Chris Jansen janstenpickle

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

Revisiting Tagless Final Interpreters

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

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

Effective Engineer - Notes

By Edmond Lau
Highly Recommended 👍
http://www.theeffectiveengineer.com/

What's an Effective Engineer?

Applied Functional Programming with Scala - Notes

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

Serialization

As all objects must be Serializable to be used as part of RDD operations in Spark, it can be difficult to work with libraries which do not implement these featuers.

Java Solutions

Simple Classes

For simple classes, it is easiest to make a wrapper interface that extends Serializable. This means that even though UnserializableObject cannot be serialized we can pass in the following object without any issue

public interface UnserializableWrapper extends Serializable {
 public UnserializableObject create(String parm1, String parm2);

To remove a submodule you need to:

Delete the relevant section from the .gitmodules file.
Stage the .gitmodules changes git add .gitmodules
Delete the relevant section from .git/config.
Run git rm --cached path_to_submodule (no trailing slash).
Run rm -rf .git/modules/path_to_submodule (no trailing slash).
Commit git commit -m "Removed submodule "
Delete the now untracked submodule files rm -rf path_to_submodule

When creating streams from an asynchronous process, the idiomatic thing is to create a stream from that process at the earliest possible stage, rather than using a queue to invert control after the fact. See the creating streams examples - generally, you just use the Process.eval and Process.repeatEval functions to build a stream by running some asynchronous task repeatedly.

That said, if you have some existing logic that you need to bind to that's already based on callbacks and side effects, you can use the functions in scalaz.stream.async. Here's an example, using a queue to invert control:

import scalaz.stream.async

val (q, src) = async.queue[Int]

// Thread 1

	#!/bin/bash
	# Cloudflare as Dynamic DNS
	# From: https://letswp.io/cloudflare-as-dynamic-dns-raspberry-pi/
	# Based on: https://gist.github.com/benkulbertis/fff10759c2391b6618dd/
	# Original non-RPi article: https://phillymesh.net/2016/02/23/setting-up-dynamic-dns-for-your-registered-domain-through-cloudflare/

	# Update these with real values
	auth_email="[email protected]"
	auth_key="global_api_key_goes_here"
	zone_name="example.com"

	import matryoshka._
	import matryoshka.data._
	import matryoshka.implicits._

	import shapeless._

	type SF[A] = Boolean :+: Int :+: String :+: List[A] :+: Map[String, A] :+: CNil


	val v0 = Coproduct[SF[Nothing]](1)