Magic words:
psql -U postgresSome 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)
There exist several DI frameworks / libraries in the Scala ecosystem. But the more functional code you write the more you'll realize there's no need to use any of them.
A few of the most claimed benefits are the following:
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.
| // Author: Aly Cerruti | |
| // Please follow along in Scastie, so you can see the output of statements: | |
| // https://scastie.scala-lang.org/CLmK5tLuRd6rxXuEnba0rQ | |
| // A Category | |
| trait Category[ | |
| // A collection of objects | |
| Obj <: AnyKind, | |
| // A constraint on those objects (Scala lacks dependent typing, so i.e. `Val = [A] =>> Monoid[A]` makes the category of Monoids) | |
| Val[_ <: Obj], |
Cats Instances for Java Time
Cats Instances for Scalacheck
What follows are some of my (very) rough thoughts on what we can and should do with respect to CPS transformation in Scala at the language level. I'll try to start with some motivation behind my thinking, as well as some rambling observations on the nature of the problem space, but don't expect too much coherence here. :-)
Async programming is hard.
Okay let's actually be more specific than that. High-performance I/O is hard. Signal multiplexing is a powerful technique for achieving high(er) performance I/O, particularly network I/O, but the tradeoff is that, in order to utilize it, the user-space programming model must allow for suspension and resumption of sequential continuations (often called "fibers" or "coroutines"). Achieving this type of programming model without significant tradeoffs in usability is what is exceptionally hard.
If that wasn't bad enough though, these problems are inextricably conflated with another set of problem spaces which are, themselves, very difficult. In
Tageless Final interpreters are an alternative to the traditional Algebraic Data Type (and generalized ADT) based implementation of the interpreter pattern. This document presents the Tageless Final approach with Scala, and shows how Dotty with it's recently added implicits functions makes the approach even more appealing. All examples are direct translations of their Haskell version presented in the Typed Tagless Final Interpreters: Lecture Notes (section 2).
The interpreter pattern has recently received a lot of attention in the Scala community. A lot of efforts have been invested in trying to address the biggest shortcomings of ADT/GADT based solutions: extensibility. One can first look at cats' Inject typeclass for an implementation of [Data Type à la Carte](http://www.cs.ru.nl/~W.Swierstra/Publications/DataTypesA
Why you prefer cats instead of zio? TF? It’s looks like zio ecosystem more widely and zio 2.0 has better performance What do you think?
Great question!
Performance-wise, it really depends on what you're doing. The problem with benchmarks (including the ones posted for ZIO and Cats Effect) is that they apply only to abstract situations, which are often nothing like what you see in real applications. A great write-up on this problem by Daniel Spiewak here, he wrote it better than I ever could: https://gist.github.com/djspiewak/f4cfc08e0827088f17032e0e9099d292
Also, this is not an app meant for production - I don't care that much about performance under load because there will be no load. And individual operations will often be bounded by I/O anyway, so the efficiency of the underlying runtime is likely not going to make a noticeable difference in use. Then again, both the client and server are JVM apps, so even the start-up penalty of the client will slow us down than picking even the least efficient e
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
