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Pros/cons Of Using Redux-saga With ES6 Generators Vs Redux-thunk With ES2017 Async/await

There is a lot of talk about the latest kid in redux town right now, redux-saga/redux-saga. It uses generator functions for listening to/dispatching actions.

Before I wrap my head around it, I would like to know the pros/cons of using redux-saga instead of the approach below where I'm using redux-thunk with async/await.

A component might look like this, dispatch actions like usual.

import { login } from 'redux/auth';

class LoginForm extends Component {

  onClick(e) {
    e.preventDefault();
    const { user, pass } = this.refs;
    this.props.dispatch(login(user.value, pass.value));
  }

  render() {
    return (<div>
        <input type="text" ref="user" />
        <input type="password" ref="pass" />
        <button onClick={::this.onClick}>Sign In</button>
    </div>);
  } 
}

export default connect((state) => ({}))(LoginForm);

Then my actions look something like this:

// auth.js

import request from 'axios';
import { loadUserData } from './user';

// define constants
// define initial state
// export default reducer

export const login = (user, pass) => async (dispatch) => {
    try {
        dispatch({ type: LOGIN_REQUEST });
        let { data } = await request.post('/login', { user, pass });
        await dispatch(loadUserData(data.uid));
        dispatch({ type: LOGIN_SUCCESS, data });
    } catch(error) {
        dispatch({ type: LOGIN_ERROR, error });
    }
}

// more actions...

// user.js

import request from 'axios';

// define constants
// define initial state
// export default reducer

export const loadUserData = (uid) => async (dispatch) => {
    try {
        dispatch({ type: USERDATA_REQUEST });
        let { data } = await request.get(`/users/${uid}`);
        dispatch({ type: USERDATA_SUCCESS, data });
    } catch(error) {
        dispatch({ type: USERDATA_ERROR, error });
    }
}

// more actions...
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Answer

In redux-saga, the equivalent of the above example would be

export function* loginSaga() {
  while(true) {
    const { user, pass } = yield take(LOGIN_REQUEST)
    try {
      let { data } = yield call(request.post, '/login', { user, pass });
      yield fork(loadUserData, data.uid);
      yield put({ type: LOGIN_SUCCESS, data });
    } catch(error) {
      yield put({ type: LOGIN_ERROR, error });
    }  
  }
}

export function* loadUserData(uid) {
  try {
    yield put({ type: USERDATA_REQUEST });
    let { data } = yield call(request.get, `/users/${uid}`);
    yield put({ type: USERDATA_SUCCESS, data });
  } catch(error) {
    yield put({ type: USERDATA_ERROR, error });
  }
}

The first thing to notice is that we're calling the api functions using the form yield call(func, ...args). call doesn't execute the effect, it just creates a plain object like {type: 'CALL', func, args}. The execution is delegated to the redux-saga middleware which takes care of executing the function and resuming the generator with its result.

The main advantage is that you can test the generator outside of Redux using simple equality checks

const iterator = loginSaga()

assetside of Redux using simple equality checks

const iterator = loginSaga()

assert.deepEqual(iterator.next().value, take(LOGIN_REQUEST))

// resume the generator with some dummy action
const mockAction = {user: '...', pass: '...'}
assert.deepEqual(
  iterator.next(mockAction).value, 
  call(request.post, '/login', mockAction)
)

// simulate an error result
const mockError = 'invalid user/password'
assert.deepEqual(
  iterator.throw(mockError).value, 
  put({ type: LOGIN_ERROR, error: mockError })
)

Note we're mocking the api call result by simply injecting the mocked data into the next method of the iterator. Mocking data is way simpler than mocking functions.

The second thing to notice is the call to yield take(ACTION). Thunks are called by the action creator on each new action (e.g. LOGIN_REQUEST). i.e. actions are continually pushed to thunks, and thunks have no control on when to stop handling those actions.

In redux-saga, generators pull the next action. i.e. they have control when to listen for some action, and when to not. In the above example the flow instructions are placed inside a while(true) loop, so it'll listen for each incoming action, which somewhat mimics the thunk pushing behavior.

The pull approach allows implementing complex control flows. Suppose for example we want to add the following requirements

  • Handle LOGOUT user action

  • upon the first successful login, the server returns a token which expires in some delay stored in a expires_in field. We'll have to refresh the authorization in the background on each expires_in milliseconds

  • Take into account that when waiting for the result of api calls (either initial login or refresh) the user may logout in-between.

How would you implement that with thunks; while also providing full test coverage for the entire flow? Here is how it may look with Sagas:

function* authorize(credentials) {
  const token = yield call(api.authorize, credentials)
  yield put( login.success(token) )
  return token
}

function* authAndRefreshTokenOnExpiry(name, password) {
  let token = yield call(authorize, {name, password})
  while(true) {
    yield call(delay, token.expires_in)
    token = yield call(authorize, {token})
  }
}

function* watchAuth() {
  while(true) {
    try {
      const {name, password} = yield take(LOGIN_REQUEST)

      yield race([
        take(LOGOUT),
        call(authAndRefreshTokenOnExpiry, name, password)
      ])

      // user logged out, next while iteration will wait for the
      // next LOGIN_REQUEST action

    } catch(error) {
      yield put( login.error(error) )
    }
  }
}

In the above example, we're expressing our concurrency requirement using race. If take(LOGOUT) wins the race (i.e. user clicked on a Logout Button). The race will automatically cancel the authAndRefreshTokenOnExpiry background task. And if the authAndRefreshTokenOnExpiry was blocked in middle of a call(authorize, {token}) call it'll also be cancelled. Cancellation propagates downward automatically.

You can find a runnable demo of the above flow

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source: stackoverflow.com
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