jOOQ Tuesdays: Daniel Dietrich Explains the Benefits of Object-Functional Programming

Welcome to the jOOQ Tuesdays series. In this series, we’ll publish an article on the third Tuesday every other month where we interview someone we find exciting in our industry from a jOOQ perspective. This includes people who work with SQL, Java, Open Source, and a variety of other related topics.


I’m very excited to feature today Daniel Dietrich whose popular library JΛVΛSLΛNG is picking up a lot of momentum among functional programming afictionados working with Java.

Daniel, you created JΛVΛSLΛNG – Object-Functional Programming in Java, a library that is becoming more and more popular among functional programmers. Why is Javaslang so popular?

Thank you Lukas for giving me the opportunity to share my thoughts.

I think that many users were disappointed about Java 8 in the whole, especially those who are already familiar with more advanced languages. The Java language architects did an awesome job. Java 8 brought groundbreaking new features like Lambdas, the new Stream API and CompletableFuture. But the new abstractions were only poorly integrated into the language from an API perspective.

There is already an increasing amount of write-ups about the disadvantages of Java 8, starting with the drawbacks of the Optional type. We read that we have to take care when using parallel Streams. These are self-made problems that keep us busy, stealing our expensive time. Javaslang provides us with alternatives.

There is no reason to reinvent the wheel. My vision is to bring as much as possible of the Scala goodness to Java. In fact Scala emerged from Java in the form of the Pizza language. Back in 2001 it had features like generics, function pointers (aka lambdas), case classes (aka value types) and pattern matching. In 2004 Java got generics, in 2014 came lambdas, and hopefully Java 10 will include value types. Scala left Java far behind. It used the last 15 year to evolve.

Object-functional programming is nothing new. It is the best of both worlds, object-oriented programming and functional programming. Scala is one of the better choices to do it on the JVM. Java’s Lambdas are an enabling feature. They allowed us to create a Javaslang API that is similar to Scala.

Java developers who get their hands on Javaslang often react in a way that I call the nice-effect: “Wow that’s nice, it feels like Scala”.

You have published a guest post on the jOOQ blog about Javaslang more than one year ago. Since then, Javaslang has moved forward quite a bit and you’ve recently published the roadmap for version 3.0. What have you done since then and where are you going?

Yes, that is true, it has changed a lot since then. We released Javaslang 1.2.2 two weeks before the first jOOQ guest post went online. Beside enriched functions that release offered popular Scala features like Option for null-safety, Try for performing computations headache-free in the presence of exceptions and a fluent pattern matching DSL. Also notably we shipped two common persistent collections, an eagerly evaluated linked List and the lazy form of it, also called Stream.

Roughly one year later we released Javaslang 2.0.0. We hardened the existing features and most notably included Future and Promise for concurrent programming and a full-fledged, Scala-like persistent collection library. Beside that, we replaced the pattern matching DSL with a more powerful pattern matching API that allows us to recursively match arbitrary object trees.

I spent a significant amount of time and energy abstracting on the type level over the mentioned features, as far as this is possible in Java. For Java developers it is not important to call things monads, sum-types or products. For example we do not need to know group theory in order to calculate 1 + 1. My duty as library developer is to make it as simple as possible for users of Javaslang to reach their goals. The need to learn new APIs and DSLs should be reduced to the minimum. This is the main reason for aligning Javaslang to Scala.

Our efforts for the next release concentrate on adding more syntactic sugar and missing persistent collections beyond those of Scala. It will be sufficient to add one import to reach 90% of Javaslang’s API. There will be new persistent collections BitSet, several MultiMaps and a PriorityQueue. We are improving the performance of our collections, most notably our persistent Vector. It will be faster than Java’s Stream for some operations and have a smaller memory footprint than Java’s ArrayList for primitive elements.

Beyond library features we pay special attention on three things: backward compatibility, controlled growth and integration aspects. Web is important. Our Jackson module ensures that all Javaslang types can be sent over the wire as serialized JSON. The next release will include a GWT module, first tests already run Javaslang in the browser. However, the Javaslang core will stay thin. It will not depend on any other libraries than the JDK.

Towards the next major release 3.0.0 I’m starting to adjust the roadmap I sketched in a previous blog post. I’ve learned that it is most important to our users that they can rely on backward compatibility. Major releases should not appear often, following the 2.x line is a better strategy. We will start to deprecate a few APIs that will be removed in a future major release. Also I keep an eye on some interesting developments that will influence the next major release. For example a new major Scala release is in the works and there are new interesting Java features that will appear in Java 10.

Looking at the current issues I don’t have to be an oracle to foresee that the next minor release 2.1.0 will take some more time. I understand that users want to start using the new Javaslang features but we need the time and the flexibility to get things right. Therefore we target a first beta release of 2.1.0 in Q4 2016.

In the meantime, there is a variety of functional(-ish) libraries for Java 8, like our own jOOλ, StreamEx, Cyclops, or the much older FunctionalJλvλ. How do all these libraries compare and how is yours different?

This question goes a little bit in the philosophical direction, maybe it is also political. These are my subjective thoughts, please treat them as such.

Humans have the ability to abstract over things. They express themselves in various ways, e.g. with painting and music. These areas split into different fields. For example in literature things are expressed in manifold ways like rhythmic prose and poetry. Furthermore different styles can be applied within these fields, like the iambic trimeter in poetry. The styles across different areas are often embossed by outer circumstances, bound to time, like an epoch.

In the area of mathematics there are also several fields, like algebra and mathematical analysis. Both have a notion of functions. Which field should I take when I want to express myself in a functional style?

Personally, I’m not able to afford the time to write non-trivial applications in each of the mentioned libraries. But I took a look at the source code and followed discussions. I see that nearly all libraries are embossed by the outer circumstance that lambdas finally made it to all curly-braces languages, especially to Java in our case. Library designers are keen to modernize their APIs in order to keep pace. But library designers are also interested in staying independent of 3rd party libraries for reasons like stability and progression.

The field of jOOQ is SQL in Java, the field of Cyclops is asynchronous systems. Both libraries are similar in the way that they adapted the new Java Lambda feature. I already mentioned that the new Java features are only poorly integrated into the language. This is the reason why we see a variety of new libraries that try to close this gap.

jOOQ needs jOOλ in order to stay independent. On the technical level StreamEx is similar to jOOλ in the way that both sit on top of Java’s Stream. They augment it with additional functionality that can be accessed using a fluent API. The biggest difference between them is that StreamEx supports parallel computations while jOOλ concentrates on sequential computations only. Looking at the SQL-ish method names it shows that jOOλ is tailored to be used with jOOQ.

Cyclops states to be the answer to the cambrian explosion of functional(-ish) libraries. It offers a facade that is backed by one of several integration modules. From the developer perspective I see this with skepticism. The one-size-fits-all approach did not work well for me in the past because it does not cover all features of the backing libraries. An abstraction layer adds another source of errors, which is unnecessary.

Many names of Cyclops look unfamiliar to me, maybe because of the huge amount of types. Looking at the API, the library seems to be a black hole, a cambrian implosion of reactive and functional features. John McClean did a great job abstracting over all the different libraries and providing a common API but I prefer to use a library directly.

FunctionalJλvλ is different. It existed long before the other libraries and has the noble goal of purely functional programming: If it does compile, it is correct. FunctionalJλvλ was originally driven by people well known from the Scala community, more specifically from the Scalaz community. Scalaz is highly influenced by Haskell, a purely functional language.

Haskell and Scala are much more expressive than Java. Porting the algebraic abstractions from Scalaz to Java turned out to be awkward. Java’s type system isn’t powerful enough, it does not allow us to reach that goal in a practical way. The committers seem to be disillusioned to me. Some state that Java is not the right language for functional programming.

Javaslang is a fresh take on porting Scala functionality to Java. At its core it is not as highly influenced by Scalaz and Haskell as FunctionalJλvλ is. However, for purely functional abstractions it offers an algebra module that depends on the core. The relation algebra/core can be compared to Scalaz/Scala.

Javaslang is similar to StreamEx in the way that it is not bound to a specific domain, in contrast to jOOλ and Cyclops. It is different from StreamEx in the sense that it does not build on top of Java’s Stream. I understand Javaslang as language addition that integrates well with existing Java features.

You have never spoken at conferences, you let other people do that for you. What’s your secret? 🙂

In fact I never attended a conference at all. My secret is to delegate the real work to others.

Joking aside, I feel more comfortable spending my time on the Javaslang source code than preparing conferences and travelling. Currently I am working on Javaslang beside my job but I’m still looking for opportunities to do it full-time.

It is awesome to see other people jumping on the Javaslang train. We receive help from all over the world. Beside IntelliJ and YourKit we recently got TouK as new sponsor and produced Javaslang stickers that are handed out at conferences.

Because of the increasing popularity of Javaslang there is also an increasing amount of questions and pull requests. Beside the conception and development I concentrate on code-reviews, discussions and managing the project.

Where do you see Java’s future with projects like Valhalla?

Java stands for stability and safety. New language features are moderately added, like salt to a soup. This is what we can expect from a future Java.

In his recent mission statement Brian Goetz gives us a great overview about the goals of Project Valhalla. From the developer point of view I really love to see that the Java language architects attach great importance to improve the expressiveness of Java. Value types for example will reduce a lot of redundant code and ceremony we are currently confronted with. It is also nice to see that value types will be immutable.

Another feature I’m really looking forward to is the extension of generics. It will allow us to remove several specializations that exist only for primitive types and void. Popular functional interfaces like Predicate, Consumer, Supplier and Runnable will be equivalent to Function. In Javaslang we currently provide additional API for performing side-effects. Having extended generics that API can be reduced to the general case, like it should have been from the beginning.

There are two more features I’m really interested in: local variable type inference, that will come to Java, and reified generics, that might come. Reified generics are needed when we want to get the type of a generic parameter at runtime. We already have type inference for lambdas. Extending it to local variables will increase conciseness and readability of method and lambda bodies while preserving type-safety. I think it is a good idea that we will still have to specify the return type of methods. It is a clear documentation of the API of an application.

I’m deeply impressed how Java and the JVM evolve over time without breaking backward compatibility. It is a safe platform we can rely on. The gap between Java and other, more modern languages is getting smaller but Java is still behind. Some popular features might never come and most probably outdated API will not get a complete refresh or a replacement. This is a field where libraries such as Javaslang can help.

(Ab)using Java 8 FunctionalInterfaces as Local Methods

If you’re programming in more advanced languages like Scala or Ceylon, or even JavaScript, “nested functions” or “local functions” are a very common idiom to you. For instance, you’ll write things like fibonacci functions as such:

def f() = {
  def g() = "a string!"
  g() + "– says g"

(Question from Stack Overflow by Aaron Yodaiken)

The f() function contains a nested g() function, which is local to the scope of the outer f() function.

In Java, there is no way to create a local function like this, but you can assign a lambda expression to a local variable and use that instead.

The above example can be translated to the following Java code:

String f() {
    Supplier<String> g = () -> "a string!";
    return g.get() + "- says g";

While this example is rather trivial, a much more useful use-case is testing. For instance, consider the following jOOλ unit test, which checks whether the Stream.close() semantics is properly implemented across all sorts of jOOλ Seq methods, that combine two streams into one:

public void testCloseCombineTwoSeqs() {
    Consumer<BiFunction<Stream<Integer>, Stream<Integer>, Seq<?>>> test = f -> {
        AtomicBoolean closed1 = new AtomicBoolean();
        AtomicBoolean closed2 = new AtomicBoolean();
        Stream s1 = Stream.of(1, 2).onClose(() -> closed1.set(true));
        Stream s2 = Stream.of(3).onClose(() -> closed2.set(true));
        try (Seq s3 = f.apply(s1, s2)) {

    test.accept((s1, s2) -> seq(s1).concat(s2));
    test.accept((s1, s2) -> seq(s1).crossJoin(s2));
    test.accept((s1, s2) -> seq(s1).innerJoin(s2, (a, b) -> true));
    test.accept((s1, s2) -> seq(s1).leftOuterJoin(s2, (a, b) -> true));
    test.accept((s1, s2) -> seq(s1).rightOuterJoin(s2, (a, b) -> true));

The local function is test, and it takes two Stream<Integer> arguments, producing a Seq<?> result.

Why not just write a private method?

Of course, this could have been solved with a private method as well, classic Java style. But sometimes, using a local scope is much more convenient, as the test Consumer (local function) does not escape the scope of this single unit test. It should be used only within this single method.

An alternative, more classic Java way would have been to define a local class, instead, and put the function inside of that. But this solution is much more lean.

One disadvantage, however, is that recursion is much harder to implement this way, in Java. See also:

Reactive Database Access – Part 3 – Using jOOQ with Scala, Futures and Actors

We’re very happy to continue our a guest post series on the jOOQ blog by Manuel Bernhardt. In this blog series, Manuel will explain the motivation behind so-called reactive technologies and after introducing the concepts of Futures and Actors use them in order to access a relational database in combination with jOOQ.

manuel-bernhardtManuel Bernhardt is an independent software consultant with a passion for building web-based systems, both back-end and front-end. He is the author of “Reactive Web Applications” (Manning) and he started working with Scala, Akka and the Play Framework in 2010 after spending a long time with Java. He lives in Vienna, where he is co-organiser of the local Scala User Group. He is enthusiastic about the Scala-based technologies and the vibrant community and is looking for ways to spread its usage in the industry. He’s also scuba-diving since age 6, and can’t quite get used to the lack of sea in Austria.

This series is split in three parts, which we have published over the past months:


In the previous two posts of this series we have introduced the benefits of reactive programming as well as two tools available for manipulating them, Futures and Actors. Now it is time to get your hands dirty, dear reader, and to create a simple application featuring reactive database access. Fear not, I will be there along the whole way to guide you through the process.

Also, the source code of this example is available on Github

Getting the tools

In order to build the application, you will need a few tools. If you haven’t worked with Scala yet, the simplest for you may be to go and grab the Typesafe Activator which is a standalone project that brings in the necessary tools to build a Scala project from scratch.

Since this is about reactive database access, you will also need a database. For the purpose of this simple example, we’re going to use Oracle Database 12c Enterprise Edition. Nah, just kidding – it might be a bit cumbersome to get this one to run on your machine. Instead we will use the excellent PostgreSQL. Make sure to install it so that you can run the psql utility from your console.

Ready? Great! Let’s have a look at what we’re going to build.

The application

The goal of our application is to fetch mentions from Twitter and store them locally so as to be able to visualize them and perform analytics on them.


The core of this mechanism will be a MentionsFetcher actor which will periodically fetch mentions from Twitter and save them in the database. Once there we can display useful information on a view.

Creating the database

The first step we’re going to take is to create the database. Create a mentions.sql file somewhere with the following content:




\connect mentions play

CREATE TABLE twitter_user (
  id bigserial primary key,
  created_on timestamp with time zone NOT NULL,
  twitter_user_name varchar NOT NULL

CREATE TABLE mentions (
  id bigserial primary key,
  tweet_id varchar NOT NULL,
  user_id bigint NOT NULL,
  created_on timestamp with time zone NOT NULL,
  text varchar NOT NULL

This script will create a play user, a mentions database as well as two tables, twitter_user and mentions.

In order to execute it, execute the following command in a terminal:

psql -f mentions.sql

(note: you might need to explictly declare which user runs this command, depending on how you have configured PostgreSQL to run)

Bootstrapping the project

Let’s create the reactive-mentions project, shall we? Assuming that you have installed the activator, run the following command in a terminal:

~/workspace » activator new reactive-mentions

This will prompt a list of templates, we are going to use the play-scala project template:

Fetching the latest list of templates...

Browse the list of templates:
Choose from these featured templates or enter a template name:
  1) minimal-akka-java-seed
  2) minimal-akka-scala-seed
  3) minimal-java
  4) minimal-scala
  5) play-java
  6) play-scala
(hit tab to see a list of all templates)
> 6
OK, application "reactive-mentions" is being created using the "play-scala" template.

At this point, a simple Play Framework project has been created in the reactive-mentions directory. If you want to, you can run this project by navigating to it and running the command activator run.

In order to work on the project, you can use one of the many IDEs that have Scala support. My personal favourite is to this day IntelliJ IDEA which does a pretty good job at this and also has built-in support for the Play Framework itself.

Setting up jOOQ

I wrote about database access in Scala about 2 years ago. There are to this day still quite a few alternatives to relational database access in Scala but at least personally I have now reached the conclusion that for the type of projects I work on, jOOQ beats them all when it comes to writing type-safe SQL. So without further ado let’s integrate it with our project.

There is an SBT plugin available for this if you would like, however for this application we will settle for a minimal, hand-crafter solution.

Bring up the build.sbt file in an editor and add adjust the libraryDependencies to look like so:

libraryDependencies ++= Seq(
  "org.postgresql" % "postgresql" % "9.4-1201-jdbc41",
  "org.jooq" % "jooq" % "3.7.0",
  "org.jooq" % "jooq-codegen-maven" % "3.7.0",
  "org.jooq" % "jooq-meta" % "3.7.0",
  specs2 % Test

If you are running the project’s console (which you can do by executing the activator command in the project’s directory) you will need to call the reload command in order for the new dependencies to be pulled in. This is true of any change you are doing to the build.sbt file. Don’t forget about it in the remainder of this article!

(note: make sure to use the version of the PostgreSQL driver that fits your version of PostgreSQL!)

Next, we need to set up jOOQ itself. For this purpose, create the file conf/mentions.xml, where conf is the directory used in the Play Framework for storing configuration-related files:

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<configuration xmlns="">

This configuration will allow to run jOOQ’s ScalaGenerator which will read the database schema and generate Scala specific classes for it, storing them in a directory accessible in the classpath and meant for generated sources.

All that is left to do is to create have a way to run jOOQ’s code generation. A simple solution that we are going to use is to create a custom SBT task in our project build. Go back to build.sbt and add the following at the end:

val generateJOOQ = taskKey[Seq[File]]("Generate JooQ classes")

val generateJOOQTask = (sourceManaged, fullClasspath in Compile, runner in Compile, streams) map { (src, cp, r, s) =>
  toError("org.jooq.util.GenerationTool", cp.files, Array("conf/mentions.xml"), s.log))
  ((src / "main/generated") ** "*.scala").get

generateJOOQ <<= generateJOOQTask

unmanagedSourceDirectories in Compile += sourceManaged.value / "main/generated"

The generateJOOQ task will run the GenerationTool using the mentions.xml file we have set-up earlier on. Let’s run it!

Start the SBT console by running the activator command in your terminal window, in the reactive-streams directory, and then run the generateJOOQ command:

[reactive-mentions] $ generateJOOQ
[info] Running org.jooq.util.GenerationTool conf/mentions.xml
[success] Total time: 1 s, completed Dec 11, 2015 2:55:08 PM

That’s it! If you want a bit more verbosity, add the following logger configuration to conf/logback.xml:

 <logger name="org.jooq" level="INFO" />

Alright, we are now ready to get to the core of our endaveour: create the actor that will pull mentions from Twitter!

Creating the MentionsFetcher actor

For the purpose of fetching mentions at regular intervals from Twitter, we will be using a simple Akka actor. Actors are meant to do a lot more powerful things than this but for the sake of introducing the concept this example will do (or so I hope).

Go ahead and add Akka as well as its logging facility as library dependencies in build.sbt:

libraryDependencies ++= Seq(
  "com.typesafe.akka" %% "akka-actor" % "2.4.1",
  "com.typesafe.akka" %% "akka-slf4j" % "2.4.1"

Next, create the file app/actors/MentionsFetcher.scala with the following content:

package actors

import actors.MentionsFetcher._
import{ActorLogging, Actor}
import org.joda.time.DateTime
import scala.concurrent.duration._

class MentionsFetcher extends Actor with ActorLogging {

  val scheduler = context.system.scheduler.schedule(
    initialDelay = 5.seconds,
    interval = 10.minutes,
    receiver = self,
    message = CheckMentions

  override def postStop(): Unit = {

  def receive = {
    case CheckMentions => checkMentions
    case MentionsReceived(mentions) => storeMentions(mentions)

  def checkMentions = ???

  def storeMentions(mentions: Seq[Mention]) = ???


object MentionsFetcher {

  case object CheckMentions
  case class Mention(id: String, created_at: DateTime, text: String, from: String, users: Seq[User])
  case class User(handle: String, id: String)
  case class MentionsReceived(mentions: Seq[Mention])


The first thing you may notice from this code is the unimplemented methods fetchMentions and storeMentions with the triple question mark ???. That’s actually valid Scala syntax: it is a method available by default which throws ascala.NotImplementedError.

The second thing I want you to notice is the companion object to the MentionsFetcher class which holds the protocol of our actor. Actors communicate using messages and even though our actor will only communicate with itself in this example it is a good idea to place it in a companion object and to import its members (via the wildcard import import actors.MentionsFetcher._) so as to keep things organized as the project grows.

Other than this, what we are doing for the moment is quite simple: we are setting up a scheduler that wakes up every 10 minutes in order to send the actor it-self the FetchMentions message. Upon receiving this message in the main receivemethod we are going to proceed to fetching the mentions from Twitter. Finally when a MentionsReceived message is received, we simply invoke the storeMentions method.

Simple enough, isn’t it? Don’t worry, things are about to get a little bit more complicated.

Fetching the mentions from Twitter

Twitter does not have an API that lets us directly fetch recent mentions. However it has an API that lets us search for Tweets and that will have to do.

Before you can go any further, if you intend to run this project, you will need to get yourself a set of keys and access tokens at If you don’t you will have to trust me that the following works.

Once you have them, add them in the file conf/application.conf like so:

# Twitter

Then, create the credentials method in MentionsFetcher:

// ...
import play.api.Play
import play.api.Play.current
import play.api.libs.oauth.{RequestToken, ConsumerKey}

class MentionsFetcher extends Actor with ActorLogging {

  // ...

  def credentials = for {
    apiKey <- Play.configuration.getString("twitter.apiKey")
    apiSecret <- Play.configuration.getString("twitter.apiSecret")
    token <- Play.configuration.getString("twitter.accessToken")
    tokenSecret <- Play.configuration.getString("twitter.accessTokenSecret")
  } yield (ConsumerKey(apiKey, apiSecret), RequestToken(token, tokenSecret))


This will allow us to place a call to Twitter’s API using the correct OAuth credentials.

Next, let’s get ready to fetch those mentions:

// ...
import akka.pattern.pipe
import org.joda.time.DateTime
import scala.util.control.NonFatal

class MentionsFetcher extends Actor with ActorLogging {

  // ...

  var lastSeenMentionTime: Option[DateTime] = Some(

  def checkMentions = {
      val maybeMentions = for {
        (consumerKey, requestToken) <- credentials
        time <- lastSeenMentionTime
      } yield fetchMentions(consumerKey, requestToken, "<yourTwitterHandleHere>", time)

      maybeMentions.foreach { mentions => { m =>
        } recover { case NonFatal(t) =>
          log.error(t, "Could not fetch mentions")
        } pipeTo self

  def fetchMentions(consumerKey: ConsumerKey, requestToken: RequestToken, user: String, time: DateTime): Future[Seq[Mention]] = ???

Do you remember the pipe pattern we talked about in the previous post about Actors? Well, here it is again!

The call we are going to make against Twitter’s API is going to be asynchronous. In other words we will not simply get aSeq[Mention] but a Future[Seq[Mention]] to work with, and the best way to deal with that one is to send ourselves a message once the Future has completed with the contents of the result.

Since things can go wrong though we also need to think about error recovery which we do here by heroically logging out the fact that we could not fetch the mentions.

You may also notice that we have introduced a lastSeenMentionTime variable. This is the means by which we are going to keep in memory the timestamp of the last mention we have seen.

In order to go ahead, one thing we need to do is to use a more recent version of the async-http-library client since there is a bug in Play 2.4.x. Add the following dependency to build.sbt:

libraryDependencies ++= Seq(
  "com.ning" % "async-http-client" % "1.9.29"

Alright, now that we are all set, let’s finally fetch those mentions!

// ...
import scala.util.control.NonFatal
import org.joda.time.format.DateTimeFormat
import play.api.libs.json.JsArray
import scala.concurrent.Future

class MentionsFetcher extends Actor with ActorLogging {

  // ...

  def fetchMentions(consumerKey: ConsumerKey, requestToken: RequestToken, user: String, time: DateTime): Future[Seq[Mention]] = {
    val df = DateTimeFormat.forPattern("EEE MMM dd HH:mm:ss Z yyyy").withLocale(Locale.ENGLISH)

      .sign(OAuthCalculator(consumerKey, requestToken))
      .withQueryString("q" -> s"@$user")
      .map { response =>
        val mentions = (response.json \ "statuses").as[JsArray] { status =>
          val id = (status \ "id_str").as[String]
          val text = (status \ "text").as[String]
          val from = (status \ "user" \ "screen_name").as[String]
          val created_at = df.parseDateTime((status \ "created_at").as[String])
          val userMentions = (status \ "entities" \ "user_mentions").as[JsArray] { user =>
            User((user \ "screen_name").as[String], ((user \ "id_str").as[String]))

          Mention(id, created_at, text, from, userMentions)



Fetching the mentions is rather straightforward thanks to Play’s WebService library. We create a signed OAuth request using our credentials and run a HTTP GET request against the search API passing as query string the @userName which will (hopefully) give us a list of Tweets mentioning a user. Lastly we do only keep those mentions that are after our last check time. Since we check every 10 minutes and since the API only returns recent tweets, this should be doing fine (unless you are very popular on Twitter and get an insane amount of replies – but this really is your own fault, then).

Setting the ExecutionContext

If you try to compile the project now (using the compile command) you will be greeted with a few compilation errors complaining about a missing ExecutionContext. Futures are a way to abstract tasks and they need something to run them. The ExecutionContext is the missing bit which will schedule the tasks to be executed.

Since we are inside of an actor we can borrow the actor’s own dispatcher:

class MentionsFetcher extends Actor with ActorLogging {

  implicit val executionContext = context.dispatcher

  // ...

We’ll talk more about Execution Contexts later on when it comes to fine-tuning the connection with the database. For the moment let us focus on actually getting to talk with the database at all.

Setting up a reactive database connection

Configuring the database connection

In order to connect to the database, we will first need to configure the connection information in conf/application.conf like so:

// ...


Creating a helper class to access the database

Play’s Database API is letting us access the configured database. We now need to do two things:

  • use jOOQ (rather than plain JDBC) to talk with the database
  • make sure we are not going to jeopardize our application by blocking while waiting for the database interaction to happen (JDBC is blocking)

For this purpose we will wrap the database operations in a Future that will run on its own ExecutionContext rather than sharing the one used by the actor or by the Play application itself.

Go ahead and create the file app/database/DB.scala:

package database

import javax.inject.Inject

import org.jooq.{SQLDialect, DSLContext}
import org.jooq.impl.DSL
import play.api.db.Database

import scala.concurrent.{ExecutionContext, Future}

class DB @Inject() (db: Database, system: ActorSystem) {

  val databaseContext: ExecutionContext = system.dispatchers.lookup("contexts.database")

  def query[A](block: DSLContext => A): Future[A] = Future {
    db.withConnection { connection =>
      val sql = DSL.using(connection, SQLDialect.POSTGRES_9_4)

  def withTransaction[A](block: DSLContext => A): Future[A] = Future {
    db.withTransaction { connection =>
      val sql = DSL.using(connection, SQLDialect.POSTGRES_9_4)


We define two methods, query and withTransaction that:

  • use a Future block in order to wrap the underlying code as a Future, thus running it asynchronously
  • use a custom databaseContext ExecutionContext in order to execute this Future
  • initialze jOOQ’s DSLContext and give access to it in the body of the expected functions

The databaseContext ExectionContext is created using Akka’s configuration capabilities. We need to add the configuration of the database dispatcher in conf/application.conf:

contexts {
    database {
        fork-join-executor {
          parallelism-max = 9

The magic number 9 doesn’t come out of nowhere. Check the excellent explanation provided by the HikariCP connection pool about connection pool sizing for more details. Those considerations are also discussed in length in Chapters 5 and 7 of Reactive Web Applications.

Wiring everything using dependency injection

Next, let’s use Play’s built-in dependency injection mechanism in order to provide our MentionsFetcher actor with a DB class. Adjust the constructor of our MentionsFetcher actor in app/actors/MentionsFetcher.scala to look like so:

// ...
import javax.inject.Inject
import play.api.db.Database

class MentionsFetcher @Inject() (database: Database) extends Actor with ActorLogging { ... }

We just need one more thing in order to bootstrap our MentionsFetcher actor: let Play know that we want to use it.

For this purpose we will declare a module and leverage the plumbing that Play provides when it comes to interacting with Akka actors. At the end of MentionsFetcher.scala (or in a new file, if you like), declare the following MentionsFetcherModule:

import play.api.libs.concurrent.AkkaGuiceSupport

class MentionsFetcherModule extends AbstractModule with AkkaGuiceSupport {
  def configure(): Unit =

Last but not least we need to tell Play that we would like to use this module. In conf/appliction.conf add the following line to do so:

play.modules.enabled += "actors.MentionsFetcherModule"

That’s it! When Play starts up it will initialize the enabled modules which in turn will lead to the actor being initialized.

We now can go ahead and use the database in order to store the fetched mentions.

Storing the mentions in the database

Thanks to jOOQ writing the statements for storing the mentions is rather easy. Since we do not want to risk storing users or mentions twice we will upsert them using the WHERE NOT EXISTS SQL clause. For the sake of recording as much data as possible we will also store all mentioned users of a Tweet.

// ...
import generated.Tables._
import org.jooq.impl.DSL._

class MentionsFetcher @Inject() (db: DB) extends Actor with ActorLogging {

  // ...

  def storeMentions(mentions: Seq[Mention]) = db.withTransaction { sql =>"Inserting potentially {} mentions into the database", mentions.size)
    val now = new Timestamp(

    def upsertUser(handle: String) = {
          select(value(now), value(handle))

    mentions.foreach { mention =>
      // upsert the mentioning users

      // upsert the mentioned users
      mention.users.foreach { user =>

      // upsert the mention


Et voilà! If you execute this code (and generate some mentions, or use an earlier timestamp for filtering) you will get some data into your database!

Let’s now query and display a few statistics in the browser.

Displaying the mentions

In order to show our mentions we will adjust the default view shown when launching the application as well as theApplication controller. Start by adjusting the template app/views/index.scala.html to look as follows:

@(mentionsCount: Int)

@main("Reactive mentions") {

    <p>You have been mentioned @mentionsCount times in the past days</p>


Next, edit the Application controller located in app/controllers/Application.scala:

package controllers

import java.sql.Timestamp
import javax.inject.Inject

import database.DB
import org.joda.time.DateTime
import play.api._
import play.api.mvc._

class Application @Inject() (db: DB) extends Controller {

  def index = Action.async { implicit request =>

    import generated.Tables._
    import org.jooq.impl.DSL._

    db.query { sql =>
      val mentionsCount =
       .where( Timestamp(




This time, we are using the query method that we have built in our DB helper. Since the result of this operation is a Future, we need to use the Action.async method of the Action which has a signature of the kind Request => Future[Response]. The execution of this query is performed by the custom ExecutionContext that we have set up for database operations and does not impede on the default ExecutionContext of the Play framework itself.

In case anything were to go wrong and the database operations were to hang on forever on the threads offered by that context, the rest of the application would not be affected (this principle is called “bulkheading” and is described a bit more in detail in Chapter 5 of Reactive Web Applications).


In this series we have explored the “Why?” of reactive applications and of asynchronous programming. In particular, we have talked about Futures and Actors, two powerful abstractions that make asynchronous programming easier to think about.

Most relational databases do not have asynchronous drivers available yet and even if there are some projects aiming at it I think it will still take some time before we’ll have a standard that will hopefully be implemented by many vendors. In the meanwhile we have seen that we can use a custom ExecutionContext in order to isolate otherwise blocking database operations.


If you liked this series and are interested in learning more on the topic, consider checking out my book which provides an introductio to building reactive web applications on the JVM. Futures are covered in Chapter 5, Actors in Chapter 6 and Database Access in Chapter 7.

Read on

Read the previous chapters of this series:

10 Java Articles Everyone Must Read

One month ago, we’ve published a list of 10 SQL Articles Everyone Must Read. A list of articles that we believe would add exceptional value to our readers on the jOOQ blog. The jOOQ blog is a blog focusing on both Java and SQL, so it is only natural that today, one month later, we’re publishing an equally exciting list of 10 Java articles everyone must read.

Note that by “must read”, we may not specifically mean the particular linked article only, but also other works from the same authors, who have been regular bloggers over the past years and never failed to produce new interesting content!

Here goes…

1. Brian Goetz: “Stewardship: the Sobering Parts”

The first blog post is actually not a blog post but a recording of a very interesting talk by Brian Goetz on Oracle’s stewardship of Java. On the jOOQ blog, we’ve been slightly critical about 1-2 features of the Java language in the past, e.g. when comparing it to Scala, or Ceylon.

Brian makes good points about why it would not be a good idea for Java to become just as “modern” as quickly as other languages. A must-watch for every Java developer (around 1h)

2. Aleksey Shipilёv: The Black Magic of (Java) Method Dispatch

In recent years, the JVM has seen quite a few improvements, including invokedynamic that arrived in Java 7 as a prerequisite for Java 8 lambdas, as well as a great tool for other, more dynamic languages built on top of the JVM, such as Nashorn.

invokedynamic is only a small, “high level” puzzle piece in the advanced trickery performed by the JVM. What really happens under the hood when you call methods? How are they resolved, optimised by the JIT? Aleksey’s article sub-title reveals what the article is really about:

“Everything you wanted to know about Black Deviously Surreptitious Magic in low-level performance engineering”

Definitely not a simple read, but a great post to learn about the power of the JVM.

Read Aleksey’s “The Black Magic of (Java) Method Dispatch

3. Oliver White: Java Tools and Technologies Landscape for 2014

We’re already in 2015, but this report by Oliver White (at the time head of ZeroTurnaround’s RebelLabs) had been exceptionally well executed and touches pretty much everything related to the Java ecosystem.

Read Oliver’s “Java Tools and Technologies Landscape for 2014

4. Peter Lawrey: Java Lambdas and Low Latency

When Aleksey has introduced us to some performance semantics in the JVM, Peter takes this one step further, talking about low latency in Java 8. We could have picked many other useful little blog posts from Peter’s blog, which is all about low-latency, high performance computing on the JVM, sometimes even doing advanced off-heap trickery.

Read Peter’s “Java Lambdas and Low Latency

5. Nicolai Parlog: Everything You Need To Know About Default Methods

Nicolai is a newcomer in the Java blogosphere, and a very promising one, too. His well-researched articles go in-depth about some interesting facts related to Java 8, digging out old e-mails from the expert group’s mailing list, explaining the decisions they made to conclude with what we call Java 8 today.

Read Nicolai’s “Everything You Need To Know About Default Methods

6. Lukas Eder: 10 Things You Didn’t Know About Java

This list wouldn’t be complete without listing another list that we wrote ourselves on the jOOQ blog. Java is an old beast with 20 years of history this year in 2015. This old beast has a lot of secrets and caveats that many people have forgotten or never thought about. We’ve uncovered them for you:

Read Lukas’s “10 Things You Didn’t Know About Java

7. Edwin Dalorzo: Why There Is Interface Pollution in Java 8

Edwin has been responding to our own blog posts a couple of times in the past with very well researched and thoroughly thought through articles, in particular about Java 8 related features, e.g. comparing Java 8 Streams with LINQ (something that we’ve done ourselves, as well).

This particular article explains why there are so many different and differently named functional interfaces in Java 8.

Read Edwin’s “Why There Is Interface Pollution in Java 8

8. Vlad Mihalcea: How Does PESSIMISTIC_FORCE_INCREMENT Lock Mode Work

When Java talks to databases, many people default to using Hibernate for convenience (see also 3. Oliver White: Java Tools and Technologies Landscape for 2014). Hibernate’s main vision, however, is not to add convenience – you can get that in many other ways as well. Hibernate’s main vision is to provide powerful means of navigating and persisting an object graph representation of your RDBMS’s data model, including various ways of locking.

Vlad is an extremely proficient Hibernate user, who has a whole blog series on how Hibernate works going. We’ve picked a recent, well-researched article about locking, but we strongly suggest you read the other articles as well:

Read Vlad’s “How Does PESSIMISTIC_FORCE_INCREMENT Lock Mode Work

9. Petri Kainulainen: Writing Clean Tests

This isn’t a purely Java-related blog post, although it is written from the perspective of a Java developer. Modern development involves testing – automatic testing – and lots of it. Petri has written an interesting blog series about writing clean tests in Java – you shouldn’t miss his articles!

Read Petri’s “Writing Clean Tests

10. Eugen Paraschiv: Java 8 Resources Collection

If you don’t already have at least 9 open tabs with interesting stuff to read after this list, get ready for a browser tab explosion! Eugen Paraschiv who maintains has been collecting all sorts of very interesting resources related to Java 8 in a single link collection. You should definitely bookmark this collection and check back frequently for interesting changes:

Read Eugen’s “Java 8 Resources Collection

Many other articles

There are, of course, many other very good articles providing deep insight into useful Java tricks. If you find you’ve encountered an article that would nicely complement this list, please leave a link and description in the comments section. Future readers will appreciate the additional insight.

jOOQ vs. Slick – Pros and Cons of Each Approach

Every framework introduces a new compromise. A compromise that is introduced because the framework makes some assumptions about how you’d like to interact with your software infrastructure.

An example of where this compromise has struck users recently is the discussion “Are Slick queries generally isomorphic to the SQL queries?“. And, of course, the answer is: No. What appears to be a simple Slick query:

val salesJoin = sales 
      join purchasers 
      join products 
      join suppliers on {
  case (((sale, purchaser), product), supplier) =>
    sale.productId === &&
    sale.purchaserId === &&
    product.supplierId ===

… turns into a rather large monster with tons of derived tables that are totally unnecessary, given the original query (formatting is mine):

select x2.x3, x4.x5, x2.x6, x2.x7 
from (
    select x8.x9 as x10, 
           x8.x11 as x12, 
           x8.x13 as x14, 
           x8.x15 as x7, 
           x8.x16 as x17, 
           x8.x18 as x3, 
           x8.x19 as x20, 
           x21.x22 as x23, 
           x21.x24 as x25, 
           x21.x26 as x6 
    from (
        select x27.x28 as x9,
               x27.x29 as x11, 
               x27.x30 as x13, 
               x27.x31 as x15, 
               x32.x33 as x16, 
               x32.x34 as x18, 
               x32.x35 as x19 
        from (
            select x36."id" as x28, 
                   x36."purchaser_id" as x29, 
                   x36."product_id" as x30, 
                   x36."total" as x31 
            from "sale" x36
        ) x27 
        inner join (
            select x37."id" as x33, 
                   x37."name" as x34, 
                   x37."address" as x35 
	    from "purchaser" x37
        ) x32 
        on 1=1
    ) x8 
    inner join (
        select x38."id" as x22, 
               x38."supplier_id" as x24, 
               x38."name" as x26 
        from "product" x38
    ) x21
    on 1=1
) x2 
inner join (
    select x39."id" as x40, 
           x39."name" as x5, 
           x39."address" as x41 
    from "supplier" x39
) x4 
on ((x2.x14 = x2.x23) 
and (x2.x12 = x2.x17)) 
and (x2.x25 = x4.x40) 
where x2.x7 >= ?

Christopher Vogt, a former Slick maintainer and still actively involved member of the Slick community, explains the above in the following words:

This means that Slick relies on your database’s query optimizer to be able to execute the sql query that Slick produced efficiently. Currently that is not always the case in MySQL

One of the main ideas behind Slick, according to Christopher, is:

Slick is not a DSL that allows you to build exactly specified SQL strings. Slick’s Scala query translation allows for re-use and composition and using Scala as the language to write your queries. It does not allow you to predict the exact sql query, only the semantics and the rough structure.

Slick vs. jOOQ

Since Christopher later on also compared Slick with jOOQ, I allowed myself to chime in and to add my two cents:

From a high level (without actual Slick experience) I’d say that Slick and jOOQ embrace compositionality equally well. I’ve seen crazy queries of several 100s of lines of [jOOQ] SQL in customer code, composed over several methods. You can do that with both APIs.

On the other hand, as Chris said: Slick has a focus on Scala collections, jOOQ on SQL tables.

  • From a conceptual perspective (= in theory), this focus shouldn’t matter.
  • From a type safety perspective, Scala collections are easier to type-check than SQL tables and queries because SQL as a language itself is rather hard to type-check given that the semantics of various of the advanced SQL clauses alter type configurations rather implicitly (e.g. outer joins, grouping sets, pivot clauses, unions, group by, etc.).
  • From a practical perspective, SQL itself is only an approximation of the original relational theories and has attained a life of its own. This may or may not matter to you.

I guess in the end it really boils down to whether you want to reason about Scala collections (queries are better integrated / more idiomatic with your client code) or about SQL tables (queries are better integrated / more idiomatic with your database).

At this point, I’d like to add another two cents to the discussion. Customers don’t buy the product that you’re selling. They never do. In the case of Hibernate, customers and users were hoping to be able to forget SQL forever. The opposite is true. As Gavin King himself (the creator of Hibernate) had told me:


Because customers and users had never listened to Gavin (and to other ORM creators), we now have what many call the object-relational impedance mismatch. A lot of unjustified criticism has been expressed against Hibernate and JPA, APIs which are simply too popular for the limited scope they really cover.

With Slick (or C#’s LINQ, for that matter), a similar mismatch is impeding integrations, if users abuse these tools for what they believe to be a replacement for SQL. Slick does a great job at modelling the relational model directly in the Scala language. This is wonderful if you want to reason about relations just like you reason about collections. But it is not a SQL API. To illustrate how difficult it is to overcome these limitations, you can browse the issue tracker or user group to learn about:

We’ll simply call this:

The Functional-Relational Impedance Mismatch

SQL is much more

Markus Winand (the author of the popular SQL Performance Explained) has recently published a very good presentation about “modern SQL”, an idea that we fully embrace at jOOQ:

We believe that APIs that have been trying to hide the SQL language from general purpose languages like Java, Scala, C# are missing out on a lot of the very nice features that can add tremendous value to your application. jOOQ is an API that fully embraces the SQL language, with all its awesome features (and with all its quirks). You obviously may or may not agree with that.

We’ll leave this article open ended, hoping you’ll chime in to discuss the benefits and caveats of each approach. Of staying close to Scala vs. staying close to SQL.

As a small teaser, however, I’d like to announce a follow-up article showing that there is no such thing as an object-relational impedance mismatch. You (and your ORM) are just not using SQL correctly. Stay tuned!

The Inconvenient Truth About Dynamic vs. Static Typing

Sometimes there are these moments of truth. They happen completely unexpectedly, such as when I read this tweet:

David is the author of the lesser-known but not at all lesser-interesting Whiley programming language, a language that has a lot of static type checking built in it. One of the most interesting features of the Whiley language is flow sensitive typing (sometimes also simply called flow typing), which is mostly useful when used along with union types. An example from the getting started guide

function indexOf(string str, char c) => null|int:

function split(string str, char c) => [string]:
  var idx = indexOf(str,c)

  // idx has type null|int
  if idx is int:

    // idx now has type int
    string below = str[0..idx]
    string above = str[idx..]
    return [below,above]

    // idx now has type null
    return [str] // no occurrence

Remember, other languages like Ceylon also know flow-sensitive typing, and even Java does to a certain extent, because Java has union types, too!

try {
catch (SQLException | IOException e) {
    if (e instanceof SQLException)
        doSomething((SQLException) e);
        doSomethingElse((IOException) e);

Granted, Java’s flow-sensitive typing is explicit and verbose. We could expect the Java compiler to infer all the types. The following should type-check and compile just as well:

try {
catch (SQLException | IOException e) {
    if (e instanceof SQLException)
        // e is guaranteed to be of type SQLException
        // e is guaranteed to be of type IOException

Flow typing or flow sensitive typing means that the compiler can infer the only possible type from the control flow of the surrounding program. It is a relatively new concept in modern languages like Ceylon, and it makes static typing extremely powerful, especially if the language also supports sophisticated type inference via var or val keywords!

JavaScript static typing with Flow

Let’s get back to David’s Tweet and have a look at what the article said about Flow:

The presence of a use of length with a null argument informs Flow that there should be a null check in that function. This version does type-check:

function length(x) {
  if (x) {
    return x.length;
  } else {
    return 0;

var total = length('Hello') + length(null);

Flow is able to infer that x cannot be null inside the if body.

That’s quite cunning. A similar upcoming feature can be observed in Microsoft’s TypeScript. But Flow is different (or claims to be different) from TypeScript. The essence of Facebook Flow can be seen in this paragraph from the official Flow announcement:

Flow’s type checking is opt-in — you do not need to type check all your code at once. However, underlying the design of Flow is the assumption that most JavaScript code is implicitly statically typed; even though types may not appear anywhere in the code, they are in the developer’s mind as a way to reason about the correctness of the code. Flow infers those types automatically wherever possible, which means that it can find type errors without needing any changes to the code at all. On the other hand, some JavaScript code, especially frameworks, make heavy use of reflection that is often hard to reason about statically. For such inherently dynamic code, type checking would be too imprecise, so Flow provides a simple way to explicitly trust such code and move on. This design is validated by our huge JavaScript codebase at Facebook: Most of our code falls in the implicitly statically typed category, where developers can check their code for type errors without having to explicitly annotate that code with types.

Let this sink in

most JavaScript code is implicitly statically typed


JavaScript code is implicitly statically typed


Programmers love type systems. Programmers love to reason formally about their data types and put them in narrow constraints to be sure the program is correct. That’s the whole essence of static typing: To make less mistakes because of well-designed data structures.

People also love to put their data structures in well-designed forms in databases, which is why SQL is so popular and “schema-less” databases will not gain more market share. Because in fact, it’s the same story. You still have a schema in a “schema-less” database, it’s just not type checked and thus leaves you all the burden of guaranteeing correctness.

On a side note: Obviously, some NoSQL vendors keep writing these ridiculous blog posts to desperately position their products, claiming that you really don’t need any schema at all, but it’s easy to see through that marketing gag. True need for schemalessness is as rare as true need for dynamic typing. In other words, when is the last time you’ve written a Java program and called every method via reflection? Exactly…

But there’s one thing that statically typed languages didn’t have in the past and that dynamically typed languages did have: Means to circumvent verbosity. Because while programmers love type systems and type checking, programmers do not love typing (as in typing on the keyboard).

Verbosity is the killer. Not static typing

Consider the evolution of Java:

Java 4

List list = new ArrayList();

// Eek. Why do I even need this Iterator?
Iterator iterator = list.iterator();
while (iterator.hasNext()) {
    // Gee, I *know* I only have strings. Why cast?
    String value = (String);

    // [...]

Java 5

// Agh, I have to declare the generic type twice!
List<String> list = new ArrayList<String>();

// Much better, but I have to write String again?
for (String value : list) {
    // [...]

Java 7

// Better, but I still need to write down two
// times the "same" List type
List<String> list = new ArrayList<>();

for (String value : list) {
    // [...]

Java 8

// We're now getting there, slowly
Stream.of("abc", "xyz").forEach(value -> {
    // [...]

On a side-note, yes, you could’ve used Arrays.asList() all along.

Java 8 is still far from perfect, but things are getting better and better. The fact that I finally do not have to declare a type anymore in a lambda argument list because it can be inferred by the compiler is something really important for productivity and adoption.

Consider the equivalent of a lambda pre-Java 8 (if we had Streams before):

// Yes, it's a Consumer, fine. And yes it takes Strings
Stream.of("abc", "xyz").forEach(new Consumer<String>(){
    // And yes, the method is called accept (who cares)
    // And yes, it takes Strings (I already say so!?)
    public void accept(String value) {
        // [...]

Now, if we’re comparing the Java 8 version with a JavaScript version:

["abc", "xyz"].forEach(function(value) {
    // [...]

We have almost reached as little verbosity as the functional, dynamically typed language that is JavaScript (I really wouldn’t mind those missing list and map literals in Java), with the only difference that we (and the compiler) know that value is of type String. And we know that the forEach() method exists. And we know that forEach() takes a function with one argument.

In the end of the day, things seem to boil down to this:

Dynamically typed languages like JavaScript and PHP have become popular mainly because they “just ran”. You didn’t have to learn all the “heavy” syntax that classic statically typed languages required (just think of Ada and PL/SQL!). You could just start writing your program. Programmers “knew” that the variables would contain strings, there’s no need to write it down. And that’s true, there’s no need to write everything down!

Consider Scala (or C#, Ceylon, pretty much any modern language):

val value = "abc"

What else can it be, other than a String?

val list = List("abc", "xyz")

What else can it be, other than a List[String]?

Note that you can still explicitly type your variables if you must – there are always those edge cases:

val list : List[String] = List[String]("abc", "xyz")

But most of the syntax is “opt-in” and can be inferred by the compiler.

Dynamically typed languages are dead

The conclusion of all this is that once syntactic verbosity and friction is removed from statically typed languages, there is absolutely no advantage in using a dynamically typed language. Compilers are very fast, deployment can be fast too, if you use the right tools, and the benefit of static type checking is huge. (don’t believe it? read this article)

As an example, SQL is also a statically typed language where much of the friction is still created by syntax. Yet, many people believe that it is a dynamically typed language, because they access SQL through JDBC, i.e. through type-less concatenated Strings of SQL statements. If you were writing PL/SQL, Transact-SQL, or embedded SQL in Java with jOOQ, you wouldn’t think of SQL this way and you’d immediately appreciate the fact that your PL/SQL, Transact-SQL, or your Java compiler would type-check all of your SQL statements.

So, let’s abandon this mess that we’ve created because we’re too lazy to type all the types (pun). Happy typing!

And if you’re reading this, Java language expert group members, please do add var and val, as well as flow-sensitive typing to the Java language. We’ll love you forever for this, promised!

When the Java 8 Streams API is not Enough

Java 8 was – as always – a release of compromises and backwards-compatibility. A release where the JSR-335 expert group might not have agreed upon scope or feasibility of certain features with some of the audience. See some concrete explanations by Brian Goetz about why …

But today we’re going to focus on the Streams API’s “short-comings”, or as Brian Goetz would probably put it: things out of scope given the design goals.

Parallel Streams?

Parallel computing is hard, and it used to be a pain. People didn’t exactly love the new (now old) Fork / Join API, when it was first shipped with Java 7. Conversely, and clearly, the conciseness of calling Stream.parallel() is unbeatable.

But many people don’t actually need parallel computing (not to be confused with multi-threading!). In 95% of all cases, people would have probably preferred a more powerful Streams API, or perhaps a generally more powerful Collections API with lots of awesome methods on various Iterable subtypes.

Changing Iterable is dangerous, though. Even a no-brainer as transforming an Iterable into a Stream via a potential method seems to risk opening pandora’s box!.

Sequential Streams!

So if the JDK doesn’t ship it, we create it ourselves!

Streams are quite awesome per se. They’re potentially infinite, and that’s a cool feature. Mostly – and especially with functional programming – the size of a collection doesn’t really matter that much, as we transform element by element using functions.

If we admit Streams to be purely sequential, then we could have any of these pretty cool methods as well (some of which would also be possible with parallel Streams):

  • cycle() – a guaranteed way to make every stream infinite
  • duplicate() – duplicate a stream into two equivalent streams
  • foldLeft() – a sequential and non-associative alternative to reduce()
  • foldRight() – a sequential and non-associative alternative to reduce()
  • limitUntil() – limit the stream to those records before the first one to satisfy a predicate
  • limitWhile() – limit the stream to those records before the first one not to satisfy a predicate
  • maxBy() – reduce the stream to the maximum mapped value
  • minBy() – reduce the stream to the minimum mapped value
  • partition() – partition a stream into two streams, one satisfying a predicate and the other not satisfying the same predicate
  • reverse() – produce a new stream in inverse order
  • skipUntil() – skip records until a predicate is satisified
  • skipWhile() – skip records as long as a predicate is satisfied
  • slice() – take a slice of the stream, i.e. combine skip() and limit()
  • splitAt() – split a stream into two streams at a given position
  • unzip() – split a stream of pairs into two streams
  • zip() – merge two streams into a single stream of pairs
  • zipWithIndex() – merge a stream with its corresponding stream of indexes into a single stream of pairs

jOOλ’s new Seq type does all that

All of the above is part of jOOλ. jOOλ (pronounced “jewel”, or “dju-lambda”, also written jOOL in URLs and such) is an ASL 2.0 licensed library that emerged from our own development needs when implementing jOOQ integration tests with Java 8. Java 8 is exceptionally well-suited for writing tests that reason about sets, tuples, records, and all things SQL.

But the Streams API just slightly feels insufficient, so we have wrapped JDK’s Streams into our own Seq type (Seq for sequence / sequential Stream):

// Wrap a stream in a sequence
Seq<Integer> seq1 = seq(Stream.of(1, 2, 3));

// Or create a sequence directly from values
Seq<Integer> seq2 = Seq.of(1, 2, 3);

We’ve made Seq a new interface that extends the JDK Stream interface, so you can use Seq fully interoperably with other Java APIs – leaving the existing methods unchanged:

public interface Seq<T> extends Stream<T> {

     * The underlying {@link Stream} implementation.
    Stream<T> stream();
	// [...]

Now, functional programming is only half the fun if you don’t have tuples. Unfortunately, Java doesn’t have built-in tuples and while it is easy to create a tuple library using generics, tuples are still second-class syntactic citizens when comparing Java to Scala, for instance, or C# and even VB.NET.


jOOλ also has tuples

We’ve run a code-generator to produce tuples of degree 1-8 (we might add more in the future, e.g. to match Scala’s and jOOQ’s “magical” degree 22).

And if a library has such tuples, the library also needs corresponding functions. The essence of these TupleN and FunctionN types is summarised as follows:

public class Tuple3<T1, T2, T3>
	Comparable<Tuple3<T1, T2, T3>>, 
	Serializable, Cloneable {
    public final T1 v1;
    public final T2 v2;
    public final T3 v3;
	// [...]


public interface Function3<T1, T2, T3, R> {

    default R apply(Tuple3<T1, T2, T3> args) {
        return apply(args.v1, args.v2, args.v3);

    R apply(T1 v1, T2 v2, T3 v3);

There are many more features in Tuple types, but let’s leave them out for today.

On a side note, I’ve recently had an interesting discussion with Gavin King (the creator of Hibernate) on reddit. From an ORM perspective, Java classes seem like a suitable implementation for SQL / relational tuples, and they are indeed. From an ORM perspective.

But classes and tuples are fundamentally different, which is a very subtle issue with most ORMs – e.g. as explained here by Vlad Mihalcea.

Besides, SQL’s notion of row value expressions (i.e. tuples) is quite different from what can be modelled with Java classes. This topic will be covered in a subsequent blog post.

Some jOOλ examples

With the aforementioned goals in mind, let’s see how the above API can be put to work by example:


// (tuple(1, "a"), tuple(2, "b"), tuple(3, "c"))
Seq.of(1, 2, 3).zip(Seq.of("a", "b", "c"));

// ("1:a", "2:b", "3:c")
Seq.of(1, 2, 3).zip(
    Seq.of("a", "b", "c"), 
    (x, y) -> x + ":" + y

// (tuple("a", 0), tuple("b", 1), tuple("c", 2))
Seq.of("a", "b", "c").zipWithIndex();

// tuple((1, 2, 3), (a, b, c))
    tuple(1, "a"),
    tuple(2, "b"),
    tuple(3, "c")

This is already a case where tuples have become very handy. When we “zip” two streams into one, we want a wrapper value type that combines both values. Classically, people might’ve used Object[] for quick-and-dirty solutions, but an array doesn’t indicate attribute types or degree.

Unfortunately, the Java compiler cannot reason about the effective bound of the <T> type in Seq<T>. This is why we can only have a static unzip() method (instead of an instance one), whose signature looks like this:

// This works
static <T1, T2> Tuple2<Seq<T1>, Seq<T2>> 
    unzip(Stream<Tuple2<T1, T2>> stream) { ... }
// This doesn't work:
interface Seq<T> extends Stream<T> {
    Tuple2<Seq<???>, Seq<???>> unzip();

Skipping and limiting

// (3, 4, 5)
Seq.of(1, 2, 3, 4, 5).skipWhile(i -> i < 3);

// (3, 4, 5)
Seq.of(1, 2, 3, 4, 5).skipUntil(i -> i == 3);

// (1, 2)
Seq.of(1, 2, 3, 4, 5).limitWhile(i -> i < 3);

// (1, 2)
Seq.of(1, 2, 3, 4, 5).limitUntil(i -> i == 3);

Other functional libraries probably use different terms than skip (e.g. drop) and limit (e.g. take). It doesn’t really matter in the end. We opted for the terms that are already present in the existing Stream API: Stream.skip() and Stream.limit()


// "abc"
Seq.of("a", "b", "c").foldLeft("", (u, t) -> t + u);

// "cba"
Seq.of("a", "b", "c").foldRight("", (t, u) -> t + u);

The Stream.reduce() operations are designed for parallelisation. This means that the functions passed to it must have these important attributes:

But sometimes, you really want to “reduce” a stream with functions that do not have the above attributes, and consequently, you probably don’t care about the reduction being parallelisable. This is where “folding” comes in.

A nice explanation about the various differences between reducing and folding (in Scala) can be seen here.


// tuple((1, 2, 3), (1, 2, 3))
Seq.of(1, 2, 3).duplicate();

// tuple((1, 3, 5), (2, 4, 6))
Seq.of(1, 2, 3, 4, 5, 6).partition(i -> i % 2 != 0)

// tuple((1, 2), (3, 4, 5))
Seq.of(1, 2, 3, 4, 5).splitAt(2);

The above functions all have one thing in common: They operate on a single stream in order to produce two new streams, that can be consumed independently.

Obviously, this means that internally, some memory must be consumed to keep buffers of partially consumed streams. E.g.

  • duplication needs to keep track of all values that have been consumed in one stream, but not in the other
  • partitioning needs to fast forward to the next value that satisfies (or doesn’t satisfy) the predicate, without losing all the dropped values
  • splitting might need to fast forward to the split index

For some real functional fun, let’s have a look at a possible splitAt() implementation:

static <T> Tuple2<Seq<T>, Seq<T>> 
splitAt(Stream<T> stream, long position) {
    return seq(stream)
          .partition(t -> t.v2 < position)
          .map((v1, v2) -> tuple(
     -> t.v1),
     -> t.v1)

… or with comments:

static <T> Tuple2<Seq<T>, Seq<T>> 
splitAt(Stream<T> stream, long position) {
    // Add jOOλ functionality to the stream
    // -> local Type: Seq<T>
    return seq(stream)
    // Keep track of stream positions
    // with each element in the stream
    // -> local Type: Seq<Tuple2<T, Long>>
    // Split the streams at position
    // -> local Type: Tuple2<Seq<Tuple2<T, Long>>,
    //                       Seq<Tuple2<T, Long>>>
          .partition(t -> t.v2 < position)
    // Remove the indexes from zipWithIndex again
    // -> local Type: Tuple2<Seq<T>, Seq<T>>
          .map((v1, v2) -> tuple(
     -> t.v1),
     -> t.v1)

Nice, isn’t it? A possible implementation for partition(), on the other hand, is a bit more complex. Here trivially with Iterator instead of the new Spliterator:

static <T> Tuple2<Seq<T>, Seq<T>> partition(
        Stream<T> stream, 
        Predicate<? super T> predicate
) {
    final Iterator<T> it = stream.iterator();
    final LinkedList<T> buffer1 = new LinkedList<>();
    final LinkedList<T> buffer2 = new LinkedList<>();

    class Partition implements Iterator<T> {

        final boolean b;

        Partition(boolean b) {
            this.b = b;

        void fetch() {
            while (buffer(b).isEmpty() && it.hasNext()) {
                T next =;

        LinkedList<T> buffer(boolean test) {
            return test ? buffer1 : buffer2;

        public boolean hasNext() {
            return !buffer(b).isEmpty();

        public T next() {
            return buffer(b).poll();

    return tuple(
        seq(new Partition(true)), 
        seq(new Partition(false))

I’ll let you do the exercise and verify the above code.

Get and contribute to jOOλ, now!

All of the above is part of jOOλ, available for free from GitHub. There is already a partially Java-8-ready, full-blown library called functionaljava, which goes much further than jOOλ.

Yet, we believe that all what’s missing from Java 8’s Streams API is really just a couple of methods that are very useful for sequential streams.

In a previous post, we’ve shown how we can bring lambdas to String-based SQL using a simple wrapper for JDBC (of course, we still believe that you should use jOOQ instead).

Today, we’ve shown how we can write awesome functional and sequential Stream processing very easily, with jOOλ.

Stay tuned for even more jOOλ goodness in the near future (and pull requests are very welcome, of course!)