Spark流示例使用其他参数调用updateStateByKey

Question

Wondering why the StatefulNetworkWordCount.scala example calls the infamous updateStateByKey() function, which is supposed to take a function only as parameter with instead:

val stateDstream = wordDstream.updateStateByKey[Int](newUpdateFunc,
  new HashPartitioner (ssc.sparkContext.defaultParallelism), true, initialRDD)

Why the need (and how does that get processed - this is not in the signature of updateStateByKey()?) to pass a partitioner, a boolean, and an RDD ?

thanks, Matt

Answer 1

It is because:

1. You see the different Spark release branch: https://github.com/apache/spark/blob/branch-1.3/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala . In Spark 1.2 this code was with just updateStateByKey receiving a single function as a parameter, while in 1.3 they have optimized it
2. Different versions of updateStateByKey exist in both 1.2 and 1.3. But in 1.2 there is no version with 4 parameters, it was introduced only in 1.3: https://github.com/apache/spark/blob/branch-1.3/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala

Here is the code:

/**
* 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.
* @param initialRDD initial state value of each key.
* @tparam S State type
*/
def updateStateByKey[S: ClassTag](
    updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)],
    partitioner: Partitioner,
    rememberPartitioner: Boolean,
    initialRDD: RDD[(K, S)]
): DStream[(K, S)] = {
    new StateDStream(self, ssc.sc.clean(updateFunc), partitioner,
    rememberPartitioner, Some(initialRDD))
}