从这节课开始，简介Spark Streaming的状态管理。
SparkStreaming 是按BatchDuration来划分Job的，但我们有时需要根据业务要求按照另外的时间周期（比如说，对过去24小时、或者过去一周的数据，等等这些大于BatchDuration的周期），对数据进行处理（比如计算最近24小时的销售额排名、今年的最新销售量等）。这需要根据之前的计算结果和新时间周期的数据，计算出新的计算结果。
updateStateByKey和mapWithState都是针对类型的数据进行操作，而RDD类本身并不对 类型的数据进行操作，所以要借助隐式转换。隐式转换放在了DStream伴生对象的区域。
object DStream {
// `toPairDStreamFunctions` was in SparkContext before 1.3 and users had to
// `import StreamingContext._` to enable it. Now we move it here to make the compiler find
// it automatically. However, we still keep the old function in StreamingContext for backward
// compatibility and forward to the following function directly.
implicit deftoPairDStreamFunctions[K, V](stream: DStream[(K, V)])
(implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null):
PairDStreamFunctions[K, V] = {
newPairDStreamFunctions[K, V](stream)
}
...
}
生成了PairDStreamFunctions对象。PairDStreamFunctions类中有updateStateByKey、mapWithState这些功能。
1.updateStateByKey解密
先看updateStateByKey：
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of each key.
* Hash partitioning is used to generate the RDDs with Spark's default number of partitions.
* @param updateFunc State update function. If `this` function returns None, then
* corresponding state key-value pair will be eliminated.
* @tparam S State type
*/
defupdateStateByKey[S: ClassTag](
updateFunc: (Seq[V], Option[S]) => Option[S]
): DStream[(K, S)] = ssc.withScope {
updateStateByKey(updateFunc,defaultPartitioner())
}
updateStateByKey返回的都是DStream类型。
根据updateFunc这个函数来更新状态。其中参数：Seq[V]是本次的数据类型，Option[S]是前次计算结果类型，本次计算结果类型也是Option[S]。
计算肯定需要Partitioner。因为Hash高效率且不做排序，默认Partitioner是HashPartitoner。
PairDStreamFunction.defaultPartitioner：
private[streaming] defdefaultPartitioner(numPartitions: Int = self.ssc.sc.defaultParallelism) = {
newHashPartitioner(numPartitions)
}
看其中返回值类型为StateDStream的updateStateByKey：
/**
* Return a new "state" DStream where the state for each key is updated by applying
* the given function on the previous state of the key and the new values of each key.
* org.apache.spark.Partitioner is used to control the partitioning of each RDD.
* @param updateFunc State update function. Note, that this function may generate a different
* tuple with a different key than the input key. Therefore keys may be removed
* or added in this way. It is up to the developer to decide whether to
* remember the partitioner despite the key being changed.
* @param partitioner Partitioner for controlling the partitioning of each RDD in the new
* DStream
* @param rememberPartitioner Whether to remember the paritioner object in the generated RDDs.
* @tparam S State type
*/
defupdateStateByKey[S: ClassTag](
updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)],
partitioner: Partitioner,
rememberPartitioner: Boolean
): DStream[(K, S)] = ssc.withScope {
newStateDStream(self, ssc.sc.clean(updateFunc), partitioner, rememberPartitioner, None)
}
看看这个StateDStream：
classStateDStream[K: ClassTag, V: ClassTag, S: ClassTag](
parent: DStream[(K, V)],
updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)],
partitioner: Partitioner,
preservePartitioning: Boolean,
initialRDD : Option[RDD[(K, S)]]
) extends DStream[(K, S)](parent.ssc) {
super.persist(StorageLevel.MEMORY_ONLY_SER)
...
是基于内存的。
所有像StateDStream这样的DStream子类都要覆写compute方法。
StateDStream.compute：
override defcompute(validTime: Time): Option[RDD[(K, S)]] = {
// Try to get the previous state RDD
getOrCompute(validTime - slideDuration) match {
case Some(prevStateRDD) => { // If previous state RDD exists
// Try to get the parent RDD
parent.getOrCompute(validTime) match {
case Some(parentRDD) => { // If parent RDD exists, then compute as usual
computeUsingPreviousRDD(parentRDD, prevStateRDD)
}
case None => { // If parent RDD does not exist
// Re-apply the update function to the old state RDD
val updateFuncLocal = updateFunc
val finalFunc = (iterator: Iterator[(K, S)]) => {
val i = iterator.map(t => (t._1, Seq[V](), Option(t._2)))
updateFuncLocal(i)
}
val stateRDD = prevStateRDD.mapPartitions(finalFunc, preservePartitioning)
Some(stateRDD)
}
}
}
case None => { // If previous session RDD does not exist (first input data)
// Try to get the parent RDD
parent.getOrCompute(validTime) match {
case Some(parentRDD) => { // If parent RDD exists, then compute as usual
initialRDD match {
case None => {
// Define the function for the mapPartition operation on grouped RDD;
// first map the grouped tuple to tuples of required type,
// and then apply the update function
val updateFuncLocal = updateFunc
val finalFunc = (iterator : Iterator[(K, Iterable[V])]) => {
updateFuncLocal (iterator.map (tuple => (tuple._1, tuple._2.toSeq, None)))
}
val groupedRDD = parentRDD.groupByKey (partitioner)
val sessionRDD = groupedRDD.mapPartitions (finalFunc, preservePartitioning)
// logDebug("Generating state RDD for time " + validTime + " (first)")
Some (sessionRDD)
}
case Some (initialStateRDD) => {
computeUsingPreviousRDD(parentRDD, initialStateRDD)
}
}
}
case None => { // If parent RDD does not exist, then nothing to do!
// logDebug("Not generating state RDD (no previous state, no parent)")
None
}
}
}
}
}
其中会用到computeUsingPreviousRDD方法。去看看。
StateDStream.computeUsingPreviousRDD：
private [this] defcomputeUsingPreviousRDD(
parentRDD : RDD[(K, V)], prevStateRDD : RDD[(K, S)]) = {
// Define the function for the mapPartition operation on cogrouped RDD;
// first map the cogrouped tuple to tuples of required type,
// and then apply the update function
val updateFuncLocal = updateFunc
val finalFunc = (iterator: Iterator[(K, (Iterable[V], Iterable[S]))]) => {
val i = iterator.map(t => {
val itr = t._2._2.iterator
val headOption = if (itr.hasNext) Some(itr.next()) else None
(t._1, t._2._1.toSeq, headOption)
})
updateFuncLocal(i)
}
val cogroupedRDD = parentRDD.cogroup(prevStateRDD, partitioner)
val stateRDD = cogroupedRDD.mapPartitions(finalFunc, preservePartitioning)
Some(stateRDD)
}
由于cogroup会对所有数据进行扫描，再按key进行分组，所以性能上会有问题。特别是随着时间的推移，这样的计算到后面会越算越慢。
所以数据量大的计算、复杂的计算，都不建议使用updateStateByKey。
2.mapWithState解密
虽然有人使用mapWithState后感觉效果还可以，但源码中仍表明，mapWithState还在试验状态。
mapWithState方法有多个。先看第一个。
PairDStreamFunctions.mapWithState：
/**
* :: Experimental ::
* Return a [[MapWithStateDStream]] by applying a function to every key-value element of
* `this` stream, while maintaining some state data for each unique key. The mapping function
* and other specification (e.g. partitioners, timeouts, initial state data, etc.) of this
* transformation can be specified using [[StateSpec]] class. The state data is accessible in
* as a parameter of type [[State]] in the mapping function.
*
* Example of using `mapWithState`:
* {{{
* // A mapping function that maintains an integer state and return a String
* def mappingFunction(key: String, value: Option[Int], state:State[Int]): Option[String] = {
* // Use state.exists(), state.get(), state.update() and state.remove()
* // to manage state, and return the necessary string
* }
*
* val spec = StateSpec.function(mappingFunction).numPartitions(10)
*
* val mapWithStateDStream = keyValueDStream.mapWithState[StateType, MappedType](spec)
* }}}
*
* @param spec Specification of this transformation
* @tparam StateType Class type of the state data
* @tparam MappedType Class type of the mapped data
*/
@Experimental
defmapWithState[StateType: ClassTag, MappedType: ClassTag](
spec:StateSpec[K, V, StateType, MappedType]
):MapWithStateDStream[K, V, StateType, MappedType] = {
new MapWithStateDStreamImpl[K, V, StateType, MappedType](
self,
spec.asInstanceOf[StateSpecImpl[K, V, StateType, MappedType]]
)
}
注释中给出了一个mapWithState使用实例。先要定义一个mappingFunction。mappingFunction的参数中，State类型的state是历史数据，相当于一个内存数据表；key指明是对state中的哪个键进行操作；value指明键值。
StateSpec类型的参数中封装了mapping功能和转换的相应配置（例如：partitioners、超时设定、初始状态数据等）。
mapWithState
返回的是MapWithStateDStream类型。
来看看State类。其中的注释有例子参考。
/**
* :: Experimental ::
* Abstract class for getting and updating the state in mapping function used in the `mapWithState`
* operation of a [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair DStream]] (Scala)
* or a [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]] (Java).
*
* Scala example of using `State`:
* {{{
* // A mapping function that maintains an integer state and returns a String
* def mappingFunction(key: String, value: Option[Int], state: State[Int]): Option[String] = {
* // Check if state exists
* if (state.exists) {
* val existingState = state.get // Get the existing state
* val shouldRemove = ... // Decide whether to remove the state
* if (shouldRemove) {
* state.remove() // Remove the state
* } else {
* val newState = ...
* state.update(newState) // Set the new state
* }
* } else {
* val initialState = ...
* state.update(initialState) // Set the initial state
* }
* ... // return something
* }
*
* }}}
*
...
sealed abstractclassState[S] {
State中有exists、get、update、remove、isTimingOut等需要在子类中覆写的方法。
State中还有个内部实现类StateImpl：
/** Internal implementation of the [[State]] interface */
private[streaming] classStateImpl[S] extends State[S] {
private var state: S = null.asInstanceOf[S]
private var defined: Boolean = false
private var timingOut: Boolean = false
private var updated: Boolean = false
private var removed: Boolean = false
// ========= Public API =========
override def exists(): Boolean = {
defined
}
...
StateImpl有一些状态变量，并且覆写了State中的方法。
回去再看
PairDStreamFunctions中的其它mapWithState方法。
@Experimental
defmapWithState[StateType: ClassTag, MappedType: ClassTag](
spec: StateSpec[K, V, StateType, MappedType]
): MapWithStateDStream[K, V, StateType, MappedType] = {
newMapWithStateDStreamImpl[K, V, StateType, MappedType](
self,
spec.asInstanceOf[StateSpecImpl[K, V, StateType, MappedType]]
)
}
先看看StateSpecImpl。StateSpecImpl是StateSpec类中的case
class。
/** Internal implementation of [[org.apache.spark.streaming.StateSpec]] interface. */
private[streaming]
case classStateSpecImpl[K, V, S, T](
function: (Time, K, Option[V], State[S]) => Option[T])extends StateSpec[K, V, S, T] {
其参数是一个函数。
StateSpecImpl中的代码片段：
require(function != null)
@volatile private var partitioner: Partitioner = null
@volatile private var initialStateRDD: RDD[(K, S)] = null
@volatile private var timeoutInterval: Duration = null
...
// ================= Private Methods =================
private[streaming] defgetFunction(): (Time, K, Option[V], State[S]) => Option[T] =function
private[streaming] def getInitialStateRDD(): Option[RDD[(K, S)]] = Option(initialStateRDD)
private[streaming] def getPartitioner(): Option[Partitioner] = Option(partitioner)
private[streaming] def getTimeoutInterval(): Option[Duration] = Option(timeoutInterval)
有一些私有变量，及其变量的获取方法。特别是有一个函数的获取方法。
再看看MapWithStateDStream的子类MapWithStateDStreamImpl：
/** Internal implementation of the [[MapWithStateDStream]] */
private[streaming] classMapWithStateDStreamImpl[
KeyType: ClassTag, ValueType: ClassTag, StateType: ClassTag, MappedType: ClassTag](
dataStream: DStream[(KeyType, ValueType)],
spec: StateSpecImpl[KeyType, ValueType, StateType, MappedType])
extends MapWithStateDStream[KeyType, ValueType, StateType, MappedType](dataStream.context) {
private valinternalStream=
newInternalMapWithStateDStream[KeyType, ValueType, StateType, MappedType](dataStream, spec)
override def slideDuration: Duration = internalStream.slideDuration
override def dependencies: List[DStream[_]] = List(internalStream)
override defcompute(validTime: Time): Option[RDD[MappedType]] = {
internalStream.getOrCompute(validTime).map { _.flatMap[MappedType] { _.mappedData } }
}
其中生成了一个DStream子类InternalMapWithStateDStream的对象。
InternalMapWithStateDStream类：
private[streaming]
classInternalMapWithStateDStream[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
parent: DStream[(K, V)], spec: StateSpecImpl[K, V, S, E])
extends DStream[MapWithStateRDDRecord[K, S, E]](parent.context) {
persist(StorageLevel.MEMORY_ONLY)
InternalMapWithStateDStream.compute：
/** Method that generates a RDD for the given time */
override defcompute(validTime: Time): Option[RDD[MapWithStateRDDRecord[K, S, E]]] = {
// Get the previous state or create a new empty state RDD
val prevStateRDD = getOrCompute(validTime - slideDuration) match {
case Some(rdd) =>
if (rdd.partitioner != Some(partitioner)) {
// If the RDD is not partitioned the right way, let us repartition it using the
// partition index as the key. This is to ensure that state RDD is always partitioned
// before creating another state RDD using it
MapWithStateRDD.createFromRDD[K, V, S, E](
rdd.flatMap { _.stateMap.getAll() }, partitioner, validTime)
} else {
rdd
}
case None =>
MapWithStateRDD.createFromPairRDD[K, V, S, E](
spec.getInitialStateRDD().getOrElse(new EmptyRDD[(K, S)](ssc.sparkContext)),
partitioner,
validTime
)
}
// Compute the new state RDD with previous state RDD and partitioned data RDD
// Even if there is no data RDD, use an empty one to create a new state RDD
val dataRDD = parent.getOrCompute(validTime).getOrElse {
context.sparkContext.emptyRDD[(K, V)]
}
val partitionedDataRDD = dataRDD.partitionBy(partitioner)
val timeoutThresholdTime = spec.getTimeoutInterval().map { interval =>
(validTime - interval).milliseconds
}
Some(newMapWithStateRDD(
prevStateRDD, partitionedDataRDD, mappingFunction, validTime, timeoutThresholdTime))
}
生成了RDD子类MapWithStateRDD的对象。
MapWithStateRDD：
private[streaming] classMapWithStateRDD[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
private var prevStateRDD: RDD[MapWithStateRDDRecord[K, S, E]],
private var partitionedDataRDD: RDD[(K, V)],
mappingFunction: (Time, K, Option[V], State[S]) => Option[E],
batchTime: Time,
timeoutThresholdTime: Option[Long]
) extends RDD[MapWithStateRDDRecord[K, S, E]](
partitionedDataRDD.sparkContext,
List(
new OneToOneDependency[MapWithStateRDDRecord[K, S, E]](prevStateRDD),
new OneToOneDependency(partitionedDataRDD))
) {
每个RDD partition是被一个MapWithStateRDDRecord类型的记录所代表，
MapWithStateRDD.compute：
override defcompute(
partition: Partition, context: TaskContext): Iterator[MapWithStateRDDRecord[K, S, E]] = {
val stateRDDPartition = partition.asInstanceOf[MapWithStateRDDPartition]
val prevStateRDDIterator = prevStateRDD.iterator(
stateRDDPartition.previousSessionRDDPartition, context)
val dataIterator = partitionedDataRDD.iterator(
stateRDDPartition.partitionedDataRDDPartition, context)
val prevRecord = if (prevStateRDDIterator.hasNext) Some(prevStateRDDIterator.next()) else None
val newRecord = MapWithStateRDDRecord.updateRecordWithData(
prevRecord,
dataIterator,
mappingFunction,
batchTime,
timeoutThresholdTime,
removeTimedoutData = doFullScan // remove timedout data only when full scan is enabled
)
Iterator(newRecord)
}
MapWithStateRDDRecord有伴生对象：
private[streaming] objectMapWithStateRDDRecord{
defupdateRecordWithData[K: ClassTag, V: ClassTag, S: ClassTag, E: ClassTag](
prevRecord: Option[MapWithStateRDDRecord[K, S, E]],
dataIterator: Iterator[(K, V)],
mappingFunction: (Time, K, Option[V], State[S]) => Option[E],
batchTime: Time,
timeoutThresholdTime: Option[Long],
removeTimedoutData: Boolean
): MapWithStateRDDRecord[K, S, E] = {
// Create a new state map by cloning the previous one (if it exists) or by creating an empty one
val newStateMap = prevRecord.map { _.stateMap.copy() }. getOrElse { new EmptyStateMap[K, S]() }
val mappedData = new ArrayBuffer[E]
val wrappedState = new StateImpl[S]()
// Call the mapping function on each record in the data iterator, and accordingly
// update the states touched, and collect the data returned by the mapping function
//
此处是mapWithState性能较好的核心代码之所在。
dataIterator.foreach { case (key, value) =>
wrappedState.wrap(newStateMap.get(key))
val returned = mappingFunction(batchTime, key, Some(value), wrappedState)
if (wrappedState.isRemoved) {
newStateMap.remove(key)
} else if (wrappedState.isUpdated
|| (wrappedState.exists && timeoutThresholdTime.isDefined)) {
newStateMap.put(key, wrappedState.get(), batchTime.milliseconds)
}
mappedData ++= returned
}
// Get the timed out state records, call the mapping function on each and collect the
// data returned
if (removeTimedoutData && timeoutThresholdTime.isDefined) {
newStateMap.getByTime(timeoutThresholdTime.get).foreach { case (key, state, _) =>
wrappedState.wrapTimingOutState(state)
val returned = mappingFunction(batchTime, key, None, wrappedState)
mappedData ++= returned
newStateMap.remove(key)
}
}
MapWithStateRDDRecord(newStateMap, mappedData)
}
借助了RDD的不变性，同时也借助了可变化特征，完成了高效的处理过程。
所以不可变的RDD也可用于处理变化的数据。
备注：
资料来源于：DT_大数据梦工厂（Spark发行版本定制）
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