Performance Optimization for Rails 8

Overview

Performance optimization focuses on:

• N+1 query detection and prevention
• Query optimization
• Memory management
• Response time improvements
• Database indexing

Quick Start

# Gemfile
group :development, :test do
  gem 'bullet'           # N+1 detection
  gem 'rack-mini-profiler' # Request profiling
  gem 'memory_profiler'  # Memory analysis
end

N+1 Query Detection and Prevention

N+1 queries occur when code loads a collection then makes a separate query for each associated record. The Bullet gem detects these automatically. Fix them with eager loading via includes, preload, or eager_load.

Eager Loading Decision Table

Method	Use When
includes	Most cases (Rails chooses best strategy)
preload	Forcing separate queries, large datasets
eager_load	Filtering on association, need single query
joins	Only need to filter, don't need association data

Key patterns: Bullet configuration, eager loading methods, scoped eager loading, counter caches, N+1 specs with query count assertions.

See references/n-plus-one.md for all code examples and patterns.

Query Optimization

Optimize queries by selecting only needed columns, using batch processing for large datasets, and choosing efficient existence checks.

Key Patterns

Pattern	Bad	Good
Column selection	User.all.map(&:name)	User.pluck(:name)
Large iterations	Event.all.each { ... }	Event.find_each { ... }
Existence checks	.any? / .present?	.exists?
Collection size	.length (loads all)	.size (smart)

Database Indexing

Add indexes for: foreign keys, columns in WHERE/ORDER BY/JOIN clauses, and unique constraints. Use composite indexes for multi-column queries. Use partial indexes for filtered subsets.

Query Analysis

Use Event.where(...).explain(:analyze) to inspect query plans. Set up slow query logging via ActiveSupport::Notifications to catch queries over a threshold.

See references/query-optimization.md for all code examples and patterns.

Memory Management and Profiling

Use memory_profiler to detect memory issues. Prefer pluck over loading full AR objects, use find_each for streaming, and use update_all / in_batches for bulk operations.

Rack Mini Profiler

Provides per-request profiling in development. Shows query count, timing, and flamegraphs (with stackprof gem). Access via the profiler badge or ?pp=flamegraph.

See references/memory-and-profiling.md for all code examples and patterns.

Quick Fixes Reference

Problem	Solution
N+1 on belongs_to	includes(:association)
N+1 on has_many	includes(:association)
Slow COUNT	Add counter_cache
Loading all columns	Use select or pluck
Large dataset iteration	Use find_each
Missing index on FK	Add index on *_id columns
Slow WHERE clause	Add index on filtered column
Loading unused associations	Remove from includes

Performance Checklist

• Bullet enabled in development/test
• No N+1 queries in critical paths
• Foreign keys have indexes
• Counter caches for frequent counts
• Eager loading in controllers
• Batch processing for large datasets
• Query analysis for slow endpoints

Workflow

1. Detect -- Enable Bullet, run specs, check Rack Mini Profiler
2. Analyze -- Use explain(:analyze), check slow query logs, profile memory
3. Fix -- Apply the appropriate pattern from the reference files
4. Verify -- Re-run specs, confirm query counts, check profiler

Reference Files

• references/n-plus-one.md -- N+1 detection, eager loading methods, Bullet config, counter caches, testing patterns
• references/query-optimization.md -- Column selection, batch processing, indexing strategies, EXPLAIN analysis, slow query logging
• references/memory-and-profiling.md -- Memory profiler usage, memory-efficient patterns, Rack Mini Profiler setup, deployment checklist