Need to make a quick json fixes – JSONPath for rescue

From time to time I have a need to do some fixes in my json data. In a world of flat files I do this with grep/sed/awk tool chain. How to handle it for JSON? Searching for a solution I came across the JSONPath. It quite mature tool (from 2007) but I haven't hear about it so I decided to share my experience with others. First of all you can try it without pain online: Full syntax is described at But also you can download python binding and run it from command line:
$ sudo apt-get install python-jsonpath-rw
$ sudo apt-get install python-setuptools
$ sudo easy_install -U jsonpath
After that you can use inside python or with simple cli wrapper:
import sys, json, jsonpath

path = sys.argv[1]

result = jsonpath.jsonpath(json.load(sys.stdin), path)
print json.dumps(result, indent=2)
… you can use it in your shell e.g. for json:
  "store": {
    "book": [
        "category": "reference",
        "author": "Nigel Rees",
        "title": "Sayings of the Century",
        "price": 8.95
        "category": "fiction",
        "author": "Evelyn Waugh",
        "title": "Sword of Honour",
        "price": 12.99
        "category": "fiction",
        "author": "Herman Melville",
        "title": "Moby Dick",
        "isbn": "0-553-21311-3",
        "price": 8.99
        "category": "fiction",
        "author": "J. R. R. Tolkien",
        "title": "The Lord of the Rings",
        "isbn": "0-395-19395-8",
        "price": 22.99
    "bicycle": {
      "color": "red",
      "price": 19.95
You can print only book nodes with price lower than 10 by:
$ jsonpath '$[?(@.price < 10)]' < books.json
    "category": "reference",
    "price": 8.95,
    "title": "Sayings of the Century",
    "author": "Nigel Rees"
    "category": "fiction",
    "price": 8.99,
    "title": "Moby Dick",
    "isbn": "0-553-21311-3",
    "author": "Herman Melville"
Have a nice JSON hacking!

Enums for scala

Scala has very limited implementation of Enumeration. Enumerated objects can't extends other classes. Partial replacement for it is to use sealed classes. You can do pattern matching on them. When you ommit some possible value you will get compiler warning for not exhaustive pattern matching. One missing feature is that you can't get sorted values of all objects extending them. You can simple got it using my (40-lines) EnumOf class from scala-enum. Examples below.


sealed abstract class Color(red: Double, green: Double, blue: Double)

object Color extends EnumOf[Color] {
case object Red extends Color(1, 0, 0)
case object Green extends Color(0, 1, 0)
case object Blue extends Color(0, 0, 1)
case object White extends Color(0, 0, 0)
case object Black extends Color(1, 1, 1)


Color.values shouldEqual List(Red, Green, Blue, White, Black)

"Blue").value shouldEqual Blue
"NotExisiting").isEmpty shouldBe true

You can also enumerate on objects nested in instances


case class DistanceFrom(srcCity: String, srcCoordinates: Coordinate) extends EnumOf[DistanceBetween] {

case object ToBerlin extends DistanceFromSrcCityTo("Berlin", Coordinate(52.5075419, 13.4251364))
case object ToNewYork extends DistanceFromSrcCityTo("New York", Coordinate(40.7033127, -73.979681))

abstract class DistanceFromSrcCityTo(val destCity: String, val destCoordinates: Coordinate) extends DistanceBetween {
override def srcCoordinates: Coordinate = DistanceFrom.this.srcCoordinates

sealed abstract class DistanceBetween {
def srcCoordinates: Coordinate

def destCity: String
def destCoordinates: Coordinate

def inKm: Int = Coordinate.distanceInKm(srcCoordinates, destCoordinates).toInt


val DistanceFromWarsaw = DistanceFrom("Warsaw", Coordinate(52.232938, 21.0611941))

DistanceFromWarsaw.ToBerlin.inKm shouldEqual
DistanceFromWarsaw.ToNewYork.inKm shouldEqual 6856 shouldEqual List(519, 6856)

micro-burn has Trello integration

After a few long evenings I've finally integrated micro-burn with Trello. All you need to run it for your Trello board is to write short configuration and run fat jar. It renders burndown chart visualising progress of cards on your board.

You can specify story points adding them in curly braces inside card title, use Scrum for Trello browser extension or define default story points number for user stories. Completed checklist items are treated as a part of work done inside card. You can manage sprints on your own: creating new, specifying start/end/name, finishing or turn on full automatic mode: sprints will be created periodically.

Sprint management in usage:

Sample for lift-ng: Micro-burn 1.0.0 released

During a last few evenings in my free time I've worked on mini-application called micro-burn. The idea of it appear from work with Agile Jira in our commercial project. This is a great tool for agile projects management. It has inline tasks edition, drag & drop board, reports and many more, but it also have a few drawbacks that turn down our team motivation.


From time to time our sprints scope is changing. It is not a big deal because we are trying to be agile :-) but Jira's burndowchart in this situation draw a peek. Because in fact that chart shows scope changes not a real burndown. It means, that chart cannot break down an x-axis if we really do more than we were planned – it always stop on at most zero. Also for better progress monitoring we've started to split our user stories to technical tasks and estimating them. Original burndowchart doesn't show points from technical tasks. I can find motivation of this – user story almost finished isn't finished at all until user can use it. But in the other hand, if we know which tasks is problematic we can do some teamwork to move it on. So I realize that it is a good opportunity to try some new approaches and tools.


I've started with lift framework. In the World of Single Page Applications, this framework has more than simple interface for serving REST services. It comes with awesome Comet support. Comet is a replacement for WebSockets that run on all browsers. It supports long polling and transparent fallback to short polling if limit of client connections exceed. In backend you can handle pushes in CometActor. For further reading take a look at Roundtrip promises But lift framework is also a kind of framework of frameworks. You can handle own abstraction of CometActors and push to client javascript that shorten up your way from server to client. So it was the trigger for author of lift-ng to make a lift with Angular integration that is build on top of lift. It provides AngularActors from which you can emit/broadcast events to scope of controller. NgModelBinders that synchronize your backend model with client scope in a few lines! I've used them to send project state (all sprints and thier details) to client and notify him about scrum board changes. My actor doing all of this hard work looks pretty small: Lift-ng also provides factories for creating of Angular services. Services could respond with futures that are transformed to Angular promises in-fly. This is all what was need to serve sprint history: And on the client side - use of service: In my opinion this two frameworks gives a huge boost in developing of web applications. You have the power of strongly typing with Scala, you can design your domain on Actors and all of this with simplicity of node.js – lack of json trasforming boilerplate and dynamic application reload.

DDD + Event Sourcing

I've also tried a few fresh approaches to DDD. I've organize domain objects in actors. There are SprintActors with encapsulate sprint aggregate root. Task changes are stored as events which are computed as a difference between two boards states. When it should be provided a history of sprint, next board states are computed from initial state and sequence of events. So I realize that the best way to keep this kind of event sourcing approach tested is to make random tests. This is a test doing random changes at board, calculating events and checking if initial state + events is equals to previously created state:

First look

Screenshot of first version:
If you want to look at this closer, check the source code or download ready to run fatjar on github.

Use asInstanceOf[T] carefully!


Scala has nice static type checking engine but from time to time there are situations when we must downcast some general object. If this casting is not possible we expect that virtual machine will throw ClassCastExeption as fast as possible. Although it is not always true. Consider code below.

Suprisingly when we run this test we will see:


Why this happens? Because type T is erasured during compile. The problem is that compiler doesn't warn about it. Method asInstanceOf[T] is treated as any other regular generic method. If we want to be noticed about type erasure we should use pattern matching:

And then during compilation we will see:

But how to fix this? We can provide implicit evidence parameter:

But we will still have no error if we cast value to generic type e.g.:

With help comes shapeless with Type safe cast. Using this approach casting will be available in compile time only when exists evidence how it is possible.


  • Use pattern matching instead of asInstanceOf[T]
  • If you are using asInstanceOf[T] make sure that target type is not erasured
  • Use ClassTag implicit evidence parameter if you are casting only to not generic types
  • Use shapeless Typeable in all other situations


Code with tests is available on GitHub

Multi phased processing in scala

Last time in our project we had to add progress bar for visualization of long time running process. Process was made of a few phases and we had to print in which phase we currently are. In first step we conclude that we need to create a class of Progress which will be passed as an implicit parameter to our service. Then we will wrap method calls be inProgress method which will notify some e.g. akka actor about phase begin and phase end.

But this approach has some disadvantages. Firstly before we start service's operation we need to init progress with count of all phases to get know ratio of progress finish. With this approach we had to add some extra counting before operation start.

If we want to keep real progress notifications the numbers of phases had to fit count of inPhase blocks. Some of phases were dynamically computed and some where omitted in case of failure validations results. This code become to be unmaintained.

We found that we need to join computation of phases with real phase processing. In this case we need to change approach from building process to building chain of phases that will run the process. Each phase will take the result of previous phase and transform it to new output. So example process will look like this:

Code giving this chain functionality looks like this:

We've used right associative operator :: for building chain of phases. "Body" of phases is piped by andThen: processPrevWrapped andThen processNext. For nil-tail we need to have a factory creating empty chain with identity "body" function.

Also if we have this kind of tool, we can modify piping code according to nature of our flow. For example if we are using scalaz.Validation we can do validating chain which will extract a success from n-step output and pass it to input of next step (like flatMap). In the other hand if n-step will return Failure, we will skip all remaining phases of validating chain.

To make building of chain more production-ready we add some extra features:
  • Chaining of chains (sth like ::: in scala Lists)
  • Transforming of input/output - for adding some "glue" code for simpler phases chaining
  • Wrapping of chains - also some "glue" code doing both input and output transformations
  • Sequencing of chains - sequenced processing of multiple phases with the same input

If you are interested in using similar approach, take a look at my github project: scala-phases-chain. If you want to integrate this tool with akka actors, simply change MultiPhasedProgress.notifyAboutStatus method to look like this:

Journal.IO 1.3 released


Just a moment ago (in February 17th) Journal.IO 1.3 has been released. Journal.IO ( is a lightweight, zero-dependency journal storage implementation written in Java. We use it in our project for storing application events (Event Sourcing pattern). It is a good, stable solution if you want to have simple in use event storage e.g. if you want to implement lightweight queuing mechanism and JMS is overhead for you.

New version resolves some bugs and improves delete operation performance. Unfortunately new version uses new log format which isn't backward compatible. Therefore we decide to write a simple migrating tool for migrate 1.2 version compatible logs to 1.3 version.


Migrator was written in groovy. It is available on github ( Also link to the tool is available from official Journal.IO homepage. To use it simply run:

oldLogsRoot is recursively scanned for logs which are migrated parallel in 5 threads (used ASYNC read mode additionally speed up this process). Migrated logs are written in the same hierarchy in newLogsRoot.

Virtual task board + info radiator

There are some posts around about various task board solutions. Besides that we use white board to sketch some designs and exchange knowledge, we use virtual board as task board and info radiator.

Simply we have a jQuery script that runs in a web browser that rotates some most important pages with our project status. These are JIRA/Greenhopper task board, Jenkins, Sonar and current app snapshot built and deployed automatically by Jenkins.

And where this board stands? In front of us, at the windowsill where every team member sees it.

What is it? An old computer with 20" display.

Our colleagues from other project had ordered about 30" monitor but they have our company owner in team so this was obvious that they should have bigger and better display ;-)