---
title: "Simplify, Batch, and Cache: How We Optimized Server-side Storefront Rendering"
description: "The post explains how a new Ruby-based server-side Storefront Renderer reduced the time required to serve Shopify storefront requests. It combines MySQL multi-statement queries, handcrafted SQL, a thin data-mapping layer built from plain old Ruby objects, query book-keeping with eager- and lazy-loading, multiple LRU caching layers, and techniques for reducing memory allocations. For a product page, one database round trip can load the product, variants, images, shop, theme, and related resources; later requests can replay previously observed queries early, while less frequently used data remains lazy-loaded. The resulting renderer serves 75% of requests in under ~45ms, 90% in under ~230ms, and 99% in under ~900ms, with average response time nearly five times faster than the previous implementation."
---

# Simplify, Batch, and Cache: How We Optimized Server-side Storefront Rendering

[Shopify](https://yomu.fyi/company/shopify) · 2023-10-18 · Dec 10, 2020

**Type:** Explainer

## Summary

The post explains how a new Ruby-based server-side Storefront Renderer reduced the time required to serve Shopify storefront requests. It combines MySQL multi-statement queries, handcrafted SQL, a thin data-mapping layer built from plain old Ruby objects, query book-keeping with eager- and lazy-loading, multiple LRU caching layers, and techniques for reducing memory allocations. For a product page, one database round trip can load the product, variants, images, shop, theme, and related resources; later requests can replay previously observed queries early, while less frequently used data remains lazy-loaded. The resulting renderer serves 75% of requests in under ~45ms, 90% in under ~230ms, and 99% in under ~900ms, with average response time nearly five times faster than the previous implementation.

## Context

The previous implementation was slower, and the team needed to improve rendering time for storefront requests while efficiently loading the data required by Shopify Liquid themes and storefront traffic.

## Approach / What changed

The renderer uses MySQL multi-statement queries and handcrafted SQL to reduce database round trips, plain old Ruby objects instead of a full ORM, query book-keeping for eager-loading, lazy-loading for less frequent data, multiple LRU caching layers, and memory-allocation profiling and benchmarks.

## Takeaways

- MySQL multi-statement queries batch-load data for a request in one database round trip, including product, variant, image, shop, and theme information for a product page.
- Query book-keeping stores the queries used during a request and replays them early for later matching requests, while data not identified for eager-loading remains lazy-loaded.
- Method-specific memory benchmarks track allocation counts and bytes within ranges, helping prevent inefficient gems and memory-heavy changes from entering the renderer.

**Tags:** [Caching](https://yomu.fyi/topic/caching), [MySQL](https://yomu.fyi/topic/mysql), [Performance](https://yomu.fyi/topic/performance), [Redis](https://yomu.fyi/topic/redis), [Ruby](https://yomu.fyi/topic/ruby)

- Source: [Shopify](https://shopify.engineering/simplify-batch-cache-optimized-server-side-storefront-rendering)
- Source URL: https://shopify.engineering/simplify-batch-cache-optimized-server-side-storefront-rendering
- Ingested by Yomu: 2026-08-31T01:10:43.642Z

[Read original post](https://shopify.engineering/simplify-batch-cache-optimized-server-side-storefront-rendering)