mravi

#! /usr/bin/env python

import redis
import random
import pylibmc
import sys

r = redis.Redis(host = 'localhost', port = 6389)
mc = pylibmc.Client(['localhost:11222'])

mravi

//==================================================================
// SPARK INSTRUMENTATION
//==================================================================

import com.codahale.metrics.{MetricRegistry, Meter, Gauge}
import org.apache.spark.{SparkEnv, Accumulator}
import org.apache.spark.metrics.source.Source
import org.joda.time.DateTime
import scala.collection.mutable

mravi

1. General Background and Overview

• 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.
• Models and Issues in Data Stream Systems
• 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
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep