Building an Offline-First React Application with React Query Library

Anton Ioffe - March 2nd 2024 - 10 minutes read

In the ever-evolving landscape of web development, the ability to deliver a seamless user experience irrespective of internet connectivity stands as a hallmark of modern, robust applications. This article delves into the art and science of building offline-first React applications, wielding the powerful capabilities of the React Query library. From setting the foundational stones of offline functionality to implementing sophisticated data fetching, caching, and mutation strategies that thrive in connectivity-challenged environments, we embark on a comprehensive journey. As we navigate through real-world challenges and pioneering best practices, you'll gain actionable insights and advanced techniques for crafting applications that are not just resilient but also ahead of their time. Join us in exploring how to harness React Query to transcend traditional boundaries of web development, ensuring your applications remain unfazed by the fickleness of internet availability.

Embracing Offline-First with React Query

Offline-first is a design paradigm where applications are built to function seamlessly, both with and without an internet connection. This approach ensures that users have continuous access to core functionalities of an app, regardless of their connectivity status. The crux of this methodology lies in prioritizing local data storage and operations, which not only addresses the challenges posed by intermittent or absent internet access but also significantly boosts the app's performance by reducing reliance on network requests.

React Query emerges as a critical tool in the arsenal for developers aiming to implement an offline-first strategy in React applications. By facilitating efficient data fetching, caching, and synchronization processes, React Query addresses the foundational needs of offline-first architecture. It allows for local data caching, which is essential for making the app's data available without an active internet connection, and seamlessly updates the cache to ensure the data remains fresh when the connection resumes.

Moreover, React Query's support for background syncing is pivotal for the offline-first approach. This feature enables applications to queue changes made while offline and synchronize them with the backend once connectivity is restored. Such a mechanism ensures that user actions are not lost or overlooked due to connectivity issues, thereby maintaining the integrity and consistency of both local and server data states.

Optimistic UI updates form another cornerstone of enhancing user experience in offline-first applications, and React Query lends robust support for implementing this pattern. By assuming that user actions will succeed without waiting for server confirmation, React Query allows for instant UI feedback. This leads to a smoother, more engaging user experience, as it minimally disrupts the user's interaction flow, even in the absence of an immediate response from the server.

In essence, React Query plays a pivotal role in realizing an effective offline-first strategy in React applications. Its comprehensive suite of features not only addresses the traditional pain points associated with developing for unreliable network conditions but also aligns with the modern expectations of a seamless user experience. By leveraging local data caching, background syncing, and optimistic UI updates, developers can craft applications that are resilient, responsive, and user-centric, transcending the limitations imposed by connectivity.

Setting up React Query for Offline Use

To begin leveraging React Query for offline use in your React application, the initial step involves installing React Query itself. This can be efficiently done via your project's package manager. Run npm install react-query or yarn add react-query to include it in your project dependencies. This installation process is fundamental, as React Query acts as the cornerstone for managing server state and caching mechanisms necessary for offline capabilities.

Once installed, the configuration of the React Query client is the next critical step. This involves creating an instance of the QueryClient and wrapping your application's root component with the QueryClientProvider, supplying the previously created client instance as a prop. This setup is crucial for React Query to initialize and start managing your application's data fetching, caching, and synchronization processes effectively.

For offline support, it's pivotal to configure cache persistence options within your React Query setup. This usually involves integrating a local storage mechanism, such as localStorage in web applications or AsyncStorage in React Native applications, with React Query’s cache. By employing a compatible persistence adapter, you're able to instruct React Query on how to store and retrieve cached data from the chosen local storage solution. This step is indispensable as it ensures that your application can access and manipulate data even when offline.

To solidify the bridge between React Query and your local storage, employing a library such as createAsyncStoragePersister from react-query/createAsyncStoragePersister is advisable for React Native applications. This library facilitates the seamless integration required for storing and retrieving query caches from AsyncStorage. During setup, you configure the persister and pass it to the QueryClient through the defaultOptions property, specifying cache time and cache synchronization strategies. Such configuration empowers your application to maintain a resilient cache that supports offline usage without compromising data integrity or user experience.

In summary, setting up React Query for offline use entails installing React Query, configuring the QueryClient, and ensuring seamless integration with a local storage mechanism through cache persistence options. By prioritizing these steps, developers can lay a robust foundation that not only enables offline data access and manipulation but also enhances the overall reliability and performance of React applications. This approach underscores the significance of a well-structured setup to harness the full potential of React Query in supporting offline functionalities.

Implementing Offline Data Fetching and Caching

To implement offline data fetching and caching effectively with React Query's useQuery hook, developers need to understand the importance of defining precise query keys and query functions. Query keys are unique identifiers for query data that enable React Query to fetch, cache, and retrieve the data efficiently. They play a crucial role in invalidating and refetching data when the data changes. A well-defined query function, on the other hand, is responsible for fetching the data from a backend API or local storage based on the application's connectivity status.

const fetchUserData = async (userId) => {
    const response = await fetch(`/api/user/${userId}`);
    if (!response.ok) {
        throw new Error('Network response was not ok');
    return response.json();

function useUserData(userId) {
    return useQuery(['userData', userId], () => fetchUserData(userId), {
        staleTime: 5 * 60 * 1000, // 5 minutes
        cacheTime: 24 * 60 * 60 * 1000, // 24 hours
        onError: (error) => console.log('Error fetching data:', error),

This code snippet demonstrates fetching user data with a unique query key composed of a base string and a dynamic userId. The configuration options staleTime and cacheTime control how long the fetched data is considered fresh and how long it is stored in the cache, respectively. These configurations are crucial for managing how data is treated in offline scenarios, determining how long cached data can be used before requiring a network fetch.

For caching strategies that optimize performance and user experience in offline scenarios, developers should consider how stale data is treated. React Query provides mechanisms for re-fetching data automatically when the application comes back online or when the data is accessed again after becoming stale. Implementing a strategy for cache invalidation is also vital. When data is mutated on the backend, corresponding cache entries should be invalidated to ensure data consistency. React Query's invalidateQueries function can be used to invalidate specific queries or all queries matching a pattern, forcing a re-fetch from the backend or local cache as appropriate.

Handling complex data synchronization needs requires careful consideration of how to resolve conflicts between cached data and updated backend data. A common approach is to prioritize user actions made offline and queue these actions for backend synchronization upon regaining connectivity. React Query’s onMutate and onSuccess options in the useMutation hook can be leveraged to manage local cache updates and backend synchronization seamlessly.

const { mutate } = useMutation(updateUserData, {
    onMutate: async (newData) => {
        await queryClient.cancelQueries(['userData',]);
        const previousUserData = queryClient.getQueryData(['userData',]);
        queryClient.setQueryData(['userData',], newData);
        return { previousUserData };
    onError: (err, newData, context) => {
        queryClient.setQueryData(['userData',], context.previousUserData);
    onSettled: () => {

    id: userId,
    newData: { /* updated user data */ },

This code example illustrates how to update user data both in the local cache and on the backend, handling potential errors by reverting to the previous data if the update fails. It highlights the balance between providing an immediate response to the user through cache updates and ensuring data consistency with the backend.

Building Robust Offline Mutation Strategies

React Query's useMutation hook serves as a cornerstone for dealing with data mutations in an offline-first environment, allowing developers to create, update, and delete data while ensuring the application remains responsive, regardless of network status. The key to crafting robust offline mutation strategies lies in effectively queuing mutation requests during offline periods and synchronizing them with the server once connectivity is re-established. This demands a methodical approach to track and store user actions performed offline, ensuring no data loss or inconsistency occurs once the app goes online.

Implementing optimistic updates plays a crucial role in enhancing the user experience by providing immediate feedback in the UI, as if the mutation succeeded without waiting for server confirmation. This involves preemptively updating the UI based on the expected outcome of the mutation request, while simultaneously queuing the actual request for when internet connectivity resumes. However, it's paramount to manage the local cache and remote database consistency meticulously, ensuring that any optimistic updates are either confirmed or rolled back based on the server's response to avoid data discrepancies.

For example, when a user modifies a record, such as marking a task as complete, the application can instantly reflect this change in the UI, while the useMutation hook queues the update operation. Upon regaining connectivity, these queued mutations are then processed. Key to this process is the mutation's onMutate option, where developers can implement the logic to optimistically update the local cache, and onSuccess and onError options to handle the synchronization outcome by either confirming the optimistic update or reverting it.

const { mutate } = useMutation(updateTaskStatus, {
    onMutate: async (newStatus) => {
        await queryClient.cancelQueries(['tasks']);
        const previousTasks = queryClient.getQueryData(['tasks']);
        queryClient.setQueryData(['tasks'], (old) => updateLocalTaskStatus(old, newStatus));
        return { previousTasks };
    onError: (err, newStatus, context) => {
        queryClient.setQueryData(['tasks'], context.previousTasks);
    onSettled: () => {

In this example, the onMutate function optimistically updates the tasks list before sending the update to the server. If the update fails (handled in onError), the changes are rolled back using the information returned by onMutate. Finally, onSettled ensures that the tasks query is invalidated, prompting a refetch to synchronize the UI with the server state.

Strategizing around these concepts requires thorough attention to the application's data flow and state management, anticipating scenarios where conflicts or data loss might occur. By leveraging React Query's capabilities with a thoughtful approach to queue management and optimistic UI techniques, developers can create seamless, robust offline-first experiences. This not only boosts the app's resilience and user satisfaction but also aligns with modern expectations of app functionality amidst fluctuating connectivity conditions.

Handling Edge Cases and Offering Best Practices

Building offline-first applications with React Query introduces unique challenges, particularly around data synchronization and version control. A common pitfall is the handling of conflicts during data synchronization. When the app regains internet access, and multiple offline mutations are synchronized with the backend, conflicts may arise. For instance, what happens if two users edit the same resource offline and then attempt to sync these changes? To mitigate this, a conflict resolution strategy should be implemented on the backend. This could involve timestamp checks, where the latest update overwrites previous ones, or a more complex merging logic that inspects the content of the mutations.

Another edge case is managing the version control of cached data. When a user accesses data offline, they work with a snapshot of the data at the time of the last update. Upon reconnecting, any changes made by other users must be synchronized. This can lead to outdated information being displayed or edited. To handle this, implement version control for your cached data. Each record can have a version number or a last updated timestamp. When the app goes online, fetch the latest data, compare versions, and update the local cache accordingly. This ensures users always work with the most up-to-date information.

Efficiently updating the UI after successful data synchronization is also crucial for maintaining a seamless user experience. After syncing offline mutations, the UI should reflect the current state without requiring a manual refresh. Leverage React Query's invalidateQueries to refetch and update the UI automatically. This not only keeps the UI in sync but also reduces the cognitive load on the user by abstracting the complexity of data management.

Regarding best practices for structuring code, it's essential to maintain a clear separation between online and offline logic. This makes your code easier to read and maintain. Utilize service workers for background syncing and caching strategies but keep the business logic within your React components or hooks. This modular approach improves code readability and makes it easier to debug and extend your application.

Finally, optimizing performance and ensuring resilience are crucial for offline-first applications. Prioritize critical data for offline access and limit the cache size to avoid storage issues on the device. Implement efficient data retrieval strategies, such as incremental loading, to minimize the initial load time and resource consumption. Regularly test your app's offline capabilities under various scenarios to ensure a robust and resilient offline-first architecture.


This article explores the concept of building offline-first React applications using the React Query library. It covers the importance of offline functionality and how React Query supports data fetching, caching, and synchronization. Key takeaways include the significance of prioritizing local data storage, the use of background syncing for maintaining data integrity, and the implementation of optimistic UI updates for a smoother user experience. The article also provides a task for readers to implement conflict resolution in data synchronization, showcasing the challenges and complexity involved in offline-first development.

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