Async Data Flow

Async Data Flow Overview

Async Data Flow lets you work with Redux in a way that stays clear, repeatable, and easy to scale. Instead of ad-hoc steps, you follow a pattern that other developers recognise immediately.

The key is to keep async data flow focused and predictable. Start from the minimal example here, then add only the complexity your situation actually needs.

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

export const fetchUser = createAsyncThunk('user/fetch', async (id) => {
  const res = await fetch(`/api/users/${id}`);
  return res.json();
});

const userSlice = createSlice({
  name: 'user',
  initialState: { data: null, status: 'idle' },
  reducers: {},
  extraReducers: (builder) => {
    builder
      .addCase(fetchUser.pending, (s) => { s.status = 'loading'; })
      .addCase(fetchUser.fulfilled, (s, a) => { s.status = 'idle'; s.data = a.payload; });
  },
});

createAsyncThunk handles async flows and dispatches pending/fulfilled/rejected actions automatically.

Async Data Flow Example

const slice = createSlice({ name, initialState, reducers });
const store = configureStore({ reducer: { key: slice.reducer } });
// dispatch(slice.actions.something())

• Start from a minimal Async Data Flow example and grow it only as needed.
• Keep things explicit so Async Data Flow behaves the same for everyone on the team.
• Name things clearly so teammates understand your Async Data Flow at a glance.
• Verify Async Data Flow works as expected before relying on it in important work.

Redux Cheatsheet

Quick Redux Toolkit reference related to async data flow.

How Async Data Flow Works in Redux

Async Data Flow follows Redux's predictable data flow: components dispatch actions, reducers compute the next state from the previous state and the action, and subscribed components re-render.

createAsyncThunk handles async flows and dispatches pending/fulfilled/rejected actions automatically.

• State lives in a single, read-only store.
• Actions are the only way to describe a change.
• Reducers are pure functions that return the next state.
• Redux Toolkit removes most boilerplate with slices and thunks.

Practical Guidance for Async Data Flow

In modern apps, async data flow should use Redux Toolkit rather than hand-written Redux. Keep state minimal, colocate logic in slices, and use RTK Query for server data.

Common Mistakes

• Skipping edge cases and error handling when using async data flow.
• Not verifying the result of async data flow before moving on.
• Over-complicating async data flow before you actually need the extra flexibility.
• Ignoring documentation, which makes async data flow hard for the next person to follow.

Key Takeaways

• Async Data Flow is a core part of working effectively with Redux.
• Start small and keep async data flow focused on a single goal.
• Apply consistent patterns so async data flow scales across your project.
• Practise and document async data flow to keep your workflow maintainable.