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Questions you might want ask your potential employer

Today's recruiting managers have become salesmen, and oftentimes will be willing to tell you more than a few "alternative truths" to get you onboard.
During an interview, one of your main goals is therefore to get wind of any such suspicious statements, and try to get to the bottom of them by kindly asking for more information.

Recent posts

Reflecting on reflection in Scala and Java

Reflection has always been a bit of a trickery, and while there may be some true to it, it does not mean one cannot master mind some cool stuff using it, and by cool I mean useful,  re-usable, elegant and the likes. In this post we put on the wizard hat and pull out some rabbits.

Problem Statement In this post we'll discuss 3 use-cases in which we want to retrieve runtime information for some generic parameters. In all three of the following use-cases, our goal is to retrieve the runtime information for the generic type parameter T.

The first use case is when you have a class deriving from a generic class, without having generic parameters of its own:
classGenericIntextendsGeneric<T> {} // one wants to know the type of T of its parentnewGenericInt()
The second use case is when you create a new instance of a generic class by providing a generic parameter:
// one wants to know the type of T inside the classnewGenericClass<Integer>()
The third use case is when you invoke …

A tale of Goblins and Streams

At QCon 2014, LinkedIn gave a talk (slides) about their Gobblin project (as well as published a blog post). Gobblin is a unified data ingestion system, aimed at providing Camus-like capabilities for sources other than Kafka. While Gobblin is a fascinating piece of engineering, what I find to be no less fascinating is the direction LinkedIn has chosen by going for a system like Gobbblin.

Keeping it together in face of a terrible codebase

This is, as a matter of fact, an industry proven method.

Sending out Storm metrics

There are a few posts talking about Storm's metrics mechanism, among which you can find Michael Noll's postJason Trost's post and the storm-metrics-statsd github project, and last but not least (or is it?)  Storm's documentation.

While all of the above provide a decent amount of information, and one is definitely encouraged to read them all before proceeding, it feels like in order to get the full picture one needs to combine them all, and even then a few bits and pieces are left missing. It is these missing bits I'll be rambling about in this post.

Dependency Injection - The good, the bad and the ugly

The Good
Dependency injection (DI, a.k.a IoC - inversion of control) is a well known technique to increase software modularity by reducing coupling between modules. To provide the benefits of DI, numerous DI frameworks have arisen (Spring, Guice, Castle Windsor, etc.) all of which essentially give you "DI capabilities" right out of the box (these frameworks tend to provide a whole lot more than just "DI capabilities", but that's not really relevant to the point I'm about to make). Now, to remove the quotes around "DI capabilities", let's define it as a DI container - a sack of objects you can manipulate using a provided API in order to wire these objects together into an object graph that makes up your application.

I've worked on quite a few projects employing Spring, so it will be my framework of reference throughout the rest of the post, but the principles and morals apply just the same.