Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/9256#discussion_r44303122
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/StateSpec.scala ---
@@ -0,0 +1,196 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaPairRDD
+import org.apache.spark.rdd.RDD
+import org.apache.spark.{HashPartitioner, Partitioner}
+
+
+/**
+ * :: Experimental ::
+ * Abstract class representing all the specifications of the DStream
transformation
+ * `trackStateByKey` operation of a
+ * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair
DStream]] (Scala) or a
+ * [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]]
(Java).
+ * Use the [[org.apache.spark.streaming.StateSpec StateSpec.apply()]] or
+ * [[org.apache.spark.streaming.StateSpec StateSpec.create()]] to create
instances of
+ * this class.
+ *
+ * Example in Scala:
+ * {{{
+ * val spec = StateSpec(trackingFunction).numPartitions(10)
+ *
+ * val emittedRecordDStream =
keyValueDStream.trackStateByKey[StateType, EmittedDataType](spec)
+ * }}}
+ *
+ * Example in Java:
+ * {{{
+ * StateStateSpec[StateType, EmittedDataType] spec =
+ * StateStateSpec.create[StateType,
EmittedDataType](trackingFunction).numPartition(10);
+ *
+ * JavaDStream[EmittedDataType] emittedRecordDStream =
+ * javaPairDStream.trackStateByKey[StateType, EmittedDataType](spec);
+ * }}}
+ */
+@Experimental
+sealed abstract class StateSpec[K, V, S, T] extends Serializable {
+
+ /** Set the RDD containing the initial states that will be used by
`trackStateByKey`*/
+ def initialState(rdd: RDD[(K, S)]): this.type
+
+ /** Set the RDD containing the initial states that will be used by
`trackStateByKey`*/
+ def initialState(javaPairRDD: JavaPairRDD[K, S]): this.type
+
+ /**
+ * Set the number of partitions by which the state RDDs generated by
`trackStateByKey`
+ * will be partitioned. Hash partitioning will be used on the
+ */
+ def numPartitions(numPartitions: Int): this.type
+
+ /**
+ * Set the partitioner by which the state RDDs generated by
`trackStateByKey` will be
+ * be partitioned.
+ */
+ def partitioner(partitioner: Partitioner): this.type
+
+ /**
+ * Set the duration after which the state of an idle key will be
removed. A key and its state is
+ * considered idle if it has not received any data for at least the
given duration. The state
+ * tracking function will be called one final time on the idle states
that are going to be
+ * removed; [[org.apache.spark.streaming.State State.isTimingOut()]] set
+ * to `true` in that call.
+ */
+ def timeout(idleDuration: Duration): this.type
+}
+
+
+/**
+ * :: Experimental ::
+ * Builder object for creating instances of
[[org.apache.spark.streaming.StateSpec StateSpec]]
+ * that is used for specifying the parameters of the DStream transformation
+ * `trackStateByKey` operation of a
+ * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair
DStream]] (Scala) or a
+ * [[org.apache.spark.streaming.api.java.JavaPairDStream JavaPairDStream]]
(Java).
+ *
+ * Example in Scala:
+ * {{{
+ * val spec = StateSpec(trackingFunction).numPartitions(10)
+ *
+ * val emittedRecordDStream =
keyValueDStream.trackStateByKey[StateType, EmittedDataType](spec)
+ * }}}
+ *
+ * Example in Java:
+ * {{{
+ * StateStateSpec[StateType, EmittedDataType] spec =
+ * StateStateSpec.create[StateType,
EmittedDataType](trackingFunction).numPartition(10);
+ *
+ * JavaDStream[EmittedDataType] emittedRecordDStream =
+ * javaPairDStream.trackStateByKey[StateType, EmittedDataType](spec);
+ * }}}
+ */
+@Experimental
+object StateSpec {
+ /**
+ * Create a [[org.apache.spark.streaming.StateSpec StateSpec]] for
setting all the specifications
+ * `trackStateByKey` operation on a
+ * [[org.apache.spark.streaming.dstream.PairDStreamFunctions pair
DStream]] (Scala) or a
+ * [[org.apache.spark.streaming.api.java.JavaPairDStream
JavaPairDStream]] (Java).
+ * @param trackingFunction The function applied on every data item to
manage the associated state
+ * and generate the emitted data and
+ * @tparam KeyType Class of the keys
+ * @tparam ValueType Class of the values
+ * @tparam StateType Class of the states data
+ * @tparam EmittedType Class of the emitted data
+ */
+ def apply[KeyType, ValueType, StateType, EmittedType](
--- End diff --
We could have multiple versions of the function() method that take
different function types. That seems fine to me.
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