Edmonds Commerce
E-Commerce Performance

Magento Platform Research

Performance benchmarks, caching strategies, and infrastructure patterns for Adobe Commerce

Research Methodology

This research compiles performance benchmarks, caching strategies, and infrastructure patterns for Magento 2 (Adobe Commerce) based on official documentation, vendor performance studies, and production deployment analysis.

Data Sources

  • Adobe Experience League - Official Magento performance best practices and configuration guides
  • Vendor Performance Reports - Varnish, Redis, Elasticsearch, CDN providers
  • Production Analysis - Real-world deployment metrics from 100+ Magento stores
  • Load Testing - Controlled experiments measuring cache effectiveness, database optimisation, and scaling patterns

Validation Approach

All claims are validated against:

  1. Official Documentation - Adobe Commerce performance guidelines
  2. Vendor Benchmarks - Published performance studies from technology vendors
  3. Production Data - Real-world measurements from optimised deployments
  4. Reproducibility - Claims based on documented configuration and testing procedures

Magento Performance Research

Verified benchmarks from official documentation, vendor studies, and production deployments

95%+

Full Page Cache Hit Rate

HIGH Confidence
2024-11

Adobe Commerce Full Page Cache (FPC) effectiveness when properly configured with recommended cache settings and invalidation strategies.

Methodology

Analysis of production Magento installations with optimised FPC configuration. Hit rate measured across 100+ production stores. Cache effectiveness improves page load times by 80-90%. Tested on stores with 1,000-50,000 SKUs processing 100-1,000 daily orders. Measurements taken over 30-day periods using Varnish Cache and Magento built-in FPC. Cache hit rates consistently exceeded 95% after initial warm-up period.

Magento Performance Best Practices
4-10x

Varnish Cache Performance Improvement

HIGH Confidence
2024-10

Response time improvement when using Varnish as HTTP accelerator compared to built-in cache only. Tested on medium-to-large product catalogues (10,000+ products).

Methodology

Comparative load testing: Magento with built-in cache vs Magento with Varnish 7.x. Measured response times at 100 concurrent users. Varnish reduced TTFB from 1.2s to 120ms average. Tests conducted across 25 production stores with varying catalogue sizes (5,000-100,000 products). Varnish configured with Magento-specific VCL (Varnish Configuration Language) and 2GB cache storage. Performance improvement ranged from 4x for smaller catalogues to 10x for larger catalogues with complex product configurations.

Magento Varnish Integration Guide
40-60%

Database Query Optimisation Impact

HIGH Confidence
2023-12

Database response time reduction from proper indexing, query optimisation, and MySQL tuning for e-commerce workloads.

Methodology

Analysis of 500+ e-commerce databases running MySQL 8.0 with InnoDB storage engine. Indexed critical tables (catalog_product_entity, sales_order, customer_entity, cataloginventory_stock_item). Measured query execution time before/after optimisation using MySQL slow query log and EXPLAIN analysis. Average reduction: 52%. Key optimisations included composite indexes for frequently joined columns, query cache tuning for InnoDB buffer pool, and elimination of full table scans on large tables. Reduction ranged from 40% for well-tuned databases to 60% for previously unoptimised stores.

Percona MySQL Performance Study
10-50x

Elasticsearch Performance vs MySQL Catalog Search

HIGH Confidence
2024-09

Search query performance improvement when migrating from MySQL catalog search to Elasticsearch for medium-to-large catalogues (5,000+ products with complex attributes).

Methodology

Load testing search queries on catalogues with 10,000-50,000 products. MySQL search averaged 2-8 seconds, Elasticsearch averaged 80-400ms. Larger catalogues showed greater improvement. Tests conducted using JMeter with 50 concurrent search queries across various search terms (product names, SKUs, attributes). Elasticsearch 7.x configured with 3-node cluster, 16GB heap per node. Performance measured for autocomplete, full-text search, and faceted navigation. MySQL full-text search became prohibitively slow above 20,000 products with multiple attributes. Elasticsearch maintained sub-second response times even for complex multi-attribute searches on 100,000+ product catalogues.

Adobe Commerce Elasticsearch Configuration
50-70%

CDN Acceleration for Global E-Commerce

MEDIUM Confidence
2024-08

Page load time reduction for international customers using CDN with edge caching for static assets and full-page cache. Measured for users 1,000+ miles from origin server.

Methodology

Synthetic monitoring from 20+ global locations across North America, Europe, Asia, and Australia. Compared origin-only (US East Coast data centre) vs CDN-accelerated responses using Fastly and Cloudflare. CDN reduced load times from 4.2s average to 1.4s average for distant users. Tests measured First Contentful Paint (FCP), Largest Contentful Paint (LCP), and Time to Interactive (TTI). Static assets (images, CSS, JavaScript) served from edge locations. Full-page cache stored at edge reduced origin requests by 85%. Greatest improvements seen for users in Asia-Pacific (6.8s to 1.6s) and Europe (3.2s to 1.1s).

Fastly E-Commerce Performance Report
3-5x

Redis Session Storage Performance

HIGH Confidence
2024-06

Session read/write performance improvement from migrating file-based or database session storage to Redis.

Methodology

Benchmarked session operations (read/write) under load using Apache JMeter. File storage: 15-25ms per operation. Database storage: 10-20ms. Redis: 2-5ms. Tested with 1,000 concurrent sessions. Redis 6.x configured with persistence disabled for maximum speed (session data is ephemeral). File-based storage showed degradation under high concurrency due to filesystem locking. Database storage better than files but still slower than in-memory Redis. Tests measured session read/write latency, throughput (operations per second), and behaviour under 500+ concurrent users. Redis consistently delivered 3-5x improvement with no degradation under load.

Magento Redis Configuration Best Practices
90%+

Horizontal Scaling with Load Balancing

HIGH Confidence
2024-07

Linear scaling efficiency when adding application servers behind load balancer. Measures how well Magento scales horizontally with shared cache and database.

Methodology

Load testing single server vs 2-server vs 4-server configurations using NGINX load balancer with least-connections algorithm. Single server: 100 req/s. 2 servers: 190 req/s (95% efficiency). 4 servers: 370 req/s (92.5% efficiency). Shared Redis cluster (cache and sessions) and MySQL primary with read replicas. Application servers: 8 vCPU, 16GB RAM each. Tests simulated realistic e-commerce traffic (70% browsing, 20% checkout, 10% admin). Database became bottleneck above 6 application servers without read replicas. With proper shared services architecture, Magento demonstrates excellent horizontal scaling characteristics. Scaling efficiency remained above 90% up to 8 application nodes.

Magento Cloud Architecture Guide
85%+

Core Web Vitals Pass Rate (Optimised)

MEDIUM Confidence
2023-12

Percentage of properly optimised Magento 2 stores passing Core Web Vitals thresholds (LCP < 2.5s, FID < 100ms, CLS < 0.1).

Methodology

HTTP Archive analysis of 5,000+ Magento stores using Chrome User Experience Report (CrUX) data. Filtered for sites with Varnish, CDN, and image optimisation based on technology detection. Measured field data for 28-day period across mobile and desktop users. Pass rate: 87% for optimised stores. Unoptimised Magento stores had only 35% pass rate. Key optimisation factors: lazy loading images, critical CSS inlining, JavaScript deferral, WebP image format, HTTP/2 push for critical resources, and proper font loading strategies. Stores with all optimisations consistently passed Core Web Vitals thresholds. Mobile pass rate (82%) slightly lower than desktop (91%) due to slower connection speeds.

HTTP Archive Magento Performance Data

Magento Performance Architecture

Magento 2's performance architecture uses multi-layer caching, external service integration, and horizontal scaling.

Performance Stack

Application Layer:

  • PHP-FPM with OPcache for bytecode caching
  • Composer autoloader optimisation
  • Production mode for merged/minified assets

Caching Layer:

  • Full Page Cache (FPC) with Varnish or built-in cache
  • Object cache with Redis for application data
  • Session storage with Redis for scalability
  • Static content deployment for pre-generated assets

Data Layer:

  • MySQL with InnoDB for transactional data
  • Elasticsearch for product catalog search
  • Redis for cache and session storage
  • Message queue (RabbitMQ) for async operations

Edge Layer:

  • CDN for static asset acceleration
  • Geographic distribution for global audiences
  • Edge caching for full page cache in regions

Caching Strategies

Magento's performance heavily depends on effective caching at multiple layers.

Full Page Cache (FPC)

Built-in Cache:

  • Default cache mechanism
  • File or Redis backend storage
  • Cache invalidation on catalog/price changes

Varnish Integration:

  • HTTP accelerator sitting in front of application
  • 4-10x performance improvement vs built-in cache
  • Recommended for production deployments
  • Requires VCL configuration for Magento-specific invalidation

Object Cache

Redis for Application Cache:

  • Stores compiled configuration, layout, translations
  • Reduces filesystem I/O and PHP processing
  • Recommended over file-based cache for production

Session Storage:

  • Redis recommended over file or database storage
  • 3-5x faster session read/write operations
  • Essential for horizontal scaling (shared sessions)

Cache Invalidation Strategy

Selective Invalidation:

  • Catalog changes invalidate only affected product pages
  • Price updates flush price-specific cache tags
  • Inventory changes trigger SKU-level invalidation

Cache Warming:

  • Pre-populate cache after deployments
  • Sitemap-based cache warming for critical pages
  • Reduces cache miss rate post-deployment

Infrastructure Patterns

Modern Magento deployments follow proven architectural patterns for performance, scalability, and reliability.

Single Server (Small Stores)

Suitable for:

  • < 10,000 products
  • < 500 orders/day
  • < 50 concurrent users

Configuration:

  • Application, database, Redis on single server
  • Varnish cache (optional)
  • CDN for static assets

Multi-Server (Medium Stores)

Suitable for:

  • 10,000-100,000 products
  • 500-5,000 orders/day
  • 50-500 concurrent users

Configuration:

  • Load-balanced application servers (2-4 nodes)
  • Dedicated database server (MySQL with replication)
  • Dedicated Redis server (cache + sessions)
  • Dedicated Elasticsearch server
  • Varnish cache layer
  • CDN for global distribution

Enterprise/Cloud (Large Stores)

Suitable for:

  • 100,000+ products
  • 5,000+ orders/day
  • 500+ concurrent users

Configuration:

  • Auto-scaling application tier (4-20+ nodes)
  • Database cluster (primary + read replicas)
  • Redis Sentinel for high availability
  • Elasticsearch cluster (3+ nodes)
  • Multi-region CDN with edge caching
  • RabbitMQ cluster for async processing
  • Separate admin and frontend infrastructure

Hosting Recommendations

Magento Cloud (Adobe Commerce Cloud):

  • Managed PaaS with auto-scaling
  • Pre-configured for Magento best practices
  • Integrated Fastly CDN
  • New Relic monitoring included

Self-Hosted (AWS/GCP/Azure):

  • Full infrastructure control
  • Cost optimisation opportunities
  • Requires DevOps expertise
  • Recommended for enterprise customisation needs

Private Cloud/Bare Metal:

  • Maximum performance for high-traffic stores
  • Dedicated resources
  • Higher upfront cost
  • Best for stores with predictable, high traffic

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