About an error accessing a field inside Tuple2 - scala

Question

i am trying to access to a field within a Tuple2 and compiler is returning me an error. The software tries to push a case class within a kafka topic, then i want to recover it using spark streaming so i can feed a machine learning algorithm and save results within a mongo instance.
Solved!
I finally solved my problem, i am going to post the final solution:
This is the github project:
https://github.com/alonsoir/awesome-recommendation-engine/tree/develop
build.sbt
name := "my-recommendation-spark-engine"
version := "1.0-SNAPSHOT"
scalaVersion := "2.10.4"
val sparkVersion = "1.6.1"
val akkaVersion = "2.3.11" // override Akka to be this version to match the one in Spark
libraryDependencies ++= Seq(
"org.apache.kafka" % "kafka_2.10" % "0.8.1"
exclude("javax.jms", "jms")
exclude("com.sun.jdmk", "jmxtools")
exclude("com.sun.jmx", "jmxri"),
//not working play module!! check
//jdbc,
//anorm,
//cache,
// HTTP client
"net.databinder.dispatch" %% "dispatch-core" % "0.11.1",
// HTML parser
"org.jodd" % "jodd-lagarto" % "3.5.2",
"com.typesafe" % "config" % "1.2.1",
"com.typesafe.play" % "play-json_2.10" % "2.4.0-M2",
"org.scalatest" % "scalatest_2.10" % "2.2.1" % "test",
"org.twitter4j" % "twitter4j-core" % "4.0.2",
"org.twitter4j" % "twitter4j-stream" % "4.0.2",
"org.codehaus.jackson" % "jackson-core-asl" % "1.6.1",
"org.scala-tools.testing" % "specs_2.8.0" % "1.6.5" % "test",
"org.apache.spark" % "spark-streaming-kafka_2.10" % "1.6.1" ,
"org.apache.spark" % "spark-core_2.10" % "1.6.1" ,
"org.apache.spark" % "spark-streaming_2.10" % "1.6.1",
"org.apache.spark" % "spark-sql_2.10" % "1.6.1",
"org.apache.spark" % "spark-mllib_2.10" % "1.6.1",
"com.google.code.gson" % "gson" % "2.6.2",
"commons-cli" % "commons-cli" % "1.3.1",
"com.stratio.datasource" % "spark-mongodb_2.10" % "0.11.1",
// Akka
"com.typesafe.akka" %% "akka-actor" % akkaVersion,
"com.typesafe.akka" %% "akka-slf4j" % akkaVersion,
// MongoDB
"org.reactivemongo" %% "reactivemongo" % "0.10.0"
)
packAutoSettings
//play.Project.playScalaSettings
Kafka Producer
package example.producer
import play.api.libs.json._
import example.utils._
import scala.concurrent.Future
import example.model.{AmazonProductAndRating,AmazonProduct,AmazonRating}
import example.utils.AmazonPageParser
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.Future
/**
args(0) : productId
args(1) : userdId
Usage: ./amazon-producer-example 0981531679 someUserId 3.0
*/
object AmazonProducerExample {
def main(args: Array[String]): Unit = {
val productId = args(0).toString
val userId = args(1).toString
val rating = args(2).toDouble
val topicName = "amazonRatingsTopic"
val producer = Producer[String](topicName)
//0981531679 is Scala Puzzlers...
AmazonPageParser.parse(productId,userId,rating).onSuccess { case amazonRating =>
//Is this the correct way? the best performance? possibly not, what about using avro or parquet? How can i push data in avro or parquet format?
//You can see that i am pushing json String to kafka topic, not raw String, but is there any difference?
//of course there are differences...
producer.send(Json.toJson(amazonRating).toString)
//producer.send(amazonRating.toString)
println("amazon product with rating sent to kafka cluster..." + amazonRating.toString)
System.exit(0)
}
}
}
This is the definition of necessary case classes (UPDATED), the file is named models.scala:
package example.model
import play.api.libs.json.Json
import reactivemongo.bson.Macros
case class AmazonProduct(itemId: String, title: String, url: String, img: String, description: String)
case class AmazonRating(userId: String, productId: String, rating: Double)
case class AmazonProductAndRating(product: AmazonProduct, rating: AmazonRating)
// For MongoDB
object AmazonRating {
implicit val amazonRatingHandler = Macros.handler[AmazonRating]
implicit val amazonRatingFormat = Json.format[AmazonRating]
//added using #Yuval tip
lazy val empty: AmazonRating = AmazonRating("-1", "-1", -1d)
}
This is the full code of the spark streaming process:
package example.spark
import java.io.File
import java.util.Date
import play.api.libs.json._
import com.google.gson.{Gson,GsonBuilder, JsonParser}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions._
import com.mongodb.casbah.Imports._
import com.mongodb.QueryBuilder
import com.mongodb.casbah.MongoClient
import com.mongodb.casbah.commons.{MongoDBList, MongoDBObject}
import reactivemongo.api.MongoDriver
import reactivemongo.api.collections.default.BSONCollection
import reactivemongo.bson.BSONDocument
import org.apache.spark.streaming.kafka._
import kafka.serializer.StringDecoder
import example.model._
import example.utils.Recommender
/**
* Collect at least the specified number of json amazon products in order to feed recomedation system and feed mongo instance with results.
Usage: ./amazon-kafka-connector 127.0.0.1:9092 amazonRatingsTopic
on mongo shell:
use alonsodb;
db.amazonRatings.find();
*/
object AmazonKafkaConnector {
private var numAmazonProductCollected = 0L
private var partNum = 0
private val numAmazonProductToCollect = 10000000
//this settings must be in reference.conf
private val Database = "alonsodb"
private val ratingCollection = "amazonRatings"
private val MongoHost = "127.0.0.1"
private val MongoPort = 27017
private val MongoProvider = "com.stratio.datasource.mongodb"
private val jsonParser = new JsonParser()
private val gson = new GsonBuilder().setPrettyPrinting().create()
private def prepareMongoEnvironment(): MongoClient = {
val mongoClient = MongoClient(MongoHost, MongoPort)
mongoClient
}
private def closeMongoEnviroment(mongoClient : MongoClient) = {
mongoClient.close()
println("mongoclient closed!")
}
private def cleanMongoEnvironment(mongoClient: MongoClient) = {
cleanMongoData(mongoClient)
mongoClient.close()
}
private def cleanMongoData(client: MongoClient): Unit = {
val collection = client(Database)(ratingCollection)
collection.dropCollection()
}
def main(args: Array[String]) {
// Process program arguments and set properties
if (args.length < 2) {
System.err.println("Usage: " + this.getClass.getSimpleName + " <brokers> <topics>")
System.exit(1)
}
val Array(brokers, topics) = args
println("Initializing Streaming Spark Context and kafka connector...")
// Create context with 2 second batch interval
val sparkConf = new SparkConf().setAppName("AmazonKafkaConnector")
.setMaster("local[4]")
.set("spark.driver.allowMultipleContexts", "true")
val sc = new SparkContext(sparkConf)
val sqlContext = new SQLContext(sc)
sc.addJar("target/scala-2.10/blog-spark-recommendation_2.10-1.0-SNAPSHOT.jar")
val ssc = new StreamingContext(sparkConf, Seconds(2))
//this checkpointdir should be in a conf file, for now it is hardcoded!
val streamingCheckpointDir = "/Users/aironman/my-recommendation-spark-engine/checkpoint"
ssc.checkpoint(streamingCheckpointDir)
// Create direct kafka stream with brokers and topics
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)
println("Initialized Streaming Spark Context and kafka connector...")
//create recomendation module
println("Creating rating recommender module...")
val ratingFile= "ratings.csv"
val recommender = new Recommender(sc,ratingFile)
println("Initialized rating recommender module...")
//THIS IS THE MOST INTERESTING PART AND WHAT I NEED!
//THE SOLUTION IS NOT PROBABLY THE MOST EFFICIENT, BECAUSE I HAD TO
//USE DATAFRAMES, ARRAYs and SEQs BUT IS FUNCTIONAL!
try{
messages.foreachRDD(rdd => {
val count = rdd.count()
if (count > 0){
val json= rdd.map(_._2)
val dataFrame = sqlContext.read.json(json) //converts json to DF
val myRow = dataFrame.select(dataFrame("userId"),dataFrame("productId"),dataFrame("rating")).take(count.toInt)
println("myRow is: " + myRow)
val myAmazonRating = AmazonRating(myRow(0).getString(0), myRow(0).getString(1), myRow(0).getDouble(2))
println("myAmazonRating is: " + myAmazonRating.toString)
val arrayAmazonRating = Array(myAmazonRating)
//this method needs Seq[AmazonRating]
recommender.predictWithALS(arrayAmazonRating.toSeq)
}//if
})
}catch{
case e: IllegalArgumentException => {println("illegal arg. exception")};
case e: IllegalStateException => {println("illegal state exception")};
case e: ClassCastException => {println("ClassCastException")};
case e: Exception => {println(" Generic Exception")};
}finally{
println("Finished taking data from kafka topic...")
}
ssc.start()
ssc.awaitTermination()
println("Finished!")
}
}
Thank you all, folks, #Yuval, #Emecas and #Riccardo.cardin.
Recommender.predict signature method looks like:
def predict(ratings: Seq[AmazonRating]) = {
// train model
val myRatings = ratings.map(toSparkRating)
val myRatingRDD = sc.parallelize(myRatings)
val startAls = DateTime.now
val model = ALS.train((sparkRatings ++ myRatingRDD).repartition(NumPartitions), 10, 20, 0.01)
val myProducts = myRatings.map(_.product).toSet
val candidates = sc.parallelize((0 until productDict.size).filterNot(myProducts.contains))
// get ratings of all products not in my history ordered by rating (higher first) and only keep the first NumRecommendations
val myUserId = userDict.getIndex(MyUsername)
val recommendations = model.predict(candidates.map((myUserId, _))).collect
val endAls = DateTime.now
val result = recommendations.sortBy(-_.rating).take(NumRecommendations).map(toAmazonRating)
val alsTime = Seconds.secondsBetween(startAls, endAls).getSeconds
println(s"ALS Time: $alsTime seconds")
result
}
//I think I've been as clear as possible, tell me if you need anything more and thanks for your patience teaching me #Yuval

Answer 1

Diagnosis
IllegalStateException suggests that you are operating over a StreamingContext that is already ACTIVE or STOPPED. see details here (lines 218-231)
java.lang.IllegalStateException: Adding new inputs, transformations, and output operations after starting a context is not supported
Code Review
By observing your code AmazonKafkaConnector , you are doing map, filter and foreachRDD into another foreachRDD over the same DirectStream object called : messages
General Advice:
Be functional my friend, by dividing your logic in small pieces for each one of the tasks you want to perform:
Streaming
ML Recommendation
Persistence
etc.
That will help you to understand and debug easier the Spark pipeline that you want to implement.

Answer 2

The problem is that the statement rdd.take(count.toInt) return an Array[T], as stated here
def take(num: Int): Array[T]
Take the first num elements of the RDD.
You're saying to your RDD to take the first n elements in it. Then, differently from what you guess, you've not a object of type Tuple2, but an array.
If you want to print each element of the array, you can use the method mkString defined on the Array type to obtain a single String with all the elements of the array.

Answer 3

It looks like what you're trying to do is is simply a map over a DStream. A map operation is a projection from type A to type B, where A is a String (that you're receiving from Kafka), and B is your case class AmazonRating.
Let's add an empty value to your AmazonRating:
case class AmazonRating(userId: String, productId: String, rating: Double)
object AmazonRating {
lazy val empty: AmazonRating = AmazonRating("-1", "-1", -1d)
}
Now let's parse the JSONs:
val messages = KafkaUtils
.createDirectStream[String, String, StringDecoder, StringDecoder]
(ssc, kafkaParams, topicsSet)
messages
.map { case (_, jsonRating) =>
val format = Json.format[AmazonRating]
val jsValue = Json.parse(record)
format.reads(jsValue) match {
case JsSuccess(rating, _) => rating
case JsError(_) => AmazonRating.empty
}
.filter(_ != AmazonRating.empty)
.foreachRDD(_.foreachPartition(it => recommender.predict(it.toSeq)))