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indexing-strategy

Design database index structures for relational and NoSQL workloads.

1|Updated May 6, 2026
Search Tags:#indexing#database#query performance#composite indexes#partial indexes#B-tree#GIN GiST BRIN
One-click install
npx skills add https://github.com/jacob-balslev/skill-graph --skill indexing-strategy-jacob-balslev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: indexing-strategy
Source: https://github.com/jacob-balslev/skill-graph/tree/main/marketplace/skills/indexing-strategy
Command: npx skills add https://github.com/jacob-balslev/skill-graph --skill indexing-strategy-jacob-balslev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Indexing strategy removes slow or unreliable data access by helping you choose which indexes to add, how to structure them, and when to remove them so queries match access patterns without exploding write cost.

Core Features & Use Cases

  • Index structure selection: Choose appropriate index types (B-tree, hash, bitmap, GIN/GiST, BRIN, LSM-tree) based on equality, range, prefix, contains, geospatial, and workload characteristics.
  • Composite and covering design: Determine column order, INCLUDE/covering behavior, and when expression/filtered indexes are preferable to broad indexing.
  • Portfolio trade-off governance: Evaluate storage, write amplification, lock/maintenance overhead, planner overhead, and monitor usage to keep only indexes that the system actually uses.

Quick Start

Use the indexing-strategy skill to design the set of indexes for your database workload, including choosing index types, composite column order, and which indexes to drop when they go unused.

Frequently Asked Questions about indexing-strategy

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

FAQPage Schema
How do I choose the right database index type for my query workload?▼

Choosing the right database index type requires matching the access pattern: B-tree for equality and range, GIN/GiST for contains and geospatial, and BRIN for large ordered sets to accelerate specific queries.

What is the correct column order for composite indexes?▼

Composite index column order should follow equality predicates first, then range or sorting columns, allowing the planner to efficiently narrow results using the leading prefix for your specific access patterns.

When should I use partial or covering indexes instead of broad indexing?▼

Partial or filtered indexes are preferable to broad indexing when queries target a specific subset of rows, reducing storage and write maintenance overhead while covering indexes avoid table lookups by including extra columns.

How do I verify if a database index is actually being used by the query planner?▼

Verify database index usage by checking query plans and index statistics to confirm the planner selects the index during execution, ensuring the access pattern matches the designed index structure.

When should I drop unused database indexes to reduce write cost?▼

Drop unused database indexes when monitoring confirms zero planner selections, eliminating unnecessary write amplification, lock maintenance overhead, and storage consumption from your portfolio.

Does database indexing strategy work for NoSQL workloads or only relational databases?▼

Database indexing strategy applies to both relational and NoSQL workloads, governing index structures to balance storage and write maintenance costs while accelerating equality, range, and prefix access patterns across systems.

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