pavel-odintsov

#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <iostream>
#include <unistd.h>

#include <boost/thread.hpp>
#include <sys/mman.h>
#include <poll.h>
#include <arpa/inet.h>

pavel-odintsov

#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <iostream>
#include <unistd.h>

#include <boost/thread.hpp>

#include <arpa/inet.h>
#include <sys/socket.h>

iand

Some notes on why Go works for me and why it might work for you if you're looking for another language to add to your repetoire. Goes without saying that this reflects my personal taste.

Go features that I particularly like

• Multicore is the future so I like that Go has concurrency built right into the core. Goroutines and channels provide a very accessible metaphor for thinking about concurrent programming. They're supported by language features that really make Go shine in this area. The select statement, for example, makes it easy to listen to and synchronise events from different concurrent threads.
• Provides both pointers and value types, but the pointers are safe and managed. Automatic memory management means its safe to return a pointer to a local variable.
• Interfaces in Go are smooth and unobtrusive. They automatically apply to anything with the right function signature so you can define interfaces that are satisfied by 3rd party code without you having to change it.
• Errors are signale

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

andrequeiroz

#The MIT License (MIT)
#
#Copyright (c) 2015 Andre Queiroz
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions: