aparrish

aparrish

marianogappa

/*
Parallel processing with ordered output in Go
(you can use this pattern by importing https://github.com/MarianoGappa/parseq)

This example implementation is useful when the following 3 conditions are true:
1) the rate of input is higher than the rate of output on the system (i.e. it queues up)
2) the processing of input can be parallelised, and overall throughput increases by doing so
3) the order of output of the system needs to respect order of input

- if 1 is false, KISS!

dpryden

import java.io.IOException;
import java.net.URLClassLoader;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.nio.file.Path;

/**
 * Example demonstrating a ClassLoader leak.
 *
 * <p>To see it in action, copy this file to a temp directory somewhere,

staltz

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.

---

zed

#!/usr/bin/env python
"""Translate text_to_translate using microsoft translator service."""
import json
import xml.etree.ElementTree as etree
try:
    from urllib import urlencode
    from urllib2 import urlopen, Request
except ImportError: # Python 3
    from urllib.parse import urlencode
    from urllib.request import urlopen, Request

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@d3/indented-tree