Compression Methods for Web Pages

The simplest but most impactful optimisation you can do in your frontend app is enable compression. Story first Our bundle was 3.5 MB. Users kept saying the site felt slow. Turned on gzip on the server → transfer dropped to 1.4 MB. No code changes. Just a config. Users instantly felt the site lighter. Why this works JS, CSS, HTML, JSON, SVG are text-heavy. Text compresses well. • Without compression → full 3.5 MB travels. • With gzip/Brotli → repeated patterns shrink → browser auto-decompresses. • Same content, 60% fewer bytes → faster FCP, LCP, TTI. What to compress ✅ HTML, CSS, JS, JSON, SVG, XML ❌ Images, videos, PDFs, fonts (already compressed) How to check Chrome DevTools → Network → click a JS/CSS file → look for Content-Encoding. If blank, you’re shipping raw bytes. Extra tip Brotli compresses 15–20% smaller than gzip. Serve .br to modern browsers, gzip as fallback. ⚡ Go ahead and check if your app already has this enabled. If not, enable it today and feel the difference yourself.

As a Frontend developer it is also important to prioritize performance optimization to ensure the web applications load quickly and provide a smooth user experience. Here's a breakdown of key techniques used for frontend performance optimization: Minification and Compression: Minification involves removing unnecessary characters (such as whitespace, comments, and unused code) from source files to reduce file size. Compression techniques like gzip or Brotli further reduce file sizes by compressing text-based resources like HTML, CSS, and JavaScript before transmitting them over the network. Smaller file sizes lead to faster download times and improved page loading speed. Image Optimization: Images often contribute significantly to page weight and load times. Optimizing images by compressing them without sacrificing quality, using appropriate image formats (such as WebP or JPEG XR), and implementing responsive image techniques (like srcset and sizes attributes) can dramatically improve performance. Additionally, lazy loading techniques delay the loading of off-screen images until they are needed, reducing initial page load times. Caching Strategies: Implementing caching strategies like browser caching, CDN caching, and server-side caching can reduce server load and speed up subsequent page loads. Leveraging HTTP caching headers such as Cache-Control and Expires allows browsers and intermediaries to store and reuse previously fetched resources, minimizing network requests. Asynchronous Loading: Loading JavaScript and CSS files asynchronously prevents them from blocking the rendering of the page, allowing critical content to display faster. Techniques like defer and async attributes for script tags and media attributes for stylesheet links enable asynchronous loading while ensuring proper execution order and avoiding render-blocking behavior. Code Splitting and Bundle Optimization: Code splitting involves breaking down large bundles of JavaScript or CSS code into smaller, more manageable chunks that can be loaded on-demand. Tools like Webpack offer features for code splitting, tree shaking (removing unused code), and optimizing bundle size, helping reduce initial load times and improve runtime performance. Critical Path Optimization: Identifying and optimizing the critical rendering path, which includes the resources necessary to render the initial view of a webpage, is crucial for improving perceived performance. Prioritizing the loading of critical resources (such as CSS and JavaScript required for above-the-fold content) and deferring non-essential resources can accelerate the time to first meaningful paint and enhance user perception of speed. #frontenddevelopment #performanceoptimization #webdevelopment #javascript

𝐁𝐨𝐨𝐬𝐭 𝐘𝐨𝐮𝐫 𝐀𝐩𝐩 𝐒𝐩𝐞𝐞𝐝: 𝐑𝐞𝐝𝐮𝐜𝐞 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐢𝐧 𝐀𝐒𝐏.𝐍𝐄𝐓 𝐂𝐨𝐫𝐞 🚀 In modern web applications, reducing latency is critical for better user experiences. One effective way to achieve this is through data compression, which reduces the size of responses sent to clients, speeding up delivery and saving bandwidth. 𝑾𝒉𝒚 𝑼𝒔𝒆 𝑰𝒕? 1️⃣ Faster Responses: Smaller payloads = quicker delivery. 2️⃣ Improved UX: Great for slow networks. 3️⃣ Bandwidth Savings: Reduced resource usage. 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐈𝐭: 1️⃣ Install the Middleware 2️⃣ Configure in Program.cs 3️⃣ Adjust Compression Levels 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬: ✅ Use Brotli (modern) and Gzip (compatible). ✅ Skip already-compressed files (images, videos). ✅ Test performance impact with tools like Lighthouse. 𝐀𝐯𝐨𝐢𝐝 𝐓𝐡𝐞𝐬𝐞 𝐏𝐢𝐭𝐟𝐚𝐥𝐥𝐬 ❌ Compressing Already-Compressed Content: Skip files like images or videos that are pre-compressed. ❌ Over-Committing to Compression: Avoid CompressionLevel.SmallestSize unless absolutely necessary—it can spike CPU usage. ❌ Ignoring HTTPS: Ensure compression works with HTTPS for modern applications. Tip: Optimize for HTTPS responses and balance CPU load with compression level. 🚀 How are you reducing latency in your apps? Let’s discuss! 👇

Have you spotted the addition of 'Transferred' data in Screaming Frog? If you're an #seo or #technicalseo interested in #webperformance, you should check it out. Comparing the difference in bytes between the 'Transferred' and 'Size' columns allows you to see if network file compression is used across different file types at scale. File compression technology, like GZIP and Brotli, allows web servers to compress file requests before streaming them to a browser. It helps to: 👉 Reduce file size 👉 Enhance transfer speed 👉 Improve bandwidth efficiency 👉 Contribute to an overall better user experience  👉 Provide faster browsing where network resources are constrained or costly Previously, I relied on Chrome DevTools to learn about file compression insights, and you could only see this data for one page at a time. 💡 Extra tip💡 Enable HTTP Headers in your crawl (Config > Spider > Extraction > URL details) and use the Content-Encoding HTTP header to check what technology is used for compression - GZIP, Brotli or none.

Originally developed by Google for web content compression, Brotli has proven to be a powerful compression algorithm for structured data storage as well. It uses a combination of dictionary-based compression and entropy coding to achieve high compression ratios, particularly for text-heavy datasets such as JSON, HTML, and log files. Its ability to compress data more efficiently than Gzip while maintaining fast decompression speeds makes it valuable for both web performance and big data applications. Brotli is especially effective for datasets where reducing network transfer time is crucial, such as cloud-based storage, web APIs, and log management. While it isn’t as commonly used in Apache Parquet as ZSTD or Snappy, it remains a strong choice for applications where bandwidth efficiency is a key factor. If you’re working with large text-based data that needs both strong compression and fast retrieval, Brotli is worth considering.