Nicolas PHUNG nsphung

The introduction to Reactive Programming you've been missing

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

Single Page Apps are ruling the world and AngularJS is leading the charge. But many of the lessons we learned in the Web 2.0 era no longer apply, and few are as drastically different as authentication.

CORS

CORS is an oft-misunderstood feature of new browsers that is configured by a remote server. CORS stands for Cross-Origin-Resource-Sharing, and was designed to make it possible to access services outside of the current origin (or domain) of the current page.

Like many browser features, CORS works because we all agree that it works. So all major browsers like Chrome, Firefox, and IE support and enforce it. By using these browsers, you benefit from the security of CORS.

That means certain browsers do not enforce it, so it is not relevant there. One large example is a native Web View for things like Cordova and Phonegap. However, these tools often have configuration options for whitelisting domains so you can add some security that way.

#Protecting the Docker daemon Socket with HTTPS

HOST-IP:172.17.42.1 VM-IP:172.17.0.2

openssl genrsa -aes256 -out ca-key.pem 2048
openssl req -new -x509 -days 365 -key ca-key.pem -sha256 -out ca.pem ```Common Name:  172.17.42.1```
openssl genrsa -out server-key.pem 2048
openssl req -subj "/CN=172.17.42.1" -new -key server-key.pem -out server.csr

echo subjectAltName = IP:172.17.42.1, IP:172.17.0.2, IP:127.0.0.1 > extfile.cnf

These are the snippets I use most of the time when administrating my ES cluster

To be updated

Settings to change before you do something

Before restarting a data node

curl -XPUT 'http://escluster:9200/_cluster/settings' -d '{

Generating Flame Graphs for Apache Spark

Flame graphs are a nifty debugging tool to determine where CPU time is being spent. Using the Java Flight recorder, you can do this for Java processes without adding significant runtime overhead.

When are flame graphs useful?

Shivaram Venkataraman and I have found these flame recordings to be useful for diagnosing coarse-grained performance problems. We started using them at the suggestion of Josh Rosen, who quickly made one for the Spark scheduler when we were talking to him about why the scheduler caps out at a throughput of a few thousand tasks per second. Josh generated a graph similar to the one below, which illustrates that a significant amount of time is spent in serialization (if you click in the top right hand corner and search for "serialize", you can see that 78.6% of the sampled CPU time was spent in serialization). We used this insight to spee

LOCATION=$(curl -s https://api.github.com/repos/<YOUR ORGANIZTION>/<YOUR REPO>/releases/latest \
| grep "zipball_url" \
| awk '{ print $2 }' \
| sed 's/,$//'       \
| sed 's/"//g' )     \
; curl -L -o <OUTPUT FILE NAME> $LOCATION

for example:

	# kafka_prod1.yaml
	#
	# The production kafka nodes for prod1

	graphite_host: <%= node[:jmxtrans][:graphite][:host] %>
	graphite_port: <%= node[:jmxtrans][:graphite][:port] %>

	# Define sthe port that the hosts in this config listen for JMX on
	# THIS PORT HAS TO BE THE SAME FOR ALL HOSTS
	query_port: 9999

	/**
	The Play (2.3) json combinator library is arguably the best in the scala world. However it doesnt
	work with case classes with greater than 22 fields.
	The following gist leverages the shapeless 'Automatic Typeclass Derivation' facility to work around this
	limitation. Simply stick it in a common location in your code base, and use like so:
	Note: ** Requires Play 2.3 and shapeless 2.1.0


	import SWrites._
	import SReads._

	import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover}
	import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
	import org.apache.spark.mllib.linalg.Vector

	import sqlContext.implicits._

	val numTopics: Int = 100
	val maxIterations: Int = 100
	val vocabSize: Int = 10000