
  Supercharge Your Web Performance: Optimizing Next.js Apps with Cloudflare Workers and Browser Caching
  If you're a web developer, you know how critical web performance is when it comes to user experience. Slow websites lead to high bounce rates, fewer conversions, and unhappy users. But what can you do to improve web app performance beyond reducing the size of your images and code?

  In this tutorial, we'll explore how to use Cloudflare Workers and browser caching to optimize Next.js web applications for lightning-fast performance. Let's get started!

  What is Next.js?
  Next.js is an increasingly popular React-based framework for building server-side rendered (SSR) web applications. When you build a Next.js app, each page is rendered on the server and sent to the client as HTML, rather than purely client-side rendering. This can bring significant performance benefits while retaining the flexibility and interactivity of React.

  Why is performance important in Next.js?
  Despite the performance benefits of Next.js, there are still some aspects you should consider to ensure your app runs smoothly and quickly.

  
    First contentful paint and time to interactive: A delay in pages loading results in a poor user experience. Users expect your pages to appear within seconds of clicking or tapping, especially on mobile devices where data is often slower.
    Repeated data fetching: Ideally, a web app should make the least amount of data requests necessary to serve its purpose. Repeated requests for the same resource (like product info or user info) slow load times further.
    Data size: The larger the amount of data that needs to load, the slower the page loads. Striving for smaller page sizes will result in faster load times for users.
  

  In short, performance is essential in any web application, and these three factors above are some of the critical aspects that you need to pay attention to.

  How can Cloudflare Workers and Browser Caching Help?
  Cloudflare Workers can be a cutting-edge solution to enhancing Next.js app performance. With their ability to modify incoming HTTP requests and outgoing responses, workers allow you to augment your app's performance in unique ways. Here are two ways to leverage workers to optimize your Next.js app.

  Benefits of Cloudflare Workers
  
    CDN Load Balancing: Single data-centers cannot handle large traffic during high demands, Cloudflare Workers can handle high burst traffic.
    Serverless: You don’t have to worry about managing operational tasks, deployment and scalability issues.
    Improved User Experience: Speeding up load times improves user experience, keeping users happy and engaged with your website.
  

  Browser Caching
  Browser caching is a technique that stores assets like images, CSS, and JavaScript files on a user's device after they visit your site for the first time. By caching these assets, the browser doesn't have to re-fetch them every time the same user returns to your site or clicks through to another page. This can significantly improve page loading speed as the browser can retrieve the files from the local cache immediately.

  However, caching can be tricky, and it's not always clear which files should be cached and for how long. Fortunately, Cloudflare Workers provide a straightforward way to handle browser caching.

  To leverage browser caching for your Next.js app using Cloudflare Workers:

  
    Add a Cache-Control header to your Next.js-generated pages using the build time configuration by modifying the next.config.js file.
    Write a Cloudflare Worker to add the Cache-Control header to non-Next.js generated resources like images, CSS, and JS files.
    Configure the worker to add the Cache-Control header when the request path matches a specific regular expression. 
  

  Step 1: Add Cache-Control Headers for Next.js Pages
  Next.js comes with an inbuilt runtime-generated cache-control header by default; however, it is possible to extend the caching times by modifying the configuration file as follows: 

  
  # next.config.js
  
  module.exports = {
    poweredByHeader: false,
    generateEtags: true,
    compress: true,
    trailingSlash: true,
    devIndicators: { autoPrerender: false },
    eslint: {
      ignoreDuringBuilds: true,
    },
    future: {
      webpack5: true,
    },
    async headers() {
      return [
        {
          source: '/:path*',
          headers: [
            {
              key: 'Cache-Control',
              value: 'public, max-age=31536000, immutable',
            },
          ],
        }
      ]
    },
  };
  

  This configuration extends the Next.js cache to 1 year, maximizing browser caching. However, some files like images and JavaScript files generated by third-party libraries might have shorter cache times. Let's take care of those in the next step.

  Step 2: Write a Cloudflare Worker to Add Cache-Control Headers for Non-Next.js Resources
  A Cloudflare Worker is a JavaScript program that executes on the Cloudflare network. Luckily, writing a worker for cache-control is simple. We'll write a worker that adds the `Cache-Control` header for files requested from the `/static` path. The worker will also add this header for `js`, `css`, and `svg` file types with `Content-Type` headers.

  
  addEventListener('fetch', event =&gt; {
    event.respondWith(handleRequest(event.request))
  })

  async function handleRequest(request) {
    const response = await fetch(request)

    const url = new URL(request.url)
    const isStaticFileRequest = url.pathname.startsWith('/static/') || /\.(js|css|svg)$/.test(response.headers.get('Content-Type'))

    if (isStaticFileRequest) {
      return new Response(response.body, {
        headers: {
          ...response.headers,
          'Cache-Control': 'public, max-age=31536000, immutable',
        },
      })
    }

    return response
  }
  

  Step 3: Deploy Your Worker
  By now, your worker code is ready, and it's time to deploy it. To do this:

  
    Log in to Cloudflare and navigate to the Workers section of your dashboard.
    Click the &quot;Create a Worker&quot; button.
    Copy the contents of the JavaScript file containing the worker code and paste it into the worker editor section.
    Save and deploy your worker.
  

  Optimizing Next.js App Performance with Cloudflare Workers
  Besides browser caching, Cloudflare Workers also allows you to cache frequently fetched data by placing your endpoint in between your API/backend and client. By doing so, you intercept fetch requests while returning the cached response, thereby utilizing less network bandwidth.

  For example, you can cut down on database calls by caching frequently accessed information from your database in memory or on Redis or Memcached to ensure speedy delivery to clients. 

  Therefore, to optimize performance in Next.js with Cloudflare Workers, we can do the following:

  
    Create a worker that intercepts the API endpoint requests.
    Cache the data in Redis or Memory to allow speedy delivery to clients.
    Set a unique cache key by hashing the requested data to avoid serving wrong data to clients.
    Set the cache expiry to determine how long the data should remain cached.
    Periodically clear your cache to avoid feeding clients outdated data.
  

  Here's an example:

  
  /**
  * Cache global variables
  */
  let cacheMemory = new Map();
  const REDIS_TTL = 60 * 60 * 24; // in seconds
  const CACHE_KEY_PREFIX = "nextjs-app:";

  /**
  * Get Redis client
  */
  function getRedisClient() {
    let redisConfig = {
      host: REDIS_HOST,
      port: REDIS_PORT,
      password: REDIS_PASSWORD,
    };
    return new Redis(redisConfig);
  }

  /**
  * Get from cache (Redis or memory)
  */
  async function getFromCache(cacheKey, defaultValue, expiry = REDIS_TTL) {
    let cachedValue;
    let isRedisEnabled = false;

    // Check if Redis is enabled
    if (REDIS_ENABLED !== undefined) {
      isRedisEnabled = (REDIS_ENABLED === 'true');
    }

    if (isRedisEnabled) {
      // Get Redis client
      let redisClient = getRedisClient();

      // Get cached value from Redis
      cachedValue = await redisClient.get(cacheKey);

      // If Redis doesn't have the item, fall back to memory
      if (cachedValue === null) {
        cachedValue = cacheMemory.get(cacheKey);
      } else {
        // Stash Redis value in local memory cache for future requests
        cacheMemory.set(cacheKey, cachedValue);
      }
    } else {
      // Fall back to simple memory cache
      cachedValue = cacheMemory.get(cacheKey);
    }

    // If we have a cached value, return it
    if (cachedValue !== undefined) {
      return JSON.parse(cachedValue);
    }

    // Otherwise, return the provided default value and add to cache
    return defaultValue;
  }

  /**
  * Save to cache (Redis or memory)
  */
  async function saveToCache(cacheKey, cacheValue, expiry = REDIS_TTL) {
    // Save to Redis
    let redisClient = getRedisClient();
    await redisClient.set(cacheKey, JSON.stringify(cacheValue), 'EX', expiry);

    // Save also to memory for faster retrieval
    cacheMemory.set(cacheKey, JSON.stringify(cacheValue));
  }

  /**
  * Clear cache by prefix
  */
  async function clearCacheByPrefix(cacheKeyPrefix, redisPattern = "*") {
    // Clear Redis cache
    let redisClient = getRedisClient();

    let keys = await redisClient.keys(buildCacheKey(cacheKeyPrefix, redisPattern));
    if (keys.length > 0) {
      await redisClient.del(keys);
    }

    // Clear memory cache
    for (const [key, value] of cacheMemory.entries()) {
      if (key.startsWith(cacheKeyPrefix)) {
        cacheMemory.delete(key);
      }
    }
  }

  /**
  * Build the cache key from the requested data
  */
  function buildCacheKey(cacheKeyPrefix, data) {
    const cacheKey = CACHE_KEY_PREFIX + cacheKeyPrefix + ':' + crypto.createHash('md5').update(JSON.stringify(data)).digest('hex');
    return cacheKey;
  }

  /**
  * The main worker function
  */
  async function handleRequest(request) {
    // Extract variables from the querystring or request body
    const { data } = await request.json();
    const uniqueIdentifier = data.identifier;

    // Get the cached response if it exists, else fetch from the server
    const cacheKey = buildCacheKey('post', uniqueIdentifier);
    const cachedResponse = await getFromCache(
      cacheKey,
      false /* do not always cache a fallback value */
    );

    if (cachedResponse !== undefined) {
      console.log('Cache HIT for ' + cacheKey);
      const response = new Response(cachedResponse, {
        headers: {
          'content-type': 'application/json',
        },
      });
      return response;
    } else {
      console.log('Cache MISS for ' + cacheKey);
      const response = await fetch(request, fallbackUrl);

      // If response is okay, cache it
      if (response.ok) {
        try {
          const jsonData = await response.json();
          await saveToCache(cacheKey, jsonData);
        } catch (e) {
          console.error('Unable to parse JSON response:', e);
        }
      }
      return response;
    }
  }

  addEventListener('fetch', event => {
    event.respondWith(handleRequest(event.request))
  });
  

  By leverage Cloudflare workers as mentioned above, we can optimize our Next.js app and reduce network traffic, all while keeping users engaged with fast load times.

  Conclusion
  Optimizing the performance of our Next.js web applications is vital to ensuring a seamless user experience. With Cloudflare Workers and browser caching strategies, we can supercharge the performance of our web apps, resulting in happy users and better conversion rates.

  Browser caching can be a quick and easy win by leveraging Cloudflare Workers, wherein we extend your browser's cache on your Next.js generated pages. Workers can also be used to cache frequently accessed data.

  While Cloudflare Workers can be an incredibly powerful tool, remember that performance optimization is a long-term game that requires monitoring and tweaking over time. By continuously analyzing and fine-tuning your Next.js app, you can build a web app that runs smoothly and consistently for your users.


<div>
  <h1>Supercharge Your Web Performance: Optimizing Next.js Apps with Cloudflare Workers and Browser Caching</h1>
  <p>If you're a web developer, you know how critical web performance is when it comes to user experience. Slow websites lead to high bounce rates, fewer conversions, and unhappy users. But what can you do to improve web app performance beyond reducing the size of your images and code?</p>

  <p>In this tutorial, we'll explore how to use Cloudflare Workers and browser caching to optimize Next.js web applications for lightning-fast performance. Let's get started!</p>

  <h2>What is Next.js?</h2>
  <p>Next.js is an increasingly popular React-based framework for building server-side rendered (SSR) web applications. When you build a Next.js app, each page is rendered on the server and sent to the client as HTML, rather than purely client-side rendering. This can bring significant performance benefits while retaining the flexibility and interactivity of React.</p>

  <h2>Why is performance important in Next.js?</h2>
  <p>Despite the performance benefits of Next.js, there are still some aspects you should consider to ensure your app runs smoothly and quickly.</p>

  <ul>
    <li><strong>First contentful paint and time to interactive:</strong> A delay in pages loading results in a poor user experience. Users expect your pages to appear within seconds of clicking or tapping, especially on mobile devices where data is often slower.</li>
    <li><strong>Repeated data fetching:</strong> Ideally, a web app should make the least amount of data requests necessary to serve its purpose. Repeated requests for the same resource (like product info or user info) slow load times further.</li>
    <li><strong>Data size:</strong> The larger the amount of data that needs to load, the slower the page loads. Striving for smaller page sizes will result in faster load times for users.</li>
  </ul>

  <p>In short, performance is essential in any web application, and these three factors above are some of the critical aspects that you need to pay attention to.</p>

  <h2>How can Cloudflare Workers and Browser Caching Help?</h2>
  <p>Cloudflare Workers can be a cutting-edge solution to enhancing Next.js app performance. With their ability to modify incoming HTTP requests and outgoing responses, workers allow you to augment your app's performance in unique ways. Here are two ways to leverage workers to optimize your Next.js app.</p>

  <h3>Benefits of Cloudflare Workers</h3>
  <ul>
    <li>CDN Load Balancing: Single data-centers cannot handle large traffic during high demands, Cloudflare Workers can handle high burst traffic.</li>
    <li>Serverless: You don’t have to worry about managing operational tasks, deployment and scalability issues.</li>
    <li>Improved User Experience: Speeding up load times improves user experience, keeping users happy and engaged with your website.</li>
  </ul>

  <h3>Browser Caching</h3>
  <p>Browser caching is a technique that stores assets like images, CSS, and JavaScript files on a user's device after they visit your site for the first time. By caching these assets, the browser doesn't have to re-fetch them every time the same user returns to your site or clicks through to another page. This can significantly improve page loading speed as the browser can retrieve the files from the local cache immediately.</p>

  <p>However, caching can be tricky, and it's not always clear which files should be cached and for how long. Fortunately, Cloudflare Workers provide a straightforward way to handle browser caching.</p>

  <p>To leverage browser caching for your Next.js app using Cloudflare Workers:</p>

  <ol>
    <li>Add a Cache-Control header to your Next.js-generated pages using the build time configuration by modifying the <code>next.config.js</code> file.</li>
    <li>Write a Cloudflare Worker to add the Cache-Control header to non-Next.js generated resources like images, CSS, and JS files.</li>
    <li>Configure the worker to add the Cache-Control header when the request path matches a specific regular expression. </li>
  </ol>

  <h4>Step 1: Add Cache-Control Headers for Next.js Pages</h4>
  <p>Next.js comes with an inbuilt runtime-generated cache-control header by default; however, it is possible to extend the caching times by modifying the configuration file as follows: </p>

  <pre><code>
  # next.config.js
  
  module.exports = {
    poweredByHeader: false,
    generateEtags: true,
    compress: true,
    trailingSlash: true,
    devIndicators: { autoPrerender: false },
    eslint: {
      ignoreDuringBuilds: true,
    },
    future: {
      webpack5: true,
    },
    async headers() {
      return [
        {
          source: '/:path*',
          headers: [
            {
              key: 'Cache-Control',
              value: 'public, max-age=31536000, immutable',
            },
          ],
        }
      ]
    },
  };
  </code></pre>

  <p>This configuration extends the Next.js cache to 1 year, maximizing browser caching. However, some files like images and JavaScript files generated by third-party libraries might have shorter cache times. Let's take care of those in the next step.</p>

  <h4>Step 2: Write a Cloudflare Worker to Add Cache-Control Headers for Non-Next.js Resources</h4>
  <p>A Cloudflare Worker is a JavaScript program that executes on the Cloudflare network. Luckily, writing a worker for cache-control is simple. We'll write a worker that adds the `Cache-Control` header for files requested from the `/static` path. The worker will also add this header for `js`, `css`, and `svg` file types with `Content-Type` headers.</p>

  <pre><code>
  addEventListener('fetch', event =&gt; {
    event.respondWith(handleRequest(event.request))
  })

  async function handleRequest(request) {
    const response = await fetch(request)

    const url = new URL(request.url)
    const isStaticFileRequest = url.pathname.startsWith('/static/') || /\.(js|css|svg)$/.test(response.headers.get('Content-Type'))

    if (isStaticFileRequest) {
      return new Response(response.body, {
        headers: {
          ...response.headers,
          'Cache-Control': 'public, max-age=31536000, immutable',
        },
      })
    }

    return response
  }
  </code></pre>

  <h4>Step 3: Deploy Your Worker</h4>
  <p>By now, your worker code is ready, and it's time to deploy it. To do this:</p>

  <ol>
    <li>Log in to Cloudflare and navigate to the Workers section of your dashboard.</li>
    <li>Click the &quot;Create a Worker&quot; button.</li>
    <li>Copy the contents of the JavaScript file containing the worker code and paste it into the worker editor section.</li>
    <li>Save and deploy your worker.</li>
  </ol>

  <h2>Optimizing Next.js App Performance with Cloudflare Workers</h2>
  <p>Besides browser caching, Cloudflare Workers also allows you to cache frequently fetched data by placing your endpoint in between your API/backend and client. By doing so, you intercept fetch requests while returning the cached response, thereby utilizing less network bandwidth.</p>

  <p>For example, you can cut down on database calls by caching frequently accessed information from your database in memory or on Redis or Memcached to ensure speedy delivery to clients. </p>

  <p>Therefore, to optimize performance in Next.js with Cloudflare Workers, we can do the following:</p>

  <ol>
    <li>Create a worker that intercepts the API endpoint requests.</li>
    <li>Cache the data in Redis or Memory to allow speedy delivery to clients.</li>
    <li>Set a unique cache key by hashing the requested data to avoid serving wrong data to clients.</li>
    <li>Set the cache expiry to determine how long the data should remain cached.</li>
    <li>Periodically clear your cache to avoid feeding clients outdated data.</li>
  </ol>

  <p>Here's an example:</p>

  <pre><code>
  /**
  * Cache global variables
  */
  let cacheMemory = new Map();
  const REDIS_TTL = 60 * 60 * 24; // in seconds
  const CACHE_KEY_PREFIX = "nextjs-app:";

  /**
  * Get Redis client
  */
  function getRedisClient() {
    let redisConfig = {
      host: REDIS_HOST,
      port: REDIS_PORT,
      password: REDIS_PASSWORD,
    };
    return new Redis(redisConfig);
  }

  /**
  * Get from cache (Redis or memory)
  */
  async function getFromCache(cacheKey, defaultValue, expiry = REDIS_TTL) {
    let cachedValue;
    let isRedisEnabled = false;

    // Check if Redis is enabled
    if (REDIS_ENABLED !== undefined) {
      isRedisEnabled = (REDIS_ENABLED === 'true');
    }

    if (isRedisEnabled) {
      // Get Redis client
      let redisClient = getRedisClient();

      // Get cached value from Redis
      cachedValue = await redisClient.get(cacheKey);

      // If Redis doesn't have the item, fall back to memory
      if (cachedValue === null) {
        cachedValue = cacheMemory.get(cacheKey);
      } else {
        // Stash Redis value in local memory cache for future requests
        cacheMemory.set(cacheKey, cachedValue);
      }
    } else {
      // Fall back to simple memory cache
      cachedValue = cacheMemory.get(cacheKey);
    }

    // If we have a cached value, return it
    if (cachedValue !== undefined) {
      return JSON.parse(cachedValue);
    }

    // Otherwise, return the provided default value and add to cache
    return defaultValue;
  }

  /**
  * Save to cache (Redis or memory)
  */
  async function saveToCache(cacheKey, cacheValue, expiry = REDIS_TTL) {
    // Save to Redis
    let redisClient = getRedisClient();
    await redisClient.set(cacheKey, JSON.stringify(cacheValue), 'EX', expiry);

    // Save also to memory for faster retrieval
    cacheMemory.set(cacheKey, JSON.stringify(cacheValue));
  }

  /**
  * Clear cache by prefix
  */
  async function clearCacheByPrefix(cacheKeyPrefix, redisPattern = "*") {
    // Clear Redis cache
    let redisClient = getRedisClient();

    let keys = await redisClient.keys(buildCacheKey(cacheKeyPrefix, redisPattern));
    if (keys.length > 0) {
      await redisClient.del(keys);
    }

    // Clear memory cache
    for (const [key, value] of cacheMemory.entries()) {
      if (key.startsWith(cacheKeyPrefix)) {
        cacheMemory.delete(key);
      }
    }
  }

  /**
  * Build the cache key from the requested data
  */
  function buildCacheKey(cacheKeyPrefix, data) {
    const cacheKey = CACHE_KEY_PREFIX + cacheKeyPrefix + ':' + crypto.createHash('md5').update(JSON.stringify(data)).digest('hex');
    return cacheKey;
  }

  /**
  * The main worker function
  */
  async function handleRequest(request) {
    // Extract variables from the querystring or request body
    const { data } = await request.json();
    const uniqueIdentifier = data.identifier;

    // Get the cached response if it exists, else fetch from the server
    const cacheKey = buildCacheKey('post', uniqueIdentifier);
    const cachedResponse = await getFromCache(
      cacheKey,
      false /* do not always cache a fallback value */
    );

    if (cachedResponse !== undefined) {
      console.log('Cache HIT for ' + cacheKey);
      const response = new Response(cachedResponse, {
        headers: {
          'content-type': 'application/json',
        },
      });
      return response;
    } else {
      console.log('Cache MISS for ' + cacheKey);
      const response = await fetch(request, fallbackUrl);

      // If response is okay, cache it
      if (response.ok) {
        try {
          const jsonData = await response.json();
          await saveToCache(cacheKey, jsonData);
        } catch (e) {
          console.error('Unable to parse JSON response:', e);
        }
      }
      return response;
    }
  }

  addEventListener('fetch', event => {
    event.respondWith(handleRequest(event.request))
  });
  </code></pre>

  <p>By leverage Cloudflare workers as mentioned above, we can optimize our Next.js app and reduce network traffic, all while keeping users engaged with fast load times.</p>

  <h2>Conclusion</h2>
  <p>Optimizing the performance of our Next.js web applications is vital to ensuring a seamless user experience. With Cloudflare Workers and browser caching strategies, we can supercharge the performance of our web apps, resulting in happy users and better conversion rates.</p>

  <p>Browser caching can be a quick and easy win by leveraging Cloudflare Workers, wherein we extend your browser's cache on your Next.js generated pages. Workers can also be used to cache frequently accessed data.</p>

  <p>While Cloudflare Workers can be an incredibly powerful tool, remember that performance optimization is a long-term game that requires monitoring and tweaking over time. By continuously analyzing and fine-tuning your Next.js app, you can build a web app that runs smoothly and consistently for your users.</p>
</div>

Learn how to leverage Cloudflare Workers and effective browser caching strategies to optimize your Next.js web applications for blazing-fast performance.

Supercharge Your Web Performance: Optimizing Next.js Apps with Cloudflare Workers and Browser Caching


  Effortless Data Fetching with SWR: A Comprehensive Guide for React Developers

  As web applications become increasingly complex, the importance of efficient data fetching only grows. Traditional solutions, such as Redux or manual Fetch requests, can be time-consuming to set up and even more so to maintain.
  
  Enter SWR, a simple yet powerful library that streamlines data fetching and optimizes caching behavior for React applications. In this comprehensive guide, we’ll dive into all things SWR: how to set it up, how to use it for optimized data fetching, and how to take advantage of real-time data updates.
  
  What is SWR?
  
  SWR, short for stale-while-revalidate, is a library that helps manage data fetching and caching within React applications. It allows developers to fetch data from various sources such as APIs, and optimize network requests using a caching strategy, all with little to no configuration. SWR also handles real-time updates by automatically revalidating data when it becomes stale or outdated.
  
  How to Install SWR
  
  To get started with SWR, we need to install it through NPM or Yarn:
  
  $ npm install swr
  
  or
  
  $ yarn add swr
  
  Using SWR for Data Fetching
  
  Now that we have SWR installed, let’s see how we can use it to fetch data inside a React component. Let’s assume we have a simple API that returns an array of objects:
  
  async function fetcher(url) {
  const res = await fetch(url);
  return res.json();
}
 
const { data, error } = useSWR('/api/data', fetcher);
 
if (error) return Failed to load data;
if (!data) return Loading...;
 
return (
  &lt;ul&gt;
    {data.map((item) => (
      &lt;li key={item.id}&gt;{item.name}&lt;/li&gt;
    ))}
  &lt;/ul&gt;
);
  
  So what’s going on in this code? First, we define an asynchronous function called `fetcher` that takes a URL and returns the parsed JSON response when called. Next, we use the `useSWR` hook to fetch data from our API endpoint at `/api/data`, using the `fetcher` function we defined earlier.
  
  The `useSWR` hook returns an object with two properties: `data` and `error`. If the fetch was successful, `data` will contain the response data, and `error` will be null. If there was an error while fetching the data, `error` will contain the error message, and `data` will be null. If the data is still loading, both `data` and `error` will be null.
  
  In the example above, we’re checking for `error` and `data`. If `error` contains a value, we display a message indicating that the data failed to load. If `data` is null, we display a message indicating that the data is still loading. If `data` contains a value, we map over the array and render each object item as an `li` element.
  
  One of the most powerful features of SWR is that it automatically caches the response data and uses it for subsequent requests. This means that if we reload the page or revisit the same endpoint, SWR will first check its cache for the data and return it if it’s available. If the data is stale, SWR will fetch the data again in the background and re-render the component when it’s available.
  
  Optimizing Caching Behavior with SWR
  
  SWR’s caching behavior can be further optimized using various configuration options. For example, we can set a time to live for the cached data using the `refreshInterval` option, which specifies the number of milliseconds between automatic revalidation:
  
  const { data } = useSWR('/api/data', fetcher, { refreshInterval: 5000 });
  
  In this example, the data will be revalidated every five seconds.
  
  Another useful option is `revalidateOnMount`, which revalidates the data when the component mounts:
  
  const { data } = useSWR('/api/data', fetcher, { revalidateOnMount: true });
  
  This can be useful for real-time data updates, as we’ll discuss in the next section.
  
  Real-time Data Updates with SWR
  
  SWR also supports real-time data updates using websockets, server-sent events, or any other protocol that can broadcast data updates to connected clients. In this example, we’ll use websockets to stream real-time updates to our data:
  
  import { useEffect, useState } from 'react';
import useSWR from 'swr';
import io from 'socket.io-client';
 
const socket = io.connect('http://localhost:3001');
 
async function fetcher(url) {
  const res = await fetch(url);
  return res.json();
}
 
export default function RealTimeData() {
  const [data, setData] = useState([]);
  const { mutate } = useSWR('/api/data', fetcher);

  useEffect(() => {
    socket.on('data:update', (newData) => {
      // Update data when new data is received over the websocket
      setData((prevData) => [...prevData, newData]);
      // Revalidate SWR cache for the new data
      mutate();
    });
  }, []);
 
  return (
    &lt;ul&gt;
      {data.map((item) => (
        &lt;li key={item.id}&gt;{item.name}&lt;/li&gt;
      ))}
    &lt;/ul&gt;
  );
}
  
  In this example, we first import the `useEffect` and `useState` hooks from React, and the `io` method to connect to the websocket server. We then define the `fetcher` function that we used in the previous example, and create a new component called `RealTimeData`. Inside this component, we define a state variable called `data`, and use the `mutate` method returned by `useSWR` to refresh the cache when we receive new data over the websocket.
  
  In the `useEffect` hook, we set up a listener for the `data:update` event, which is broadcast by the websocket server whenever new data is available. When we receive new data, we update the `data` state using the `setData` method, and revalidate the SWR cache using the `mutate` method.
  
  Finally, we render the `data` array as a list of `li` elements, just like in the previous example.
  
  Conclusion
  
  SWR is a powerful tool for streamlining data fetching and optimizing caching behavior for React applications. With SWR, React developers can focus on building great user experiences rather than worrying about low-level networking details. Whether you’re building a simple CRUD app or a complex real-time dashboard, SWR has you covered.
  
  We hope this comprehensive guide has helped you get started with SWR and shown you how to take advantage of its powerful features. If you have any questions or feedback, please feel free to leave a comment!


<div>
  <h1>Effortless Data Fetching with SWR: A Comprehensive Guide for React Developers</h1>

  <p>As web applications become increasingly complex, the importance of efficient data fetching only grows. Traditional solutions, such as Redux or manual Fetch requests, can be time-consuming to set up and even more so to maintain.</p>
  
  <p>Enter SWR, a simple yet powerful library that streamlines data fetching and optimizes caching behavior for React applications. In this comprehensive guide, we’ll dive into all things SWR: how to set it up, how to use it for optimized data fetching, and how to take advantage of real-time data updates.</p>
  
  <h2>What is SWR?</h2>
  
  <p>SWR, short for stale-while-revalidate, is a library that helps manage data fetching and caching within React applications. It allows developers to fetch data from various sources such as APIs, and optimize network requests using a caching strategy, all with little to no configuration. SWR also handles real-time updates by automatically revalidating data when it becomes stale or outdated.</p>
  
  <h2>How to Install SWR</h2>
  
  <p>To get started with SWR, we need to install it through NPM or Yarn:</p>
  
  <pre><code>$ npm install swr</code></pre>
  
  <p>or</p>
  
  <pre><code>$ yarn add swr</code></pre>
  
  <h2>Using SWR for Data Fetching</h2>
  
  <p>Now that we have SWR installed, let’s see how we can use it to fetch data inside a React component. Let’s assume we have a simple API that returns an array of objects:</p>
  
  <pre><code>async function fetcher(url) {
  const res = await fetch(url);
  return res.json();
}
 
const { data, error } = useSWR('/api/data', fetcher);
 
if (error) return <div>Failed to load data</div>;
if (!data) return <div>Loading...</div>;
 
return (
  &lt;ul&gt;
    {data.map((item) => (
      &lt;li key={item.id}&gt;{item.name}&lt;/li&gt;
    ))}
  &lt;/ul&gt;
);</code></pre>
  
  <p>So what’s going on in this code? First, we define an asynchronous function called `fetcher` that takes a URL and returns the parsed JSON response when called. Next, we use the `useSWR` hook to fetch data from our API endpoint at `/api/data`, using the `fetcher` function we defined earlier.</p>
  
  <p>The `useSWR` hook returns an object with two properties: `data` and `error`. If the fetch was successful, `data` will contain the response data, and `error` will be null. If there was an error while fetching the data, `error` will contain the error message, and `data` will be null. If the data is still loading, both `data` and `error` will be null.</p>
  
  <p>In the example above, we’re checking for `error` and `data`. If `error` contains a value, we display a message indicating that the data failed to load. If `data` is null, we display a message indicating that the data is still loading. If `data` contains a value, we map over the array and render each object item as an `li` element.</p>
  
  <p>One of the most powerful features of SWR is that it automatically caches the response data and uses it for subsequent requests. This means that if we reload the page or revisit the same endpoint, SWR will first check its cache for the data and return it if it’s available. If the data is stale, SWR will fetch the data again in the background and re-render the component when it’s available.</p>
  
  <h2>Optimizing Caching Behavior with SWR</h2>
  
  <p>SWR’s caching behavior can be further optimized using various configuration options. For example, we can set a time to live for the cached data using the `refreshInterval` option, which specifies the number of milliseconds between automatic revalidation:</p>
  
  <pre><code>const { data } = useSWR('/api/data', fetcher, { refreshInterval: 5000 });</code></pre>
  
  <p>In this example, the data will be revalidated every five seconds.</p>
  
  <p>Another useful option is `revalidateOnMount`, which revalidates the data when the component mounts:</p>
  
  <pre><code>const { data } = useSWR('/api/data', fetcher, { revalidateOnMount: true });</code></pre>
  
  <p>This can be useful for real-time data updates, as we’ll discuss in the next section.</p>
  
  <h2>Real-time Data Updates with SWR</h2>
  
  <p>SWR also supports real-time data updates using websockets, server-sent events, or any other protocol that can broadcast data updates to connected clients. In this example, we’ll use websockets to stream real-time updates to our data:</p>
  
  <pre><code>import { useEffect, useState } from 'react';
import useSWR from 'swr';
import io from 'socket.io-client';
 
const socket = io.connect('http://localhost:3001');
 
async function fetcher(url) {
  const res = await fetch(url);
  return res.json();
}
 
export default function RealTimeData() {
  const [data, setData] = useState([]);
  const { mutate } = useSWR('/api/data', fetcher);

  useEffect(() => {
    socket.on('data:update', (newData) => {
      // Update data when new data is received over the websocket
      setData((prevData) => [...prevData, newData]);
      // Revalidate SWR cache for the new data
      mutate();
    });
  }, []);
 
  return (
    &lt;ul&gt;
      {data.map((item) => (
        &lt;li key={item.id}&gt;{item.name}&lt;/li&gt;
      ))}
    &lt;/ul&gt;
  );
}</code></pre>
  
  <p>In this example, we first import the `useEffect` and `useState` hooks from React, and the `io` method to connect to the websocket server. We then define the `fetcher` function that we used in the previous example, and create a new component called `RealTimeData`. Inside this component, we define a state variable called `data`, and use the `mutate` method returned by `useSWR` to refresh the cache when we receive new data over the websocket.</p>
  
  <p>In the `useEffect` hook, we set up a listener for the `data:update` event, which is broadcast by the websocket server whenever new data is available. When we receive new data, we update the `data` state using the `setData` method, and revalidate the SWR cache using the `mutate` method.</p>
  
  <p>Finally, we render the `data` array as a list of `li` elements, just like in the previous example.</p>
  
  <h2>Conclusion</h2>
  
  <p>SWR is a powerful tool for streamlining data fetching and optimizing caching behavior for React applications. With SWR, React developers can focus on building great user experiences rather than worrying about low-level networking details. Whether you’re building a simple CRUD app or a complex real-time dashboard, SWR has you covered.</p>
  
  <p>We hope this comprehensive guide has helped you get started with SWR and shown you how to take advantage of its powerful features. If you have any questions or feedback, please feel free to leave a comment!</p>
</div>

Discover the simple yet powerful data fetching library called SWR for use within your React applications. Learn how to streamline your data fetching process, optimize caching behavior, and set up real-time data updates with ease.

Effortless Data Fetching with SWR: A Comprehensive Guide for React Developers



  Efficient Data Fetching: Demystifying React Query and SWR for Fullstack Developers

  Fetching and managing data is a critical part of building web applications, whether you're creating a simple blog or a complex e-commerce platform. However, if done inefficiently, it can lead to poor user experience, performance issues, and maintenance headaches.
  
  Fortunately, there are some modern and powerful libraries that can help you with data fetching, caching, and synchronization, such as React Query and SWR. In this article, we'll explore what these libraries are, how they differ, and how to use them in your fullstack React applications.

  

  What is React Query?

  React Query is a library that simplifies and streamlines data fetching in your React components. It provides a declarative and hook-based API that allows you to fetch data from one or multiple sources, such as REST APIs, GraphQL servers, or local storage. React Query also offers features like caching, pagination, polling, and optimistic updates, which can improve the performance and user experience of your app.

  Basic Usage

  Let's start by looking at a simple example of fetching data with React Query. Suppose we have a component that displays a list of posts fetched from a REST API:

  {`
    import React from 'react';
    import { useQuery } from 'react-query';
    
    const fetchPosts = async () => {
      const response = await fetch('/api/posts');
      const data = response.json();
      return data;
    };
    
    const PostList = () => {
      const { data, isLoading, error } = useQuery('posts', fetchPosts);
    
      if (isLoading) return Loading...;
      if (error) return {error.message};
    
      return (
        
          {data.map(post => (
            {post.title}
          ))}
        
      );
    };
  `}

  In this example, we define a fetchPosts function that uses the Fetch API to retrieve the list of posts from our API. Then, we use the useQuery hook from React Query to fetch the data and cache it under the key 'posts'. We also handle the loading and error states with conditional rendering.

  Caching

  One of the most useful features of React Query is caching. Once you fetch data with a certain key, React Query will automatically store it in a cache, and subsequent requests with the same key will be served from the cache instead of the network, unless the data is stale. This can significantly reduce the number of network requests and speed up your app.

  You can configure the caching behavior of React Query in various ways, such as setting a TTL (time-to-live) for the data, keeping only a certain number of items in the cache, or using a custom cache provider. You can also manually invalidate or refetch the data, for example when the user takes a certain action or when a related data source changes.

  Pagination

  Another common use case of data fetching in web applications is pagination, where you fetch only a portion of the data at a time, and load more data when the user reaches the end of the current page. React Query provides built-in support for pagination, either by using an offset-based or a cursor-based approach.

  To use offset-based pagination with React Query, you can pass a page number and a page size as variables to the query key, and use them to compute the range of data to fetch:

  {`
    const fetchPosts = async (key, page = 1, pageSize = 10) => {
      const offset = (page - 1) * pageSize;
      const response = await fetch(`/api/posts?offset=${offset}&limit=${pageSize}`);
      const data = response.json();
      return data;
    };

    const PostList = () => {
      const { data, isLoading, isError, error } = useQuery(['posts', page], fetchPosts);

      // ...
    };
  `}

  In this example, we use the page and pageSize variables as part of the query key, so that React Query knows when to refetch the data with a different page number. We also pass the same variables to the fetchPosts function to compute the offset and page size for our API call.

  Polling

  If you need to continuously fetch and update data in your app, for example to display real-time information or notifications, you can use polling with React Query. Polling is a technique where you periodically fetch data from a server at a specific interval, and refresh the UI with the new data.

  React Query provides a useQuery option called refetchInterval, which allows you to set the interval in milliseconds between successive fetches. You can also use other options like refetchIntervalInBackground, refetchOnMount, and refetchOnWindowFocus to fine-tune the polling behavior. If you need more control over the polling process, you can use the pollingInterval option and manually call the refetch function to trigger a new fetch:

  {`
    const PostList = () => {
      const { data, isLoading, isError, error, refetch } = useQuery('posts', fetchPosts, {
        refetchInterval: 3000,
      });

      // ...

      return (
        <>
          
            {data.map(post => (
              {post.title}
            ))}
          

          Refresh
        
      );
    };
  `}

  In this example, we set the refetchInterval option to 3000 milliseconds (3 seconds), and provide a refetch function that can be called manually to force a new fetch. We also render a "Refresh" button that triggers the refetch function when clicked. This way, the user can choose to refresh the data at any time, or let the automatic polling do its job.

  

  What is SWR?

  SWR (short for "stale-while-revalidate") is another library that aims to simplify data fetching and caching in React applications. It uses a similar hook-based API to React Query, but with a different approach to caching and synchronization. SWR focuses on optimizing the balance between responsiveness and consistency of your app, by leveraging a local cache, a remote cache, and a globally consistent data layer.

  Basic Usage

  Let's see a simple example of using SWR to fetch data from a REST API:

  {`
    import React from 'react';
    import useSWR from 'swr';
    
    const fetcher = async (url) => {
      const response = await fetch(url);
      const data = response.json();
      return data;
    };
    
    const PostList = () => {
      const { data, error } = useSWR('/api/posts', fetcher);
    
      if (error) return {error.message};
    
      return (
        
          {data.map(post => (
            {post.title}
          ))}
        
      );
    };
  `}

  In this example, we define a fetcher function that takes a URL and returns the response as JSON. We use the useSWR hook from SWR to fetch the data from the URL, and let SWR handle the caching and synchronization for us. We also handle the error state with conditional rendering.

  Caching

  SWR has a built-in caching mechanism that operates on a per-resource basis. When you fetch data with SWR, it first checks if the data is available in its local cache (which can be in-memory or localStorage), and returns it if it's not stale. Then, it sends a request to the API to get the latest data, and updates the cache with the new data. If the API responds with a 304 "Not Modified" status, it means the data is still fresh, and SWR keeps using the previous data.

  You can configure the caching behavior of SWR in various ways, such as setting a TTL, enabling or disabling revalidation on focus or reconnection, or providing a custom cache provider. You can also manually mutate the cache with new data, for example when the user updates a resource locally or when a WebSocket sends a new message.

  Synchronization

  One of the unique features of SWR is its ability to synchronize data across different instances of your app, even if they are running in different tabs or devices. This is achieved by using a globally consistent data layer, which is a distributed cache that implements a "push-based" approach to updating data. Whenever a mutation occurs on one instance of your app, such as creating a new post or updating a comment, the data layer broadcasts the change to all other instances that are currently active, and triggers a revalidation on their caches. This ensures that all users of your app see the same data in real-time, without the need for manual refreshing or polling.

  Optimistic Updates

  SWR also supports optimistic updates, which is a technique that improves the perceived speed and interactivity of your app by immediately updating the UI with new data before the server confirms the mutation. This can give the user an instant feedback that their action was successful, and avoid the frustration of waiting for the server to respond. Optimistic updates can be implemented by using the mutate function provided by the useSWR hook:

  {`
    const PostList = () => {
      const { data, mutate } = useSWR('/api/posts', fetcher);

      const handleCreatePost = async (title, content) => {
        const response = await fetch('/api/posts', {
          method: 'POST',
          body: JSON.stringify({ title, content }),
        });

        if (response.ok) {
          const newPost = await response.json();
          // Update the cache optimistically
          mutate((data) => [newPost, ...data], false);
        }
      };

      // ...

      return (
        <>
          

          
            {data.map(post => (
              {post.title}
            ))}
          
        
      );
    };
  `}

  In this example, we define a handleCreatePost function that sends a POST request to the API to create a new post. If the request succeeds, we update the SWR cache optimistically by calling the mutate function with a new data object that includes the newly created post. The second parameter of mutate is set to false to avoid a revalidation until the server confirms the mutation. We also render a PostForm component that allows the user to create new posts.

  

  Which one to use?

  Now that we've seen the main features and usage patterns of both React Query and SWR, you might wonder which one to choose for your next project. The answer depends on your specific needs and preferences, but here are some general guidelines:

  
    Use React Query if you value simplicity, flexibility, and performance. React Query is easy to learn and use, supports a wide range of data sources and features, and has a lean and efficient implementation. It's also well-suited for handling complex queries and mutations, and provides a lot of customization options.
    Use SWR if you value consistency, synchronization, and developer experience. SWR is designed for optimizing the balance between local and remote caching, provides a seamless integration with global data layers like Apollo or Firebase Realtime Database, and has a powerful toolset for debugging and error handling. It's also very intuitive to use, especially if you're familiar with React state management.
  

  Of course, you don't have to choose only one library and use it for all data fetching needs. You can mix and match React Query and SWR, or even use other libraries like Axios or Fetch directly, depending on the context and requirements of your app. The important thing is to keep your data fetching code clean, modular, and reusable, so that you can focus on the business logic and user experience of your app.

  

  Conclusion

  Data fetching is a crucial aspect of building web applications, and should be done efficiently and effectively. React Query and SWR are two valuable libraries that can help you with that, by providing simple, powerful, and flexible abstractions for fetching, caching, and syncing data in your React components. Whether you prefer the simplicity and customization of React Query, or the consistency and synchronization of SWR, you now have the tools to choose the best option for your specific use case. Happy coding!



<div>

  <h1>Efficient Data Fetching: Demystifying React Query and SWR for Fullstack Developers</h1>

  <p>Fetching and managing data is a critical part of building web applications, whether you're creating a simple blog or a complex e-commerce platform. However, if done inefficiently, it can lead to poor user experience, performance issues, and maintenance headaches.</p>
  
  <p>Fortunately, there are some modern and powerful libraries that can help you with data fetching, caching, and synchronization, such as React Query and SWR. In this article, we'll explore what these libraries are, how they differ, and how to use them in your fullstack React applications.</p>

  <hr>

  <h2>What is React Query?</h2>

  <p>React Query is a library that simplifies and streamlines data fetching in your React components. It provides a declarative and hook-based API that allows you to fetch data from one or multiple sources, such as REST APIs, GraphQL servers, or local storage. React Query also offers features like caching, pagination, polling, and optimistic updates, which can improve the performance and user experience of your app.</p>

  <h3>Basic Usage</h3>

  <p>Let's start by looking at a simple example of fetching data with React Query. Suppose we have a component that displays a list of posts fetched from a REST API:</p>

  <pre><code class="language-jsx">{`
    import React from 'react';
    import { useQuery } from 'react-query';
    
    const fetchPosts = async () => {
      const response = await fetch('/api/posts');
      const data = response.json();
      return data;
    };
    
    const PostList = () => {
      const { data, isLoading, error } = useQuery('posts', fetchPosts);
    
      if (isLoading) return <p>Loading...</p>;
      if (error) return <p>{error.message}</p>;
    
      return (
        <ul>
          {data.map(post => (
            <li key={post.id}>{post.title}</li>
          ))}
        </ul>
      );
    };
  `}</code></pre>

  <p>In this example, we define a <code>fetchPosts</code> function that uses the Fetch API to retrieve the list of posts from our API. Then, we use the <code>useQuery</code> hook from React Query to fetch the data and cache it under the key <code>'posts'</code>. We also handle the loading and error states with conditional rendering.</p>

  <h3>Caching</h3>

  <p>One of the most useful features of React Query is caching. Once you fetch data with a certain key, React Query will automatically store it in a cache, and subsequent requests with the same key will be served from the cache instead of the network, unless the data is stale. This can significantly reduce the number of network requests and speed up your app.</p>

  <p>You can configure the caching behavior of React Query in various ways, such as setting a TTL (time-to-live) for the data, keeping only a certain number of items in the cache, or using a custom cache provider. You can also manually invalidate or refetch the data, for example when the user takes a certain action or when a related data source changes.</p>

  <h3>Pagination</h3>

  <p>Another common use case of data fetching in web applications is pagination, where you fetch only a portion of the data at a time, and load more data when the user reaches the end of the current page. React Query provides built-in support for pagination, either by using an offset-based or a cursor-based approach.</p>

  <p>To use offset-based pagination with React Query, you can pass a page number and a page size as variables to the query key, and use them to compute the range of data to fetch:</p>

  <pre><code class="language-jsx">{`
    const fetchPosts = async (key, page = 1, pageSize = 10) => {
      const offset = (page - 1) * pageSize;
      const response = await fetch(`/api/posts?offset=${offset}&limit=${pageSize}`);
      const data = response.json();
      return data;
    };

    const PostList = () => {
      const { data, isLoading, isError, error } = useQuery(['posts', page], fetchPosts);

      // ...
    };
  `}</code></pre>

  <p>In this example, we use the <code>page</code> and <code>pageSize</code> variables as part of the query key, so that React Query knows when to refetch the data with a different page number. We also pass the same variables to the <code>fetchPosts</code> function to compute the offset and page size for our API call.</p>

  <h3>Polling</h3>

  <p>If you need to continuously fetch and update data in your app, for example to display real-time information or notifications, you can use polling with React Query. Polling is a technique where you periodically fetch data from a server at a specific interval, and refresh the UI with the new data.</p>

  <p>React Query provides a <code>useQuery</code> option called <code>refetchInterval</code>, which allows you to set the interval in milliseconds between successive fetches. You can also use other options like <code>refetchIntervalInBackground</code>, <code>refetchOnMount</code>, and <code>refetchOnWindowFocus</code> to fine-tune the polling behavior. If you need more control over the polling process, you can use the <code>pollingInterval</code> option and manually call the <code>refetch</code> function to trigger a new fetch:</p>

  <pre><code class="language-jsx">{`
    const PostList = () => {
      const { data, isLoading, isError, error, refetch } = useQuery('posts', fetchPosts, {
        refetchInterval: 3000,
      });

      // ...

      return (
        <>
          <ul>
            {data.map(post => (
              <li key={post.id}>{post.title}</li>
            ))}
          </ul>

          <button onClick={refetch}>Refresh</button>
        </>
      );
    };
  `}</code></pre>

  <p>In this example, we set the <code>refetchInterval</code> option to 3000 milliseconds (3 seconds), and provide a <code>refetch</code> function that can be called manually to force a new fetch. We also render a "Refresh" button that triggers the <code>refetch</code> function when clicked. This way, the user can choose to refresh the data at any time, or let the automatic polling do its job.</p>

  <hr>

  <h2>What is SWR?</h2>

  <p>SWR (short for "stale-while-revalidate") is another library that aims to simplify data fetching and caching in React applications. It uses a similar hook-based API to React Query, but with a different approach to caching and synchronization. SWR focuses on optimizing the balance between responsiveness and consistency of your app, by leveraging a local cache, a remote cache, and a globally consistent data layer.</p>

  <h3>Basic Usage</h3>

  <p>Let's see a simple example of using SWR to fetch data from a REST API:</p>

  <pre><code class="language-jsx">{`
    import React from 'react';
    import useSWR from 'swr';
    
    const fetcher = async (url) => {
      const response = await fetch(url);
      const data = response.json();
      return data;
    };
    
    const PostList = () => {
      const { data, error } = useSWR('/api/posts', fetcher);
    
      if (error) return <p>{error.message}</p>;
    
      return (
        <ul>
          {data.map(post => (
            <li key={post.id}>{post.title}</li>
          ))}
        </ul>
      );
    };
  `}</code></pre>

  <p>In this example, we define a <code>fetcher</code> function that takes a URL and returns the response as JSON. We use the <code>useSWR</code> hook from SWR to fetch the data from the URL, and let SWR handle the caching and synchronization for us. We also handle the error state with conditional rendering.</p>

  <h3>Caching</h3>

  <p>SWR has a built-in caching mechanism that operates on a per-resource basis. When you fetch data with SWR, it first checks if the data is available in its local cache (which can be in-memory or localStorage), and returns it if it's not stale. Then, it sends a request to the API to get the latest data, and updates the cache with the new data. If the API responds with a 304 "Not Modified" status, it means the data is still fresh, and SWR keeps using the previous data.</p>

  <p>You can configure the caching behavior of SWR in various ways, such as setting a TTL, enabling or disabling revalidation on focus or reconnection, or providing a custom cache provider. You can also manually mutate the cache with new data, for example when the user updates a resource locally or when a WebSocket sends a new message.</p>

  <h3>Synchronization</h3>

  <p>One of the unique features of SWR is its ability to synchronize data across different instances of your app, even if they are running in different tabs or devices. This is achieved by using a globally consistent data layer, which is a distributed cache that implements a "push-based" approach to updating data. Whenever a mutation occurs on one instance of your app, such as creating a new post or updating a comment, the data layer broadcasts the change to all other instances that are currently active, and triggers a revalidation on their caches. This ensures that all users of your app see the same data in real-time, without the need for manual refreshing or polling.</p>

  <h3>Optimistic Updates</h3>

  <p>SWR also supports optimistic updates, which is a technique that improves the perceived speed and interactivity of your app by immediately updating the UI with new data before the server confirms the mutation. This can give the user an instant feedback that their action was successful, and avoid the frustration of waiting for the server to respond. Optimistic updates can be implemented by using the <code>mutate</code> function provided by the <code>useSWR</code> hook:</p>

  <pre><code class="language-jsx">{`
    const PostList = () => {
      const { data, mutate } = useSWR('/api/posts', fetcher);

      const handleCreatePost = async (title, content) => {
        const response = await fetch('/api/posts', {
          method: 'POST',
          body: JSON.stringify({ title, content }),
        });

        if (response.ok) {
          const newPost = await response.json();
          // Update the cache optimistically
          mutate((data) => [newPost, ...data], false);
        }
      };

      // ...

      return (
        <>
          <PostForm onCreate={handleCreatePost} />

          <ul>
            {data.map(post => (
              <li key={post.id}>{post.title}</li>
            ))}
          </ul>
        </>
      );
    };
  `}</code></pre>

  <p>In this example, we define a <code>handleCreatePost</code> function that sends a POST request to the API to create a new post. If the request succeeds, we update the SWR cache optimistically by calling the <code>mutate</code> function with a new data object that includes the newly created post. The second parameter of <code>mutate</code> is set to <code>false</code> to avoid a revalidation until the server confirms the mutation. We also render a <code>PostForm</code> component that allows the user to create new posts.</p>

  <hr>

  <h2>Which one to use?</h2>

  <p>Now that we've seen the main features and usage patterns of both React Query and SWR, you might wonder which one to choose for your next project. The answer depends on your specific needs and preferences, but here are some general guidelines:</p>

  <ul>
    <li>Use React Query if you value simplicity, flexibility, and performance. React Query is easy to learn and use, supports a wide range of data sources and features, and has a lean and efficient implementation. It's also well-suited for handling complex queries and mutations, and provides a lot of customization options.</li>
    <li>Use SWR if you value consistency, synchronization, and developer experience. SWR is designed for optimizing the balance between local and remote caching, provides a seamless integration with global data layers like Apollo or Firebase Realtime Database, and has a powerful toolset for debugging and error handling. It's also very intuitive to use, especially if you're familiar with React state management.</li>
  </ul>

  <p>Of course, you don't have to choose only one library and use it for all data fetching needs. You can mix and match React Query and SWR, or even use other libraries like Axios or Fetch directly, depending on the context and requirements of your app. The important thing is to keep your data fetching code clean, modular, and reusable, so that you can focus on the business logic and user experience of your app.</p>

  <hr>

  <h2>Conclusion</h2>

  <p>Data fetching is a crucial aspect of building web applications, and should be done efficiently and effectively. React Query and SWR are two valuable libraries that can help you with that, by providing simple, powerful, and flexible abstractions for fetching, caching, and syncing data in your React components. Whether you prefer the simplicity and customization of React Query, or the consistency and synchronization of SWR, you now have the tools to choose the best option for your specific use case. Happy coding!</p>

</div>

Discover the power of React Query and SWR, two modern and efficient libraries to fetch, cache, and sync data in your React applications. Learn how to integrate them into your fullstack web apps and leverage their features to create a seamless user experience.

Efficient Data Fetching: Demystifying React Query and SWR for Fullstack Developers


  Supercharge Your Web App's Performance: Implementing GraphQL Pagination and Caching with Hasura
  As web applications start to grow, they usually become slower, especially when trying to retrieve a large amount of data. To tackle this problem, we need to optimize our web application's performance by implementing pagination and caching strategies. In this article, we'll learn how to implement these strategies with GraphQL, React, and Next.js using Hasura as our backend.

  What is Hasura?
  Hasura is an open-source engine that connects to your databases and gives you instant GraphQL APIs for your applications. It provides real-time GraphQL APIs, authorization and access control, and event triggers to build modern applications. With Hasura, you can build scalable, secure, and performant applications with minimal setup time.

  Implementing GraphQL Pagination
  Pagination is a mechanism that allows us to retrieve data in smaller and more manageable chunks instead of one large dataset. It helps in reducing server load and improving application performance. With GraphQL, implementing pagination is quite straightforward. We can use relay-style pagination, which is a standard way of handling pagination in GraphQL APIs. It has four main fields:
  
    edges - contains the actual data and a cursor for each item
    nodes - contains the actual data
    pageInfo - contains information about the current page, such as hasNextPage and hasPreviousPage
    totalCount - the total number of items available in the dataset
  

  Let's take an example of a GraphQL query requesting a list of products with a limit of 10 items per page:

  
    query {
      products(limit: 10, offset: 0) {
        totalCount
        edges {
          cursor
          node {
            id
            name
            price
          }
        }
        pageInfo {
          hasNextPage
          hasPreviousPage
        }
      }
    }
  

  In the code above, we're requesting a list of products with a limit of 10 items per page and an offset of 0. The response will contain the total number of products available, the data for the first 10 items, and information about the current page. To get the next page, we only need to change the offset value to 10:

  
    query {
      products(limit: 10, offset: 10) {
        totalCount
        edges {
          cursor
          node {
            id
            name
            price
          }
        }
        pageInfo {
          hasNextPage
          hasPreviousPage
        }
      }
    }
  

  It's that simple. With GraphQL, we can easily implement pagination without worrying about the complexity of managing offsets and limits.

  Implementing Caching with Hasura
  Caching is the process of storing frequently used data in memory to improve application performance. It reduces the number of requests that our application needs to make to the server and can dramatically improve response times. With Hasura, we can implement caching easily using a combination of Postgres features and Hasura-specific configuration.

  Hasura-specific Configuration
  Hasura provides a caching mechanism that caches the results of queries in memory for a specified amount of time. It helps to reduce the load on the database and speed up result retrieval. We can configure the cache's behavior and duration using custom Hasura SQL commands.

  Let's take an example of a query that retrieves information about a product with a given ID:

  
    query {
      product(id: "123") {
        id
        name
        price
        description
      }
    }
  

  We can configure Hasura to cache the results of this query for 5 minutes using the following SQL command:

  
    COMMENT on function product (id text) is 'route: /product, cache_ttl: 300';
  

  With this configuration, Hasura will cache the results of the product query for 5 minutes (300 seconds) before updating it from the database.

  Postgres Features
  Postgres has several built-in features that we can use to further improve caching. The two most important features are:

  
    Materialized Views - allows us to pre-calculate the results of a query and store them as a table. It helps in reducing the time it takes to execute complex queries.
    Indexes - allows us to index specific columns to speed up search queries. Indexes are very useful when working with large datasets.
  

  We can use these Postgres features to optimize our queries and speed up data retrieval. For example, let's take an example of a query that retrieves the top-selling products:

  
    query {
      topSellingProducts(limit: 10) {
        id
        name
        price
      }
    }
  

  We can utilize Postgres Materialized Views to pre-calculate the top-selling products and index the 'sold_quantity' column for faster search:

  
    CREATE MATERIALIZED VIEW top_selling_products AS (
      SELECT id, name, price, sold_quantity
      FROM products
      ORDER BY sold_quantity DESC
      LIMIT 10
    );

    CREATE INDEX ON top_selling_products(sold_quantity);
  

  This configuration will create a Materialized View that pre-calculates the top selling products and indexes the 'sold_quantity' column for faster search. We can now modify our GraphQL query to retrieve data from the Materialized View instead of querying the database:

  
    query {
      topSellingProductsMV {
        id
        name
        price
      }
    }
  

  With this configuration, our query will retrieve data from the Materialized View instead of querying the database, resulting in faster response times and reduced load on the database.

  Conclusion
  Implementing pagination and caching strategies are essential to optimizing our web application's performance. With GraphQL, React, and Next.js, we can easily implement these strategies using Hasura as our backend. By implementing pagination, we can reduce server load and improve application performance, while caching helps to reduce the number of requests our application needs to make to the server and can dramatically improve response times. When used together, these strategies can help us build scalable, secure, and performant applications.


<div>
  <h1>Supercharge Your Web App's Performance: Implementing GraphQL Pagination and Caching with Hasura</h1>
  <p>As web applications start to grow, they usually become slower, especially when trying to retrieve a large amount of data. To tackle this problem, we need to optimize our web application's performance by implementing pagination and caching strategies. In this article, we'll learn how to implement these strategies with GraphQL, React, and Next.js using Hasura as our backend.</p>

  <h2>What is Hasura?</h2>
  <p>Hasura is an open-source engine that connects to your databases and gives you instant GraphQL APIs for your applications. It provides real-time GraphQL APIs, authorization and access control, and event triggers to build modern applications. With Hasura, you can build scalable, secure, and performant applications with minimal setup time.</p>

  <h2>Implementing GraphQL Pagination</h2>
  <p>Pagination is a mechanism that allows us to retrieve data in smaller and more manageable chunks instead of one large dataset. It helps in reducing server load and improving application performance. With GraphQL, implementing pagination is quite straightforward. We can use relay-style pagination, which is a standard way of handling pagination in GraphQL APIs. It has four main fields:</p>
  <ul>
    <li>edges - contains the actual data and a cursor for each item</li>
    <li>nodes - contains the actual data</li>
    <li>pageInfo - contains information about the current page, such as hasNextPage and hasPreviousPage</li>
    <li>totalCount - the total number of items available in the dataset</li>
  </ul>

  <p>Let's take an example of a GraphQL query requesting a list of products with a limit of 10 items per page:</p>

  <pre><code>
    query {
      products(limit: 10, offset: 0) {
        totalCount
        edges {
          cursor
          node {
            id
            name
            price
          }
        }
        pageInfo {
          hasNextPage
          hasPreviousPage
        }
      }
    }
  </code></pre>

  <p>In the code above, we're requesting a list of products with a limit of 10 items per page and an offset of 0. The response will contain the total number of products available, the data for the first 10 items, and information about the current page. To get the next page, we only need to change the offset value to 10:</p>

  <pre><code>
    query {
      products(limit: 10, offset: 10) {
        totalCount
        edges {
          cursor
          node {
            id
            name
            price
          }
        }
        pageInfo {
          hasNextPage
          hasPreviousPage
        }
      }
    }
  </code></pre>

  <p>It's that simple. With GraphQL, we can easily implement pagination without worrying about the complexity of managing offsets and limits.</p>

  <h2>Implementing Caching with Hasura</h2>
  <p>Caching is the process of storing frequently used data in memory to improve application performance. It reduces the number of requests that our application needs to make to the server and can dramatically improve response times. With Hasura, we can implement caching easily using a combination of Postgres features and Hasura-specific configuration.</p>

  <h3>Hasura-specific Configuration</h3>
  <p>Hasura provides a caching mechanism that caches the results of queries in memory for a specified amount of time. It helps to reduce the load on the database and speed up result retrieval. We can configure the cache's behavior and duration using custom Hasura SQL commands.</p>

  <p>Let's take an example of a query that retrieves information about a product with a given ID:</p>

  <pre><code>
    query {
      product(id: "123") {
        id
        name
        price
        description
      }
    }
  </code></pre>

  <p>We can configure Hasura to cache the results of this query for 5 minutes using the following SQL command:</p>

  <pre><code>
    COMMENT on function product (id text) is 'route: /product, cache_ttl: 300';
  </code></pre>

  <p>With this configuration, Hasura will cache the results of the product query for 5 minutes (300 seconds) before updating it from the database.</p>

  <h3>Postgres Features</h3>
  <p>Postgres has several built-in features that we can use to further improve caching. The two most important features are:</p>

  <ul>
    <li>Materialized Views - allows us to pre-calculate the results of a query and store them as a table. It helps in reducing the time it takes to execute complex queries.</li>
    <li>Indexes - allows us to index specific columns to speed up search queries. Indexes are very useful when working with large datasets.</li>
  </ul>

  <p>We can use these Postgres features to optimize our queries and speed up data retrieval. For example, let's take an example of a query that retrieves the top-selling products:</p>

  <pre><code>
    query {
      topSellingProducts(limit: 10) {
        id
        name
        price
      }
    }
  </code></pre>

  <p>We can utilize Postgres Materialized Views to pre-calculate the top-selling products and index the 'sold_quantity' column for faster search:</p>

  <pre><code>
    CREATE MATERIALIZED VIEW top_selling_products AS (
      SELECT id, name, price, sold_quantity
      FROM products
      ORDER BY sold_quantity DESC
      LIMIT 10
    );

    CREATE INDEX ON top_selling_products(sold_quantity);
  </code></pre>

  <p>This configuration will create a Materialized View that pre-calculates the top selling products and indexes the 'sold_quantity' column for faster search. We can now modify our GraphQL query to retrieve data from the Materialized View instead of querying the database:</p>

  <pre><code>
    query {
      topSellingProductsMV {
        id
        name
        price
      }
    }
  </code></pre>

  <p>With this configuration, our query will retrieve data from the Materialized View instead of querying the database, resulting in faster response times and reduced load on the database.</p>

  <h2>Conclusion</h2>
  <p>Implementing pagination and caching strategies are essential to optimizing our web application's performance. With GraphQL, React, and Next.js, we can easily implement these strategies using Hasura as our backend. By implementing pagination, we can reduce server load and improve application performance, while caching helps to reduce the number of requests our application needs to make to the server and can dramatically improve response times. When used together, these strategies can help us build scalable, secure, and performant applications.</p>
</div>

Learn how to optimize your web application's performance by implementing GraphQL pagination and caching strategies using the Hasura backend for your React and Next.js applications.