GraphQL Java 6.o

I’ve been a fan of GraphQL ever since I first tried it.  I push back against RESTful APIs to anyone that will listened (or not).  I’ve written a few post about it (GraphQL: Java Server, JavaScript Client, GraphQL: Java Schema AnnotationsGraphQL: A Java Server in Ratpack).  What I haven’t done, is stay current.  I got hooked on graphql-java at version 3.X and decided the annotations were the best way to go, and sadly the annotations development stalled and made upgrades tricky, and so I didn’t.  But it was a constant nagging itch, to upgrade, and finally I did.

This post will discuss a Ratpack server, using GraphQL-java 6.0. I should note, that as I did this work, the annotations finally release an upgrade. Doh.

GraphQL Java 6.0

I committed to upgrade. The annotations had not kept up so this meant a bit of a rewrite.  Normally I’m pretty suspicious of gratuitous annotation use.  They often mask too much of what’s really going on, and they tend to spray the information about one concern throughout your code, making it hard to locate coherent answers on a topic.  That was exactly the case here.  Leaving the annotations behind meant:

  • I had to figure out what previous magic I now had to handle on my own.
  • I had to determine just how deeply into my code they’d rooted themselves.

I tried to approach it intelligently, but in the end I went with brute force, I removed the dependency, and kept trying to build the project, ripping out references, until, while no longer working, the project built and the annotations were gone.  Then I set about fixing what I’d broken.

What Was Missing?

Basically without the annotations there were two things I needed to repair:

  • Defining the query schema
  • Wiring the queries up to the data sources

Defining Your Schema

GraphQL-java 6.0 supports a domain specific language for defining your schema known as IDL.  It’s a major win.  First, it gets your schema, which is by definition, a single concept, into one place, and makes it coherent.  Second, they didn’t go off and write “Yet Another DSL” but instead supported one that while not part of the official spec, is part of the reference implementation, and has traction in the community. Nice.

Wiring Up Your Data Sources

The best practice for this now is using the “DataFetcher” interface. The name is a bit misleading, since these aren’t just for your queries (i.e. fetching data) but also for your mutations (modifying data).  The name is weak, but the interface and it’s use is a breeze.

To the Code

I did all this work on my snippets server kata project, so for a richer example go there, but for the sake of clarity here will look at the more concise Ratpack GraphQL Server example.

The Dispatcher

This didn’t change hardly at all.  It’s still as simple as grappig a JSON map, pulling out the query and variables, and executing them:

Pretty straight forward.

Defining the Schema: IDL

So in this trivial example all I have are Company entities, defined with this bean:

And all I wanted to support was, get one, get all, save one, delete one.  So I needed to define my Company type, two queries, and two mutations. Defining this in IDL was easy:

Loading The Schema

I just tucked my schema definition into my resources folder and then:

Wiring The Data to The Schema

In GraphQL-java, the way I chose to do this is with DataFetcher implementations. So for example, to find a company, by name, from a map it would look about like:

So that’s the way to “fetch” the data, but how do you connect this to your schema? You define a “RuntimeWiring”:

And then you associate that wiring with your schema you loaded:

And Then…

Well that’s it.  You’ve:

  • Created a GraphQL dispather
  • Defined your entites
  • Defined your GraphQL schema
  • Created queries
  • Instantiated the schema, wired in the queries

Done.  Take a look at my GraphQL server in Ratpack for the complete working code.

 

 

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Travis-CI to Docker Hub

More and more of my work involved docker images.  I consume them and produce them. My standard open source project CI/CD tool chain stack is Java, Gradle, GitHub, Travis-CI, CodeCov and Bintray.  End to end free and functional.

Recently I moved my snippets server app into a docker image.  This added Docker Hub to my stack, and happily it was an easy addition because of Gradle and Travis-CI.

Setting up the Build

A quick search and review turned up the gradle-docker-plugin.  With this plugin and access to a docker engine you can create, run and push docker images easily. The docs for the plugin will walk you through how to add it to your build.gradle. Also note, to use the types in the tasks below, you’ll need proper import statements. My build.gradle is pretty clean, but I’ll walk through some details below.

Creating the Dockerfile

The plugin is pretty flexible, so the following notes are not the answer but my answer.  Rather than create a fixed Dockerfile, I create mine on the fly from gradle:

task createDockerfile(type: Dockerfile) {
    def labels = [maintainer: 'nwillc@gmail.com']

    destFile = project.file('build/docker/Dockerfile')
    from 'anapsix/alpine-java:8_server-jre_unlimited'
    label labels
    exposePort 4567
    runCommand 'mkdir -p /opt/service/db'
    volume '/opt/service/db'
    copyFile "libs/$project.name-$project.version-standalone.jar", '/opt/service'
    workingDir '/opt/service'
    defaultCommand '--port', '4567', '--store', 'H2'
    entryPoint 'java', '-Djava.awt.headless=true', '-jar', "$project.name-$project.version-standalone.jar"
}

There’s a fair bit going on there so let’s walk through it.  First off, I’m creating the Dockerfile down in my build directory.  Then I’m using the plugin to do standard Dockerfile operations like setting the base from image, creating folders, copying in artifacts, and setting up the command and entry point.  The plugin sticks pretty close to the dockerfile DSL so you should be able to pick it up easily.  It’s worth noting that because this is in gradle, I can use the groovy variables to denote things like the artifact name etc.

Creating the Image

With the task to create the Dockerfile done, building an image is trivial.

task buildImage(type: DockerBuildImage, dependsOn: [assemble, createDockerfile]) {
    inputDir = project.file("build")
    dockerFile = createDockerfile.destFile
    tag = "nwillc/$project.name:$project.version"
}

So here I just indicate where I’ll root the build, in the build folder, and grab the previously created Dockerfile, and tag the image. Running this task will create your artifact, create your Dockerfile, and build the image.

Pushing the Image

I push my images into docker hub’s free public area. So, all I need to add to my build is info about my credentials and a push task.

docker {
    registryCredentials {
        url = 'https://index.docker.io/v1/'
        username = 'nwillc'
        password = System.getenv('DOCKER_PASSWD')
        email = 'nwillc@gmail.com'
    }
}
task pushImage(type: DockerPushImage, dependsOn: buildImage) {
    imageName buildImage.tag
}

Note I grab the password from an environment variable. That keeps it out of my github repo and you can set these in a secure manner in Travis-CI.

Running the Build and Doing the Deploy

With your build.gradle ready to go, and your DOCKER_PASSWD set you can now locally do a ./gradlew pushImage and it should all work, ending up with the image in docker hub.

But now let’s get our CI/CD working. Travis-CI has all you need supported. Set the DOCKER_PASSWD in your Travis-CI account’s profile, and then add the relevant bits to your .travis.yml, here are the key elements:

sudo: required
services: docker
after_success:
  - docker login -u nwillc -p ${DOCKER_PASSWD}
  - ./gradlew pushImage

You’ll need sudo, you have to indicate you’re using the docker service, you’ll need to login to docker hub, and finally push the image after successful build.

Done

With your build.gradle, and .travis.yml enhanced, it’s done. Every push to github builds and tests and if everything is happy your docker hub image is updated.

Information Graveyard

I’m trying to learn how to write a skill for Amazon’s Alexa, taking the tried and true approach of searching for tutorials on the internet.  At this point it’s been only frustration.  I’ve found both Amazon written tutorials and third party ones.  Not a single one yet has provided instructions that correspond to the current Amazon tools.  Some are relatively recent, or at least claim to have been recently updated, but not a one has actually provided a working example.  It’s not a matter of slight differences that can be worked around, each one has had at least one step that didn’t seem to correspond to anything in the AWS console as it is today.

Keeping posts up to date is work, I realize. I’m guilty too, of leaving out of date documentation out in the wild, but I make an effort to be responsible, and I’m not expecting revenue from my posts.  How is it that even Amazon’s own tutorials are completely borked?  I tried this about two months back and it was the same story. Since then both the tutorials and AWS tools have been updated, but the new combination is no more workable than the prior.

Some products are notably bad on this point.  Amazon’s SDKs and tools are a consistent pain point. The Spring ecosystem too is bad.  JBoss a mess.  The problem also is made worse by how the developers refactor code and API.  Making changes and improvements in a way that facilitated migrations is a skill.  I wish Amazon acquired that skill.

Compromises

I hit on a really good article on the Law of Demeter. If you’re not familiar with it read that article, and if you do you may find my discussion with the author in the comments. The discussion was around the how rigidly you took the term Law.

Why Quibble The Word Law?

I code mostly in Java, classified as an object-oriented language, and I’ve coded in the OO paradigm in C++, Objective-C and Smalltalk too.  But I started in procedural (Pascal and C) and I’ve worked with functional (Lisp, SML), and smatterings of other languages with their styles too.  They all have their strong points. I’ve learned tactics and patterns from them all and when I’m encounter a situation where one applies, if the current tools can implement the tactic I use it.  I’m not saying anything astonishing here, modern tools are rarely purist in their approaches anymore.

The Law of Demeter is a good OO maxim, but if you’re writing code that handles serialized data, whether if be a RESTful service, or data store persistence, etc. you’ll likely be dealing with composited data (addresses, inside accounts, inside portfolios etc.).  Accessing portfolio.account.address.state violates the Law of Demeter. There are patterns to mitigate some of the issues here, like Data Transfer Objects, the Visitor Pattern, or Facade Pattern,  but depending on the situation some of these cures are worse than the problem.

In Summary

Keep the Law of Demeter in mind as you write/review your code. If it’s been rampantly ignored that certainly is a code smell.  But paradigm “laws” are for purists, and writing software is a pragmatic process… so… yeah… it’s a maxim.

Revisiting Immutables

A while back I looked into the immutables project for Java. It lets you define immutable objects in a very simple way.  Depending on your programming background you might not even see the role of an immutable object, but as more languages and patterns concern themselves with more rigorous and safer practices you may find the call for immutability in your Java.

When I looked into the project I was impressed, but at the time, the current build tools and IDEs struggled with the preprocessing requirements, and Java 7 didn’t complement it either.  All that has changed, and this project is now one I’m adding to my short list of favorites.

What You Get

I’m not going to go into a lot of detail here, there are plenty of good sources for that just a google away, but here’s the concept in brief.  The project lets you define an immutable object by simply adding annotations to an interface or abstract class, for example:

@Value.Immutable
publice interface Item {
  String getName();
  Set<String> getTags();
  Optional<String> getDescription();
}

With that definition in place, a preprocessor will create you:

  • A immutable class that implements your interface
  • A very clean and powerful builder implementation with slick support for collections, defaults, optionals and more.

With the example above you can start using the immutable easily:

Item item = ImmutableItem.builder()
                         .name("Veggie Pizza")
                         .addTag("mushrooms")
                         .addTag("peppers")
                         .description("A pizza with vegetables.")
                         .build();

System.out.println("Order " + item.getName() + " it's a "
                       item.getDescritpion().orElse("nice choice"));

The Mechanics

The mechanics of using the project, at least with gradle, are now as simple as:

buildscript {
    dependencies {
        classpath 'gradle.plugin.org.inferred:gradle-processors:1.2.11'
    }
}

apply plugin: 'org.inferred.processors'

dependencies {
    processor 'org.immutables:value:2.4.6'
}

And the generated code appeared in IntelliJ seamlessly!

With Java 8

Java 8 complements the project in at least a couple of ways.  Java now has its own Optional class so you don’t need to pull in another projects for that. But what I found nicer was using default methods.  The project has long (always?) supported abstract classes as well as interfaces and so there was a way to add pure implementation code to the immutables. But interfaces provide more inheritance flexibility and now with Java 8’s default methods you can get the best if both, interfaces benefits, with accompanying method implementations!

So…

I highly recommend giving the project a test run and incorporating immutables in your toolbox.

GraphQL: A Java Server in Ratpack

[The post is out of date, please read the update]

I previously wrote about an implementation of a GraphQL server in Java.  That post is showing age because the code is part of a kata project and constantly evolves.

A Concise Example

So, I’ve created a new concise example in GitHub that exemplifies using:

The example is just the code needed to manage a list of companies in memory. It implements basic CRUD operations but with an extensible pattern.

Grab The Code

It’s all covered in ~300 lines of code:

  1. A package with a GraphQL Schema, using a Query and a Mutation class
  2. A GraphQLHandler that dispatches POST requests to GraphQL
  3. An Application that creates the Ratpack server for the GraphQLHandler

The README covers how to run it, and there are a series sample requests included.

First Project With Ratpack

One of my trade skills is server side Java, implying writing services in Java.  Recently they’ve mostly been servlet based microservices. I’ve used the Spark Framework a lot, but as clean as that framework is, there’s no denying that servlets are the man behind the curtain, and you can’t avoid paying him attention any time you do anything of substance. Servlets work well enough, but they are showing their age, and so I always keep an eye open for other light weight service architectures.

Ratpack

When I saw Ratpack I decided to give it a go. It bills itself as “Simple, lean & powerful HTTP apps”.  It’s built on newer and a carefully curated selection of technologies: Java 8, Netty, Guice, asynchronicity, event driven … looked promising.

Giving it a Try

I took my standard approach to any existing evaluation, and migrated one on my kata projects over to it.  The obvious choice was my snippets service.  I created a feature branch, and damned if just a few hours later I didn’t have a version of the service that I felt was cleaner and faster, and the branch was merged to master.

Likes

What I liked about Ratpack:

  • Appeared to live up to its credo, simple, lean and powerful.
  • Seemed to produce a quick app.
  • Clearly not a servlets facade. The APIs are largely consistent and you don’t immediately hit the “and here’s where it becomes servlets” boundaries over and over.
  • Documented, both an online manual and Javadocs.

The Imperfections

Here are the rough edges from my perspective:

  • The documentation. Yup, it’s a like and a dislike. The manual is useful, but it leans towards “here’s a cool way to do this cool thing.” When I wanted to do the pedestrian “serve static content from inside a fat jar”,  I had to hit Google and hunt around various boards.
  • The Gradle syntactic sugar that magically pulls in dependencies.  I wish they just listed the dependencies needed and left it to you to include them.  I really don’t want magic in my gradle build, where some dependencies are implied, but most you need to list. I prefer less magic, a little more work, but consistency.

To Keep in Mind

I was migrating Java 8 code which had lambdas sprinkled throughout.  In at least one place things started happening out of order. I had to keep in mind that Ratpack leans towards asynchronous/reactive paradigms and that some of my lambdas where now off in other threads happening in parallel and I had to make sure they had completed before using their results.

The Bits

If you’re curious to see what the Ratpack based version of my snippets server looks like it’s in my github.