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designing-search-and-indexes

Design search architectures and indexing strategies for PostgreSQL, Elasticsearch, and Meilisearch.

Updated Apr 14, 2026
Search Tags:#search#elasticsearch#index#ranking#hybrid#facets#meilisearch
One-click install
npx skills add https://github.com/somachak/claude-code-skills-db --skill designing-search-and-indexes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: designing-search-and-indexes
Source: https://github.com/somachak/claude-code-skills-db/tree/main/skills/data/designing-search-and-indexes
Command: npx skills add https://github.com/somachak/claude-code-skills-db --skill designing-search-and-indexes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designers and engineers need robust search architectures to deliver fast, relevant results at scale.

Core Features & Use Cases

  • Decision framework for choosing among PostgreSQL full-text search, Elasticsearch, Meilisearch, or hybrid approaches.
  • Ranking and facets guidance for relevant results and interactive discovery.
  • Use cases include product catalogs, documents, and user data requiring vector or lexical retrieval.

Quick Start

Choose a storage and indexing strategy (e.g., PostgreSQL full-text search, Elasticsearch) and implement a basic search index for your dataset.

Frequently Asked Questions about designing-search-and-indexes

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

FAQPage Schema
How do I design a search architecture that scales with Elasticsearch and PostgreSQL?▼

To design search architecture, apply a modular query pipeline with indexing strategies like PostgreSQL full-text or Elasticsearch. This approach delivers fast, relevant search results at scale across product catalogs and user datasets.

What is the best way to choose between Elasticsearch and Meilisearch for search indexing?▼

Choosing between Elasticsearch and Meilisearch requires a decision framework evaluating ranking, facets, and hybrid retrieval needs. The best backend choice matches your dataset complexity, whether handling product catalogs, documents, or vector and lexical retrieval.

How do I implement hybrid retrieval and ranking for fast search results?▼

Implement hybrid retrieval by combining vector and lexical search techniques within a modular query pipeline. Apply ranking and facets guidance to ensure relevant results and interactive discovery across your indexed datasets.

Does PostgreSQL full-text search work for large product catalogs and user data?▼

PostgreSQL full-text search works for large datasets when combined with robust indexing strategies. It supports product catalogs and user data, though Elasticsearch or Meiliserve may be better for complex ranking and facets at scale.

How do I plan autocomplete and modular query pipelines for search features?▼

Plan autocomplete and modular query pipelines by structuring indexing strategies to support fast, relevant results. This search architecture enables interactive discovery using ranking and facets across backends like Elasticsearch and Meilisearch.

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