This is a rewrite of RxJava-Android-Samples by Kaushik Gopal in Kotlin
Just like Kaushik, RxJava samples. This repository is filled with useful real-world examples of using RxJava with Android. It's usually in a constant state of "Work in Progress" (WIP).
Kaushik also gave a talk at the San Francisco Android User Group about warming up to RxJava. Here is a link to the video and slides.
I wanted to have the freedom to Kotlin-ize the examples. I also wanted to replace ButterKnife and xml layouts with Anko. Lastly it was a good opportunity to get to know the RxJava examples in more detail and also learn both Kotlin and Anko
I will try and keep in sync with the examples in the Java repo
A common requirement is to offload lengthy heavy I/O intensive operationsacc to a background thread (non-UI thread) and feed the results back to the UI/main thread, on completion. This is a demo of how long-running operations can be offloaded to a background thread. After the operation is done, we resume back on the main thread. All using RxJava! Think of this as a replacement to AsyncTasks.
The long operation is simulated by a blocking Thread.sleep call (since this is done in a background thread, our UI is never interrupted).
To really see this example shine. Hit the button multiple times and see how the button click (which is a UI operation) is never blocked because the long operation only runs in the background.
This is a demo of how events can be accumulated using the "buffer" operation.
A button is provided and we accumulate the number of clicks on that button, over a span of time and then spit out the final results.
If you hit the button once, you'll get a message saying the button was hit once. If you hit it 5 times continuously within a span of 2 seconds, then you get a single log, saying you hit that button 5 times (vs 5 individual logs saying "Button hit once").
Note:
If you're looking for a more foolproof solution that accumulates "continuous" taps vs just the number of taps within a time span, look at the EventBus Demo where a combo of the publish and buffer operators is used. For a more detailed explanation, you can also have a look at this blog post.
This is a demo of how events can be swallowed in a way that only the last one is respected. A typical example of this is instant search result boxes. As you type the word "Bruce Lee", you don't want to execute searches for B, Br, Bru, Bruce, Bruce, Bruce L ... etc. But rather intelligently wait for a couple of moments, make sure the user has finished typing the whole word, and then shoot out a single call for "Bruce Lee".
As you type in the input box, it will not shoot out log messages at every single input character change, but rather only pick the lastly emitted event (i.e. input) and log that.
This is the debounce/throttleWithTimeout method in RxJava.
Retrofit from Square is an amazing library that helps with easy networking (even if you haven't made the jump to RxJava just yet, you really should check it out). It works even better with RxJava and these are examples hitting the GitHub API, taken straight up from the android demigod-developer Jake Wharton's talk at Netflix. You can watch the talk at this link. Incidentally, my motivation to use RxJava was from attending this talk at Netflix.
Since it was a presentation, Jake only put up the most important code snippets in his slides. Also he uses Java 8 in them, so I flushed those examples out in good old Java 6. (Note: you're most likely to hit the GitHub API quota pretty fast so send in an OAuth-token as a parameter if you want to keep running these examples often).
Orchestrating Observables. Make parallel network calls, then combine the result into a single data point (flatmap + zip)
The below ascii diagram expresses the intention of our next example with panache. f1,f2,3,f4,f5 are essentially network calls that when made, give back a result that's needed for a future calculation.
(flatmap)
f1 ___________________ f3 _______
(flatmap) | (zip)
f2 ___________________ f4 _______| ___________ final output
\ |
\____________ f5 _______|
The code for this example has already been written by one Mr.skehlet in the interwebs. Head over to the gist for the code. It's written in pure Java (6) so it's pretty comprehensible if you've understood the previous examples. I'll flush it out here again when time permits or I've run out of other compelling examples.
Auto-updating views are a pretty cool thing. If you've dealt with Angular JS before, they have a pretty nifty concept called "two-way data binding", so when an HTML element is bound to a model/entity object, it constantly "listens" to changes on that entity and auto-updates its state based on the model. Using the technique in this example, you could potentially use a pattern like the Presentation View Model pattern with great ease.
While the example here is pretty rudimentary, the technique used to achieve the double binding using a Publish Subject is much more interesting.
This is an example of polling using RxJava Schedulers. This is useful in cases, where you want to constantly poll a server and possibly get new data. The network call is "simulated" so it forces a delay before return a resultant string.
Have a look at the accompanying blog posts for details on this demo:
- Implementing an event bus with RxJava
- DebouncedBuffer used for the fancier variant of the demo
- share/publish/refcount
Form validation - using .combineLatest
Thanks to Dan Lew for giving me this idea in the fragmented podcast - episode #5 (around the 4:30 mark).
.combineLatest allows you to monitor the state of multiple observables at once compactly at a single location. The example demonstrated shows how you can use .combineLatest to validate a basic form. There are 3 primary inputs for this form to be considered "valid" (an email, a password and a number). The form will turn valid (the text below turns blue :P) once all the inputs are valid. If they are not, an error is shown against the invalid inputs.
We have 3 independent observables that track the text/input changes for each of the form fields (RxAndroid's WidgetObservable comes in handy to monitor the text changes). After an event change is noticed from all 3 inputs, the result is "combined" and the form is evaluated for validity.
Note that the Func3 function that checks for validity, kicks in only after ALL 3 inputs have received a text change event.
The value of this technique becomes more apparent when you have more number of input fields in a form. Handling it otherwise with a bunch of booleans makes the code cluttered and kind of difficult to follow. But using .combineLatest all that logic is concentrated in a nice compact block of code (I still use booleans but that was to make the example more readable).
Retrieve data first from a cache, then a network call - using .concat
Using concat, you can retrieve information from an observable first (presumably this one is fast like retrieveing from a disk cache) and show preliminary data to a user. Subsequently, when the longer running 2nd observable is complete (say a network call), you can update the results on the interface using the latest information.
For the purposes of illustration i use an in-memory List (not an actual disk cache), then shoot out a real network call to the github api so it gives you a feel of how this can really be applied in production apps.
Update:
After a conversation I had with @artem_zin, we arrived at an alternative solution to the same problem. One that used the .merge operator instead.
The concat (and the equivalent startWith) opeartor is strictly sequential, meaning all of the items emitted by the first Observable are emitted strictly before any of the items from the second Observable are emitted. So assuming the first observable (for some strange reason) takes really long to run through all its items, even if the first few items from the second observable have come down the wire it will forcibly be queued.
The merge operator on the other hand interleaves items as they are emitted. The problem here though is if for some strange reason an item is emitted by the cache or slower observable after the newer/fresher observable, it will overwrite the newer content. To account for this you have to monitor the "resultAge" somehow. This is demonstrated in the updated solution PseudoCacheMergeFragment.
This is a super simple and straightforward example which shows you how to use RxJava's timer, interval and delay operators to handle a bunch of cases where you want to run a task at specific intervals. Basically say NO to Android TimerTasks.
Cases demonstrated here:
- run a single task after a delay of 2s, then complete
- run a task constantly every 1s (there's a delay of 1s before the first task fires off)
- run a task constantly every 1s (same as above but there's no delay before the first task fires off)
- run a task constantly every 3s, but after running it 5 times, terminate automatically
Exponential backoff is a strategy where based on feedback from a certain output, we alter the rate of a process (usually reducing the number of retries or increasing the wait time before retrying or re-executing a certain process).
The concept makes more sense with examples. RxJava makes it (relatively) simple to implement such a strategy. My thanks to Mike for suggesting the idea.
Say you have a network failure. A sensible strategy would be to NOT keep retrying your network call every 1 second. It would be smart instead (nay... elegant!) to retry with increasing delays. So you try at second 1 to execute the network call, no dice? try after 10 seconds... negatory? try after 20 seconds, no cookie? try after 1 minute. If this thing is still failing, you got to give up on the network yo!
We simulate this behaviour using RxJava with the retryWhen operator.
RetryWithDelay code snippet courtesy:
- http://stackoverflow.com/a/25292833/159825
- Another excellent implementation via @sddamico : https://gist.github.com/sddamico/c45d7cdabc41e663bea1
Another variant of the exponential backoff strategy is to execute an operation for a given number of times but with delayed intervals. So you execute a certain operation 1 second from now, then you execute it again 10 seconds from now, then you execute the operation 20 seconds from now. After a grand total of 3 times you stop executing.
Simulating this behavior is actually way more simpler than the prevoius retry mechanism. You can use a variant of the delay operator to achieve this.
A common question that's asked when using RxJava in Android is, "how do i resume the work of an observable if a configuration change occurs (activity rotation, language locale change etc.)?".
This example shows you one strategy viz. using retained Fragments. I started using retained fragments as "worker fragments" after reading this fantastic post by Alex Lockwood quite sometime back.
Hit the start button and rotate the screen to your heart's content; you'll see the observable continue from where it left off.
There are certain quirks about the "hotness" of the source observable used in this example. Check my blog post out where I explain the specifics.
Examples that I would like to have here, but haven't found the time yet to flush out.
a. Simple pagination b. Optimized pagination
I try to ensure the examples are not overly contrived but reflect a real-world usecase. If you have similar useful examples demonstrating the use of RxJava, feel free to send in a pull request.
I'm wrapping my head around RxJava too so if you feel there's a better way of doing one of the examples mentioned above, open up an issue explaining how. Even better, send a pull request.
Licensed under the Apache License, Version 2.0 (the "License"). You may obtain a copy of the License at
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