---
title: "Accelerate search queries with full-text search indexes on Databricks"
description: "Databricks introduces full-text search indexes in Beta on Databricks Runtime 18.2 to accelerate substring and keyword queries on large open-format tables without changing their layouts. The indexes tokenize text columns into a compact lookup structure mapping tokens to matching rows; at query time, the engine uses it to identify candidate files and skip most of the table. They are maintained asynchronously, require no query hints, preserve complete results when stale by scanning indexed and non-indexed data as needed, and support Unity Catalog managed Delta and Iceberg tables on serverless and classic compute. A Trust and Safety team reported a substring search running more than 100x faster on a petabyte-scale table, while Liquid clustering remains complementary because it optimizes column-value filters rather than text within fields."
---

# Accelerate search queries with full-text search indexes on Databricks

[Databricks](https://yomu.fyi/company/databricks) · Yu Xu, Yingyi Bu, Ivan Vezilić · Jun 16, 2026

**Type:** Announcement

## Summary

Databricks introduces full-text search indexes in Beta on Databricks Runtime 18.2 to accelerate substring and keyword queries on large open-format tables without changing their layouts. The indexes tokenize text columns into a compact lookup structure mapping tokens to matching rows; at query time, the engine uses it to identify candidate files and skip most of the table. They are maintained asynchronously, require no query hints, preserve complete results when stale by scanning indexed and non-indexed data as needed, and support Unity Catalog managed Delta and Iceberg tables on serverless and classic compute. A Trust and Safety team reported a substring search running more than 100x faster on a petabyte-scale table, while Liquid clustering remains complementary because it optimizes column-value filters rather than text within fields.

## Context

As tables reach hundreds of gigabytes or terabytes and beyond, text searches can scan far more data than necessary. Teams have used duplicate tables, external search systems such as Elasticsearch or Splunk, or specialized table layouts to address slow lookups.

## Approach / What changed

Databricks stores full-text indexes separately from base tables, tokenizes text columns, and maps tokens to matching rows. The query engine automatically consults available indexes to skip files, while asynchronous maintenance avoids slowing writes and stale indexes remain correct by including indexed and non-indexed portions as needed.

## Takeaways

- Full-text search indexes target substring and keyword queries, including high-cardinality lookups across multiple text columns.
- Indexes are maintained asynchronously and selected automatically by the query engine, with no query hints required.
- A Trust and Safety team achieved more than 100x faster substring-search performance on a petabyte-scale table.

**Tags:** [Apache Iceberg](https://yomu.fyi/topic/apache-iceberg), [Databricks](https://yomu.fyi/topic/databricks), [Delta Lake](https://yomu.fyi/topic/delta-lake), [Search](https://yomu.fyi/topic/search)

- Source: [Databricks](https://www.databricks.com/blog/accelerate-search-queries-full-text-search-indexes-databricks)
- Source URL: https://www.databricks.com/blog/accelerate-search-queries-full-text-search-indexes-databricks
- Ingested by Yomu: 2026-08-30T17:02:11.318Z

[Read original post](https://www.databricks.com/blog/accelerate-search-queries-full-text-search-indexes-databricks)