Page Speed

Page speed refers to the measurement of how quickly the content on a webpage loads, specifically the time it takes for the complete display of the content. Elements such as HTML code, CSS, JavaScript files, images, videos, and other multimedia components impact the page speed. Pages that have longer loading times tend to experience higher bounce rates, lower average time spent on the page, and decreased conversions. Additionally, slower-loading pages tend to rank poorly on search engine result pages.

To improve page speed, consider the following steps:

Begin by measuring the speed of your pages using tools like PageInsight, GTMatrix, or similar tools. These tools can provide insights on areas that need improvement to enhance page speed. Dynatrace is another tool that can be utilized.

If you are using a popular framework or content management platform, consult their documentation for performance optimization best practices.

The following recommendations can have a significant impact on reducing page load speed:

• Optimize images and videos: Images, graphics, photos, and videos enhance user engagement but often consist of large files that can slow down a website. Reduce image file size by sending appropriately sized images at the correct resolution for the user's screen. Compress images using formats like JPG. Compress videos using video compression tools to improve speed.
• Progressive image loading technique: Implement progressive image loading to prioritize the display of visible content and delay the loading of images below the fold.
• Serve images in next-gen formats: Utilize next-generation image formats, such as WebP, to further optimize image delivery.
• Enable compression: Compress website files, such as CSS, JavaScript, and HTML, to reduce their size. Techniques like Gzip compression can minimize HTTP requests and decrease server response time. On the client side, browsers unzip the files and deliver the contents.
• Cache your web pages: Caching is an effective method to deliver web pages quickly. Utilize a hosting provider that offers caching capabilities or use caching plugins. Content Delivery Networks (CDNs) can also assist in delivering content from nearest edge locations.
• Reduce the number of plugins: Excessive plugins on a website consume more resources and can slow down performance. Remove unused plugins and evaluate the performance impact of new plugins before adding them.
• Minify CSS, JavaScript, and HTML: Minify your code by removing unnecessary characters, spaces, comments, formatting, and unused code.
• Enable browser caching: Enable browser caching to store files such as stylesheets, images, and JavaScript files in the user's browser, allowing for faster subsequent page loads.
• Reduce redirects: Unnecessary redirects trigger additional HTTP requests and slow down performance. Eliminate unnecessary redirects to improve page speed.
• Lazy loading: Implement lazy loading for content and script files that are not immediately needed in the visible portion of the initial page render. This asynchronous download allows the browser to download content in the background while rendering the visible part of the page. It is particularly useful for pages with extensive content or when content is shown based on user actions like scrolling or mouse movement.
• Avoid render-blocking JavaScript: Render-blocking JavaScript can hinder page speed as browsers must execute it before rendering the page for users. Minimize or defer render-blocking JavaScript to improve page speed.
• Reduce server response time: Optimize your server configuration, database, and other relevant factors to ensure fast content delivery and reduce server response time.
• Use the right hosting for your site: Assess your hosting platform, such as dedicated, shared, or Virtual Private Servers (VPS), and consider moving to a performance-optimized hosting solution if necessary.

Optimize Your API Response Time

API Response Time

API Response time refers to the overall duration taken by a system to provide a response to a API request. For instance, it represents the time elapsed between calling an API and receiving the resulting output, which can be in the form of XML, JSON, or other media types. Factors that influence the response time include network bandwidth, user volume, types and quantities of requests submitted, and the size of the requests. Research indicates that websites with longer loading times experience higher bounce rates and tend to rank poorly on search engine result pages.

To enhance API response time, it is recommended to follow these steps:

Begin by measuring and monitoring the response times of your APIs, and establish alert systems. Conduct load, stress, and endurance testing when necessary.

If you are using a popular framework, data source, or content management platform, refer to their documentation for performance optimization best practices.

Implement the following recommended practices

• Configure caching for faster data retrieval: If certain requests frequently yield the same response, caching the response can prevent excessive queries to the data source. Periodically expire the cached responses or force expiration when relevant data updates occur.
• Eliminate unnecessary data from the response: Transmitting large amounts of data takes more time. By reducing the payload size sent from the server to the client device, you can minimize the waiting time for users. Pay special attention to small-sized responses for mobile devices. Utilize PATCH requests whenever appropriate.
• Compress data: Compression can decrease the time it takes for a device to download data from the server since fewer bytes are being transmitted.
• Ensure a faster and reliable network: Slow or unreliable networks directly impact API performance. Invest in the appropriate infrastructure to maintain the desired level of performance.
• Implement pagination for large payloads: This approach significantly reduces the payload size, particularly for customers with extensive data histories.
• Break down APIs into microservices: Divide a single monolithic API into multiple microservices, each handling specific module APIs to access and retrieve relevant data. This approach enables separate scaling based on demand.
• Use connection pooling: Employ connection pooling to connect to the data source, as creating and closing database connections can consume considerable time.
• Deploy auto-scaling: Utilize auto-scaling groups to deploy APIs, allowing them to scale between instances according to normal and peak usage periods throughout the day.