Redux Toolkit's createAsyncThunk for API Polling

Anton Ioffe - January 11th 2024 - 10 minutes read

In the ever-evolving landscape of modern web development, fine-tuning asynchronous operations is paramount to building responsive and reliable applications. JavaScript developers wielding the power of Redux Toolkit are poised at the frontier of state-of-the-art data management techniques. Prepare to dive deep into the nuances of leveraging createAsyncThunk for efficient, effective, and sophisticated API polling strategies. This article charts a course through the practical implementation of API polling, from the automated simplicity afforded by createAsyncThunk to the finesse required for performance optimization and advanced error handling. Each section is meticulously crafted, not only to enhance your technical know-how but also to equip you with an arsenal of best practices and design patterns ready to deploy in real-world applications. Engage with this comprehensive guide to master asynchronous interactions and elevate your Redux Toolkit prowess to new heights.

Leveraging createAsyncThunk for Effective API Polling

Leveraging createAsyncThunk from Redux Toolkit offers a streamlined approach to handle the intricacies of API polling. Traditional Redux thunks require meticulous crafting of action types for "pending," "fulfilled," and "rejected" states alongside matching action creators, which can quickly become cumbersome. The introduction of createAsyncThunk abstracts this repetitive boilerplate by automatically generating these actions based on the life cycle of a returned Promise during an asynchronous operation, thus simplifying the state management process during polling.

When implementing API polling, developers historically managed loading states and error handling manually. This entailed dispatching separate actions at various stages of the async request. With createAsyncThunk, the loading state is automatically tracked. It dispatches "pending" actions before a request and "fulfilled" or "rejected" actions after resolving or rejecting the Promise. This automation reduces the likelihood of common polling mistakes, such as mismanaging application states, which can lead to an unresponsive UI or other state-related issues.

A practical example of API polling with createAsyncThunk combines the utility with useEffect and a recurrent execution mechanism such as setInterval or recursive setTimeout to initiate and manage the polling process. The payload creation callback is tasked with performing the async request and then dispatched repeatedly according to the defined interval. Developers also need to implement cleanup logic within useEffect's return function to prevent unnecessary network traffic and resource retention when the component unmounts or the polling condition is met.

const pollData = createAsyncThunk(
  async (jobId, { rejectWithValue }) => {
    try {
      const response = await fetchData(`/api/jobs/${jobId}`);
      // Condition to stop polling should be checked here
      if (!response.ok) {
        throw new Error('Polling error');
      // Proceed with JSON parsing or other processing logic
      return response.json();
    } catch (error) {
      return rejectWithValue(error.message);
// Setting up polling in a React component
useEffect(() => {
  const intervalId = setInterval(() => {
    // Additional logic to check if polling should stop
  }, pollingInterval);

  // Cleanup function to clear the interval
  return () => clearInterval(intervalId);
}, [jobId, dispatch, pollingInterval]);

While createAsyncThunk greatly aids the management of async operations typical in polling, developers still need to ensure that this polling is properly orchestrated, taking care to avoid excessive network requests, and to manage resource cleanup effectively to prevent memory bloat.

Incorporating createAsyncThunk in your polling logic eliminates a significant amount of manual and error-prone coding. Nevertheless, while this utility simplifies several aspects of async logic, developers must still diligently manage polling intervals and lifecycle to optimize application performance. The abstraction createAsyncThunk provides is powerful, but exercising control over the polling frequency and responding appropriately to the polling result remains a deliberate exercise in state management.

Architecting Reducers for API Polling Scenarios

In architecting reducers for API polling scenarios with Redux Toolkit's createAsyncThunk, it is crucial to precisely handle the distinct states of an API request. This involves structuring your reducer logic to cope with the transient nature of polling where data is fetched at regular intervals. The fundamental states to consider are loading, which indicates a fetch is in progress, success, which reflects the completion of a fetch with data retrieval, and error, for handling any issues that occur during the attempt.

A common approach for managing these states is to use the extraReducers property provided by createSlice. This field allows you to augment your slice with additional cases that respond to the action types dispatched by createAsyncThunk. Here, you handle the .pending, .fulfilled, and .rejected action types to update your state to reflect the current status of the asynchronous requests. This approach centralizes the reducer logic associated with an async operation, aiding maintainability and reducing complexity.

Consider this real-world reducer pattern which uses createAsyncThunk:

import { createAsyncThunk, createSlice } from '@reduxjs/toolkit';

// Define the initial state for the polling
const initialState = {
  data: null,
  loading: false,
  error: null

// Create the thunk for polling our API
export const pollData = createAsyncThunk(
  async (arg, { dispatch, getState }) => {
    // Perform the API call
    const response = await fetch('');
    const data = await response.json();
    return data; // Returns a promise with the data

// Slice definition with extraReducers to handle async states
const dataSlice = createSlice({
  name: 'data',
  reducers: {},
  extraReducers: (builder) => {
      .addCase(pollData.pending, (state) => {
        state.loading = true;
        state.error = null;
      .addCase(pollData.fulfilled, (state, action) => { = action.payload; // update data with the result
        state.loading = false;
      .addCase(pollData.rejected, (state, action) => {
        state.error = action.error.message;
        state.loading = false; = null;

export default dataSlice.reducer;

In this pattern, while an API call is pending, the state's loading is set to true, and error to null, reflecting an ongoing process. Upon fulfillment, data is populated with the result, and loading returns to false. Rejection sets the error state with the encountered error message and resets data to null. This concise harnessing of createAsyncThunk's dispatched actions through extraReducers ensures a clean and modular reducer that directly mirrors the lifecycle of the API polling operation.

One must also take precautionary measures to mitigate potential coding mistakes such as mutating state directly or mismanaging asynchronous logic. Within the Redux Toolkit, state is immutable, requiring the use of Immer, which underlies createSlice, to draft state updates. Direct mutation outside of this paradigm will cause errors and unstable application behavior. Correct usage entails returning a new state object or modifying the draft state and letting Immer handle immutability concerns.

When considering polling, challenging thought-provoking questions include: How do we efficiently mitigate the risk of overlapping requests if a poll takes longer than the interval time? What strategies do we employ to manage memory and cleanup in event-driven environments like React, keeping in mind component lifecycle and cleanups? How can we optimize selectors to prevent unnecessary re-renders when polling-induced state updates occur? Addressing these questions within our reducer and component design can lead to resilient, scalable polling mechanisms within our applications.

Optimizing Polling Performance and Handling Cancellation

Efficient API polling is contingent on the delicate balance between keeping data fresh and minimizing the performance drag associated with repeated network requests. One of the most common problems encountered during the process is the risk of memory leaks which may occur if polling tasks are not properly managed. Ensuring that polling events do not pile up is crucial, as abandoned setTimeout calls can lead to increased memory usage and even application crashes if left unchecked.

With Redux Toolkit's createAsyncThunk, developers have a powerful middleware at their disposal to handle complex asynchronous workflows such as polling. When integrating createAsyncThunk with React's useEffect hook, you can initiate polling and define cancellation logic in response to component lifecycle events. This guards against redundant requests and potential memory leaks by canceling ongoing polling tasks when components unmount or when a specific condition — such as obtaining the necessary data — is met.

A typical mistake in setting up polling is neglecting to control the execution flow. This can be mitigated by using a cancellable async action, like createAsyncThunk, which supports the abort controller pattern out-of-the-box. Through this pattern, developers can send an abort signal to their fetch request, allowing the polling to be stopped under set conditions without having to worry about pending asynchronous operations potentially leading to unexpected side effects.

A further consideration is optimizing the polling action to prevent unnecessary component re-renders, which hampers performance. Using memoization techniques offered by createSelector, or optimizing state updates to minimize object references changes can result in performance gains. Ensuring that data is handled efficiently, and only necessary updates are triggering re-renders, plays a significant role in enhancing the user experience in applications that rely heavily on updating states due to polling.

In conclusion, while API polling with Redux Toolkit can simplify async data management, it is critical to lay emphasis on implementing a robust cancellation strategy and performance optimizations. It requires a keen understanding of how to orchestrate and clean up the polling process, which createAsyncThunk elegantly offers. Keeping tabs on these aspects will lead to a durable, responsive application that can manage data fetching at regular intervals effectively.

Design Patterns and Best Practices for Polling in Real-World Applications

When dealing with API polling in real-world applications, modularity and reusability of the polling logic are of paramount importance. It is essential to encapsulate the polling mechanism, making it easily adaptable to various parts of the application. One efficient way to accomplish this is by using action creators that abstract the setup and teardown of the polling process.

const pollActionCreator = (actionToDispatch, interval) => {
    let intervalId = null;
    return {
        start: dispatch => {
            if (intervalId !== null) {
            intervalId = setInterval(() => dispatch(actionToDispatch()), interval);
        stop: () => {
            intervalId = null;

In the snippet above, pollActionCreator returns an object with start and stop methods for initiation, control, and clean-up of the polling process. This separation of concerns leads to maintainable and scalable code. Each time a component needs to poll, it can call start to initiate the process and stop to terminate it, preventing unintentional overlaps and memory leaks.

A common mistake is not properly handling the lifecycle of a poll, resulting in multiple intervals running concurrently which could create performance issues. The correct approach is to ensure that any existing poll is cleared before a new one starts. This can be handled within a React component using lifecycle hooks.

const dispatch = useDispatch();
const myAsyncAction = () => {/*...async action code...*/};
const pollingActions = useMemo(
  () => pollActionCreator(myAsyncAction, 5000),

useEffect(() => {
  return pollingActions.stop;
}, [pollingActions]);

Here, useEffect is utilized to guarantee that the poll starts when the component is mounted and is cleaned up when the component will unmount. The useMemo hook ensures that our pollingActions object is stable across re-renders, preventing unnecessary polls.

Furthermore, developers should strive to minimize the footprint of their polling logic in the global application state. One approach is to keep the polling-relevant state locally within the polling module or a custom hook, exposing only necessary actions for starting and stopping the poll, and then dispatching the retrieved data into the global store.

function usePolling(dataSelector, asyncPollAction, interval) {
  const { data, loading, error } = useSelector(dataSelector);
  const dispatch = useDispatch();

  useEffect(() => {
    const pollingActions = pollActionCreator(asyncPollAction, interval);

    return pollingActions.stop;
  }, [dispatch, asyncPollAction, interval]);

  return { data, loading, error };

Using the usePolling custom hook, a developer can effortlessly initiate polling inside any component, keeping the async logic bundled together, promoting clarity, and ease of testing.

In order to yield well-architected, modular, and reusable code, it's vital to debate the tradeoffs of local vs global state management, especially in the context of polling where the state is inherently transient and prone to rapid change. How will your components respond to these changes, and what can be abstracted to minimize performance impacts? By deliberating on such questions, one can fine-tune the balance between local component state and the wider application state, striking the perfect chord of efficiency and elegance in their polling strategy.

Beyond Basics: Advanced Polling Techniques and Error Handling

When delving into advanced polling techniques, an astute approach is to implement a dynamic polling interval that adapts based on certain conditions. A common strategy is exponential backoff, where the polling interval increases exponentially after each failed request, up to a maximum limit. This not only aids in mitigating server overload scenarios but also provides a more considerate use of network resources during periods of disconnection or when the service is down for maintenance.

let retryCount = 0;
const maxRetries = 5;
const baseInterval = 1000; // Initial polling interval
let timeoutId = null; // Keep track of the timeout to clear it later

function schedulePolling() {
  // Calculate a backoff interval
  const backoffInterval = Math.min(baseInterval * (2 ** retryCount), 30000); // Cap at 30 seconds
  clearTimeout(timeoutId); // Prevent memory leaks by clearing previous timeout
  timeoutId = setTimeout(pollForUpdates, backoffInterval);

async function pollForUpdates() {
  try {
    const data = await fetchData();
    retryCount = 0; // Reset retry count after a successful fetch
    schedulePolling(); // Schedule the next poll
  } catch (error) {
    if (retryCount < maxRetries) {
      schedulePolling(); // Retry with increased interval

function cancelPolling() {
  clearTimeout(timeoutId); // Clear the timeout to prevent the next poll

For more granular control, consider adaptive polling, where the interval varies in response to certain conditions or content received from the server. For instance, less frequent updates during off-peak hours or more frequent when expecting critical updates. To implement this, incorporate server-side logic that indicates to the front-end the suggested next polling time, allowing the server to have control over client-side polling activity.

Robust error handling is also a pivotal component of an advanced polling setup. Besides network errors, one must handle application-specific errors and situations where no new data is available. It is advisable to leverage try-catch blocks and if necessary, introduce error thresholds to cease polling after a certain number of consecutive failures, thus preventing an endless loop of fruitless requests.

const errorThreshold = 3;
let errorCount = 0;

async function pollForUpdates() {
  try {
    const response = await fetchData();
    if (response.hasUpdates) {
      errorCount = 0;
    const nextPollInMillis = response.nextPollInMillis || baseInterval;
    schedulePolling(nextPollInMillis); // Schedule polling with dynamic or default interval
  } catch (error) {
    if (errorCount < errorThreshold) {
      // Function to decide what the retry interval should be
      const retryInterval = calculateRetryInterval(errorCount);
    } else {
      cancelPolling(); // Cease polling if error threshold is reached

In elaborate polling scenarios, managing state becomes critical. Developers should responsibly track ongoing polls, ensuring that each is accounted for and cancellable when no longer needed. When managing multiple, concurrent polling operations, consider deduplicating requests that may cause race conditions or superfluous network traffic. It is advisable to maintain a registry of active polls and enforce a single active poll per unique request to guard against such issues.

Lastly, never underestimate the cognitive overhead of sprawling, complex polling logic spread across your application. Keeping your polling logic concise and contained not only enhances readability but also facilitates easier unit testing and debugging. Where possible, abstract polling mechanisms into reusable services or hooks that encapsulate polling details, providing clean, declarative interfaces for components to trigger updates. This encourages separation of concerns and leads to a maintainable and stable application architecture.


This article explores the use of Redux Toolkit's createAsyncThunk for API polling in modern web development. It delves into the benefits of using createAsyncThunk, such as simplifying state management and automating the handling of loading states and errors. The article also discusses how to architect reducers for API polling scenarios and optimize polling performance. It concludes with suggestions for designing modular and reusable polling logic and implementing advanced techniques like dynamic polling intervals and error handling. A challenging task for the reader to consider is implementing a deduplication strategy to prevent race conditions and unnecessary network requests in multiple concurrent polling operations.

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