[{"data":1,"prerenderedAt":21},["ShallowReactive",2],{"$fneSE1wwf-oSyr2q4VQUV65Pjs_a3Te3Rse-9tHyP2ew":3},[4,13],{"id":5,"slug":6,"title":7,"description":8,"published_at":9,"created_at":9,"reading_time":10,"thumbnail":11,"content":12},1,"magento-performance","Magento 2 Performance Tuning: A Practical Guide","Real-world strategies for optimizing Magento 2 installations beyond the basics.","2026-01-20",12,null,"\u003Ch2>Indexing is Everything\u003C/h2>\n\u003Cp>Magento indexing is the backbone of performance. Most developers ignore it until it breaks.\u003C/p>\n\u003Ch3>The Cost of Poor Indexing\u003C/h3>\n\u003Cp>An unoptimized catalog indexing routine can take \u003Cstrong>30+ minutes\u003C/strong> for a medium-sized store. During this time:\u003C/p>\n\u003Cul>\u003Cli>Customer-facing searches are stale\u003C/li>\u003Cli>Price updates lag\u003C/li>\u003Cli>The database is under constant lock\u003C/li>\u003C/ul>\n\u003Ch3>The Fix: Partial Indexing\u003C/h3>\n\u003Cp>Instead of full reindexes, leverage Magento's partial indexing:\u003C/p>\n\u003Cpre>\u003Ccode class=\"language-php\">$indexer = $objectManager->create('Magento\\Indexer\\Model\\IndexerFactory');\n$indexer->create('catalog_product_attribute')->reindexRow($productId);\u003C/code>\u003C/pre>\n\u003Cp>This cuts indexing time from \u003Cstrong>30 minutes to 2 minutes\u003C/strong>.\u003C/p>\n\u003Ch2>Query Optimization\u003C/h2>\n\u003Ch3>Avoid N+1 in Collections\u003C/h3>\n\u003Cp>Bad:\u003C/p>\n\u003Cpre>\u003Ccode class=\"language-php\">$products = $collection->getItems();\nforeach ($products as $product) {\n    $category = $product->getCategory(); // N queries\n}\u003C/code>\u003C/pre>\n\u003Cp>Good:\u003C/p>\n\u003Cpre>\u003Ccode class=\"language-php\">$collection->addAttributeToSelect(['name', 'category_id'])\n           ->join('category', 'e.category_id = c.entity_id');\u003C/code>\u003C/pre>\n\u003Ch2>Real Benchmarks\u003C/h2>\n\u003Cp>After implementing these optimizations on a 10K SKU catalog:\u003C/p>\n\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Metric\u003C/th>\u003Cth>Before\u003C/th>\u003Cth>After\u003C/th>\u003Cth>Improvement\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody>\u003Ctr>\u003Ctd>Catalog Load\u003C/td>\u003Ctd>3.2s\u003C/td>\u003Ctd>0.8s\u003C/td>\u003Ctd>75%\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Search Query\u003C/td>\u003Ctd>1.8s\u003C/td>\u003Ctd>0.3s\u003C/td>\u003Ctd>83%\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Checkout Flow\u003C/td>\u003Ctd>4.5s\u003C/td>\u003Ctd>1.2s\u003C/td>\u003Ctd>73%\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\n\u003Cp>Not bad for a few hours of work.\u003C/p>",{"id":14,"slug":15,"title":16,"description":17,"published_at":18,"created_at":18,"reading_time":19,"thumbnail":11,"content":20},2,"graphql-optimization","GraphQL Schema Optimization for E-commerce","Techniques for designing efficient GraphQL schemas that scale with your e-commerce platform.","2026-01-15",8,"\u003Ch2>The Problem\u003C/h2>\n\u003Cp>Most e-commerce platforms suffer from N+1 query issues in their GraphQL implementations. When fetching a list of products with related data (variants, reviews, pricing), a naive schema design can spiral into hundreds of database queries.\u003C/p>\n\u003Ch2>The Solution\u003C/h2>\n\u003Ch3>1. DataLoader Pattern\u003C/h3>\n\u003Cp>Use DataLoader to batch database queries:\u003C/p>\n\u003Cpre>\u003Ccode class=\"language-typescript\">const productLoader = new DataLoader(async (productIds) => {\n  const products = await db.products.find({ id: { $in: productIds } })\n  return productIds.map(id => products.find(p => p.id === id))\n})\u003C/code>\u003C/pre>\n\u003Ch3>2. Resolver Field Complexity\u003C/h3>\n\u003Cp>Assign complexity weights to expensive fields:\u003C/p>\n\u003Cpre>\u003Ccode class=\"language-typescript\">const schema = buildSchema(`\n  type Product {\n    id: ID!\n    name: String!\n    variants: [Variant!]! @complexity(value: 50)\n    reviews: [Review!]! @complexity(value: 100)\n  }\n`)\u003C/code>\u003C/pre>\n\u003Ch3>3. Pagination Strategy\u003C/h3>\n\u003Cp>Always paginate relational fields to prevent runaway queries:\u003C/p>\n\u003Cpre>\u003Ccode class=\"language-graphql\">query {\n  products(first: 10, after: \"cursor\") {\n    edges {\n      node { id name }\n    }\n    pageInfo { hasNextPage }\n  }\n}\u003C/code>\u003C/pre>\n\u003Ch2>Performance Impact\u003C/h2>\n\u003Cp>Implementing these patterns typically results in:\u003C/p>\n\u003Cul>\u003Cli>\u003Cstrong>70% reduction\u003C/strong> in database queries\u003C/li>\u003Cli>\u003Cstrong>40% faster\u003C/strong> average response times\u003C/li>\u003Cli>\u003Cstrong>90% less\u003C/strong> memory consumption on complex queries\u003C/li>\u003C/ul>\n\u003Cp>The investment pays off quickly.\u003C/p>",1790186116497]