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# Basic text search with relevancy for MongoDB. 
# See http://blog.tty.nl/2010/02/08/simple-ranked-text-search-for-mongodb/
# Copythingie 2010 - Ward Bekker - [email protected]

#create (or empty) a docs collection
doc_col = MongoMapper.connection.db('example_db').collection('docs')
doc_col.remove({})

#add some sample data
doc_col.insert({ "txt" => "it is what it is"})

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/* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  */
/*                                                                                                */
/*  Simple node js module to get distance between two coordinates.                                */
/*                                                                                                */
/*  Code transformed from Chris Veness example code - please refer to his website for licensing   */
/*  questions.                                                                                    */
/*                                                                                                */
/*                                                                                                */
/*  Latitude/longitude spherical geodesy formulae & scripts (c) Chris Veness 2002-2011            */
/*   - www.movable-type.co.uk/scripts/latlong.html                                                */

viirya

use strict;
use JSON;
use Data::Dumper;

open(TWEET_STAT, "<$ARGV[0]");

my $rows = [];

while (<TWEET_STAT>) {

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# See: http://www.lecloud.net/post/61401763496/install-update-to-python-2-7-and-latest-pip-on-ec2
# install build tools 
sudo yum install make automake gcc gcc-c++ kernel-devel git-core -y 

# install python 2.7 and change default python symlink 
# python27-devel or python27-python-devel.x86_64
sudo yum install python27-devel -y 
sudo rm /usr/bin/python
sudo ln -s /usr/bin/python2.7 /usr/bin/python 

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# Update EC2 packages

sudo yum install cmake boost-devel.x86_64 boost-python.x86_64 boost-serialization.x86_64 -y 

sudo yum install swig blas-devel.x86_64 lapack-devel.x86_64 -y

# Install Python packages

sudo pip install numpy bitarray

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Basic environment

Instance: p2.xlarge

AMI ID: ubuntu/images/hvm-ssd/ubuntu-xenial-16.04-amd64-server-20170221

EBS volume for root: 30GB

Install ubuntu packages

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================================================================================================
filter & aggregate without group
================================================================================================

OpenJDK 64-Bit Server VM 1.8.0_212-8u212-b03-0ubuntu1.18.04.1-b03 on Linux 4.15.0-1021-aws
Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
range/filter/sum:                         Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
range/filter/sum wholestage off                   46264          47546        1814         45.3          22.1       1.0X
range/filter/sum wholestage on                     3156           3523         206        664.5           1.5      14.7X

viirya

1. Install minikube
2. Start minikube with enough cpus and memory
   minikube start --memory='8196mb' --cpus=4
3. The Pod of spark doesn't specify systemaccount, so it is "default". Spark will create pod. So we should give enough
   permission to "default" systemaccount. Create role by kubectl and bind the role to systemaccount default
   kubectl create role default --verb=get,list,watch,create,update,patch,delete --resource=pods,pods/status
   kubectl create rolebinding default-binding --role=default --serviceaccount=default:default --namespace=default
4. Build Spark images. Remember to build PySpark image too.
   ./bin/docker-image-tool.sh -m -t dev -p resource-managers/kubernetes/docker/src/main/dockerfiles/spark/bindings/python/Dockerfile build

viirya

import java.io.{ByteArrayOutputStream, File}
import java.nio.charset.StandardCharsets
import java.sql.{Date, Timestamp}
import java.util.UUID
import java.util.concurrent.atomic.AtomicLong

import scala.util.Random

import org.scalatest.Matchers._