natewave

package natewave

import akka.Done

import scala.concurrent.Future
import scala.util.{Failure, Success}

object GraphTest extends App {
  import akka.actor.ActorSystem
  import akka.stream._

natewave

def upsert[Pk, A](dbContext: DbContext[Pk, A], x: A): Query[Unit, Connection] =
      queryBuilder.write { implicit connection => implicit ec =>
        val params = dbContext.params(x)
        val keys = params.map(_.name)
        val cols = keys.mkString("(", ",", ")")
        val placeholders = keys.mkString("({", "},{", "})")
        val values = keys.flatMap { key =>
          params.find(_.name == key).map(DbContext.anormNamedParameter)
        }

natewave

def getCommittedOffsetsForConsumer(consumerConfig: KafkaConsumerConfig, topicPartitions: Seq[TopicPartition]): Map[TopicPartition, Long] = {
    val result = withCustomStringConsumer(consumer => {
      consumer.assign(topicPartitions.toList.asJava)
      val tps = topicPartitions.map( tp => {
        val offsetAndMetadata = consumer.committed(tp)
        val startOffset: Long = KafkaCluster.getLogBeginningOffset(consumerConfig.topic).values.head
        
        Option(offsetAndMetadata) match {
          case None => {
            // log "TopicPartition $tp doesn't have committed offsets"

natewave

sealed trait NodeState
trait Follower extends NodeState
trait Candidate extends NodeState
trait Leader extends NodeState

final class NotLeader extends NodeState with Follower with Candidate
final class NotFollower extends NodeState with Candidate with Leader
final class NotCandidate extends NodeState with Follower with Candidate

class Node[State <: NodeState] private () {

natewave

import scala.concurrent.{ Future, ExecutionContext }

object GroupedAsync {
  def sequencePar[K, V, R](fs: ((K, V)) => Future[(K, R)])(input: Seq[(K, V)])(implicit ec: ExecutionContext): Future[Seq[(K, R)]] = {
    val result: Future[Seq[(K, R)]] = {
        val grouped: Map[K, Seq[(K, V)]] = input.groupBy(_._1)

        val futurePerKeyMap: Map[K, Future[Seq[(K, R)]]] = grouped.mapValues { elements =>
          val l: Future[Seq[(K, R)]] = elements.foldLeft(Future(Seq.empty[(K, R)])) {
            (previousFuture, next) =>

natewave

class KafkaListener(saveProgress: Array[OffsetRange] => Boolean) extends SparkListener with Logging {
  private val runningJobs = collection.mutable.Set.empty[SparkListenerJobStart]
  private val kafkaRDDs = collection.mutable.Map.empty[Int, Array[OffsetRange]]

  override def onJobStart(jobStart: SparkListenerJobStart) {
    runningJobs += jobStart
  }

  override def onJobEnd(jobEnd: SparkListenerJobEnd) {
    runningJobs.find(_.jobId == jobEnd.jobId).map { job =>

natewave

val sRule = implicitly[RuleLike[AvroValue, String]]
val drRule = implicitly[RuleLike[AvroValue, DefectResult]]
def kTransform(g: GenericRecord) = RecordTransformations.convert(g, sRule)
def vTransform(g: GenericRecord) = RecordTransformations.convert(g, drRule)

val defectResults: DStream[(String, DefectResult)] = KafkaCommons.read[String, DefectResult](consumerConfig, ssc)(kTransform, vTransform)

natewave

def read[A, B](consumerConfig: ConsumerConfig, streamingContext: StreamingContext, maxRate: Int = DEFAULT_MAX_RATE)
  (keyRule:GenericRecord => VA[A], valueRule: GenericRecord => VA[B]): DStream[(A, B)] = {
    val sparkConf = streamingContext.sparkContext.getConf
    val appName = sparkConf.get("spark.app.name")

    val offsetsCoordinator = OffsetsCoordinator.get(
      channel = OffsetsCoordinator.newChannel(consumerConfig.host, consumerConfig.port),
      clientId = consumerConfig.clientId,
      groupId = consumerConfig.groupId)

natewave

class KafkaCustomDecoder[T](implicit keyRule: RuleLike[AvroValue, T]) extends kafka.serializer.Decoder[VA[T]] with Logging {
  import kafka.message.Message
  import java.util.Properties

  def fromAvro(value: GenericRecord) =
    Avro.fromAvro[T](Avro.wrap(value))

  def fromBytes(bytes: Array[Byte]): VA[T] = {
    val props: Properties = new Properties()
    props.put("schema.registry.url", "http://localhost:8081")

natewave

def read[A, B](consumerConfig: ConsumerConfig, streamingContext: StreamingContext, maxRate: Int = DEFAULT_MAX_RATE)
  (implicit keyRule: RuleLike[AvroValue, A], valueRule: RuleLike[AvroValue, B]): DStream[(VA[A], VA[B])] = {
    val sparkConf = streamingContext.sparkContext.getConf
    val appName = sparkConf.get("spark.app.name")

    val offsetsCoordinator = OffsetsCoordinator.get(
      channel = OffsetsCoordinator.newChannel(consumerConfig.host, consumerConfig.port),
      clientId = consumerConfig.clientId,
      groupId = consumerConfig.groupId)