcing General Availability of ClickHouse Full-text Search

[Clickhouse](https://yomu.fyi/company/clickhouse) · Melvyn Peignon · Mar 10, 2026

**Type:** Announcement

## Summary

ClickHouse has reached general availability for its native Full-text Search feature, designed to accelerate token-based text filtering across large datasets. The capability relies on deterministic inverted indexes rather than probabilistic Bloom filter skip indexes, mapping individual tokens directly to row numbers. It supports plain strings, string arrays, and map keys or values while allowing configurable pre-processing and tokenization using standard SQL expressions. The system does not implement relevance scoring or store positional data for ranking, focusing instead on rapid filtering paired with large-scale analytical aggregations. Testing shows significant reductions in scanned granules alongside substantial latency improvements, though text indexes require more storage than Bloom filters.

## Context

Accelerating string search in large text datasets previously relied on Bloom filter skip indexes, which are probabilistic, operate at the coarse granule level, risk false positives, lack multi-token search support, and require careful tuning.

## Approach / What changed

ClickHouse implemented native inverted text indexes that map tokens directly to row numbers during insertion or materialization, allowing precise deterministic lookups across strings, string arrays, and maps using configurable SQL tokenization pipelines.

## Takeaways

- ClickHouse Full-text Search uses deterministic inverted indexes mapping tokens to row numbers, eliminating index-level false positives seen in Bloom filters.
- The implementation accelerates token filtering alongside real-time analytical aggregation, omitting relevance scoring mechanisms such as BM25 or TF-IDF.
- Inverted text indexes introduce a storage trade-off, occupying significantly more storage space than Bloom filter skip indexes.

**Tags:** [Observability](https://yomu.fyi/topic/observability), [Performance](https://yomu.fyi/topic/performance), [Scalability](https://yomu.fyi/topic/scalability), [Search](https://yomu.fyi/topic/search)

- Source: [Clickhouse](https://clickhouse.com/blog/full-text-search-ga-release)
- Source URL: https://clickhouse.com/blog/full-text-search-ga-release
- Ingested by Yomu: 2026-08-28T01:21:50.925Z

[Read original post](https://clickhouse.com/blog/full-text-search-ga-release)