Implementing Advanced Server Rendering Techniques with React Query Library in React

Anton Ioffe - March 1st 2024 - 10 minutes read

In the ever-evolving landscape of modern web development, React applications stand at the forefront, continually pushing the boundaries of performance and user experience. This article embarks on a deep dive into the realm of React Query, specifically tailored for leveraging its full potential in server-side rendering scenarios. From unraveling the essentials of data fetching strategies to mastering advanced optimization techniques, we will navigate through the intricacies of server-state hydration and tackle the common challenges faced when integrating React Query in SSR. Prepare to elevate your React applications by implementing these cutting-edge server rendering techniques, equipped with practical code examples and real-world solutions designed to optimize data management and enhance application efficiency like never before.

React Query Essentials for Server-Side Rendering (SSR)

React Query has revolutionized the way developers handle server-side data in React applications, offering robust solutions for fetching, caching, and updating data. Particularly in server-side rendering (SSR) scenarios, React Query's mechanisms are invaluable, ensuring that data fetched from the server is efficiently managed and rendered to the client. The library's approach to managing asynchronous data with its use of hooks reduces the complexity and boilerplate code that traditionally comes with handling server state. This facilitates a more streamlined development process, enabling developers to focus on building feature-rich, reactive applications.

In SSR, the initial page load's performance is critical for user experience and SEO. React Query's caching capabilities shine here by reducing the number of unnecessary server requests. When a component requests data that has already been fetched, React Query serves the cached data, thus saving precious server resources and time. This feature is particularly useful in React applications that are heavily reliant on dynamic data, as it significantly improves the efficiency of data retrieval processes.

Integrating React Query into an SSR React project requires a few prerequisites to be in place. The foundational step is setting up the QueryClient, an essential part of React Query's architecture that manages queries' lifecycle and caching mechanisms. For SSR, a unique instance of QueryClient must be created for each request to ensure data isolation between requests, preventing data leaks between different users.

Configuring React Query for an SSR setup involves ensuring that server-fetched data is seamlessly transferred to the client. This process, often referred to as hydration, is critical for matching the server-rendered content with the client-side React tree. Properly setting up hydration with React Query involves serializing the server-side fetched data and ensuring the client-side QueryClient is initialized with this state. This setup guarantees that data fetched on the server is accurately reflected on the client, providing a consistent user experience across the board.

Lastly, React Query distinguishes itself in SSR scenarios through its automatic background refetching policies. The library intelligently determines when to refetch data, based on factors such as window focus or network status changes. This ensures that the application data remains fresh without manual intervention from developers, thereby optimizing the application's performance and reliability. Combined with its caching and updating mechanisms, React Query offers a comprehensive solution for managing server-side data in SSR applications, making it an indispensable tool in modern React development.

Implementing Data Fetching Strategies in SSR

In server-side rendering (SSR) environments, data fetching strategies must be mindfully chosen to balance performance with user experience. React Query offers several approaches such as prefetching, parallel queries, and dependent queries. Each comes with its distinct advantages and potential drawbacks. Prefetching is especially beneficial for improving the perceived performance. By fetching data before it's actually rendered, applications can ensure that data is immediately available for rendering, eliminating the wait time for the user. However, over-prefetching might lead to unnecessary data fetching, which could impact server performance.

Parallel queries allow multiple data fetching operations to occur simultaneously, significantly speeding up the overall load time when multiple, independent datasets are needed to render a page. This approach maximizes the utilization of network and server resources, which could lead to faster user experiences. The downside, however, lies in the increased complexity of error handling, as developers must manage multiple asynchronous operations concurrently.

Dependent queries introduce a sequential approach to data fetching, where some queries depend on the results of others. This is particularly useful when data from one query must be present to correctly fetch another. While this approach ensures data consistency and integrity, it can introduce latency since each request waits for its predecessor, potentially degrading the user experience.

Implementing these strategies in a React SSR application with the useQuery hook involves careful consideration of SSR-specific configurations to ensure seamless synchronization between server and client-side states. For instance, when prefetching data, developers might use queryClient.prefetchQuery to fetch data on the server and pass the queryClient to the component through context to utilize the prefetched data on the client side.

const fetchProjects = async () => {
    const response = await fetch('/api/projects');
    return response.json();

export function ProjectsPage() {
    const { data: projects } = useQuery('projects', fetchProjects);
    return (
            { => (
                <li key={}>{}</li>

In the example above, server-side data fetching strategies can be customized depending on the project's requirements. Developers are encouraged to weigh the trade-offs between initial load time, user experience, and server strain when selecting a fetching strategy. While prefetching and parallel queries can significantly enhance the speed and responsiveness of SSR applications, leveraging dependent queries ensures data accuracy at the slight cost of increased fetching time. These strategies, when judiciously applied, can lead to highly efficient and user-friendly server-rendered React applications.

Server-State Hydration Patterns

Server-rendered React applications often face the challenge of ensuring that the data available on the server matches what's rendered on the client. This process, known as hydration, is pivotal when utilizing React Query in server-side rendering (SSR) scenarios. The concept of hydration with React Query is rooted in the transition from server-to-client rendering, which addresses the need to preserve state between these environments. Achieving this synchronization means implementing patterns that maintain consistency, especially concerning server-state.

React Query simplifies this process through its dehydration and rehydration processes. Dehydration allows for a snapshot of the server's query cache to be taken, effectively 'freezing' the current state. This state can then be sent alongside the server-side rendered application to the client. On the client-side, React Query provides the Hydrate component, which rehydrates the application using the previously frozen state. This ensures that the client-side application can immediately access the server-state without needing to refetch the data, thereby maintaining consistency between server and client states.

To illustrate, consider a React application that fetches a list of todos from an API on the server-side and renders them. By using React Query's dehydrate function, the fetched data can be serialized and passed to the client. Within the client, the Hydrate component deserializes this state and initializes the client-side QueryClient with it. This seamless process requires minimal boilerplate, significantly enhancing the developer experience and application performance by eliminating redundant data fetching.

import { dehydrate, Hydrate, QueryClient, useQuery } from 'react-query';

// Server-side
const queryClient = new QueryClient();
await queryClient.prefetchQuery('todos', fetchTodos);
const dehydratedState = dehydrate(queryClient);

// Passed to the client (e.g., as a prop or through a global window object)
// Client-side
function App({ dehydratedState }) {
  return (
    <QueryClientProvider client={new QueryClient()}>
      <Hydrate state={dehydratedState}>
        <Todos />

A noteworthy aspect of server-state hydration with React Query is the critical role of query keys in ensuring data integrity. Query keys must uniquely identify the data being fetched to prevent mismatches and ensure that the hydrated state corresponds accurately to the server's state. Incorrect or non-unique query keys can lead to hydration errors, where the client might either display stale data or incorrectly refetch data that was already fetched server-side.

The practice of server-state hydration with React Query encapsulates the complexities of managing consistency across server and client rendering. By recognizing the nuanced challenges—such as ensuring data integrity through unique query keys—developers can leverage React Query to its fullest, enhancing SSR applications' performance, modularity, and user experience.

Advanced Optimization Techniques with React Query in SSR

In the realm of server-side rendering (SSR) with React, leveraging advanced optimization techniques provided by React Query can yield significant improvements in application performance and efficiency. One such technique involves optimizing custom query functions, which allows developers to reduce overhead and improve data retrieval times. By carefully crafting query functions with performance in mind, such as minimizing dependency on external data or avoiding unnecessary computations, applications can serve content faster and more reliably. For example, using memoization within your query functions can prevent redundant calculations by caching the results based on specific arguments, thereby speeding up consecutive calls with the same parameters.

const useCustomData = (dataId) => {
    const fetchCustomData = async () => {
        const cachedResult = memoizeFetch(dataId);
        if (cachedResult) return cachedResult; // Return from cache if available
        const response = await fetchData('/api/data/' + dataId);

    return useQuery(['customData', dataId], fetchCustomData);

Selective data prefetching stands out as another potent optimization strategy. By intelligently prefetching data needed for subsequent pages or components, applications can dramatically improve user experience by making these resources appear to load instantaneously. This technique requires developers to anticipate user actions to some degree and prefetch data accordingly, without overburdening the server with unnecessary requests. React Query's prefetchQuery method is instrumental in this approach, allowing for data to be fetched and cached before it's explicitly requested by a component. This strategy not only reduces load times but also minimizes the impact on server resources as prefetching can be strategically scheduled during off-peak times or when network conditions are most favorable.

// Prefetch data for a product detail page when hovering over a product link
productLink.addEventListener('mouseover', async () => {
    await queryClient.prefetchQuery(['product', productId], fetchProduct);

The role of automatic garbage collection in React Query is another cornerstone for managing memory and resource usage efficiently across SSR applications. React Query automatically removes stale or unused data from the cache, ensuring that the memory footprint remains low, and application performance stays high. This feature is particularly useful in SSR contexts where the volume of data fetched from the server can be substantial. Developers have the option to customize this behavior based on their specific needs, such as defining how long unused data should remain in cache before being collected, essentially tailoring the garbage collection strategy to the unique performance and resource usage profile of the application.

Moreover, maintaining code readability and modularity while employing these optimization techniques is paramount. Structuring code in a way that separates concerns and clearly delineates React Query logic from UI components can greatly enhance maintainability. This is especially true when implementing complex caching strategies or custom query functions. By encapsulating React Query logic within custom hooks or utilities, developers can keep their components clean and focused on rendering logic, making the codebase easier to navigate and understand.

Lastly, a thoughtful approach to optimization with React Query in SSR should always consider the balance between performance gain and complexity cost. While it's tempting to leverage all available optimization features, developers must critically assess the impact on code complexity and weigh it against the performance benefits. This requires a nuanced understanding of both the application's requirements and user expectations, prompting developers to question whether an optimization genuinely enhances the user experience or merely adds unnecessary complexity to the codebase.

Troubleshooting Common SSR Challenges with React Query

One of the common pitfalls when integrating React Query with server-side rendering is managing hydration mismatches. When the data fetched on the server does not match the initially rendered data on the client, React throws warnings and errors, potentially leading to a broken user experience. To mitigate this, ensure that the data fetched during server rendering is identical to the initial client-side fetch by using the same query keys and parameters. For example, instead of dynamically generating query keys or parameters based on client-side inputs, predefine them or derive them from the URL or initial state shared between the server and client.

// Server-side
const serverQueryKey = ['todos', { userId: 1 }];
await queryClient.prefetchQuery(serverQueryKey, fetchTodos);

// Client-side
const clientQueryKey = ['todos', { userId: 1 }];
useQuery(clientQueryKey, fetchTodos);

Another challenge is inconsistent server-client state synchronization. This issue arises when client-side interactions lead to state changes that are not reflected in the server-rendered content, causing the client to revert to the server state upon rehydration. To address this, utilize React Query's mutations with optimistic updates to immediately reflect changes on the client side, and ensure that these mutations invalidate related queries to fetch the updated data from the server.

function useAddTodo() {
    return useMutation(addTodo, {
        // Optimistically update the cache
        onMutate: async newTodo => {
            await queryClient.cancelQueries('todos');
            const previousTodos = queryClient.getQueryData('todos');
            queryClient.setQueryData('todos', old => [...old, newTodo]);
            return { previousTodos };
        // Rollback on error
        onError: (err, newTodo, context) => {
            queryClient.setQueryData('todos', context.previousTodos);
        // Invalidate queries to refetch updated data
        onSettled: () => {

Improper query invalidation can also lead to stale data being presented to the user. A common mistake is not invalidating related queries after a mutation or fetching related data. A best practice is to specify which queries need to be invalidated in the mutation's onSettled method, ensuring that the cache is always fresh and reflects the most current state from the server.

The issue of query key mismatches is another area where developers often stumble. Each use of useQuery and useMutation needs to follow a consistent convention for keys to avoid unmatched or overlapping queries, leading to unpredictable caching and fetching behaviors. Structuring query keys as arrays with a predictable order and set of parameters can prevent mismatches.

// Correct key structure
const queryKey = ['user', userId, { filters }];

// Incorrect, prone to mismatches
const queryKey = `user-${userId}-${JSON.stringify(filters)}`;

Lastly, thought must be given to how server-client state synchronization impacts the overall project architecture. Developers should question whether their current data fetching and state management patterns support or hinder SSR with React Query. Are there opportunities to simplify or enhance data consistency? Evaluating the trade-offs of different approaches in the context of your application's requirements can lead to more robust and maintainable solutions.


This article explores how to implement advanced server rendering techniques with the React Query library in React applications. The article covers the essentials of React Query for server-side rendering (SSR), including data fetching strategies, server-state hydration patterns, advanced optimization techniques, and troubleshooting common SSR challenges. Key takeaways include the benefits of React Query's caching capabilities, the importance of proper hydration for maintaining data consistency, and the potential optimizations that can be applied to improve performance. A challenging technical task for readers is to implement selective data prefetching in SSR applications using React Query, strategically prefetching data for subsequent pages or components to enhance user experience.

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