---
name: magento-performance-a4bf90957013
description: >
  Optimize Magento 2 performance — full page cache (Varnish), Redis, indexer
  tuning, JavaScript/CSS optimization, database optimization, and profiling. Use
  when diagnosing slow pages, optimizing load times, or configuring caching.
---

# Magento 2 Performance Optimization

## Before writing code

**Fetch live docs**:
1. Web-search `site:experienceleague.adobe.com commerce performance` for performance best practices
2. Web-search `site:experienceleague.adobe.com commerce configuration cache` for cache configuration
3. Web-search `site:developer.adobe.com commerce php development cache` for cache development guide

## Full Page Cache (FPC)

### Varnish (Recommended for Production)

- Serves cached pages from RAM before hitting the web server
- Dramatically reduces TTFB (time to first byte)
- Magento generates VCL (Varnish Configuration Language) files
- Configure at Stores > Configuration > Advanced > System > Full Page Cache
- Generate VCL: `bin/magento varnish:vcl:generate`

### Built-in FPC (Fallback)

- Filesystem-based cache — slower than Varnish
- Suitable for development only
- No separate service required

### Cache Invalidation

- Automatic on entity save (products, categories, CMS)
- Manual: `bin/magento cache:clean full_page`
- Cache tags track which pages contain which entities
- Hole-punching for dynamic content (customer name, cart count) via private content/sections

## Redis / Valkey

### Cache Backend

Store all Magento caches in Redis for fast reads:
- Configuration cache, layout cache, block HTML cache
- Configure in `app/etc/env.php` under `cache`

### Session Storage

Store PHP sessions in Redis instead of filesystem/database:
- Faster session reads/writes
- Better for load-balanced environments
- Configure in `app/etc/env.php` under `session`

## Indexer Tuning

- **Update by Schedule** (production) — cron-based, uses changelogs
- **Update on Save** (development) — immediate but resource-intensive
- Monitor indexer status: `bin/magento indexer:status`
- Tune cron frequency for indexer jobs

## JavaScript and CSS Optimization

### Production Mode

Static content is pre-deployed and minified:
```bash
bin/magento setup:static-content:deploy --jobs=N
```

### Minification

- JS minification: enabled via admin config
- CSS minification: enabled via admin config
- HTML minification: configurable

### Bundling

- Built-in JS bundling (basic)
- Advanced bundling with Magepack (community tool) for page-specific bundles
- Critical CSS extraction for above-the-fold rendering

### Hyva Performance Gain

If using Hyva themes: ~5 HTTP requests vs ~230, ~0.4MB vs ~3MB — eliminates most JS optimization needs.

## Database Optimization

- Use MySQL 8.0+/MariaDB 10.6+ for latest optimizer improvements
- Tune InnoDB buffer pool size (ideally 70-80% of available RAM on dedicated DB servers) and use MySQL 8.0 optimizer hints where applicable
- Optimize slow queries: enable slow query log, analyze with `EXPLAIN`
- Clean log tables periodically (cron handles this)
- Use persistent database connections in `app/etc/env.php`

## Profiling Tools

- **Magento Profiler**: enable in `app/etc/env.php` or via `bin/magento dev:profiler:enable`
- **New Relic**: APM integration built into Magento
- **Blackfire.io**: PHP profiling
- **MySQL slow query log**: database bottlenecks
- **Varnish stats**: cache hit rates

## Best Practices

- Always use production mode in production
- Varnish + Redis is the standard production stack
- Set all indexers to "Update by Schedule"
- Pre-deploy static content during build (not on production server)
- Monitor cache hit rates — low rates indicate misconfigured invalidation
- Profile before optimizing — measure, don't guess
- Use CDN for static assets
- Enable HTTP/2 on the web server
- Consider Hyva for frontend performance

Fetch the performance documentation for exact configuration paths, VCL generation options, and Redis configuration format before optimizing.


---
Source attribution: OrcaQubits
Original project: https://github.com/OrcaQubits/agentic-commerce-skills-plugins/blob/30cd25c74c8aa7dc49b3ec4847d463f6104439f6/dist/codex/magento2-commerce/skills/magento-performance/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2024-2026 OrcaQubits AI (https://orcaqubits-ai.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: OrcaQubits
Original project: https://github.com/OrcaQubits/agentic-commerce-skills-plugins/blob/30cd25c74c8aa7dc49b3ec4847d463f6104439f6/dist/codex/magento2-commerce/skills/magento-performance/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Backend Developer, Data Analyst, DevOps / SRE, Growth Product Manager, Product Manager
Specialties: Backend, Database
Tools: MySQL, Redis, Varnish
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: b8ef08d644097885950f93364826924eaf383bcc91408925c66f4dafb2aea47e