Roman Pearah neverfox

nest

A multi-level groupBy for arrays inspired by D3's nest operator.

Nesting allows elements in an array to be grouped into a hierarchical tree structure; think of it like the GROUP BY operator in SQL, except you can have multiple levels of grouping, and the resulting output is a tree rather than a flat table. The levels in the tree are specified by key functions.

Virtual Hosts on nginx (CSC309)

When hosting our web applications, we often have one public IP address (i.e., an IP address visible to the outside world) using which we want to host multiple web apps. For example, one may wants to host three different web apps respectively for example1.com, example2.com, and example1.com/images on the same machine using a single IP address.

How can we do that? Well, the good news is Internet browsers

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

Deprecating `transferPropsTo`

It's a common pattern in React to wrap a component in an abstraction. The outer component exposes a simple property to do something that might have more complex implementation details.

We used to have a helper function called transferPropsTo. We no longer support this method. Instead you're expected to use a generic object helper to merge props.

render() {
 return Component(Object.assign({}, this.props, { more: 'values' }));

The Anatomy of an Onyx Program

In this tutorial, we'll take an in-depth view of what's happening when you execute a simple Onyx program. All of the code can be found in the Onyx Starter repository if you'd like to follow along. The code uses the development environment with HornetQ and ZooKeeper running in memory, so you don't need additional dependencies to run the example for yourself on your machine.

The Workflow

At the core of the program is the workflow - the flow of data that we ingest, apply transformations to, and send to an output for storage. In this program, we're going to ingest some sentences from an input source, split the sentence into individual words, play with capitalization, and add a suffix. Finally, we'll send the transformed data to an output source.

Let's examine the workflow pictorially:

	# Sublime package NVM node path configuration
	# Save this file in:
	# ~/Library/Application\ Support/Sublime\ Text\ 3/Packages/node_env.py
	import os

	os.environ["PATH"] = "/Users/YOUR_USERNAME/.nvm/v0.10.26/bin:/Users/cranemes/.nvm/v0.10.26/lib:/Users/YOUR_USERNAME/bin:/usr/local/sbin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin"
	print("PATH=" + os.environ["PATH"])

	(ns om-data.core
	(:require [om.core :as om :include-macros true]
	[om.dom :as dom :include-macros true]
	[datascript :as d]))

	(enable-console-print!)

	(def schema {})
	(def conn (d/create-conn schema))

	(ns reagent-test.core
	(:require [reagent.core :as reagent :refer [atom]]
	[datascript :as d]
	[cljs-uuid-utils :as uuid]))

	(enable-console-print!)

	(defn bind
	([conn q]
	(bind conn q (atom nil)))

	; Input via stdin
	; Formatted as described in reddit.com/r/dailyprogrammer/comments/20cydp/14042014_challenge_152_hard_minimum_spanning_tree/

	(defn with-index [coll]
	(map-indexed vector coll))

	(defn get-adjacency []
	(with-index (map with-index
	(repeatedly (read-string (read-line))
	#(map read-string (clojure.string/split (read-line) #", *"))))))

	(comment ; Fun with transducers, v2

	;; Still haven't found a brief + approachable overview of Clojure 1.7's new
	;; transducers in the particular way I would have preferred myself - so here goes:

	;;;; Definitions
	;; Looking at the `reduce` docstring, we can define a 'reducing-fn' as:
	(fn reducing-fn ([]) ([accumulation next-input])) -> new-accumulation
	;; (The `[]` arity is actually optional; it's only used when calling
	;; `reduce` w/o an init-accumulator).