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javascript: Promise.all([import('https://unpkg.com/turndown@6.0.0?module'), import('https://unpkg.com/@tehshrike/readability@0.2.0'), ]).then(async ([{ | |
default: Turndown | |
}, { | |
default: Readability | |
}]) => { | |
/* Optional vault name */ | |
const vault = ""; | |
/* Optional folder name such as "Clippings/" */ |
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import os | |
import urllib | |
import urllib2 | |
import base64 | |
import json | |
import sys | |
import argparse | |
try: | |
import requests | |
except ImportError: |
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
\
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$ ./bin/spark-shell | |
14/04/18 15:23:49 INFO spark.HttpServer: Starting HTTP Server | |
14/04/18 15:23:49 INFO server.Server: jetty-7.x.y-SNAPSHOT | |
14/04/18 15:23:49 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:49861 | |
Welcome to | |
____ __ | |
/ __/__ ___ _____/ /__ | |
_\ \/ _ \/ _ `/ __/ '_/ | |
/___/ .__/\_,_/_/ /_/\_\ version 0.9.1 | |
/_/ |
- 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.
- 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=rep1&t
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
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import numpy as np | |
import sys | |
import matplotlib.pyplot as plot | |
import csv | |
# read the arguments - need two files | |
if len(sys.argv) < 3: | |
quit('python parse.py [raw data file] [mahout output]') | |
# read the files |
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# MAC manipulators | |
alias random_mac='sudo ifconfig en0 ether `openssl rand -hex 6 | sed "s/\(..\)/\1:/g; s/.$//"`' | |
alias restore_mac='sudo ifconfig en0 ether YOUR_ORIGINAL_MAC_ADDRESS_GOES_HERE' |
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Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
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