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$>echo "{nThreads:2,w:true,r:false,mmf:true}" > perfsession1.js | |
$> | |
$>mongoperf < perfsession1.js |
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$>echo "{nThreads:2,w:true,r:false,mmf:false,fileSizeMB:10000}" > perfsession1.js | |
$> | |
$>mongoperf < perfsession1.js |
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mongoperf < perfsession1.js | |
mongoperf | |
use -h for help | |
options: | |
{ nThreads: 2, w: true, r: false, mmf: true, fileSizeMB: 256 } | |
creating test file size:256MB ... | |
testing... | |
new thread, total running : 1 | |
9922222 ops/sec | |
11197085 ops/sec |
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{ | |
"_id" : ObjectId("53c281bb2239c277d82494a3"), | |
"station_id" : 8449130, | |
"name" : "Nantucket Island", | |
"lon" : -70.0967, | |
"products" : [ | |
{ | |
"v" : 72.1, | |
"t" : ISODate("2014-07-13T12:42:00Z"), | |
"name" : "water_temperature", |
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{ | |
"_id" : ObjectId("53744abad3a11771ea04bc32"), | |
"-name" : "Unalaska", // station Name | |
"-ID" : "9462620", // station ID | |
"metadata" : { | |
"location" : { | |
"lat" : "53.88", // Geo Lat | |
"long" : "-166.537", // Geo Long | |
"state" : "AK" | |
}, |
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{ | |
"metadata": { | |
"name": "", | |
"signature": "sha256:a90804b68c0c02c405e62b261bc333673f0be01d2a735136d4799cd5e506f42d" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ |
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# this script installs jupyter on Rackspace CBD | |
# 2015 kcg | |
# the trick here is to install python 3.3 with pip and not ruin the default python install | |
# then simply run jupyterhub | |
# this is designed to be run as a postinstall script when provisioning a cluster. | |
if [ $HOSTNAME == "GATEWAY-1" ] | |
then | |
# pre-requisites |
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import sys | |
import os | |
sys.path.append("/usr/lib/spark-yarn/python") | |
os.environ["SPARK_HOME"]="/usr/lib/spark-yarn/" | |
from pyspark import SparkContext |
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// mongodb 2.4.8 | |
// fragmented | |
// initial setting of random rand of 5000 | |
> use sbtest | |
switched to db sbtest | |
> db.stats() | |
{ | |
"db" : "sbtest", |
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hour,avg_neg,avg_neutral,avg_pos | |
0,0.489045047812703,0.572904921392379,0.510954952187297 | |
1,0.45979209657117,0.55928348584548,0.54020790342883 | |
2,0.452954920632673,0.564895009665459,0.547045079367327 | |
3,0.4886087921913,0.473499704794948,0.5113912078087 | |
4,0.486929135727515,0.470766998533493,0.513070864272485 | |
5,0.516430239594934,0.633499700744954,0.483569760405066 | |
6,0.476850687564712,0.605231004647229,0.523149312435288 | |
7,0.485249046873756,0.504506815590507,0.514750953126244 | |
8,0.485469075837924,0.53258626109772,0.514530924162076 |
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