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DataProvider.scala
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DataProvider.scala
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package com.lightbend.scala.kafka.client
import java.io.{ByteArrayOutputStream, File}
import java.nio.file.{Files, Paths}
import com.google.protobuf.ByteString
import com.lightbend.java.configuration.kafka.ApplicationKafkaParameters._
import com.lightbend.model.modeldescriptor.ModelDescriptor
import com.lightbend.model.winerecord.WineRecord
import com.lightbend.scala.kafka.{KafkaLocalServer, MessageSender}
import scala.concurrent.Future
import scala.io.Source
import scala.concurrent.ExecutionContext.Implicits.global
/**
* Created by boris on 5/10/17.
*
* Application publishing models from /data directory to Kafka
*/
object DataProvider {
val file = "data/winequality_red.csv"
var dataTimeInterval = 1000 * 1 // 1 sec
val directory = "data/"
val tensorfile = "data/optimized_WineQuality.pb"
var modelTimeInterval = 1000 * 60 * 5 // 5 mins
def main(args: Array[String]) {
println(s"Using kafka brokers at ${KAFKA_BROKER}")
println(s"Data Message delay $dataTimeInterval")
println(s"Model Message delay $modelTimeInterval")
val kafka = KafkaLocalServer(true)
kafka.start()
kafka.createTopic(DATA_TOPIC)
kafka.createTopic(MODELS_TOPIC)
println(s"Cluster created")
publishData()
publishModels()
while(true)
pause(600000)
}
def publishData() : Future[Unit] = Future {
val sender = MessageSender(KAFKA_BROKER)
val bos = new ByteArrayOutputStream()
val records = getListOfDataRecords(file)
var nrec = 0
while (true) {
records.foreach(r => {
bos.reset()
r.writeTo(bos)
sender.writeValue(DATA_TOPIC, bos.toByteArray)
nrec = nrec + 1
if (nrec % 10 == 0)
println(s"printed $nrec records")
pause(dataTimeInterval)
})
}
}
def publishModels() : Future[Unit] = Future {
val sender = MessageSender(KAFKA_BROKER)
val files = getListOfModelFiles(directory)
val bos = new ByteArrayOutputStream()
while (true) {
files.foreach(f => {
// PMML
val pByteArray = Files.readAllBytes(Paths.get(directory + f))
val pRecord = ModelDescriptor(
name = f.dropRight(5),
description = "generated from SparkML", modeltype = ModelDescriptor.ModelType.PMML,
dataType = "wine"
).withData(ByteString.copyFrom(pByteArray))
bos.reset()
pRecord.writeTo(bos)
sender.writeValue(MODELS_TOPIC, bos.toByteArray)
println(s"Published Model ${pRecord.description}")
pause(modelTimeInterval)
})
// TF
val tByteArray = Files.readAllBytes(Paths.get(tensorfile))
val tRecord = ModelDescriptor(name = tensorfile.dropRight(3),
description = "generated from TensorFlow", modeltype = ModelDescriptor.ModelType.TENSORFLOW,
dataType = "wine").withData(ByteString.copyFrom(tByteArray))
bos.reset()
tRecord.writeTo(bos)
sender.writeValue(MODELS_TOPIC, bos.toByteArray)
println(s"Published Model ${tRecord.description}")
pause(modelTimeInterval)
}
}
private def pause(timeInterval : Long): Unit = {
try {
Thread.sleep(timeInterval)
} catch {
case _: Throwable => // Ignore
}
}
def getListOfDataRecords(file: String): Seq[WineRecord] = {
var result = Seq.empty[WineRecord]
val bufferedSource = Source.fromFile(file)
for (line <- bufferedSource.getLines) {
val cols = line.split(";").map(_.trim)
val record = new WineRecord(
fixedAcidity = cols(0).toDouble,
volatileAcidity = cols(1).toDouble,
citricAcid = cols(2).toDouble,
residualSugar = cols(3).toDouble,
chlorides = cols(4).toDouble,
freeSulfurDioxide = cols(5).toDouble,
totalSulfurDioxide = cols(6).toDouble,
density = cols(7).toDouble,
pH = cols(8).toDouble,
sulphates = cols(9).toDouble,
alcohol = cols(10).toDouble,
dataType = "wine"
)
result = record +: result
}
bufferedSource.close
result
}
private def getListOfModelFiles(dir: String): Seq[String] = {
val d = new File(dir)
if (d.exists && d.isDirectory) {
d.listFiles.filter(f => (f.isFile) && (f.getName.endsWith(".pmml"))).map(_.getName)
} else {
Seq.empty[String]
}
}
}