Java in 2015 – Major happenings

2015 was the year where Java the language, platform, ecosystem and community continue to dominate the software landscape, with only Javascript having a similar sized impact on the industry. In case you missed the highlights of 2015, here’s some of the major happenings that occurred.

Java 20 years old and still not dead yet!

Java turned 20 this year and swept back to the top of the Tiobe index in December 2015. Although the Tiobe index is hardly a 100% peer reviewed scientific methodology, it is seen as a pretty strong barometer for the health of a language/platform. So what the heck happened to boost Java so dramatically again?

Firstly, the release of Java 8 the previous year was adopted by mainstream Java enterprise shops. The additional functional capabilities of Lambdas combined with the new Streams and Collections framework breathed a new lease of life into the language. Although Java 8 is not as rich in its feature set as say Scala or Python it is seen as the steady workhorse that now has at least some feature parity with more aggressive languages. Enterprises love a stable platform and it’s unlikely that Java will be disappearing any time soon.

Secondly, Java has become a strong platform to use for infrastructure platforms/frameworks. Many popular NoSQL, datagrid solutions such as Apache Cassandra, Hazelcast are written in Java, again due to its stability and strong threading and networking support. CI tools such as Jenkins are widely adopted and of course business productivity tools such as Atlassian’s JIRA are again Java based.

Oracle guts its Java evangelism team

Oracle fired much of its Java evangelism team just before JavaOne which wasn’t the greatest PR move by the stewards of Java. Over the subsequent months it became clearer that this wasn’t a step by Oracle to reduce its engineering efforts into Java but there were nervous times for much of the community as they feared the worst. A salient reminder that big corporations don’t always get their left hand talking to their right!

Java 9 delay announced

In the “We’re not really surprised” bucket came the announcement the Java 9 will be delayed until March 2017 in order to ensure that the new modularisation system will not break the millions of Java applications running out there today.

Although the technical work of Jigsaw is progressing nicely, the entire ecosystem will need to test on the new system. The Quality group in OpenJDK is leading this effort. I highly recommend you contact them to be part of the early access and feedback loop.

OpenJDK supports further mobile platforms

The creation of the OpenJDK mobile project came as a surprise to many and although it doesn’t represent a change in Oracle’s business direction it was a wlecome release of code to enable Java on ARM, Android and iOS platforms. There’s much technical work to do but it will be interesting to watch if the software community at large picks up on this new support and tries Java out as a language for the iOS and Android platforms in 2016 and beyond. There is a possibility that OpenFX (JavaFX) combined with Java mobile on iOS or Android may entice a slew of developers to this ‘new’ platform.

Was I right about 2015?

It’s always fun to look at past predictions, let’s see how I did!

  1. I expected 2015 to be a little bit quieter. Well I clearly got that wrong! Despite no major releases for ME, SE or EE, the excitement of celebrating 20 years of Java and a surge of new developers using Java 8 meant 2015 was busier than ever.
  2. Embracing Javascript for the front end. This trend continues and stacks such as JHipster show the new love affair that Java developers have with Javascript.
  3. Devops toolchains to the fore. Docker continues to steamroll ahead in terms of popularity and Java developers are especially starting to use Docker in test environments to avoid polluting environments with variations in Java runtimes, web servers, data stores etc.
  4. IoT and Java to be a thing. Nope, not yet! Perhaps in 2016 with the new Mobile Java project in OpenJDK and further refinement of Java ME, we may start to see serious inroads.

I’m not going to make any predictions for 2016 as I clearly need to stick to my day job 🙂

One final important note. Project Jigsaw is the modularisation story for Java 9 that will massively impact tool vendors and day to day developers alike. The community at large needs your help to help test out early builds of Java 9 and to help OpenJDK developers and tool vendors ensure that IDEs, build tools and applications are ready for this important change. You can join us in the Adoption Group at OpenJDK. I hope everyone has a great holiday break – I look forward to seeing the Twitter feeds and the GitHub commits flying around in 2016 :-).

Cheers,
Martijn (CEO – jClarity, Java Champion & Diabolical Developer)

This post is part of the Java Advent Calendar and is licensed under the Creative Commons 3.0 Attribution license. If you like it, please spread the word by sharing, tweeting, FB, G+ and so on!

Kotlin for Android Developers

We Android Developers have a difficult situation regarding our language limitation. As you may know, current Android development only support Java 6 (with some small improvements from Java 7), so we need to deal every day with a really old language that cuts our productivity and forces us to write tons of boilerplate and fragile code that it’s difficult to read an maintain.

Hopefully, at the end of the day we’re running over a Java Virtual Machine, so technically anything that can be run in a JVM is susceptible of being used to develop Android Apps. There are many languages that generate bytecode a JVM can execute, so some alternatives are starting to become popular these days, and Kotlin is one of them.

What is Kotlin?

Kotlin is a language that runs on the JVM. It’s being created by Jetbrains, the company behind powerful tools such as IntelliJ, one of the most famous IDEs for Java developers.

Kotlin is a really simple language. One of it’s main goals is to provide a powerful language with a simple and reduced syntax. Some of it’s features are:

  • It’s lightweight: this point is very important for Android. The library we need to add to our projects is as small as possible. In Android we have hard restrictions regarding method count, and Kotlin only adds around 6000 extra methods.
  • It’s interoperable: Kotlin is able to communicate with Java language seamlessly. This means we can use any existing Java library in our Kotlin code, so even though the language is young, we already have thousands of libraries we can work with. Besides, Kotlin code can also be used from Java code, which means we can create software that uses both languages. You can start writing new features in Kotlin and keep the rest of codebase in Java.
  • It’s a strongly-typed language: though you barely need to specify any types throughout the code, because the compiler is able to infer the type of variables or the return types of the functions in almost every situations. So you get the best of both worlds: a concise and safe language.
  • It’s null safe: One of the biggest problems of Java is null. You can’t specify when a variable or parameter can be null, so lots of NullPointerException will happen, and they are really hard to detect while coding. Kotlin uses explicit nullity, which will force us check nulls when necessary.

Kotlin is currently in version 1.0.0 Beta 3, but can expect the final version very soon. It’s quite ready for production anyway, there are already many companies successfully using it.

Why Kotlin is great for Android?

Basically because all its features fit perfectly well in the Android ecosystem. The library is small enough to let us work without proguard during development. It’s size is equivalent to support-v4 library, and there are some other libraries we use in amost every projects that are even bigger.

Besides, Android Studio (the official Android IDE) is built over IntelliJ. This means our IDE have an excellent support to work with this language. We can configure our project in seconds and keep using the IDE as we are used to do. We can keep using Gradle and all the run and debug features the IDE provides. It’s literally the same as writing the App in Java.

And obviously, thanks to its interoperability, we can use the Android SDK without any problems from Kotlin code. In fact, some parts of the SDK are even easier to use, because the interoperability is intelligent, and it for instance maps getters and setters to Kotlin properties, or let us write listeners as closures.

How to start using Kotlin in Android

It’s really easy. Just follow these steps:

  • Download Kotlin plugin from the IDE plugins sections
  • Create a Kotlin class in your module
  • Use the action “Configure Kotlin in Project…”
  • Enjoy

Some features

Kotlin has a lot of awesome features I won’t be able to explain here today. If you want to continue learning about it, you can check my blog and read my book. But today I’ll explain some interesting stuff I hope it makes you want more.

Null safety

As I mentioned before, Kotlin is null safe. If a type can be null we need to specify it by setting an ? after the type. From that point, every time we want to use a variable that uses that type, we need to check nullity.

For instance, this code won’t compile:

var artist: Artist? = null

artist.print()

The second line will show an error, because the nullity wasn’t checked. We could do something like this:

if (artist != null) {

    artist.print()

}

This shows another great Kotlin feature: Smart casting. If we’ve checked the type of a variable, we don’t need to cast it inside the scope of that check. So we now can use artist as variable of type Artist inside the if. This works with any other check we may do (like after checking the instance type).

We have a simpler way to check nullity, by using ? before calling a function of the object. And we can even provide an alternative by using the Elvis operator ?:

val name = artist?.name ?: ""

Data classes

In Java, if we want to create a data class, or POJO class (a class that only saves some state), we’d need to create a class with lots fields, getters and setters, and probably a toString and an equals class:

public class Artist {
    private long id;
    private String name;
    private String url;
    private String mbid;

    public long getId() {
        return id;
    }

    public void setId(long id) {
        this.id = id;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public String getUrl() {
        return url;
    }

    public void setUrl(String url) {
        this.url = url;
    }

    public String getMbid() {
        return mbid;
    }

    public void setMbid(String mbid) {
        this.mbid = mbid;
    }

    @Override public String toString() {
        return "Artist{" +
                "id=" + id +
                ", name='" + name + '\'' +
                ", url='" + url + '\'' +
                ", mbid='" + mbid + '\'' +
                '}';
    }
}

In Kotlin, all the previous code can be substituted by this:

data class Artist (

    var id: Long,
    var name: String,
    var url: String,
    var mbid: String)

Kotlin uses properties instead of fields. A property is basically a field plus its getter and setter. We can declare those properties directly in the constructor, that you can see is defined right after the name of the class, saving us some lines if we are not modifying the entry values.

The data modifier provides some extra features: a readable toString(), an equals() based on the properties defined in the constructor, a copy function, and even a set of component functions that let us split an object into variables. Something like this:

val (id, name, url, mbid) = artist

Interoperability

We have some great interoperability features that help a lot in Android. One of them is the mapping of interfaces with a single method to a lambda. So a click listener like this one:

view.setOnClickListener(object : View.OnClickListener {
    override fun onClick(v: View) {
        toast("Click")

    }

})

can be converted into this:

view.setOnClickListener { toast("Click") }

Besides, getters and setters are mapped automatically to properties. This doesn’t add any kind of overhead, because the bytecode will in fact just call to the original getters and setters. These are some examples:

supportActionBar.title = title
textView.text = title
contactsList.adapter = ContactsAdapter()

Lambdas

Lambdas will save tons of code, but the important thing is that it will let us do things that are impossible (or too verbose) without them. With them we can start thinking in a more functional way. A lambda is simply a way to specify a type that defines a function. We can for instance define a variable like this:

val listener: (View) -> Boolean

This is a variable that is able to declare a function that receives a view and returns a function. A closure is the way we have to define what the function will do:

val listener = { view: View -> view is TextView }

The previous function will get a View and return true if the view is an instance of TextView. Ad the compiler is able to infer the type, we don’t need to specify it. We can be more explicit if we want by the way:

val listener: (View) -> Boolean = { view -> view is TextView }

With lambdas, we can prevent the use of callback interfaces. We can just set the function we want to be called after and operation finishes:

fun asyncOperation(value: Int, callback: (Boolean) -> Unit) {
    ...
    callback(true)

}

asyncOperation(5) { result -> println("result: $result") }

But there is a simpler alternative, because if a function only has one parameter, we can use the reserved word it:

asyncOperation(5) { println("result: $it") }

Collections

Collections in Kotlin are really powerful. They are written over Java collections, so it means when we get a result from any Java library (or the Android SDK for instance), we still be able to use all the functions Kotlin provides.

The available collections we have are:

  • Iterable
  • Collection
  • List
  • Set
  • Map

And we can apply a lot of operations to them. These are a few of them:

  • filter
  • sort
  • map
  • zip
  • dropWhile
  • first
  • firstOrNull
  • last
  • lastOrNull
  • fold
    …

You may see the complete set of operations in this article. So a complex operation such as a filters, a sort and a transformation can be quite explicitly defined:

parsedContacts
    .filter { it.name != null && it.image != null }
    .sortedBy { it.name }
    .map { Contact(it.id, it.name!!, it.image!!) }

We can define new immutable lists in a simple way:

val list = listOf(1, 2, 3, 4, 5)

Or if we want it to be mutable (we can add and remove items), we have a very nice way to access and modify the items, the same way we’d do with an array:

mutableList[0] = 1
val first = mutableList[0]

And the same thing with maps:

map["key"] = 1
val value = map["key"]

This is possible because we can overload some basic operators when implementing our own classes.

Extension functions

Extensions functions will let us add extra behaviour to classes we can’t modify, because they belong to a library or an SDK for instance.

We could create an inflate() function for ViewGroup class:

fun ViewGroup.inflate(layoutRes: Int): View {
    return LayoutInflater.from(context).inflate(layoutRes, this, false)
}

And from now on, we can just use it as any other method:

val v = parent.inflate(R.layout.view_item)

Or even a loadUrl function to an ImageView. We can make use of Picasso library inside the function:

fun ImageView.loadUrl(url: String) {
    Picasso.with(context).load(url).into(this)
}

All ImageViews can use this function now:

contactImage.loadUrl(contact.imageUrl)

Interface

Interfaces in Kotlin can contain code, which simulates a simple multiple inheritance. A class can be composed by the code of many classes, not just a parent. The interfaces can’t, however, keep state. So if we define a property in an interface, the class that implements it must override that property and provide a value.

An example could be a ToolbarManager class that will deal with the Toolbar:

interface ToolbarManager {

    val toolbar: Toolbar


    fun initToolbar() {
        toolbar.inflateMenu(R.menu.menu_main)
        toolbar.setOnMenuItemClickListener {
            when (it.itemId) {
                R.id.action_settings -> App.instance.toast("Settings")
                else -> App.instance.toast("Unknown option")
            }
            true
        }
    }
}

This interface can be used by all the activities or fragments that use a Toolbar:

class MainActivity : AppCompatActivity(), ToolbarManager {

    override val toolbar by lazy { find<Toolbar>(R.id.toolbar) }


    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)
        initToolbar()
        ...
    }
}


When expression

When is the alternative to switch in Java, but much more powerful. It can literally check anything. A simple example:

val cost = when(x) {
    in 1..10 -> "cheap"
    in 10..100 -> "regular"
    in 100..1000 -> "expensive"
    in specialValues -> "special value!"
    else -> "not rated"
}

We can check that a number is inside a range, or even inside a collection (specialValues is a list). But if we don’t set the parameter to when, we can just check whatever we need. Something as crazy as this would be possible:

val res = when {
    x in 1..10 -> "cheap"
    s.contains("hello") -> "it's a welcome!"
    v is ViewGroup -> "child count: ${v.getChildCount()}"
    else -> ""
}

Kotlin Android Extensions

Another tool the Kotlin team provides for Android developers. It will be able to read an XML and inject a set of properties into an activity, fragment or view with the views inside the layout casted to its proper type.

If we have this layout:

<FrameLayout
    xmlns:android="..."
    android:id="@+id/frameLayout"
    android:orientation="vertical"
    android:layout_width="match_parent"
    android:layout_height="match_parent">

    <TextView
        android:id="@+id/welcomeText"
        android:layout_width="wrap_content"
        android:layout_height="wrap_content"/>

</FrameLayout>

We just need to add this synthetic import:

import kotlinx.android.synthetic.main.*

And from that moment, we can use the views in our Activity:

override fun onCreate(savedInstanceState: Bundle?) {
    super<BaseActivity>.onCreate(savedInstanceState)
    setContentView(R.id.main)
    frameLayout.setVisibility(View.VISIBLE)
    welcomeText.setText("I´m a welcome text!!")
}

It’s that simple.

Anko

Anko is a library the Kotlin team is developing to simplify Android development. It’s main goal is to provide a DSL to declare views using Kotlin code:

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

But it includes many other useful things. For instance, a great way to navigate to other activities:

startActivity<DetailActivity>("id" to res.id, "name" to res.name)

It just receives a set of Pairs an adds them to a bundle when creating the intent to navigate to the activity (specified as the type of the function).

We also have direct access to system services:

context.layoutInflater
context.notificationManager
context.sensorManager
context.vibrator

Or easy ways to create toasts and alerts:

toast(R.string.message)
longToast("Wow, such a duration")


alert("Yes /no Alert") {
    positiveButton("Yes") { submit() }
    negativeButton("No") {}
}.show()

And one I love, an simple easy DSL to deal with asynchrony:

async {
    val result = longRequest()
    uiThread { bindForecast(result) }
}

It also provides a good set of tools to work with SQLite and cursors. The ManagedSQLiteOpenHelper provides a use method which will receive the database and can call directly to its functions:

dbHelper.use {
    select("TABLE_NAME").where("_id = {id}", "id" to 20)
}

As you can see, it has a nice select DSL, but also a simple create function:

db.createTable("TABLE_NAME", true,
        "_id" to INTEGER + PRIMARY_KEY,
        "name" to TEXT)

When you are dealing with a cursor, you can make use of some extension functions such as parseList, parseOpt or parseClass, that will help with parsing the result.

Conclusion

As you can see, Kotlin simplifies Android development in many different points. It will boost your productivity and will let you solve usual problems in a very different and simpler way.

My recommendation is that you at least try it and play a little with it. It’s a really fun language and very easy to learn. If you think this language is for you, you may continue learning it by getting Kotlin for Android Developers book.

Composing Multiple Async Results via an Applicative Builder in Java 8

A few months ago, I put out a publication where I explain in detail an abstraction I came up with named Outcome, which helped me A LOT to code without side-effects by enforcing the use of semantics. By following this simple (and yet powerful) convention, I ended up turning any kind of failure (a.k.a. Exception) into an explicit result from a function, making everything much easier to reason about. I don’t know you but I was tired of dealing with exceptions that teared everything down, so I did something about it, and to be honest, it worked really well. So before I keep going with my tales from the trenches, I really recommend going over that post. Now let’s solve some asynchronous issues by using eccentric applicative ideas, shall we?

Something wicked this way comes

Life was real good, our coding was fast-paced,  cleaner and composable as ever, but, out of the blue, we stumble upon a “missing” feature (evil laughs please): we needed to combine several asynchronous Outcome instances in a non-blocking fashion….

ohgodwhy

Excited by the idea, I got down to work. I experimented for a fair amount of time seeking for a robust and yet simple way of expressing these kind of situations; while the new ComposableFuture API turned out to be much nicer that I expected (though I still don’t understand why they decided to use names like applyAsync  or thenComposeAsync instead of map or flatMap), I always ended up with implementations too verbose and repetitive comparing to some stuff I did with Scala, but after some long “Mate” sessions, I had my “Hey! moment”: Why not using something similar to an applicative?

The problem

Suppose that we have these two asynchronous results

and a silly entity called Message

I need something that given textf and numberf it will give me back something like

//After combining textf and numberf
CompletableFuture<Outcome<Message>> message = ....

So I wrote a letter to Santa Claus:

  1. I want to asynchronously format the string returned by textf using the number returned by numberf only when both values are available, meaning that both futures completed successfully and none of the outcomes did fail. Of course, we need to be non-blocking.
  2. In case of failures, I want to collect all failures that took place during the execution of textf and/or numberf and return them to the caller, again, without blocking at all.
  3. I don’t want to be constrained by the number of values to be combined,  it must be capable of handling a fair amount of asynchronous results. Did I say without blocking? There you go…
  4. Not die during the attempt.

waaat

Applicative  builder to the rescue

If you think about it, one simple way to put what we’re trying to achieve is as follows:

// Given a String -> Given a number -> Format the message
f: String -> Integer -> Message

Checking the definition of  f, it is saying something like: “Given a String, I will return a function that takes an Integer as parameter, that when applied, will return an instance of type Message“, this way, instead of waiting for all values to be available at once, we can partially apply one value at a time, getting an actual description of the construction process of a Message instance. That sounded great.

To achieve that, it would be really awesome if we could take the construction lambda Message:new and curry it, boom!, done!, but in Java that’s impossible (to do in a generic, beautiful and concise way), so for the sake of our example, I decided to go with our beloved Builder pattern, which kinda does the job:

And here’s the WannabeApplicative<T> definition

public interface WannabeApplicative<V>
{
    V apply();
}

Disclamer: For those functional freaks out there, this is not an applicative per se, I’m aware of that, but I took some ideas from it an adapted them according to the tools that the language offered me out of the box. So, if you’re feeling curious, go check this post for a more formal example.

If you’re still with me, we could agree that we’ve done nothing too complicated so far, but now we need to express a building step, which, remember, needs to be non-blocking and capable to combine any previous failure that might have took place in other executions with potentially new ones. So, in order to do that, I came up with something as follows:

First of all, we’ve got two functional interfaces: one is Partial<B>, which represents a lazy application of a value to a builder, and the second one, MergingStage<B,V>, represents the “how” to combine both the builder and the value. Then, we’ve got a method called value that, given an instance of type CompletableFuture<Outcome<V>>, it will return an instance of type MergingStage<B,V>, and believe or not, here’s where the magic takes place. If you remember the MergingState definition, you’ll see it’s a BiFunction, where the first parameter is of type Outcome<B> and the second one is of type Outcome<V>. Now, if you follow the types, you can tell that we’ve got two things: the partial state of the building process on one side (type parameter B)  and a new value that need to be applied to the current state of the builder (type parameter V), so that, when applied, it will generate a new builder instance with the “next state in the building sequence”, which is represented by Partial<B>. Last but not least, we’ve got the stickedTo method, which basically is a (awful java) hack to stick to a specific applicative type (builder) while defining building step. For instance, having:

I can define partial value applications to any Builder instance as follows:

See that we haven’t built anything yet, we just described what we want to do with each value when the time comes, we might want to perform some validations before using the new value (here’s when Outcome plays an important role) or just use it as it is, it’s really up to us, but the main point is that we haven’t applied anything yet. In order to do so, and to finally tight up all loose ends, I came up with some other definition, which looks as follows:

Hope it’s not that overwhelming, but I’ll try to break it down as clearer as possible. In order to start specifying how you’re going to combine the whole thing together, you will start by calling begin with an instance of type WannabeApplicative<V>, which, in our case, type parameter V is equal to Builder.

FutureCompositions<Message, Builder> ab = begin(Message.applicative())

See that, after you invoke begin, you will get a new instance of FutureCompositions with a lazily evaluated partial state inside of it, making it the one and only owner of the whole building process state, and that was the ultimate goal of everything we’ve done so far, to fully gain control over when and how things will be combined. Next, we must specify the values that we want to combine, and that’s what the binding method is for:

ab.binding(textToApply)
  .binding(numberToApply);

This is how we supply our builder instance with all the values that need to be merged together along with the specification of what’s supposed to happen with each one of them, by using our previously defined Partial instances. Also see that everything’s still lazy evaluated, nothing has happened yet, but still we stacked all “steps” until we finally decide to materialize the result, which will happen when you call perform.

CompletableFuture<Outcome<Message>> message = ab.perform();

From that very moment everything will unfold,  each building stage will get evaluated, where failures could be returned and collected within an Outcome instance or simply the newly available values will be supplied to the target builder instance, one way or the other, all steps will be executed until nothing’s to be done. I will try to depict what just happened as follows

applicative

If you pay attention to the left side of the picture, you can easily see how each step gets “defined” as I showed before, following the previous “declaration” arrow direction, meaning, how you actually described the building process. Now, from the moment that you call perform, each applicative instance (remember Builder in our case) will be lazily evaluated in the opposite direction:  it will start by evaluating the last specified stage in the stack, which will then proceed to evaluate the next one and so forth up to the point where we reach the “beginning” of the building definition, where it will start to unfold o roll out evaluation each step up to the top, collecting everything  it can by using the MergingStage specification.

And this is just the beginning….

I’m sure a lot could be done to improve this idea, for example:

  • The two consecutive calls to dependingOn at CompositionSources.values() sucks, too verbose to my taste, I must do something about it.
  • I’m not quite sure to keep passing Outcome instances to a MergingStage, it would look cleaner and easier if we unwrap the values to be merged before invoking it and just return Either<Failure,V> instead – this will reduce complexity and increase flexibility on what’s supposed to happen behind the scenes.
  • Though using the Builder pattern did the job, it feels old-school, I would love to easily curry constructors, so in my to-do list is to check if jOOλ or Javaslang have something to offer on that matter.
  • Better type inference so that the any unnecessary noise gets remove from the code, for example, the stickedTo method, it really is a code smell, something that I hated from the first place. Definitely need more time to figure out an alternative way to infer the applicative type from the definition itself.

You’re more than welcome to send me any suggestions and comments you might have. Cheers and remember…..

index

@gszeliga

Sources

 

Reactive Development Using Vert.x

Lately, it seems like we’re hearing about the latest and greatest frameworks for Java. Tools like Ninja, SparkJava, and Play; but each one is opinionated and make you feel like you need to redesign your entire application to make use of their wonderful features. That’s why I was so relieved when I discovered Vert.x. Vert.x isn’t a framework, it’s a toolkit and it’s un-opinionated and it’s liberating. Vert.x doesn’t want you to redesign your entire application to make use of it, it just wants to make your life easier. Can you write your entire application in Vert.x? Sure! Can you add Vert.x capabilities to your existing Spring/Guice/CDI applications? Yep! Can you use Vert.x inside of your existing JavaEE applications? Absolutely! And that’s what makes it amazing.

Background

Vert.x was born when Tim Fox decided that he liked a lot of what was being developed in the NodeJS ecosystem, but he didn’t like some of the trade-offs of working in V8: Single-threadedness, limited library support, and JavaScript itself. Tim set out to write a toolkit which was unopinionated about how and where it is used, and he decided that the best place to implement it was on the JVM. So, Tim and the community set out to create an event-driven, non-blocking, reactive toolkit which in many ways mirrored what could be done in NodeJS, but also took advantage of the power available inside of the JVM. Node.x was born and it later progressed to become Vert.x.

Overview

Vert.x is designed to implement an event bus which is how different parts of the application can communicate in a non-blocking/thread safe manner. Parts of it were modeled after the Actor methodology exhibited by Eralng and Akka. It is also designed to take full advantage of today’s multi-core processors and highly concurrent programming demands. As such, by default, all Vert.x VERTICLES are implemented as single-threaded by default. Unlike NodeJS though, Vert.x can run MANY verticles in MANY threads. Additionally, you can specify that some verticles are “worker” verticles and CAN be multi-threaded. And to really add some icing on the cake, Vert.x has low level support for multi-node clustering of the event bus via the use of Hazelcast. It has gone on to include many other amazing features which are too numerous to list here, but you can read more in the official Vert.x docs.

The first thing you need to know about Vert.x is, similar to NodeJS, never block the current thread. Everything in Vert.x is set up, by default, to use callbacks/futures/promises. Instead of doing synchronous operations, Vert.x provides async methods for doing most I/O and processor intensive operations which might block the current thread. Now, callbacks can be ugly and painful to work with, so Vert.x optionally provides an API based on RxJava which implements the same functionality using the Observer pattern. Finally, Vert.x makes it easy to use your existing classes and methods by providing the executeBlocking(Function f) method on many of it’s asynchronous APIs. This means you can choose how you prefer to work with Vert.x instead of the toolkit dictating to you how it must be used.

The second thing to know about Vert.x is that it composed of verticles, modules, and nodes. Verticles are the smallest unit of logic in Vert.x, and are usually represented by a single class. Verticles should be simple and single-purpose following the UNIX Philosophy. A group of verticles can be put together into a module, which is usually packaged as a single JAR file. A module represents a group of related functionality which when taken together could represent an entire application or just a portion of a larger distributed application. Lastly, nodes are single instances of the JVM which are running one or more modules/verticles. Because Vert.x has clustering built-in from the ground up, Vert.x applications can span nodes either on a single machine or across multiple machines in multiple geographic locations (though latency can hider performance).

Example Project

Now, I’ve been to a number of Meetups and conferences lately where the first thing they show you when talking about reactive programming is to build a chat room application. That’s all well and good, but it doesn’t really help you to completely understand the power of reactive development. Chat room apps are simple and simplistic. We can do better. In this tutorial, we’re going to take a legacy Spring application and convert it to take advantage of Vert.x. This has multiple purposes: It shows that the toolkit is easy to integrate with existing Java projects, it allows us to take advantage of existing tools which may be entrenched parts of our ecosystem, and it also lets us follow the DRY principle in that we don’t have to rewrite large swathes of code to get the benefits of Vert.x.

Our legacy Spring application is a contrived simple example of a REST API using Spring Boot, Spring Data JPA, and Spring REST. The source code can be found in the “master” branch HERE. There are other branches which we will use to demonstrate the progression as we go, so it should be simple for anyone with a little experience with git and Java 8 to follow along. Let’s start by examining the Spring Configuration class for the stock Spring application.


@SpringBootApplication
@EnableJpaRepositories
@EnableTransactionManagement
@Slf4j
public class Application {
    public static void main(String[] args) {
        ApplicationContext ctx = SpringApplication.run(Application.class, args);

        System.out.println("Let's inspect the beans provided by Spring Boot:");

        String[] beanNames = ctx.getBeanDefinitionNames();
        Arrays.sort(beanNames);
        for (String beanName : beanNames) {
            System.out.println(beanName);
        }
    }

    @Bean
    public DataSource dataSource() {
        EmbeddedDatabaseBuilder builder = new EmbeddedDatabaseBuilder();
        return builder.setType(EmbeddedDatabaseType.HSQL).build();
    }

    @Bean
    public EntityManagerFactory entityManagerFactory() {
        HibernateJpaVendorAdapter vendorAdapter = new HibernateJpaVendorAdapter();
        vendorAdapter.setGenerateDdl(true);

        LocalContainerEntityManagerFactoryBean factory = new LocalContainerEntityManagerFactoryBean();
        factory.setJpaVendorAdapter(vendorAdapter);
        factory.setPackagesToScan("com.zanclus.data.entities");
        factory.setDataSource(dataSource());
        factory.afterPropertiesSet();

        return factory.getObject();
    }

    @Bean
    public PlatformTransactionManager transactionManager(final EntityManagerFactory emf) {
        final JpaTransactionManager txManager = new JpaTransactionManager();
        txManager.setEntityManagerFactory(emf);
        return txManager;
    }
}

As you can see at the top of the class, we have some pretty standard Spring Boot annotations. You’ll also see an @Slf4j annotation which is part of the lombok library, which is designed to help reduce boiler-plate code. We also have @Bean annotated methods for providing access to the JPA EntityManager, the TransactionManager, and DataSource. Each of these items provide injectable objects for the other classes to use. The remaining classes in the project are similarly simplistic. There is a Customer POJO which is the Entity type used in the service. There is a CustomerDAO which is created via Spring Data. Finally, there is a CustomerEndpoints class which is the JAX-RS annotated REST controller.

As explained earlier, this is all standard fare in a Spring Boot application. The problem with this application is that for the most part, it has limited scalability. You would either run this application inside of a Servlet container, or with an embedded server like Jetty or Undertow. Either way, each requests ties up a thread and is thus wasting resources when it waits for I/O operations.

Switching over to the Convert-To-Vert.x-Web branch, we can see that the Application class has changed a little. We now have some new @Bean annotated methods for injecting the Vertx instance itself, as well as an instance of ObjectMapper (part of the Jackson JSON library). We have also replaced the CustomerEnpoints class with a new CustomerVerticle. Pretty much everything else is the same.

The CustomerVerticle class is annotated with @Component, which means that Spring will instantiate that class on startup. It also has it’s start method annotated with @PostConstruct so that the Verticle is launched on startup. Looking at the actual content of the code, we see our first bits of Vert.x code: Router.

The Router class is part of the vertx-web library and allows us to use a fluent API to define HTTP URLs, methods, and header filters for our request handling. Adding the BodyHandler instance to the default route allows a POST/PUT body to be processed and converted to a JSON object which Vert.x can then process as part of the RoutingContext. The order of routes in Vert.x CAN be significant. If you define a route which has some sort of glob matching (* or regex), it can swallow requests for routes defined after it unless you implement chaining. Our example shows 3 routes initially.


    @PostConstruct
    public void start() throws Exception {
        Router router = Router.router(vertx);
        router.route().handler(BodyHandler.create());
        router.get("/v1/customer/:id")
                .produces("application/json")
                .blockingHandler(this::getCustomerById);
        router.put("/v1/customer")
                .consumes("application/json")
                .produces("application/json")
                .blockingHandler(this::addCustomer);
        router.get("/v1/customer")
                .produces("application/json")
                .blockingHandler(this::getAllCustomers);
        vertx.createHttpServer().requestHandler(router::accept).listen(8080);
    }

Notice that the HTTP method is defined, the “Accept” header is defined (via consumes), and the “Content-Type” header is defined (via produces). We also see that we are passing the handling of the request off via a call to the blockingHandler method. A blocking handler for a Vert.x route accepts a RoutingContext object as it’s only parameter. The RoutingContext holds the Vert.x Request object, Response object, and any parameters/POST body data (like “:id”). You’ll also see that I used method references rather than lambdas to insert the logic into the blockingHandler (I find it more readable). Each handler for the 3 request routes is defined in a separate method further down in the class. These methods basically just call the methods on the DAO, serialize or deserialize as needed, set some response headers, and end() the request by sending a response. Overall, pretty simple and straightforward.


    private void addCustomer(RoutingContext rc) {
        try {
            String body = rc.getBodyAsString();
            Customer customer = mapper.readValue(body, Customer.class);
            Customer saved = dao.save(customer);
            if (saved!=null) {
                rc.response().setStatusMessage("Accepted").setStatusCode(202).end(mapper.writeValueAsString(saved));
            } else {
                rc.response().setStatusMessage("Bad Request").setStatusCode(400).end("Bad Request");
            }
        } catch (IOException e) {
            rc.response().setStatusMessage("Server Error").setStatusCode(500).end("Server Error");
            log.error("Server error", e);
        }
    }

    private void getCustomerById(RoutingContext rc) {
        log.info("Request for single customer");
        Long id = Long.parseLong(rc.request().getParam("id"));
        try {
            Customer customer = dao.findOne(id);
            if (customer==null) {
                rc.response().setStatusMessage("Not Found").setStatusCode(404).end("Not Found");
            } else {
                rc.response().setStatusMessage("OK").setStatusCode(200).end(mapper.writeValueAsString(dao.findOne(id)));
            }
        } catch (JsonProcessingException jpe) {
            rc.response().setStatusMessage("Server Error").setStatusCode(500).end("Server Error");
            log.error("Server error", jpe);
        }
    }

    private void getAllCustomers(RoutingContext rc) {
        log.info("Request for all customers");
        List customers = StreamSupport.stream(dao.findAll().spliterator(), false).collect(Collectors.toList());
        try {
            rc.response().setStatusMessage("OK").setStatusCode(200).end(mapper.writeValueAsString(customers));
        } catch (JsonProcessingException jpe) {
            rc.response().setStatusMessage("Server Error").setStatusCode(500).end("Server Error");
            log.error("Server error", jpe);
        }
    }

“But this is more code and messier than my Spring annotations and classes”, you might say. That CAN be true, but it really depends on how you implement the code. This is meant to be an introductory example, so I left the code very simple and easy to follow. I COULD use an annotation library for Vert.x to implement the endpoints in a manner similar to JAX-RS. In addition, we have gained a massive scalability improvement. Under the hood, Vert.x Web uses Netty for low-level asynchronous I/O operations, thus providing us the ability to handle MANY more concurrent requests (limited by the size of the database connection pool).

We’ve already made some improvement to the scalability and concurrency of this application by using the Vert.x Web library, but we can improve things a little more by implementing the Vert.x EventBus. By separating the database operations into Worker Verticles instead of using blockingHandler, we can handle request processing more efficiently. This is show in the Convert-To-Worker-Verticles branch. The application class has remained the same, but we have changed the CustomerEndpoints class and added a new class called CustomerWorker. In addition, we added a new library called Spring Vert.x Extension which provides Spring Dependency Injections support to Vert.x Verticles. Start off by looking at the new CustomerEndpoints class.


    @PostConstruct
    public void start() throws Exception {
        log.info("Successfully create CustomerVerticle");
        DeploymentOptions deployOpts = new DeploymentOptions().setWorker(true).setMultiThreaded(true).setInstances(4);
        vertx.deployVerticle("java-spring:com.zanclus.verticles.CustomerWorker", deployOpts, res -> {
            if (res.succeeded()) {
                Router router = Router.router(vertx);
                router.route().handler(BodyHandler.create());
                final DeliveryOptions opts = new DeliveryOptions()
                        .setSendTimeout(2000);
                router.get("/v1/customer/:id")
                        .produces("application/json")
                        .handler(rc -> {
                            opts.addHeader("method", "getCustomer")
                                    .addHeader("id", rc.request().getParam("id"));
                            vertx.eventBus().send("com.zanclus.customer", null, opts, reply -> handleReply(reply, rc));
                        });
                router.put("/v1/customer")
                        .consumes("application/json")
                        .produces("application/json")
                        .handler(rc -> {
                            opts.addHeader("method", "addCustomer");
                            vertx.eventBus().send("com.zanclus.customer", rc.getBodyAsJson(), opts, reply -> handleReply(reply, rc));
                        });
                router.get("/v1/customer")
                        .produces("application/json")
                        .handler(rc -> {
                            opts.addHeader("method", "getAllCustomers");
                            vertx.eventBus().send("com.zanclus.customer", null, opts, reply -> handleReply(reply, rc));
                        });
                vertx.createHttpServer().requestHandler(router::accept).listen(8080);
            } else {
                log.error("Failed to deploy worker verticles.", res.cause());
            }
        });
    }

The routes are the same, but the implementation code is not. Instead of using calls to blockingHandler, we have now implemented proper async handlers which send out events on the event bus. None of the database processing is happening in this Verticle anymore. We have moved the database processing to a Worker Verticle which has multiple instances to handle multiple requests in parallel in a thread-safe manner. We are also registering a callback for when those events are replied to so that we can send the appropriate response to the client making the request. Now, in the CustomerWorker Verticle we have implemented the database logic and error handling.

@Override
public void start() throws Exception {
    vertx.eventBus().consumer("com.zanclus.customer").handler(this::handleDatabaseRequest);
}

public void handleDatabaseRequest(Message<Object> msg) {
    String method = msg.headers().get("method");

    DeliveryOptions opts = new DeliveryOptions();
    try {
        String retVal;
        switch (method) {
            case "getAllCustomers":
                retVal = mapper.writeValueAsString(dao.findAll());
                msg.reply(retVal, opts);
                break;
            case "getCustomer":
                Long id = Long.parseLong(msg.headers().get("id"));
                retVal = mapper.writeValueAsString(dao.findOne(id));
                msg.reply(retVal);
                break;
            case "addCustomer":
                retVal = mapper.writeValueAsString(
                                    dao.save(
                                            mapper.readValue(
                                                    ((JsonObject)msg.body()).encode(), Customer.class)));
                msg.reply(retVal);
                break;
            default:
                log.error("Invalid method '" + method + "'");
                opts.addHeader("error", "Invalid method '" + method + "'");
                msg.fail(1, "Invalid method");
        }
    } catch (IOException | NullPointerException e) {
        log.error("Problem parsing JSON data.", e);
        msg.fail(2, e.getLocalizedMessage());
    }
}

The CustomerWorker worker verticles register a consumer for messages on the event bus. The string which represents the address on the event bus is arbitrary, but it is recommended to use a reverse-tld style naming structure so that it is simple to ensure that the addresses are unique (“com.zanclus.customer”). Whenever a new message is sent to that address, it will be delivered to one, and only one, of the worker verticles. The worker verticle then calls handleDatabaseRequest to do the database work, JSON serialization, and error handling.

There you have it. You’ve seen that Vert.x can be integrated into your legacy applications to improve concurrency and efficiency without having to rewrite the entire application. We could have done something similar with an existing Google Guice or JavaEE CDI application. All of the business logic could remain relatively untouched while we tried in Vert.x to add reactive capabilities. The next steps are up to you. Some ideas for where to go next include Clustering, WebSockets, and VertxRx for ReactiveX sugar.

How jOOQ Allows for Fluent Functional-Relational Interactions in Java 8

In this year’s Java Advent Calendar, we’re thrilled to have been asked to feature a mini-series showing you a couple of advanced and very interesting topics that we’ve been working on when developing jOOQ.

The series consists of:

Don’t miss any of these!

How jOOQ allows for fluent functional-relational interactions in Java 8

In yesterday’s article, we’ve seen How jOOQ Leverages Generic Type Safety in its DSL when constructing SQL statements. Much more interesting than constructing SQL statements, however, is executing them.

Yesterday, we’ve seen a sample PL/SQL block that reads like this:

BEGIN
FOR rec IN (
SELECT first_name, last_name FROM customers
UNION
SELECT first_name, last_name FROM staff
)
LOOP
INSERT INTO people (first_name, last_name)
VALUES rec.first_name, rec.last_name;
END LOOP;
END;

And you won’t be surprised to see that the exact same thing can be written in Java with jOOQ:

for (Record2<String, String> rec : 
dsl.select(CUSTOMERS.FIRST_NAME, CUSTOMERS.LAST_NAME).from(CUSTOMERS)
.union(
select(STAFF.FIRST_NAME, STAFF.LAST_NAME ).from(STAFF))
) {
dsl.insertInto(PEOPLE, PEOPLE.FIRST_NAME, PEOPLE.LAST_NAME)
.values(rec.getValue(CUSTOMERS.FIRST_NAME), rec.getValue(CUSTOMERS.LAST_NAME))
.execute();
}

This is a classic, imperative-style PL/SQL inspired approach at iterating over result sets and performing actions 1-1.

Java 8 changes everything!

With Java 8, lambdas appeared, and much more importantly, Streams did, and tons of other useful features. The simplest way to migrate the above foreach loop to Java 8’s “callback hell” would be the following

dsl.select(CUSTOMERS.FIRST_NAME, CUSTOMERS.LAST_NAME).from(CUSTOMERS)
.union(
select(STAFF.FIRST_NAME, STAFF.LAST_NAME ).from(STAFF))
.forEach(rec -> {
dsl.insertInto(PEOPLE, PEOPLE.FIRST_NAME, PEOPLE.LAST_NAME)
.values(rec.getValue(CUSTOMERS.FIRST_NAME), rec.getValue(CUSTOMERS.LAST_NAME))
.execute();
}

This is still very simple. How about this. Let’s fetch a couple of records from the database, stream them, map them using some sophisticated Java function, reduce them into a batch update statement! Whew… here’s the code:

dsl.selectFrom(BOOK)
.where(BOOK.ID.in(2, 3))
.orderBy(BOOK.ID)
.fetch()
.stream()
.map(book -> book.setTitle(book.getTitle().toUpperCase()))
.reduce(
dsl.batch(update(BOOK).set(BOOK.TITLE, (String) null).where(BOOK.ID.eq((Integer) null))),
(batch, book) -> batch.bind(book.getTitle(), book.getId()),
(b1, b2) -> b1
)
.execute();

Awesome, right? Again, with comments

// Here, we simply select a couple of books from the database
dsl.selectFrom(BOOK)
.where(BOOK.ID.in(2, 3))
.orderBy(BOOK.ID)
.fetch()

// Now, we stream the result as a Java 8 Stream
.stream()

// Now we map all book titles using the "sophisticated" Java function
.map(book -> book.setTitle(book.getTitle().toUpperCase()))

// Now, we reduce the books into a batch update statement...
.reduce(

// ... which is initialised with empty bind variables
dsl.batch(update(BOOK).set(BOOK.TITLE, (String) null).where(BOOK.ID.eq((Integer) null))),

// ... and then we bind each book's values to the batch statement
(batch, book) -> batch.bind(book.getTitle(), book.getId()),

// ... this is just a dummy combiner function, because we only operate on one batch instance
(b1, b2) -> b1
)

// Finally, we execute the produced batch statement
.execute();

Awesome, right? Well, if you’re not too functional-ish, you can still resort to the “old ways” using imperative-style loops. Perhaps, your coworkers might prefer that:

BatchBindStep batch = dsl.batch(update(BOOK).set(BOOK.TITLE, (String) null).where(BOOK.ID.eq((Integer) null))),

for (BookRecord book :
dsl.selectFrom(BOOK)
.where(BOOK.ID.in(2, 3))
.orderBy(BOOK.ID)
) {
batch.bind(book.getTitle(), book.getId());
}

batch.execute();

So, what’s the point of using Java 8 with jOOQ?

Java 8 might change a lot of things. Mainly, it changes the way we reason about functional data transformation algorithms. Some of the above ideas might’ve been a bit over the top. But the principal idea is that whatever is your source of data, if you think about that data in terms of Java 8 Streams, you can very easily transform (map) those streams into other types of streams as we did with the books. And nothing keeps you from collecting books that contain changes into batch update statements for batch execution.

Another example is one where we claimed that Java 8 also changes the way we perceive ORMs. ORMs are very stateful, object-oriented things that help manage database state in an object-graph representation with lots of nice features like optimistic locking, dirty checking, and implementations that support long conversations. But they’re quite terrible at data transformation. First off, they’re much much inferior to SQL in terms of data transformation capabilities. This is topped by the fact that object graphs and functional programming don’t really work well either.

With SQL (and thus with jOOQ), you’ll often stay on a flat tuple level. Tuples are extremely easy to transform. The following example shows how you can use an H2 database to query for INFORMATION_SCHEMA meta information such as table names, column names, and data types, collect those information into a data structure, before mapping that data structure into new CREATE TABLE statements:

DSL.using(c)
.select(
COLUMNS.TABLE_NAME,
COLUMNS.COLUMN_NAME,
COLUMNS.TYPE_NAME
)
.from(COLUMNS)
.orderBy(
COLUMNS.TABLE_CATALOG,
COLUMNS.TABLE_SCHEMA,
COLUMNS.TABLE_NAME,
COLUMNS.ORDINAL_POSITION
)
.fetch() // jOOQ ends here
.stream() // Streams start here
.collect(groupingBy(
r -> r.getTableName(),
LinkedHashMap::new,
mapping(
r -> r,
toList()
)
))
.forEach(
(table, columns) -> {
// Just emit a CREATE TABLE statement
System.out.println(
"CREATE TABLE " + table + " (");

// Map each "Column" type into a String
// containing the column specification,
// and join them using comma and
// newline. Done!
System.out.println(
columns.stream()
.map(col -> " " + col.getName() +
" " + col.getType())
.collect(Collectors.joining(",n"))
);

System.out.println(");");
}
);

The above statement will produce something like the following SQL script:

CREATE TABLE CATALOGS(
CATALOG_NAME VARCHAR
);
CREATE TABLE COLLATIONS(
NAME VARCHAR,
KEY VARCHAR
);
CREATE TABLE COLUMNS(
TABLE_CATALOG VARCHAR,
TABLE_SCHEMA VARCHAR,
TABLE_NAME VARCHAR,
COLUMN_NAME VARCHAR,
ORDINAL_POSITION INTEGER,
COLUMN_DEFAULT VARCHAR,
IS_NULLABLE VARCHAR,
DATA_TYPE INTEGER,
CHARACTER_MAXIMUM_LENGTH INTEGER,
CHARACTER_OCTET_LENGTH INTEGER,
NUMERIC_PRECISION INTEGER,
NUMERIC_PRECISION_RADIX INTEGER,
NUMERIC_SCALE INTEGER,
CHARACTER_SET_NAME VARCHAR,
COLLATION_NAME VARCHAR,
TYPE_NAME VARCHAR,
NULLABLE INTEGER,
IS_COMPUTED BOOLEAN,
SELECTIVITY INTEGER,
CHECK_CONSTRAINT VARCHAR,
SEQUENCE_NAME VARCHAR,
REMARKS VARCHAR,
SOURCE_DATA_TYPE SMALLINT
);

That’s data transformation! If you’re as excited as we are, read on in this article how this example works exactly.

Conclusion

Java 8 has changed everything in the Java ecosystem. Finally, we can implement functional, transformative algorithms easily using Streams and lambda expressions. SQL is also a very functional and transformative language. With jOOQ and Java 8, you can extend data transformation directly from your type safe SQL result into Java data structures, back into SQL. These things aren’t possible with JDBC. These things weren’t possible prior to Java 8.

jOOQ is free and Open Source for use with Open Source databases, and it offers commercial licensing for use with commercial databases.

For more information about jOOQ or jOOQ’s DSL API, consider these resources:

Stay tuned for tomorrow’s article “How jOOQ helps pretend that your stored procedures are a part of Java”
This post is part of the Java Advent Calendar and is licensed under the Creative Commons 3.0 Attribution license. If you like it, please spread the word by sharing, tweeting, FB, G+ and so on!