Milad Khajavi khajavi

Moved to git repository: https://github.com/denji/golang-tls

Generate private key (.key)

# Key considerations for algorithm "RSA" ≥ 2048-bit
openssl genrsa -out server.key 2048

# Key considerations for algorithm "ECDSA" ≥ secp384r1
# List ECDSA the supported curves (openssl ecparam -list_curves)

byte-size tuts

by alexander white ©

golang tutorials

Rough Notes about CQRS and ES

Once upon a time…

I once took notes (almost sentence by sentence with not much editing) about the architectural design concepts - Command and Query Responsibility Segregation (CQRS) and Event Sourcing (ES) - from a presentation of Greg Young and published it as a gist (with the times when a given sentence was heard).

I then found other summaries of the talk and the gist has since been growing up. See the revisions to know the changes and where they came from (aka the sources).

It seems inevitable to throw Domain Driven Design (DDD) in to the mix.

	package akkahttptest

	import akka.actor.ActorSystem
	import akka.http.Http
	import akka.stream.FlowMaterializer
	import akka.http.server._
	import akka.http.marshalling.PredefinedToResponseMarshallers._
	import akka.stream.scaladsl.{HeadSink, Source}

	object Proxy extends App {

	package thunder.streaming

	import org.apache.spark.{SparkConf, Logging}
	import org.apache.spark.rdd.RDD
	import org.apache.spark.SparkContext._
	import org.apache.spark.streaming._
	import org.apache.spark.streaming.dstream.DStream
	import org.apache.spark.mllib.clustering.KMeansModel

	import scala.util.Random.nextDouble

	package test;

	/**
	* Created by russellspitzer on 12/4/14.
	*/

	import java.io.Serializable;
	import java.util.List;
	import org.apache.spark.SparkConf;
	import org.apache.spark.api.java.JavaSparkContext;

	import org.apache.spark.sql._
	val hc = new org.apache.spark.sql.hive.HiveContext(sc)

	val schema = StructType(Seq(StructField("dual", StringType, true)))
	val rowRDD = sc.parallelize(Seq(Row("dual")))
	val schemaRDD = hc.applySchema(rowRDD, schema)
	schemaRDD.registerTempTable("dual")

	hc.hql("select 'hello world' from dual").collect

	import os
	import sys

	# Set the path for spark installation
	# this is the path where you have built spark using sbt/sbt assembly
	os.environ['SPARK_HOME'] = "/Users/vvlad/spark/spark-1.0.2"

	# Append to PYTHONPATH so that pyspark could be found
	sys.path.append("/Users/vvlad/spark/spark-1.0.2/python")
	sys.path.append("/Users/vvlad/spark/spark-1.0.2/python/lib/py4j-0.8.1-src.zip")

	package org.facboy.spark;

	import org.apache.spark.SparkConf;
	import org.apache.spark.api.java.JavaPairRDD;
	import org.apache.spark.api.java.JavaRDD;
	import org.apache.spark.api.java.JavaSparkContext;

	import scala.Tuple2;
	import scala.reflect.ClassTag;
	import scala.reflect.ClassTag$;

	val num = 1 to 100
	//num: scala.collection.immutable.Range.Inclusive = Range(1,2,3,...,100)

	val numRDD = sc.parallelize(num)
	//numRDD: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[11] at parallelize at <console>:14

	val numFileter = numRDD.filter(_ < 10)
	//numFileter: org.apache.spark.rdd.RDD[Int] = FilteredRDD[12] at filter at <console>:16

	val numMap = numFileter.map(_ + 10)