Spark

Storm

開發語言：Scala

開發語言：Clojure

編程模型：DStream

編程模型：Spout/Bolt

Transformation

Meaning

map(func)

Return a new DStream by passing each element of the source DStream through a function func.

flatMap(func)

Similar to map, but each input item can be mapped to 0 or more output items.

filter(func)

Return a new DStream by selecting only the records of the source DStream on which func returns true.

repartition(numPartitions)

Changes the level of parallelism in this DStream by creating more or fewer partitions.

union(otherStream)

Return a new DStream that contains the union of the elements in the source DStream and otherDStream.

count()

Return a new DStream of single-element RDDs by counting the number of elements in each RDD of the source DStream.

reduce(func)

Return a new DStream of single-element RDDs by aggregating the elements in each RDD of the source DStream using a function func (which takes two arguments and returns one). The function should be associative so that it can be computed in parallel.

countByValue()

When called on a DStream of elements of type K, return a new DStream of (K, Long) pairs where the value of each key is its frequency in each RDD of the source DStream.

reduceByKey(func, [numTasks])??

When called on a DStream of (K, V) pairs, return a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Note: By default, this uses Spark's default number of parallel tasks (2 for local mode, and in cluster mode the number is determined by the config property spark.default.parallelism) to do the grouping. You can pass an optional numTasks argument to set a different number of tasks.

join(otherStream, [numTasks])

When called on two DStreams of (K, V) and (K, W) pairs, return a new DStream of (K, (V, W)) pairs with all pairs of elements for each key.

cogroup(otherStream, [numTasks])

When called on a DStream of (K, V) and (K, W) pairs, return a new DStream of (K, Seq[V], Seq[W]) tuples.

transform(func)?????

Return a new DStream by applying a RDD-to-RDD function to every RDD of the source DStream. This can be used to do arbitrary RDD operations on the DStream.

updateStateByKey(func)

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 for the key. This can be used to maintain arbitrary state data for each key.

Output Operation

Meaning

print()

Prints the first ten elements of every batch of data in a DStream on the driver node running the streaming application. This is useful for development and debugging.

saveAsTextFiles(prefix, [suffix])

Save this DStream's contents as text files. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".

saveAsObjectFiles(prefix, [suffix])

Save this DStream's contents as SequenceFiles of serialized Java objects. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".

saveAsHadoopFiles(prefix, [suffix])

Save this DStream's contents as Hadoop files. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".

foreachRDD(func)

The most generic output operator that applies a function, func, to each RDD generated from the stream. This function should push the data in each RDD to an external system, such as saving the RDD to files, or writing it over the network to a database. Note that the function func is executed in the driver process running the streaming application, and will usually have RDD actions in it that will force the computation of the streaming RDDs.