Prerequisite Reducer Concepts

As described in Reducers, a Redux reducer function:

  • Should have a signature of (previousState, action) => newState, similar to the type of function you would pass to Array.prototype.reduce(reducer, ?initialValue)
  • Should be "pure", which means the reducer:
    • Does not perform side effects (such as calling API's or modifying non-local objects or variables).
    • Does not call non-pure functions (like Date.now or Math.random).
    • Does not mutate its arguments. If the reducer updates state, it should not modify the existing state object in-place. Instead, it should generate a new object containing the necessary changes. The same approach should be used for any sub-objects within state that the reducer updates.
Note on immutability, side effects, and mutation

Mutation is discouraged because it generally breaks time-travel debugging, and React Redux's connect function:

  • For time traveling, the Redux DevTools expect that replaying recorded actions would output a state value, but not change anything else. Side effects like mutation or asynchronous behavior will cause time travel to alter behavior between steps, breaking the application.
  • For React Redux, connect checks to see if the props returned from a mapStateToProps function have changed in order to determine if a component needs to update. To improve performance, connect takes some shortcuts that rely on the state being immutable, and uses shallow reference equality checks to detect changes. This means that changes made to objects and arrays by direct mutation will not be detected, and components will not re-render.

Other side effects like generating unique IDs or timestamps in a reducer also make the code unpredictable and harder to debug and test.

Because of these rules, it's important that the following core concepts are fully understood before moving on to other specific techniques for organizing Redux reducers:

Redux Reducer Basics

Key concepts:

Pure Functions and Side Effects

Key Concepts:

Immutable Data Management

Key Concepts:

Normalizing Data

Key Concepts: