RxJS proper way to merge 3 arrays into one, as an Observable -


hi have 3 subjects ( observables )

 const arr1$ = new behaviorsubject([])  const arr2$ = new behaviorsubject([])  const arr3$ = new behaviorsubject([])

let's these subjects contain data

arr1 = ['1','2'],  arr2 = ['3','7'],  arr3 = ['4']

i want create observable (mergedobservable) combines latest these 3 observables, merges them 1 array, result 1 array this: ['1','2','3','7','4']

so can achieve  this.mergedobservable.subscribe( mergedarray => {      console.log(mergedarray) }) // ['1','2','3','7','4']

i struggling how make 'mergedobservable'. have tried combinelatest(arr1$,arr2$,arr3$) , still need 'chain' more functionality merge 3.

thanks!

combinelatest takes optional project function can used modify combined value.

you can specify project function uses array.prototype.reduce flatten arrays single array:

const arr1$ = new rx.behaviorsubject(['1','2']);  const arr2$ = new rx.behaviorsubject(['3','7']);  const arr3$ = new rx.behaviorsubject(['4']);    rx.observable    .combinelatest(      arr1$, arr2$, arr3$,      (...arrays) => arrays.reduce((acc, array) => [...acc, ...array], [])    )    .subscribe(value => console.log(value));
.as-console-wrapper { max-height: 100% !important; top: 0; }
<script src="https://unpkg.com/rxjs/bundles/rx.min.js"></script>


Comments

Post a Comment

Popular posts from this blog

Is there a better way to structure post methods in Class Based Views -

i'm using class based views in django , never specify url in ajax calls , omit action parameter in form because know these requests go through post method of class based view. notice view - profile page make many requests depending on user updating on page - post method become convoluted , if/else mess example: def post(self, request, *args, **kwargs): # handle user changing available date if request.post.get('availabledate') != none: self.updateavailabledate(request) return jsonresponse({'result':'success'}) if request.post.get('newprojectname') != none: creative_user = creativeuserprofile.objects.get(id = request.user.id) project = project.create_project(creative_user, request.post['newprojectname']) project.save() return jsonresponse({'projectid': project.id }) if request.post.get('projectimage') != none , request.files['file']: proj...
Read more

php - Cannot override Laravel Spark authentication with own implementation -

during login process of laravel spark need request token of remote api. api stores exact same users , passwords laravel spark application. therefore need username , non hashed password of user during authentication process. i thought overriding authenticated method solution problem. in routes/web.php override post /login endpoint , pointing endpoint own logincontroller: <?php namespace app\http\controllers\auth; use illuminate\support\facades\log; use illuminate\http\request; use laravel\spark\http\controllers\auth\logincontroller sparklogincontroller; class logincontroller extends sparklogincontroller { /** * create new login controller instance. * * @return void */ public function __construct() { parent::__construct(); } /** * handle successful authentication attempt. * * @param request $request * @param \illuminate\contracts\auth\authenticatable $user * @return response */ public fu...
Read more

What is happening when Matlab is starting a "parallel pool"? -

running parallel cpu processes in matlab starts command parpool() according documentation , function: [creates] special job on pool of workers, , [connects] matlab client parallel pool. this function takes bit of time execute, on order of 30 seconds. in other multi-cpu paradigms openmp, parallel execution seems totally transparent -- i've never noticed behavior analogous matlab (granted i'm not experienced parallel programming). so, happening between time parpool() called , when finishes executing? takes long? parallel computing toolbox enables run matlab code in parallel using several different paradigms (e.g. jobs , tasks, parfor , spmd , parfeval , batch processing), , run either locally (parallelised across cores in local machine) or remotely (parallelised across machines in cluster - either 1 own, or 1 in cloud). in of these cases, code run on matlab workers , copies of matlab without interactive desktop. if you're intending run on remo...
Read more