clickzetta-index-manager

Name: clickzetta-index-manager
Availability: InStock
Author: qiliang

Manage Bloom Filter, inverted, and vector indexes for ClickZetta Lakehouse tables.

8|3|Updated Mar 19, 2026

Search Tags:#vector #index #query-performance #lakehouse #bloomfilter #inverted #build-index

Ready to run

npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-index-manager

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clickzetta-index-manager
Source: https://github.com/yunqiqiliang/clickzetta-skills/tree/main/clickzetta-index-manager
Command: npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-index-manager

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides guidance on selecting and creating the right index types (Bloom Filter, inverted, and vector) to accelerate ClickZetta Lakehouse queries, and offers practical workflows for creating, building, viewing, and dropping indexes for both new and existing data.

Core Features & Use Cases

Index type selection: Guidance on Bloom Filter for high-cardinality equal lookups, inverted for text search, and vector for semantic similarity.
Index creation & maintenance: Create, view, and delete indexes with CREATE/Bloom Filter/INVERTED/VECTOR constructs, and maintain storage with BUILD INDEX where supported.
Operational workflows: Build for existing data, monitor progress, and apply per-partition indexing for large tables.

Quick Start

Create a Bloom Filter index on a high-cardinality column and then build the index for existing data as needed.

Frequently Asked Questions about clickzetta-index-manager

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

When should I use a Bloom filter, inverted, or vector index in a lakehouse?▼

Use a Bloom filter index for high-cardinality equality lookups, an inverted index for text search, and a vector index for semantic similarity queries across large datasets.

How do I build an index on existing data in ClickZetta Lakehouse tables?▼

You can build an index on existing data by executing the BUILD INDEX command on your ClickZetta Lakehouse tables to apply the index to current records and maintain storage efficiently.

How can I view or drop indexes on ClickZetta Lakehouse tables?▼

You can view existing indexes using SHOW INDEX or DESC INDEX commands, and remove them using DROP INDEX to manage query performance and storage overhead on your tables.

Does ClickZetta support per-partition indexing for large lakehouse tables?▼

Yes, ClickZetta Lakehouse supports applying per-partition indexing for large tables, allowing you to build and maintain indexes on specific partitions to optimize query performance.

Which index type is best for accelerating text search across large datasets?▼

An inverted index is the best choice for accelerating text search across large datasets, as it is specifically designed to optimize text-based query performance in ClickZetta Lakehouse.

Why is my vector similarity search slow on ClickZetta Lakehouse tables?▼

Vector similarity search may be slow without a dedicated vector index; creating a vector index on the target column accelerates semantic similarity queries across large datasets.