paf31

This blog post series has moved here.

You might also be interested in the 2016 version.

tokestermw

import json
import urlparse
from itertools import chain
flatten = chain.from_iterable

from nltk import word_tokenize

from gensim.corpora import Dictionary
from gensim.models.ldamodel import LdaModel
from gensim.models.tfidfmodel import TfidfModel

acolyer

Internet Scale Services Checklist

A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."

• http://mvdirona.com/jrh/talksandpapers/jamesrh_lisa.pdf

Basic tenets

• Does the design expect failures to happen regularly and handle them gracefully?
• Have we kept things as simple as possible?

avernet

/*
 * ====================================================================
 *
 *  Licensed to the Apache Software Foundation (ASF) under one or more
 *  contributor license agreements.  See the NOTICE file distributed with
 *  this work for additional information regarding copyright ownership.
 *  The ASF licenses this file to You under the Apache License, Version 2.0
 *  (the "License"); you may not use this file except in compliance with
 *  the License.  You may obtain a copy of the License at
 *

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

ayosec

package test

import org.apache.commons.compress.archivers.ArchiveStreamFactory
import org.apache.commons.compress.archivers.ArchiveInputStream
import org.apache.commons.compress.archivers.ArchiveEntry
import org.apache.commons.compress.compressors.CompressorStreamFactory
import scala.util.Try
import scala.util.Success
import scala.util.Failure
import java.io.InputStream

millermedeiros

Setup Mac OS X

I've done the same process every couple years since 2013 (Mountain Lion, Mavericks, High Sierra, Catalina) and I updated the Gist each time I've done it.

I kinda regret for not using something like Boxen (or anything similar) to automate the process, but TBH I only actually needed to these steps once every couple years...

mrflip

Tuning Storm+Trident

Tuning a dataflow system is easy:

The First Rule of Dataflow Tuning:
* Ensure each stage is always ready to accept records, and
* Deliver each processed record promptly to its destination

jbilcke

execfile("/your/path/to/videomaker.py")
videomaker(
    ts_min=1352261778000, # "from" timestamp..
    ts_max=1352262378000, # .."to" timestamp
    frames=20, # number of images in the video. eg 200 frames for a video at 20 frames per seconds = 10 seconds of video
    output_prefix="/path/to/output/dir/frame_", # path where to write the png. images will be prefixed with "frame_" 
    output_format=".png" # you probably want to leave png here
)

why-not

"""making a dataframe"""
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

"""quick way to create an interesting data frame to try things out""" 
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

"""convert a dictionary into a DataFrame"""
"""make the keys into columns"""
df = pd.DataFrame(dic, index=[0])