jonathana

import sys, os, glob
from datetime import datetime

sys.path.append(os.environ.get("MAHOUT_CORE"))
for jar in glob.glob(os.environ.get("MAHOUT_JAR_DIR") + "/*.jar"):
	sys.path.append(jar)
	
from org.apache.mahout.common import RandomUtils
from org.apache.mahout.cf.taste.common import TasteException
from org.apache.mahout.cf.taste.eval import *

jboner

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

jashkenas

# 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'

magsol

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

willurd

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.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

debasishg

General Background and Overview

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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

ceteri

$ ./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
      /_/

tsiege

ANNOUNCEMENT

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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andrewxhill

import os
import urllib
import urllib2
import base64
import json
import sys
import argparse
try:
    import requests
except ImportError:

kracekumar

Writing better python code

---

Swapping variables

Bad code