gigamap

Build and query massive datasets with index-backed, lazily-loaded collections.

Updated Apr 24, 2026

Search Tags:#query optimization #indexing #scalability #search #big data #large datasets

Ready to run

npx skills add https://github.com/cyrock-ai/eclipse-store-skills --skill gigamap

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gigamap
Source: https://github.com/cyrock-ai/eclipse-store-skills/tree/main/skills/gigamap
Command: npx skills add https://github.com/cyrock-ai/eclipse-store-skills --skill gigamap

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the efficient management and querying of massive datasets through lazily-loaded, index-backed collections.

Core Features & Use Cases

Indexed Large Collections: Support for billions of entities with multiple index types including bitmap, Lucene, and vector.
Complex Querying: Facilitate advanced boolean combinations, filtering, and sub-queries across various index types.
Use Case: Use this skill to design a recommendation system that indexes millions of products with search, filtering, and similarity queries in a single framework.

Quick Start

Use the gigamap skill to create, index, and query datasets with complex multi-type filters and searches.

Frequently Asked Questions about gigamap

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

FAQPage Schema

How do I manage and query large datasets without running out of memory?▼

To manage large datasets with a minimal memory footprint, use lazily-loaded, index-backed collections. This approach supports scalable querying across massive datasets without loading everything into memory.

How do I perform complex boolean logic and filtering on big data collections?▼

You can apply complex boolean logic and filtering on big data collections by utilizing index-backed structures. This facilitates advanced boolean combinations and sub-queries across various index types for efficient data retrieval.

Does this approach support vector and bitmap indexing for recommendation systems?▼

Yes, indexed large collections support bitmap, Lucene, and vector index types. This allows you to design recommendation systems that execute search, filtering, and similarity queries within a single framework.

What is the best way to index millions of products for search and similarity queries?▼

The best way to index millions of products is using an index-backed collection supporting bitmap, Lucene, and vector indexes. This enables combined search, filtering, and similarity queries across massive datasets within a single framework.

Can I use lazily-loaded collections for AI-driven search scenarios?▼

Yes, lazily-loaded collections are suitable for AI-driven search scenarios. They ensure scalable management of billions of entities with a minimal memory footprint while supporting efficient data retrieval.