Archive for category Android

Easy SQLite on Android with RxJava

Whenever I consider using an ORM library on my Android projects, I always end up abandoning the idea and rolling my own layer instead for a few reasons:

  • My database models have never reached the level of complexity that ORM’s help with.
  • Every ounce of performance counts on Android and I can’t help but fear that the SQL generated will not be as optimized as it should be.

Recently, I started using a pretty simple design pattern that uses Rx to offer what I think is a fairly simple way of managing your database access with RxJava. I’m calling this the “Async Rx Read” design pattern, which is a horrible name but the best I can think of right now.

Easy reads

One of the important design principles on Android is to never perform I/O on the main thread, and this obviously applies to database access. RxJava turns out to be a great fit for this problem.

I usually create one Java class per table and these tables are then managed by my SQLiteOpenHelper. With this new approach, I decided to extend my use of the helper and make it the only point of access to anything that needs to read or write to my SQL tables.

Let’s consider a simple example: a USERS table managed by the UserTable class:

// UserTable.java
List<User> getUsers(SQLiteDatabase db, String userId) {
  // select * from users where _id = {userId}
}

The problem with this method is that if you’re not careful, you will call it on the main thread, so it’s up to the caller to make sure they are always invoking this method on a background thread (and then to post their UI update back on the main thread, if they are updating the UI). Instead of relying on managing yet another thread pool or, worse, using AsyncTask, we are going to rely on RxJava to take care of the threading model for us.

Let’s rewrite this method to return a callable instead:

// UserTable.java
Callable<List<User>> getUsers(SQLiteDatabase db, String userId) {
  return new Callable<List<User>>() {
    @Override
    public List<User> call() {
      // select * from users where _id is userId
    }
  }
}

In effect, we simply refactored our method to return a lazy result, which makes it possible for the database helper to turn this result into an Observable:

// MySqliteOpenHelper.java
Observable<List<User>> getUsers(String userId) {
  return makeObservable(mUserTable.getUsers(getReadableDatabase(), userId))
    .subscribeOn(Schedulers.computation()) // note: do not use Schedulers.io()
}

Notice that on top of turning the lazy result into an Observable, the helper forces the subscription to happen on a background thread (the computation scheduler here; do not use Schedulers.io() because it’s backed by an unbounded executor). This guarantees that callers don’t have to worry about ever blocking the main thread.

Finally, the makeObservable method is pretty straightforward (and completely generic):

// MySqliteOpenHelper.java
private static <T> Observable<T> makeObservable(final Callable<T> func) {
  return Observable.create(
      new Observable.OnSubscribe<T>() {
          @Override
          public void call(Subscriber<? super T> subscriber) {
            try {
              subscriber.onNext(func.call());
            } catch(Exception ex) {
              Log.e(TAG, "Error reading from the database", ex);
            }
          }
    });
}

At this point, all our database reads have become observables that guarantee that the queries run on a background thread. Accessing the database is now pretty standard Rx code:

// DisplayUsersFragment.java
@Inject
MySqliteOpenHelper mDbHelper;

// ...

mDbHelper.getUsers(userId)
  .observeOn(AndroidSchedulers.mainThread())
  .subscribe(new Action1<List<User>>()) {
    @Override
    public void onNext(List<User> users) {
      // Update our UI with the users
    }
  }
}

And if you don’t need to update your UI with the results, just observe on a background thread.

Since your database layer is now returning observables, it’s trivial to compose and transform these results as they come in. For example, you might decide that your ContactTable is a low layer class that should not know anything about your model (the User class) and that instead, it should only return low level objects (maybe a Cursor or ContentValues). Then you can use use Rx to map these low level values into your model classes for an even cleaner separation of layers.

Two additional remarks:

  1. Your Table Java classes should contain no public methods: only package protected methods (which are accessed exclusively by your Helper, located in the same package) and private methods. No other classes should ever access these Table classes directly.

  2. This approach is extremely compatible with dependency injection: it’s trivial to have both your database helper and your individual tables injected (additional bonus: with Dagger 2, your tables can have their own component since the database helper is the only refence needed to instantiate them).

This is a very simple design pattern that has scaled remarkably well for our projects while fully enabling the power of RxJava. I also started extending this layer to provide a flexible update notification mechanism for list view adapters (not unlike what SQLBrite offers), but this will be for a future post.

This is still a work in progress, so feedback welcome!

Anko for Android

I’m really excited to see JetBrains continue to invest in Android. Not long ago, they release Anko, a library that uses Kotlin’s DSL functionalities to offer a different way to define your layouts than XML:

verticalLayout {
    val name = editText()
    button("Say Hello") {
        onClick { toast("Hello, ${name.text}!") }
    }
}

To be honest, I’m still a bit skeptical of the value that such a DSL provides for layout definitions (the XML file based layout that Android supports is extremely flexible to manage layouts for different displays) but JetBrains just released a new version of Anko and the direction this library is taking is now becoming much more interesting to me.

With the latest additions, Anko is widening its scope beyond layout definition and adding a set of helper functions that make Android development on Kotlin even cleaner and more concise. The combination of extension functions and the DSL notation really shine for Android development. Here are a few examples:

val flowers = listOf("Chrysanthemum", "Rose", "Hyacinth")
selector("What is your favorite flower?", flowers) { i ->
    toast("So your favorite flower is ${flowers[i]}, right?")
}

Doing away with the pesky TAG requirement for logging:

class SomeActivity : Activity(), AnkoLogger {
    fun someMethod() {
        info("Info message")
        debug(42) // .toString() method will be called automatically
    }
}

Check out the announcement for more snippets, or head directly to Anko’s project page.

Android, Rx and Kotlin: part 3

This is part three of my Rx – Android – Kotlin series (part 1, part 2).

This article has three parts, much like a magic trick:

  • The Pledge: we will implement a simple GUI operation with Rx and Kotlin on Android.
  • The Turn: we will create a brand new Rx operator.
  • The Prestige: we will use a standard feature of Kotlin for a final coup de grâce in code elegance.

Let’s get started.

The Pledge

We want to detect triple taps. There are various ways to define what a triple tap is so let’s just settle on a simple one: three taps are considered a triple tap if the first and last tap are separated by less than five hundred milliseconds.

With a traditional (non Rx) implementation, you would probably have two fields maintaining the timestamps of two taps ago [t-2] and that from one tap ago [t-1]. When a new tap arrives, [t], you compare the timestamp of [t] and [t-2] and if it’s less than five hundred milliseconds, we have a triple tap. Then you shift down the timestamps: [t-2] receives [t-1] and [t-1] receives the current timestamp. Repeat.

This approach is pretty standard but it’s also ugly: it bleeds state into your class and it allows for little flexibility in terms of filtering, transforming and composing.

Let’s look at this problem from an Rx perspective.

The Turn

As I explained in the first article of this series, we need to change our perspective to find out how to implement this with Rx. We are now looking for emitters and consumers of events.

The emitter is pretty obvious: it’s a tap. On Android, we can just create an Observable from a View with the convenient RxAndroid project. Once we receive the tap events from the view, we need to use an operator which will pass them in sets of threes. This is sometimes referred to as a “sliding window”. For example, if you have an Observable that emits the numbers from 0 to 5, you would expect a sliding window of size 3 to receive the following parameters:

[0, 1, 2]
[1, 2, 3]
[2, 3, 4]
[3, 4, 5]

Any list that is not of size three is ignored. Then, whenever our operator is notified, we simply extract the timestamps of the first and last index of that list and we see if the difference is less than five hundred milliseconds.

For the sake of learning, we’ll assume that such an operator doesn’t already exist and we’ll implement it ourselves (as it turns out, this is not true, jump to the bottom of this article if you want to find out).

Implementing an Rx operator

I don’t want to dive too deep into the internals of how Rx works so I’ll just focus on the important parts here. Fundamentally, an operator is like a Callable: it receives parameters and it returns a value. That value is a Subscriber which needs to implement methods you are probably familiar with now after a few hours spent on Rx: onNext(), onCompleted() and onError().

We’re just going to focus on onNext() for now in order to keep this article short and because this is where our main logic resides. The implementation is pretty simple:

class SwSubscriber<T> (val child: Subscriber<in List<T>>, val size: Int)
        : Subscriber<T>(child)
{
    val buffer = ArrayList<T>(size)

    override fun onNext(t: T) {
        buffer.add(t)
        if (buffer.size() == size) {
            child.onNext(ArrayList(buffer))
            buffer.remove(0)
        }
    }

    override fun onCompleted() { /* ... */ }

    override fun onError(e: Throwable?) { /* ... */ }
}

We maintain a buffer of size three and we accumulate the elements from the Observable in it. When that buffer reaches the size three, we emit it and we shift the buffer. Note that when I call onNext() on the buffer, I pass a copy of the list and not the list itself. This is just a precaution against the fact that the subscriber might decide to alter the list I pass them, which would break our operator.

Now that we have a subscriber, we can create our operator:

class SwOperator<T>(val n: Int) : Observable.Operator<List<T>, T> {
    override fun call(child: Subscriber<in List<T>>): Subscriber<in T>? =
            SwSubscriber(child, n)
}

And this is how we use it:

Observable.just(1, 2, 3, 4, 5)
    .lift(SwOperator(3))
    .subscribe { println("Received ${it}") }

… which prints:

Received [1, 2, 3]
Received [2, 3, 4]
Received [3, 4, 5]

The Prestige

We’re not quite done yet: you might have noticed that because our new function is, obviously, not part of the Observable class, we need to use a helper method called lift() in order to insert our code in a composition chain. Of course, we can always send a pull request to the project to have our new operator added, but we don’t have to wait: we can define an extension method in Kotlin to do just that:

fun <T> Observable<T>.slidingWindow(n: Int) =
    lift(SwOperator(n))

And now we can write:

Observable.just(1, 2, 3, 4, 5)
    .slidingWindow(3))
    .subscribe { println("Received ${it}")

Our new operator looks just as if it were part of the Rx library.

Android

Let’s close the loop on the initial problem so we can move on to more interesting things: now we want to use our sliding window to report triple taps.

Using RxAndroid, we have the convenient clicks() method that turns taps from a View into a stream of events. Unfortunately, these events are simple wrappers that don’t carry with them the timestamp of the tap, but that’s not really a problem: we just need to convert our stream of events into a stream of timestamps and we can add our logic then:

    ViewObservable.clicks(view)
	    .map { System.currentTimeMillis() }
	    .slidingWindow(3)
        .filter { it.get(2) - it.get(0) < 500 }
        .subscribe { println("Triple tap!") }
)

This is another example of the power of composition: no need to create our own events or our own wrappers. When you look at problems in the form of streams of data that you can transform at will, a lot of problems become trivial.

Rx and the sliding window

Note that Rx already has the functionality we implemented in this article: it’s called buffer() and you pass it not just the window size but also by how many elements you want to skip. buffer() behaves a bit differently from our implementation, though, by notifying subscribers of windows that are smaller than the desired size:

        Observable.just(1, 2, 3, 4, 5)
            .buffer(3, 1)
            .subscribe { p("Received ${it}")

prints:

[main] Received [1, 2, 3]
[main] Received [2, 3, 4]
[main] Received [3, 4, 5]
[main] Received [4, 5]
[main] Received [5]

Wrapping up

The concept of a sliding window is very common in all domains. Detecting multiple taps in a user interface is an example but you could also conceive using this to determine the bounding box of multiple taps. Stepping out from the graphical domain, your stream could contain geo locations and you could use the sliding window to determine instant or average speed. A few years ago, I actually posed a coding challenge to implement a sliding window in order to throttle calls to a server. Needless to say, I would use Rx for this today.

Hopefully, this article gave you some insight on the benefits that Rx can bring to your application, regardless of the domain. Revisiting problems with the Rx philosophy is always an interesting exercise which often results in simpler, more elegant solutions.

Read part 1, part 2.

Android, Rx and Kotlin: a case study

There are countless introductions to Rx and quite a few discussing Rx on Android, so instead of writing another one, I decided to come up with a simple activity and go through the entire exercise of implementing it using Rx and Kotlin. I’m sure you’ll be able to follow these article easily even if you’re not familiar with Kotlin because the syntax is remarkably similar to what you would write if you were using Java 8 (just with fewer semi colons).

The activity

It’s shown at the top of this article. It’s pretty simple while covering some important basics and, as we’ll find out along the way, the devil is in the details. Here is a simple functional specification of this activity:

  • The user types a nick name in an EditText. The activity asks the server if such a name is known and if it is, it returns a user record.
  • At this point, the user can “Add” this person as a friend, which triggers another call to the server to verify that the addition is allowed. The server returns a simple “ok” or “error” if that person can’t be added as a friend.

A few additional details:

  • We want to display a progress bar during all server calls.
  • Since we don’t want to bother the server too much, we only start sending requests for names that have three or more characters (we’ll add to this condition later).

Without Rx

Implementing this activity with “regular” Android practices is straightforward:

  • Add a listener to the EditText so we get notified each time a character is typed.
  • Send a network request with an AsyncTask.
  • When the result comes, update the UI (on the main thread and not in whatever thread AsyncTask ended up running).
  • Enable the “Add friend” button and listen to it, calling the server if pressed to verify that the friend can be added (one more AsyncTask + main thread post tap dance).

This implementation is straightforward but it’s very scattered. You will very likely end up with a lot of empty methods added just to satisfy listener requirements, storing state in fields to communicate between the various asynchronous tasks and making sure your threading model is sound. In other words, a lot of boiler plate, a messy mix of headless and graphical logic and graphical update code spread a bit everywhere.

There is a better way.

Kotlin and Android

I found Kotlin to be a very good match for Android even in the early days but recently, the Kotlin team has really cranked up their support for Android and adding specific functionalities for the platform in their tooling, which makes Kotlin even more of a perfect match for Android.

Kotlin M11 was released about a week ago and it added a functionality that makes the appeal of Kotlin absolutely irresistible for Android: automatically bound resources. Here is how it works.

Suppose you define the following View in your layout activity_search.xml:

<Button ...
    android:id="@+id/addFriendButton"
>

All you need to do is add a special kind of import to your source:

import kotlinx.android.synthetic.activity_search.addFriendButton

and the identifier addFriendButton becomes magically available everywhere in your source, with the proper type. This basically obsoletes ButterKnife/KotterKnife (well, not quite, there’s still OnClick which is pretty nice. Besides, Jake tells me he has something in the works). And if you press Ctrl-b on such an identifier, Android Studio takes you directly to the layout file where that View is defined. Very neat.

The server

For the purpose of this article, I’m just mocking the server. Here is its definition:

trait Server {
    fun findUser(name: String) : Observable<JsonObject>
    fun addFriend(user: User) : Observable<JsonObject>
}

If you think this definition looks a lot like a Retrofit service interface, it’s because it is. If you’re not using Retrofit, you should. Right now, I’ll be mocking this server by returning a set of hardcoded answers and also making each call sleep one second to simulate latency (and so we can see the progress bar on the screen). Note that each call on this interface returns an Observable, so they fit right in with our Rx implementation.

In this example, I hardcoded the server to know about two friends (“cedric” and “jon”) but only “cedric” can be added as a friend.

The Rx mindset

Switching to the Rx mindset requires you to start thinking in terms of event sources (observables) and listeners (subscribers). If this idea doesn’t sound that novel, it’s because it’s not. This model was already being advocated in the book “Design Patterns” in 1994 and even in the early versions of Java twenty years ago (and no doubt you can find traces of it in the literature before that). However, Rx introduces new concepts in this idea that we’ll explore in this series.

So let’s rethink our activity in Rx terms: what are event sources (I’ll use the name “observable” from now on) in this activity?

I can count four observables:

  1. First, we have the EditText: whenever a new character is typed, it emits an event that contains the entire text typed so far. We can emit a new name once we have more than three characters.
  2. Next is the name observable, which calls the server and emits the JsonObject it receives in response.
  3. Next in the chain, we have a “user” Observable, which maps the JsonObject into a User instance with the name and id of that person.
  4. Finally, the “Add friend” button is another observable: if the user presses that button, we make another call to the server with the User we have and we update our UI based on the results.

There are various ways we can break this problem into observables and the final result depends on how you want to handle various tasks, which observables you want to reuse, the threading model you prefer, etc…

Implementation

The “EditText” Observable

Let’s start with our EditText. Its implementation is pretty straightforward:

WidgetObservable.text(editText)
    .doOnNext { e: OnTextChangeEvent ->
        addFriendButton.setEnabled(false)
        loading.setVisibility(View.INVISIBLE)
    }
    .map { e: OnTextChangeEvent -> e.text().toString() }
    .filter { s: String -> s.length() >= 3 }
    .subscribe { s: String -> mNameObservable.onNext(s) }

Let’s go through each line in turn:

  • WidgetObservable turns an ordinary View into an Observable. It’s a wrapper coming from RxAndroid that calls all subscribers with the full text whenever a new character is typed.
  • Next we reset the UI to its default state.
  • The map line converts an OnTextChangeEvent into a string. It’s a purely convenience step, so that the next operators in the chain can deal with the context of the EditText as a String instead of having to repeatedly extract it from the event (e.text().toString() everywhere).
  • Next, if the text is less than three characters long, we stop right there.
  • Finally, we subscribe to the value we have obtained if we have reached this far: a string that’s at least three characters long. We pass this string to our name observable.

The “name” Observable

val mNameObservable: BehaviorSubject<String> = BehaviorSubject.create()

A BehaviorSubject is a special kind of Observable that you can send events to after its creation. I like to think of it as an event bus, except that it’s focused on a very specific kind of events (as opposed to an event bus that is used to post pretty much anything to). Using a Subject here allows me to create that Observable early and only post new events to it as they come in, which is what we did with the snippet of code above.

Let’s see how we use that Observable now, by simply subscribing to it, which means receiving names that have three or more characters in them. All we do is call the server and pass the result to the “user” observable:

mNameObservable
    .subscribe{ s: String ->
        mServer.findUser(s).subscribe { jo: JsonObject ->
            mUserObservable.onNext(jo)
        }
    }

We’re not quite done, though: we actually have another subscriber to that Observable:

mNameObservable
    .subscribe { s: String ->
        loading.setVisibility(View.VISIBLE)
    }

We need to let the user know we just issued a network call, so we show the progress bar (this is a suboptimal implementation, this logic should be done at the server level, but we’ll save this for later).

Note that I’m intentionally hiding a very important part of this logic in order to stay focused on the topic, and this also explains why we have two separate subscriptions. I explain why at the end of this article.

The “User” Observable

Next, we have the User Observable, which gets notified when the server sends us a response to the query “Does the user named ‘foo’ exist?”:

mUserObservable
    .map { jo: JsonObject ->
        if (mServer.isOk(jo)) {
            User(jo.get("id").getAsString(), jo.get("name").getAsString())
        } else {
            null
        }
    }
    .subscribe { user: User? ->
        addFriendButton.setEnabled(user != null)
        loading.setVisibility(View.INVISIBLE)
        mUser = user
    }

This Observable does two things: it updates our UI and it maps the JsonObject response to our data class User. If the call was a success, we assign this value to the field mUser.

The “Add friend” Observable

Finally, we have the “Add friend” button, which only gets enabled once we have a valid User. If the user presses that button, we issue another call to the server to request that person to be added as a friend and then we update the UI depending on the response:

ViewObservable.clicks(addFriendButton)
    .subscribe { e: OnClickEvent ->
        mServer.addFriend(mUser!!)
            .subscribe { jo: JsonObject ->
                val toastText: String
                if (mServer.isOk(jo)) {
                    toastText = "Friend added id: " + jo.get("id").getAsString()
                    editText.setText("")
                } else {
                    toastText = "ERROR: Friend not added"
                }
                Toast.makeText(this, toastText, Toast.LENGTH_LONG).show();
            }
    }

Stepping back

This is a very different implementation of how you would write the code with regular Android calls, but in the end, it’s not just compact but it also divides our entire logic in four very distinct components that interact with each other in very clear ways. This is the macro level. At the micro level, these four components are not just self contained, they are also highly configurable thanks to operators, operations you can insert between your Observable and your Subscriber and which transform the data in ways that are easier for you to handle. I only have one such example of this in the code above (transforming an OnTextChangeEvent into a String) but you get the idea.

Another benefit that should be immediately obvious to you even if you don’t buy into the whole Rx paradigm shift yet is that thanks to Rx, we now have a universal language for observables and observers. I’m sure that right now, your Android code base contains a lot of such interfaces, all with subtly different method names and definitions and all needing some adapaters to be inserted before they can talk to each other. If ever you felt the need to write an interface with a method called "onSomethingHappened()“, Rx will be an immediate improvement.

Operators

I have barely touched upon operators and this topic in itself is worthy of an entire book but I’d like to spend a few minutes to give a quick example of the power of operators.

Going back to our activity, remember that as soon as the user types more than three characters, we send a query to the network for each character typed. This is a bit wasteful: a lot of phone users are very fast typists and if they type letters in quick succession, we could be sparing our server some extra work. For example, how about we define that we only call the server when the user stopped typing?

How do we define “stopped typing”? Let’s say that we’ll decide that the user stopped typing if two keypresses are separated by more than 500ms. This way, a quick typing of “cedric” will result in just one server call instead of four without this function. How do we go about implementing this?

Again, the traditional approach would mean that each time our text change listener is invoked, we compare the current time with the timestamp of the last character typed and if it exceeds a certain value, we trigger the event.

As it turns out, observables have an operator that does this for us called debounce(), so our code becomes:

WidgetObservable.text(editText)
    .doOnNext {(e: OnTextChangeEvent) ->
        addFriendButton.setEnabled(false)
        loading.setVisibility(View.INVISIBLE)
    }
    .map { e: OnTextChangeEvent -> e.text().toString() }
    .filter { s: String -> s.length() >= 3 }
    .debounce(500, TimeUnit.MILLISECONDS)
    .subscribe { s: String -> mNameObservable.onNext(s) }

I know what you’re thinking: “You just picked a problem that could be solved by an operator that already exists”. Well, kind of, but that’s not my point. First of all, this is not a made up problem and I’m sure a lot of developers who write user interface code will agree that this is pretty common.

However, my point is more general than this: the debounce() operator has nothing to do with Android. It is defined on Observable, which is the base library we are using. It’s an operator that’s generally useful to have on any source that emits events. These events might be graphical in nature (as is the case here) or of any other kind, such as a stream of network requests, coordinates of a mouse cursor or capturing data from a geolocation sensor. debounce() represents the general need for getting rid of redundancies in streams.

Not only do we get to reuse an existing implementation without having to rewrite it ourselves, we preserve the locality of our implementation (with the traditional approach, you would probably have polluted your class with a couple of fields) and we maintain the composition abilities of our observable computations. For example, do you need to make sure that the user is not adding themselves before calling the server? Easy:

WidgetObservable.text(editText)
    // ...
    .filter { s: String -> s.length() >= 3 }
    .filter { s: String -> s != myName }

Wrapping up

At this point, you should probably take a look at the full source so you can step back and see how all these pieces fit together. It’s obvious to me that the view of decomposing your problem in terms of observables that emit values that subscribers observe is extremely powerful and leads to cleaner, more self contained and composable code. The non-Rx version of this class feels incredibly messy to me (a lot more fields holding state, methods added just to satisfy slightly incompatible listener interfaces and a total lack of composability).

Having said that, our work here is not over:

  • The threading model is all wrong. I intentionally omitted any thread consideration in this article in order to stay on topic but the code as it is right now is extremely suboptimal. It works but you shouldn’t ship it in its current form. In the next installment, I will show how Rx makes it trivial to get Android’s multithreaded model right.
  • There is a lot of UI code peppered through the business logic. It’s messy, it obfuscates how our UI works and it’s hard to maintain. We’ll have to fix that as well.

Stay tuned!

Update: Discussion on reddit.

Read part 2.

Android and Amazon

Interesting piece from Jean-Louis Gassée, commenting on the various tech events of last week. However, I have to disagree with his take on the Amazon/Google relationship regarding Amazon’s new tablet, the Fire.

Jean-Louis quotes the following excerpt from Michael Mace:

“The problem is that Amazon is using Android as just an OS, not using the Google-branded services and application store that Google layers on top of the OS (link). Although Google touted the openness of Android when it was first launched, the reality is that Google is using it as a Trojan horse to force its services onto hardware. What Amazon did with Android is very threatening to Google, and so you’re not likely to hear a lot of supportive words from them.”

I really don’t see the threat, here, especially not the one coming from Amazon’s Silk browser being used as a cloaking proxy for its users. Yes, Google will not be getting browser hits directly from Fire users, but let’s face it: no matter how successful the Fire will be (and I bet it will be very successful), it will never go very far in terms of browser market share compared to smartphone access or even PC access. As for Amazon, I bet they will keep getting most of their data from non-Fire users connecting to Amazon.com by a very, very wide margin.

As for Android: Google’s mobile OS was designed from the very first day as an open platform that anyone, vendor, carrier or third party, can use in their own product without giving so much as a phone call to Google to ask permission. In a famous tweet from a year ago, Andy Rubin gave his definition of ‘open’:

the definition of open: “mkdir android ; cd android ; repo init -u git://android.git.kernel.org/platform/manifest.git ; repo sync ; make”

I think it’s nice, and it’s certainly one definition of ‘free’, but hardly the one that really matters. The important aspect of Android’s freedom lies in its license and the fact that every day, it’s being used as the foundation of products that even Google has never heard of, and probably never will until they ship.

Amazon is not the first company to go down that route. Just this past week, Boeing announced that their next generation of in-flight software will be based on Android. I don’t know if Google was involved in this project or not (I’m betting not) but I’m not seeing any outrage from tech pundits about this decision. Why is Amazon’s Fire different?

It’s a non-story.

Very soon, companies announcing that they are choosing Android for their next generation of products and they did not cooperate with Google to do so will be as interesting to read as an article about what operating system your toaster runs on.

The bottom line is that every new product that comes out running on Android solidifies its success in the market, and by extension, Google’s.

Microsoft and Samsung ink a cross licensing deal

Microsoft signs an Android related patent deal with Samsung:

Microsoft is announcing today the biggest Android-related patent deal to date, signing a broad cross-licensing agreement with Samsung.

With the deal, Microsoft will get royalty revenue on every Android smartphone and tablet that Samsung sells. Redmond already has a deal with another major handset maker — HTC — that sells both Android and Windows Phone devices.

Maybe I’m interpreting this wrong, but I see this as a move that further solidifies Android’s presence in the mobile market. The more players are making money off it, the stronger the Android brand becomes. Whatever way you look at it, it’s another very strong play by Microsoft, which has been making some very smart decisions these past years to hedge its bets.

Overall, it’s good for Android, good for Microsoft and good for Samsung. The effect of this deal on Apple is a bit unclear, although the strengthening of a coalition of competitors is never a good thing.

I couldn’t help but chuckle at this quote from the Windows Phone president, though:

“Microsoft and Samsung see the opportunity for dramatic growth in Windows Phones and we’re investing to make that a reality,” Windows Phone unit president Andy Lees said in a statement.

It’s easy to see an opportunity for dramatic growth when you start from zero. Whether that growth will materialize is a different story.