Announcing Krush 1.0

You may know Nussknacker, which is our open-source tool for scenario authoring, but we have a bunch of other projects we want to share with the community. One of these is Krush, which is our attempt to solve data access in Kotlin using the Exposed framework underneath. It is now 3 years in development and is mostly feature-complete, so we decided that this could be a good time to release the 1.0 version. 🎉🎉🎉

yeah, nussknacker is cool, but have you heard of krush?

This release is mostly infrastructure-centric, we introduced support for Kotlin 1.7 and switched our build scripts to Kotlin Gradle. You can read more details in the Changelog.

In case you want to give it a try, a good resource could be project’s README, an introductory post from our blog and some example
repositories if you’d prefer to just dig into code.

Let us know about your thoughts on GitHub or ping us on Twitter!

You May Also Like

Clojure web development – state of the art

It’s now more than a year that I’m getting familiar with Clojure and the more I dive into it, the more it becomes the language. Once you defeat the “parentheses fear”, everything else just makes the difference: tooling, community, good engineering practices. So it’s now time for me to convince others. In this post I’ll try to walktrough a simple web application from scratch to show key tools and libraries used to develop with Clojure in late 2015.

Note for Clojurians: This material is rather elementary and may be useful for you if you already know Clojure a bit but never did anything bigger than hello world application.

Note for Java developers: This material shows how to replace Spring, Angular, grunt, live-reload with a bunch of Clojure tools and libraries and a bit of code.

The repo with final code and individual steps is here.

Bootstrap

I think all agreed that component is the industry standard for managing lifecycle of Clojure applications. If you are a Java developer you may think of it as a Spring (DI) replacement - you declare dependencies between “components” which are resolved on “system” startup. So you just say “my component needs a repository/database pool” and component library “injects” it for you.

To keep things simple I like to start with duct web app template. It’s a nice starter component application following the 12-factor philosophy. So let’s start with it:

lein new duct clojure-web-app +example

The +example parameter tells duct to create an example endpoint with HTTP routes - this would be helpful. To finish bootstraping run lein setup inside clojure-web-app directory.

Ok, let’s dive into the code. Component and injection related code should be in system.clj file:

(defn new-system [config]
  (let [config (meta-merge base-config config)]
    (-> (component/system-map
         :app  (handler-component (:app config))
         :http (jetty-server (:http config))
         :example (endpoint-component example-endpoint))
        (component/system-using
         {:http [:app]
          :app  [:example]
          :example []}))))

In the first section you instantiate components without dependencies, which are resolved in the second section. So in this example, “http” component (server) requires “app” (application abstraction), which in turn is injected with “example” (actual routes). If your component needs others, you just can get then by names (precisely: by Clojure keywords).

To start the system you must fire a REPL - interactive environment running within context of your application:

lein repl

After seeing prompt type (go). Application should start, you can visit http://localhost:3000 to see some example page.

A huge benefit of using component approach is that you get fully reloadable application. When you change literally anything - configuration, endpoints, implementation, you can just type (reset) in REPL and your application is up-to-date with the code. It’s a feature of the language, no JRebel, Spring-reloaded needed.

Adding REST endpoint

Ok, in the next step let’s add some basic REST endpoint returning JSON. We need to add 2 dependencies in project.clj file:

:dependencies
 ...
  [ring/ring-json "0.3.1"]
  [cheshire "5.1.1"]

Ring-json adds support for JSON for your routes (in ring it’s called middleware) and cheshire is Clojure JSON parser (like Jackson in Java). Modifying project dependencies if one of the few tasks that require restarting the REPL, so hit CTRL-C and type lein repl again.

To configure JSON middleware we have to add wrap-json-body and wrap-json-response just before wrap-defaults in system.clj:

(:require 
 ...
 [ring.middleware.json :refer [wrap-json-body wrap-json-response]])

(def base-config
   {:app {:middleware [[wrap-not-found :not-found]
                      [wrap-json-body {:keywords? true}]
                      [wrap-json-response]
                      [wrap-defaults :defaults]]

And finally, in endpoint/example.clj we must add some route with JSON response:

(:require 
 ...
 [ring.util.response :refer [response]]))

(defn example-endpoint [config]
  (routes
    (GET "/hello" [] (response {:hello "world"}))
    ...

Reload app with (reset) in REPL and test new route with curl:

curl -v http://localhost:3000/hello

< HTTP/1.1 200 OK
< Date: Tue, 15 Sep 2015 21:17:37 GMT
< Content-Type: application/json; charset=utf-8
< Set-Cookie: ring-session=37c337fb-6bbc-4e65-a060-1997718d03e0;Path=/;HttpOnly
< X-XSS-Protection: 1; mode=block
< X-Frame-Options: SAMEORIGIN
< X-Content-Type-Options: nosniff
< Content-Length: 151
* Server Jetty(9.2.10.v20150310) is not blacklisted
< Server: Jetty(9.2.10.v20150310)
<
* Connection #0 to host localhost left intact
{"hello": "world"}

It works! In case of any problems you can find working version in this commit.

Adding frontend with figwheel

Coding backend in Clojure is great, but what about the frontend? As you may already know, Clojure could be compiled not only to JVM bytecode, but also to Javascript. This may sound familiar if you used e.g. Coffescript. But ClojureScript philosophy is not only to provide some syntax sugar, but improve your development cycle with great tooling and fully interactive development. Let’s see how to achieve it.

The best way to introduce ClojureScript to a project is figweel. First let’s add fighweel plugin and configuration to project.clj:

:plugins
   ...
   [lein-figwheel "0.3.9"]

And cljsbuild configuration:

:cljsbuild
    {:builds [{:id "dev"
               :source-paths ["src-cljs"]
               :figwheel true
               :compiler {:main       "clojure-web-app.core"
                          :asset-path "js/out"
                          :output-to  "resources/public/js/clojure-web-app.js"
                          :output-dir "resources/public/js/out"}}]}

In short this tells ClojureScript compiler to take sources from src-cljs with figweel support and but resulting JavaScript into resources/public/js/clojure-web-app.js file. So we need to include this file in a simple HTML page:

<!DOCTYPE html>
<head>
</head>
<body>
  <div id="main">
  </div>
  <script src="js/clojure-web-app.js" type="text/javascript"></script>
</body>
</html>

To serve this static file we need to change some defaults and add corresponding route. In system.clj change api-defaults to site-defaults both in require section and base-config function. In example.clj add following route:

(GET "/" [] (io/resource "public/index.html")

Again (reset) in REPL window should reload everything.

But where is our ClojureScript source file? Let’s create file core.cljs in src-cljs/clojure-web-app directory:

(ns ^:figwheel-always clojure-web-app.core)

(enable-console-print!)

(println "hello from clojurescript")

Open another terminal and run lein fighweel. It should compile ClojureScript and print ‘Prompt will show when figwheel connects to your application’. Open http://localhost:3000. Fighweel window should prompt:

To quit, type: :cljs/quit
cljs.user=>

Type (js/alert "hello"). Boom! If everything worked you should see and alert in your browser. Open developers console in your browser. You should see hello from clojurescript printed on the console. Change it in core.cljs to (println "fighweel rocks") and save the file. Without reloading the page your should see updated message. Figweel rocks! Again, in case of any problems, refer to this commit.

In the next post I’ll show how to fetch data from MongoDB, serve it with REST to the broser and write ReactJs/Om components to render it. Stay tuned!

Confitura 2013 afterthoughts

Confitura, the biggest free-of-charge Java conference in Europe, took place on the 6th of July in Warsaw. TouK's presence was heavy, with 5 separate talks, all chosen in call for papers, no sponsored bullshit. We were sponsoring deck chairs during the...Confitura, the biggest free-of-charge Java conference in Europe, took place on the 6th of July in Warsaw. TouK's presence was heavy, with 5 separate talks, all chosen in call for papers, no sponsored bullshit. We were sponsoring deck chairs during the...

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()
  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()
  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.