Kevin Olson keolson

Adrian -

I appreciate that you spent time in writing this post. I know I've been up until 2am writing similarly long ones as well. I will take responsibility for having what is likely an irrational response (I blame Twitter for that) to the term "NoOps", but I invite you to investigate why that might be. I'm certainly not the only one who feels this way, apparently, and thus far have decided this issue is easily the largest distraction in my field I've encountered in recent years. I have had the option to simply ignore my opposition to the term, and just let the chips fall where they may with how popular the term "NoOps" may or may not get. I have obviously not taken that option in the past, but I plan to in the future.

You're not an analyst saying "NoOps". Analysts are easy (for me) to ignore, because they're not practitioners. We have expectations of engineering maturity from practitioners in this field of web engineering, especially those we consider leaders. I don't have any expectations from analysts,

	#!comment: This is a list of the top 100,000 most frequently-used English words
	#!comment: according to Wiktionary.
	#!comment:
	#!comment: It was compiled in August 2005 and coalesced into a handy list for
	#!comment: use in John the Ripper.
	#!comment:
	#!comment:
	#!comment: Pull date: Sun Jan 15 22:03:54 2012 GMT
	#!comment:
	#!comment: Sources:

	import spark.streaming.{Seconds, StreamingContext}
	import spark.storage.StorageLevel
	import spark.streaming.examples.twitter.TwitterInputDStream
	import com.twitter.algebird._
	import spark.streaming.StreamingContext._
	import spark.SparkContext._

	/**
	* Example of using CountMinSketch monoid from Twitter's Algebird together with Spark Streaming's
	* TwitterInputDStream

	// An Example is an observation with optional target value and features in the form of a vector of Doubles
	case class Example(target: Option[Double] = None, features: Vector[Double])

	// Base model API looks something like:
	abstract class BaseModel(val modelSettings: Settings)
	extends Serializable
	with Logging {

	def fit(data: RDD[Example])

	require 'rubygems'
	require 'mechanize'

	FIRST_NAME = 'FIRST_NAME'
	LAST_NAME = 'LAST_NAME'
	PHONE = 'PHONE'
	EMAIL = '[email protected]'

	PARTY_SIZE = 2
	SCHEDULE_RANGE = { :start_time => '19:00', :end_time => '20:30' }

	public class HCatInputFormat extends InputFormat<SerializableWritable<Writable>, HCatRecord> {

	private final org.apache.hcatalog.mapreduce.HCatInputFormat input;

	public HCatInputFormat() {
	input = new org.apache.hcatalog.mapreduce.HCatInputFormat();
	}

	@Override
	public RecordReader<SerializableWritable<Writable>, HCatRecord> createRecordReader(

Kevin Olson keolson

Day 1

The Tidyverse and the Future of the Monitoring Toolchain

Martyrs on Film: Learning to hate the #oncallselfie

Monitoring Things At Your Day Job

	// Python RDD creation functions //

	// SequenceFile converted to Text and then to String
	def sequenceFileAsText(path: String) = {
	implicit val kcm = ClassManifest.fromClass(classOf[Text])
	implicit val fcm = ClassManifest.fromClass(classOf[SequenceFileAsTextInputFormat])

	new JavaPairRDD(sc
	.newAPIHadoopFile[Text, Text, SequenceFileAsTextInputFormat](path)
	.map{ case (k, v) => (k.toString, v.toString) }

	<style type="text/css">
	#dashboard_list ul {
	margin:20px, 40px, 40px, 10px;
	overflow:hidden;
	}
	#dashboard_list li {
	line-height:1.5em;
	float:left;
	display:inline;
	}

	# Helper function to plot a decision boundary.
	# If you don't fully understand this function don't worry, it just generates the contour plot below.
	def plot_decision_boundary(pred_func):
	# Set min and max values and give it some padding
	x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
	y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
	h = 0.01
	# Generate a grid of points with distance h between them
	xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
	# Predict the function value for the whole gid