djspiewak

Git DMZ Flow

I've been asked a few times over the last few months to put together a full write-up of the Git workflow we use at RichRelevance (and at Precog before), since I have referenced it in passing quite a few times in tweets and in person. The workflow is appreciably different from GitFlow and its derivatives, and thus it brings with it a different set of tradeoffs and optimizations. To that end, it would probably be helpful to go over exactly what workflow benefits I find to be beneficial or even necessary.

• Two developers working on independent features must never be blocked by each other
  • No code freeze! Ever! For any reason!
• A developer must be able to base derivative work on another developer's work, without waiting for any third party
• Two developers working on inter-dependent features (or even the same feature) must be able to do so without interference from (or interfering with) any other parties
• Developers must be able to work on multiple features simultaneously, or at lea

djspiewak

Introduction to scalaz-stream

Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.

The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca

Kartones

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

• -E: will describe the underlaying queries of the \ commands (cool for learning!)
• -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

sadache

This post aims to document a practical design implementation we came up with when designing some APIs in Scala. Of course the concept is not Scala specific and applies to other functional languages.

If you don't want to go into the full introduction, this post talks about how Applicatives are too restrictive and breaking them into two independent components can be interesting for Contravariant and Invariant Functors. Jump to implementation attached.

We are taking JSON serialization/deserialization as a motivating example.

JsValue is the name of the type that represents the JSON AST. What we need is to be able to read a JsValue and produce an A:

trait Reads[A]{

milessabin

trait Companion[T] {
  type C
  def apply() : C
}

object Companion {
  implicit def companion[T](implicit comp : Companion[T]) = comp()
}

object TestCompanion {