{"id":552315,"date":"2026-10-08T07:23:53","date_gmt":"2026-10-08T07:23:53","guid":{"rendered":"https:\/\/webkul.com\/blog\/?p=552315"},"modified":"2026-10-08T07:24:02","modified_gmt":"2026-10-08T07:24:02","slug":"optimize-next-js-caching-1-million-product","status":"publish","type":"post","link":"https:\/\/webkul.com\/blog\/optimize-next-js-caching-1-million-product\/","title":{"rendered":"How We Optimized Next.js Caching for 1 Million Product"},"content":{"rendered":"\n

Running an eCommerce store with one million products creates a very different performance challenge from running a small catalog.<\/p>\n\n\n\n

Pages need to load quickly, product data needs to stay reasonably fresh, and the backend must handle traffic without processing the same requests repeatedly.<\/p>\n\n\n\n

While building a headless Bagisto storefront<\/a> with Next.js, we needed a caching strategy that could scale with a large catalog without making deployments slower or putting unnecessary load on the backend.<\/p>\n\n\n\n

The solution was not a single caching technique. We combined request-level caching, cache tags, on-demand page generation, a small in-memory cache, and webhook-based revalidation.<\/p>\n\n\n\n

If you’re building a headless storefront, our guide on How to Build eCommerce in Next.js<\/a> covers the foundation.<\/p>\n\n\n\n

The Challenge of a One-Million-Product Catalog<\/h2>\n\n\n\n

A large product catalog creates two problems that need to be solved together: performance and freshness<\/strong>.<\/p>\n\n\n\n

Pre-building every product page during deployment is not practical when the catalog contains one million products. Build times can increase significantly, and deployments become harder to manage.<\/p>\n\n\n\n

Fetching product data from the backend on every request creates a different problem. The backend has to process repeated requests for the same data, increasing server load and potentially increasing response times.<\/p>\n\n\n\n

Caching everything indefinitely is not the answer either. Product prices, inventory, availability, and content can change.<\/p>\n\n\n\n

The real challenge is finding the right balance between speed and freshness.<\/p>\n\n\n\n

\"text<\/figure>\n\n\n\n

Why We Stopped Treating Cache as One Thing<\/h2>\n\n\n\n

Not all ecommerce data changes at the same frequency. Store configurations may remain unchanged for days.<\/p>\n\n\n\n

Product information can change a few times throughout the day. Cart and checkout data can change every second.<\/p>\n\n\n\n

Because of this, we moved away from a single caching strategy and started caching data based on how often it changes.<\/p>\n\n\n\n

This simple decision became the foundation of our entire optimization approach.<\/p>\n\n\n\n

One Entry Point for Every GraphQL Request<\/h2>\n\n\n\n

To keep caching consistent, every server-side GraphQL request goes through a single helper function.<\/p>\n\n\n\n

This helper wraps Next.js caching APIs and applies cache tags, cache keys, and cache lifetimes automatically.<\/p>\n\n\n\n

graphqlRequest( \nGET_PRODUCT_BY_URL_KEY, \n{ urlKey }, \n{ tags: [\"products\", `product-${urlKey}`],\n life: \"hours\",\n } );<\/pre>\n\n\n\n
This approach ensures every request follows the same caching rules.<\/p>\n\n\n\n
It also gives us one place to update caching behavior when requirements change.<\/p>\n\n\n\n
Why Cache Tags Matter<\/h3>\n\n\n\n
Cache tags allow us to invalidate specific content when data changes. Instead of clearing the entire cache, we can refresh only the affected product.<\/p>\n\n\n\n
This keeps cache invalidation fast and efficient.<\/p>\n\n\n\n
Building Stable Cache Keys<\/h3>\n\n\n\n
A cache is only effective when identical requests generate identical keys. We generate cache keys using the GraphQL query and its variables in a stable, sorted format. As a result, duplicate requests always hit the same cache entry.<\/p>\n\n\n\n
For large catalogs, this significantly reduces unnecessary backend traffic.<\/p>\n\n\n\n
Match Cache Lifetime to the Data<\/h2>\n\n\n\n
We assign cache durations based on how frequently data changes.<\/p>\n\n\n\n
\/\/ Human-friendly cache lifetimes\nseconds \u2192 10 s\nminutes \u2192 60 s  \nhours \u2192 3600 s\ndays \u2192 86400 s<\/pre>\n\n\n\n
Here is how we assign them across the catalog.<\/p>\n\n\n\n
Data<\/th>Lifetime<\/th><\/tr><\/thead>
Store config, menus<\/td>days<\/td><\/tr>
Product pages<\/td>hours<\/td><\/tr>
Category listings<\/td>minutes<\/td><\/tr>
Search and filters<\/td>seconds<\/td><\/tr>
Cart, customer<\/td>never cached<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
The rule is easy to remember. Cache for as long as the data stays true.<\/p>\n\n\n\n
Generating Pages On Demand<\/h2>\n\n\n\n
The biggest optimization was avoiding the need to pre-build a million pages. We only generate a small set of popular products during deployment.<\/p>\n\n\n\n
All other product pages are generated when they receive their first visit.<\/p>\n\n\n\n
export const dynamic = \"force-static\";\nexport const revalidate = 86400;\nexport const dynamicParams = true;<\/pre>\n\n\n\n
This keeps build times fast regardless of catalog size.<\/p>\n\n\n\n
Once generated, pages are cached and served like static content.<\/p>\n\n\n\n
Using a Small Memory Cache<\/h2>\n\n\n\n
The same product can be requested multiple times during a render cycle. To avoid duplicate requests, we use a lightweight LRU cache.<\/p>\n\n\n\n
const productCache =\n  new LRUCache<ProductNode>(100, 10);<\/pre>\n\n\n\n
This reduces unnecessary backend calls while keeping memory usage predictable.<\/p>\n\n\n\n
Keeping Cached Content Fresh<\/h2>\n\n\n\n
Long cache times are only safe if you can clear them on demand.<\/p>\n\n\n\n
Bagisto sends a webhook whenever a product or collection changes.<\/p>\n\n\n\n
if ( isProductUpdate || isCollectionUpdate ) \n{\n revalidatePath(\"\/\", \"layout\");\n }<\/pre>\n\n\n\n
This allows product updates to appear quickly without waiting for cache expiration.<\/p>\n\n\n\n
Final Thoughts<\/h2>\n\n\n\n
Supporting one million products in Next.js does not mean building one million pages during every deployment or sending every request directly to the backend.<\/p>\n\n\n\n
Centralized caching, on-demand page generation, cache tagging, webhook revalidation, and strict handling of user data helped us keep the storefront fast while maintaining fresh content.<\/p>\n\n\n\n
For large headless ecommerce<\/a> implementations, these patterns provide a practical way to scale Next.js without sacrificing performance or maintainability.<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Running an eCommerce store with one million products creates a very different performance challenge from running a small catalog. Pages need to load quickly, product data needs to stay reasonably fresh, and the backend must handle traffic without processing the same requests repeatedly. While building a headless Bagisto storefront with Next.js, we needed a caching […]<\/a><\/p>\n","protected":false},"author":770,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-552315","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nHow We Optimized Next.js Caching for 1 Million Product - Webkul Blog<\/title>\n<meta name=\"description\" content=\"Learn how we optimized Next.js caching for a 1 million product Bagisto storefront using cache tags, on-demand page generation, ISR, webhooks, and smart cache lifetimes to improve performanc\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/webkul.com\/blog\/optimize-next-js-caching-1-million-product\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How We Optimized Next.js Caching for 1 Million Product - 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