fedesilva

package com.batanga.eng.interop

import java.util.function.{BiFunction, BiPredicate, Function => JFunction, Predicate => JPredicate}
import java.util.stream.{Stream => JStream}

import scala.collection.JavaConverters._

/** Interoperability with java functional interfaces and stream api.
 * Created by f on 10/30/14.
 */

fedesilva

#Contoller configuration
#Mon Dec 29 22:50:54 UYST 2014
# ~/Library/Application Support/.ultratron_3.03/controls.txt
controller2.button.7=start
controller2.button.6=back
controller2.button.5=action
controller2.button.4=firebutton
controller2.axis.4=fireaxis
controller2.axis.3=aimX
controller2.axis.2=aimY

fedesilva

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

fedesilva

f@Motoko:~$ ping google.com
PING google.com (200.40.0.90): 56 data bytes
Request timeout for icmp_seq 0
64 bytes from 200.40.0.90: icmp_seq=0 ttl=57 time=1632.718 ms
64 bytes from 200.40.0.90: icmp_seq=1 ttl=57 time=1169.396 ms
64 bytes from 200.40.0.90: icmp_seq=2 ttl=57 time=165.587 ms
64 bytes from 200.40.0.90: icmp_seq=3 ttl=57 time=157.983 ms
64 bytes from 200.40.0.90: icmp_seq=4 ttl=57 time=81.226 ms
64 bytes from 200.40.0.90: icmp_seq=5 ttl=57 time=1147.733 ms
64 bytes from 200.40.0.90: icmp_seq=6 ttl=57 time=473.100 ms

fedesilva

import util.{Solutions, ScalaMeetupSpec}

/** Tests to validate your implementation
  *
  */
class FinancialReaderTest extends ScalaMeetupSpec with Solutions {

  import FinancialReader._

  "Financial" should "for get correct files for a list symbols " in {

fedesilva

package scalax.collection
import scala.collection.mutable.ListBuffer

/** FoldTransformers and the views based on them are a Scala
 *  adaptation, and to some degree an extension, of Rich Hickey's
 *  transducers for Clojure. They show that the concepts can be
 *  implemented in a type-safe way, and that the implementation is
 *  quite beautiful.
 */
object FoldingViews {

fedesilva

POST /logstash-batanga-*/_search?search_type=count
{
   "query": {
      "filtered": {
         "query": {
            "match_all": {}
         },
         "filter": {
            "and": {
               "filters": [

fedesilva

POST /logstash-batanga-*/_search?search_type=count
{
   "query": {
      "filtered": {
         "query": {
            "match_all": {}
         },
         "filter": {
            "exists": {
               "field": "btng.elapsedtime"

fedesilva

POST _search
{
   "query": {
       "filtered": {
          "query": {
            "match_all": {}
          },
          "filter": {}
       }
   }

fedesilva

import spark.SparkContext
import SparkContext._

/**
 * A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]]
 * to Spark.
 * Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]]
 */
object MovieSimilarities {