It’s simplistic API makes it limiting for a few reasons:
Callers must always know the preference key and type.
No support for storing custom types out of the box.
Callers cannot listen for changes to individual keys.
RxPreferences is a new(ish) library that builds on top of SharedPreferences to solve these problems, and takes it further by integrating with RxJava.
SharedPreferences requires callers to always know what key identifies a preference when they get or save a preference. Callers also need to keep track of what type was used for a preference (did the preference use a float or int), which can lead to subtle bugs.
RxPreferences introduces a Preference class, that identifies key used to store it and the type of data it holds, making it easier to spot such bugs at compile time. RxSharedPreferences provides factory methods to promote preferences to objects.
The Preference class provides methods that replace their counterparts in SharedPreferences and SharedPreferences.Editor. This makes it convenient to use them as the source of truth, instead of sharing String constants throughout your app.
SharedPreferences restricts you to a set of limited types — boolean, float, int, long, String and Set<String>. Trying to persist custom types is doable, but looks awkward.
RxPreferences introduces a pluggable Adapter abstraction. An Adapter can store and retrieve values of an arbitrary type, and consolidates your serialization logic into a single location.
RxPreferences provides built in adapters for all the types suppored by SharedPreferences and enums. Writing a custom adapter is trivial. You can even use your own favorite serialization library!
Then, simply let RxPreferences know which adapter you want to use.
The Preference class integrates with RxJava, and lets you observe changes to a single preference directly. Internally, RxPreferences shares a single listener amongst all Preference objects to avoid unnecessary work.
RxPreferences also lets you take actions on preferences to update or delete values. This makes it straightforward to set up complex pipelines by combining it with other libraries in the RxJava family.
For example, RxPreferences and RxBinding can be combined to hand roll your own simplified CheckBoxPreference.
RxPreferences makes it convenient to interact with SharedPreferences, and integrating with RxJava makes it easy to express complex logic that would otherwise have been tedious and brittle. RxPreferences is available on Maven Central. Check the Github repo or u2020 to see more examples.
When you create a new Observable, you’ll often want to clean up whenever a Subscriber unsubscribes from the Observable. The Subscriber class exposes a handy add method that does exactly what we want.
For instance, here’s how you would create an Observable that emits the keys that were changed for some SharedPreferences. It registers a new OnSharedPreferenceChangeListener when a subscriber subscribes, and automatically unregisters the listener when the subscriber unsubscribes.
On the Observer side of things, you can also enqueue actions to be executed each time a Subscriber unsubscribes from an Observable with the doOnUnSubscribe method.
JSON is a popular format data exchange format between services. Naturally, we use JSON for our public HTTP API. Most of our client libraries are a wrapper around this with a bit of sugar mixed in.
If you’re an Android application developer, you should use Gson, Jackson or one of the numerous databinding libraries. They’re fast, (relatively) tiny, and provide simple, yet powerful APIs. Unfortunately library developers don’t have the luxury of bundling such large libraries. So we turned to APIs available in the core Android SDK.
Android ships two JSON APIs. org.json is a simple tree API, while JsonReader/JsonWriter is a lower level streaming API, available only on Android 3.0+. Our first versions of the SDK used org.json, since it was available on all versions Android, and was in use by a lot of our bundled integrations, such as Mixpanel. Although this was convenient, it resulted in a sub-optimal experience for clients. Since we let users pass in their metadata, I wanted to hide our implementation details for the next version and expose a simpler API. One of my prototypes essentially tried to replicate Gson. I liked the idea of databinding so clients didn’t have to learn any new APIs and could use their POJOs instead.
Trying to re-create Gson was not only a daunting task, but impractical (we were trying to keep the method count down after all) and wasteful. We didn’t need to support top level generic types, @SerializedName equivalent, field arrays, custom type adapters, and probably quite a few other use cases. By constraining the problem, I prototyped a implementation that does the job. It use the JsonReader/JsonWriter to read/write the JSON and combines it with the reflection API to do databinding. It’s also very similar (albeit much more complex) to how Gson approaches the issue. If you’ve wondered what Gson does under the hood, this might be a good place to start. Here’s a snippet from the class to show how serialization works.
Pretty easy, eh? My favourite part is that it can be hidden behind a Converter interface!
This interface can be exposed to clients. It lets us provide an implementation ready to use out of the box, but clients can plug in their own implementations if they need more complex use cases. Here’s an implementation if they’re using Gson.
Although it lost out to another approach, it was a fun little exercise to come up with this class.