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def min_max_hashes(text, window=60):
    hashes = [murmurhash(text[i:i+window]) for i in range(len(text)-window+1)]
    return [min(hashes), max(hashes)]

def shingleprints(text):
    min1, max1 = min_max_hashes(text[0:len(text)/2])
    min2, max2 = min_max_hashes(text[len(text)/2:])
    # combine pairs, using your favorite hash-value combiner
    return [hash_combine(min1, min2),
            hash_combine(min1, max2),

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def minhash(text, window=25):  # assume len(text) > 50
    hashes = [murmurhash(text[i:i+window]) for i in range(len(text)-window+1)]
    return set(sorted(hashes)[0:20])

def similarity(text1, text2):
    hashes1 = minhash(text1)
    hashes2 = minhash(text2)
    return len(hashes1 & hashes2) / len(hashes1)

A = "one two three four five six seven eight nine ten eleven twelve thirteen fourteen fifteen sixteen seventeen eighteen"

reynoldsm88

AWS Golang SDK examples

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#!/usr/bin/python
#
#  Copyright 2017 Otto Seiskari
#  Licensed under the Apache License, Version 2.0.
#  See http://www.apache.org/licenses/LICENSE-2.0 for the full text.
#
#  This file is based on
#  https://github.com/swagger-api/swagger-ui/blob/4f1772f6544699bc748299bd65f7ae2112777abc/dist/index.html
#  (Copyright 2017 SmartBear Software, Licensed under Apache 2.0)
#

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package org.apache.spark.countSerDe

import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.types._
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
import org.apache.spark.sql.expressions.MutableAggregationBuffer
import org.apache.spark.sql.expressions.UserDefinedAggregateFunction

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curl http://spark-cluster-ip:6066/v1/submissions/status/driver-20151008145126-0000

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organization := "net.seratch"

name := "sandbox"

version := "0.1"

scalaVersion := "2.9.1"

libraryDependencies ++= Seq(
  "junit"          % "junit"     % "4.9"   withSources(),

reynoldsm88

Spark internals through code

Nothing gives you more detail about spark internals than actually reading it source code. In addition, you get to learn many design techniques and improve your scala coding skills. These are the random notes I make while reading the spark code. The best way to comprehend the notes is to load spark code into an IDE, e.g. IntelliJ, and navigate the code on the side.

Genesis - creation of a spark cluster

The scripts for creating a spark cluster are: start-master.sh and start-slave.sh. Read them carefully, and you can see that both scripts are very similar except the values for $CLASS variable. For start-master.sh, the value is CLASS="org.apache.spark.deploy.master.Master", while the value for start-slave.sh is shown below with more context.

# NOTE: This exact class name is matched downstream by SparkSubmit.