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package spark.streaming
import spark.RDD
import spark.Logging
import spark.SparkEnv
import spark.SparkContext
import spark.storage.StorageLevel
import scala.collection.mutable.Queue
import java.io.InputStream
import java.util.concurrent.atomic.AtomicInteger
import org.apache.hadoop.io.LongWritable
import org.apache.hadoop.io.Text
import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
import org.apache.flume.source.avro.AvroFlumeEvent
import org.apache.hadoop.fs.Path
import java.util.UUID
import spark.util.MetadataCleaner
/**
* A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic
* information (such as, cluster URL and job name) to internally create a SparkContext, it provides
* methods used to create DStream from various input sources.
*/
class StreamingContext private (
sc_ : SparkContext,
cp_ : Checkpoint,
batchDur_ : Time
) extends Logging {
/**
* Creates a StreamingContext using an existing SparkContext.
* @param sparkContext Existing SparkContext
* @param batchDuration The time interval at which streaming data will be divided into batches
*/
def this(sparkContext: SparkContext, batchDuration: Time) = this(sparkContext, null, batchDuration)
/**
* Creates a StreamingContext by providing the details necessary for creating a new SparkContext.
* @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
* @param frameworkName A name for your job, to display on the cluster web UI
* @param batchDuration The time interval at which streaming data will be divided into batches
*/
def this(master: String, frameworkName: String, batchDuration: Time) =
this(StreamingContext.createNewSparkContext(master, frameworkName), null, batchDuration)
/**
* Re-creates a StreamingContext from a checkpoint file.
* @param path Path either to the directory that was specified as the checkpoint directory, or
* to the checkpoint file 'graph' or 'graph.bk'.
*/
def this(path: String) = this(null, CheckpointReader.read(path), null)
initLogging()
if (sc_ == null && cp_ == null) {
throw new Exception("Streaming Context cannot be initilalized with " +
"both SparkContext and checkpoint as null")
}
protected[streaming] val isCheckpointPresent = (cp_ != null)
val sc: SparkContext = {
if (isCheckpointPresent) {
new SparkContext(cp_.master, cp_.framework, cp_.sparkHome, cp_.jars)
} else {
sc_
}
}
protected[streaming] val env = SparkEnv.get
protected[streaming] val graph: DStreamGraph = {
if (isCheckpointPresent) {
cp_.graph.setContext(this)
cp_.graph.restoreCheckpointData()
cp_.graph
} else {
assert(batchDur_ != null, "Batch duration for streaming context cannot be null")
val newGraph = new DStreamGraph()
newGraph.setBatchDuration(batchDur_)
newGraph
}
}
protected[streaming] val nextNetworkInputStreamId = new AtomicInteger(0)
protected[streaming] var networkInputTracker: NetworkInputTracker = null
protected[streaming] var checkpointDir: String = {
if (isCheckpointPresent) {
sc.setCheckpointDir(StreamingContext.getSparkCheckpointDir(cp_.checkpointDir), true)
cp_.checkpointDir
} else {
null
}
}
protected[streaming] var checkpointInterval: Time = if (isCheckpointPresent) cp_.checkpointInterval else null
protected[streaming] var receiverJobThread: Thread = null
protected[streaming] var scheduler: Scheduler = null
def remember(duration: Time) {
graph.remember(duration)
}
def checkpoint(dir: String, interval: Time = null) {
if (dir != null) {
sc.setCheckpointDir(StreamingContext.getSparkCheckpointDir(dir))
checkpointDir = dir
checkpointInterval = interval
} else {
checkpointDir = null
checkpointInterval = null
}
}
protected[streaming] def getInitialCheckpoint(): Checkpoint = {
if (isCheckpointPresent) cp_ else null
}
protected[streaming] def getNewNetworkStreamId() = nextNetworkInputStreamId.getAndIncrement()
/**
* Create an input stream that pulls messages form a Kafka Broker.
*
* @param host Zookeper hostname.
* @param port Zookeper port.
* @param groupId The group id for this consumer.
* @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
* in its own thread.
* @param initialOffsets Optional initial offsets for each of the partitions to consume.
* By default the value is pulled from zookeper.
* @param storageLevel RDD storage level. Defaults to memory-only.
*/
def kafkaStream[T: ClassManifest](
hostname: String,
port: Int,
groupId: String,
topics: Map[String, Int],
initialOffsets: Map[KafkaPartitionKey, Long] = Map[KafkaPartitionKey, Long](),
storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2
): DStream[T] = {
val inputStream = new KafkaInputDStream[T](this, hostname, port, groupId, topics, initialOffsets, storageLevel)
graph.addInputStream(inputStream)
inputStream
}
def networkTextStream(
hostname: String,
port: Int,
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
): DStream[String] = {
networkStream[String](hostname, port, SocketReceiver.bytesToLines, storageLevel)
}
def networkStream[T: ClassManifest](
hostname: String,
port: Int,
converter: (InputStream) => Iterator[T],
storageLevel: StorageLevel
): DStream[T] = {
val inputStream = new SocketInputDStream[T](this, hostname, port, converter, storageLevel)
graph.addInputStream(inputStream)
inputStream
}
def flumeStream (
hostname: String,
port: Int,
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2): DStream[SparkFlumeEvent] = {
val inputStream = new FlumeInputDStream(this, hostname, port, storageLevel)
graph.addInputStream(inputStream)
inputStream
}
def rawNetworkStream[T: ClassManifest](
hostname: String,
port: Int,
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
): DStream[T] = {
val inputStream = new RawInputDStream[T](this, hostname, port, storageLevel)
graph.addInputStream(inputStream)
inputStream
}
/**
* Creates a input stream that monitors a Hadoop-compatible filesystem
* for new files and executes the necessary processing on them.
*/
def fileStream[
K: ClassManifest,
V: ClassManifest,
F <: NewInputFormat[K, V]: ClassManifest
](directory: String): DStream[(K, V)] = {
val inputStream = new FileInputDStream[K, V, F](this, directory)
graph.addInputStream(inputStream)
inputStream
}
def textFileStream(directory: String): DStream[String] = {
fileStream[LongWritable, Text, TextInputFormat](directory).map(_._2.toString)
}
/**
* Creates a input stream from an queue of RDDs. In each batch,
* it will process either one or all of the RDDs returned by the queue
*/
def queueStream[T: ClassManifest](
queue: Queue[RDD[T]],
oneAtATime: Boolean = true,
defaultRDD: RDD[T] = null
): DStream[T] = {
val inputStream = new QueueInputDStream(this, queue, oneAtATime, defaultRDD)
graph.addInputStream(inputStream)
inputStream
}
def queueStream[T: ClassManifest](array: Array[RDD[T]]): DStream[T] = {
val queue = new Queue[RDD[T]]
val inputStream = queueStream(queue, true, null)
queue ++= array
inputStream
}
def union[T: ClassManifest](streams: Seq[DStream[T]]): DStream[T] = {
new UnionDStream[T](streams.toArray)
}
/**
* Registers an input stream that will be started (InputDStream.start() called) to get the
* input data.
*/
def registerInputStream(inputStream: InputDStream[_]) {
graph.addInputStream(inputStream)
}
/**
* Registers an output stream that will be computed every interval
*/
def registerOutputStream(outputStream: DStream[_]) {
graph.addOutputStream(outputStream)
}
protected def validate() {
assert(graph != null, "Graph is null")
graph.validate()
assert(
checkpointDir == null || checkpointInterval != null,
"Checkpoint directory has been set, but the graph checkpointing interval has " +
"not been set. Please use StreamingContext.checkpoint() to set the interval."
)
}
/**
* This function starts the execution of the streams.
*/
def start() {
if (checkpointDir != null && checkpointInterval == null && graph != null) {
checkpointInterval = graph.batchDuration
}
validate()
val networkInputStreams = graph.getInputStreams().filter(s => s match {
case n: NetworkInputDStream[_] => true
case _ => false
}).map(_.asInstanceOf[NetworkInputDStream[_]]).toArray
if (networkInputStreams.length > 0) {
// Start the network input tracker (must start before receivers)
networkInputTracker = new NetworkInputTracker(this, networkInputStreams)
networkInputTracker.start()
}
Thread.sleep(1000)
// Start the scheduler
scheduler = new Scheduler(this)
scheduler.start()
}
/**
* This function stops the execution of the streams.
*/
def stop() {
try {
if (scheduler != null) scheduler.stop()
if (networkInputTracker != null) networkInputTracker.stop()
if (receiverJobThread != null) receiverJobThread.interrupt()
sc.stop()
logInfo("StreamingContext stopped successfully")
} catch {
case e: Exception => logWarning("Error while stopping", e)
}
}
}
object StreamingContext {
protected[streaming] def createNewSparkContext(master: String, frameworkName: String): SparkContext = {
// Set the default cleaner delay to an hour if not already set.
// This should be sufficient for even 1 second interval.
if (MetadataCleaner.getDelaySeconds < 0) {
MetadataCleaner.setDelaySeconds(60)
}
new SparkContext(master, frameworkName)
}
implicit def toPairDStreamFunctions[K: ClassManifest, V: ClassManifest](stream: DStream[(K,V)]) = {
new PairDStreamFunctions[K, V](stream)
}
protected[streaming] def rddToFileName[T](prefix: String, suffix: String, time: Time): String = {
if (prefix == null) {
time.millis.toString
} else if (suffix == null || suffix.length ==0) {
prefix + "-" + time.milliseconds
} else {
prefix + "-" + time.milliseconds + "." + suffix
}
}
protected[streaming] def getSparkCheckpointDir(sscCheckpointDir: String): String = {
new Path(sscCheckpointDir, UUID.randomUUID.toString).toString
}
}
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