
  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.