Kafka：ZK+Kafka+Spark Streaming集群环境搭建（三十）：使用flatMapGroupsWithState替换agg

flatMapGroupsWithState的出现解决了什么问题：

flatMapGroupsWithState的出现在spark structured streaming原因(从spark.2.2.0开始才开始支持)：

1）可以实现agg函数；

2）就目前最新spark2.3.2版本来说在spark structured streming中依然不支持对dataset多次agg操作

，而flatMapGroupsWithState可以替代agg的作用，同时它允许在sink为append模式下在agg之前使用。

注意：尽管允许agg之前使用，但前提是：输出（sink）方式Append方式。

flatMapGroupsWithState的使用示例（从网上找到）：

参考：《https://jaceklaskowski.gitbooks.io/spark-structured-streaming/spark-sql-streaming-KeyValueGroupedDataset-flatMapGroupsWithState.html》

说明：以下示例代码实现了“select deviceId,count(0) as count from tbName group by deviceId.”。

1）spark2.3.0版本下定义一个Signal实体类：

scala> spark.version
res0: String = 2.3.0-SNAPSHOT

import java.sql.Timestamp
type DeviceId = Int
case class Signal(timestamp: java.sql.Timestamp, value: Long, deviceId: DeviceId)

2）使用Rate source方式生成一些测试数据（随机实时流方式），并查看执行计划：

// input stream
import org.apache.spark.sql.functions._
val signals = spark.
  readStream.
  format("rate").
  option("rowsPerSecond", 1).
  load.
  withColumn("value", $"value" % 10).  // <-- randomize the values (just for fun)
  withColumn("deviceId", rint(rand() * 10) cast "int"). // <-- 10 devices randomly assigned to values
  as[Signal] // <-- convert to our type (from "unpleasant" Row)
scala> signals.explain
== Physical Plan ==
*Project [timestamp#0, (value#1L % 10) AS value#5L, cast(ROUND((rand(4440296395341152993) * 10.0)) as int) AS deviceId#9]
+- StreamingRelation rate, [timestamp#0, value#1L]

3）对Rate source流对象进行groupBy，使用flatMapGroupsWithState实现agg

// stream processing using flatMapGroupsWithState operator
val device: Signal => DeviceId = { case Signal(_, _, deviceId) => deviceId }
val signalsByDevice = signals.groupByKey(device)

import org.apache.spark.sql.streaming.GroupState
type Key = Int
type Count = Long
type State = Map[Key, Count]
case class EventsCounted(deviceId: DeviceId, count: Long)
def countValuesPerKey(deviceId: Int, signalsPerDevice: Iterator[Signal], state: GroupState[State]): Iterator[EventsCounted] = {
  val values = signalsPerDevice.toList
  println(s"Device: $deviceId")
  println(s"Signals (${values.size}):")
  values.zipWithIndex.foreach { case (v, idx) => println(s"$idx. $v") }
  println(s"State: $state")

  // update the state with the count of elements for the key
  val initialState: State = Map(deviceId -> 0)
  val oldState = state.getOption.getOrElse(initialState)
  // the name to highlight that the state is for the key only
  val newValue = oldState(deviceId) + values.size
  val newState = Map(deviceId -> newValue)
  state.update(newState)

  // you must not return as it's already consumed
  // that leads to a very subtle error where no elements are in an iterator
  // iterators are one-pass data structures
  Iterator(EventsCounted(deviceId, newValue))
}
import org.apache.spark.sql.streaming.{GroupStateTimeout, OutputMode}

val signalCounter = signalsByDevice.flatMapGroupsWithState(
  outputMode = OutputMode.Append,
  timeoutConf = GroupStateTimeout.NoTimeout)(func = countValuesPerKey)

4）使用Console Sink方式打印agg结果：

import org.apache.spark.sql.streaming.{OutputMode, Trigger}
import scala.concurrent.duration._
val sq = signalCounter.
  writeStream.
  format("console").
  option("truncate", false).
  trigger(Trigger.ProcessingTime(10.seconds)).
  outputMode(OutputMode.Append).
  start

5）console print

...
-------------------------------------------
Batch:
-------------------------------------------
+--------+-----+
|deviceId|count|
+--------+-----+
+--------+-----+
...
// :: INFO StreamExecution: Streaming query made progress: {
  "id" : "a43822a6-500b-4f02-9133-53e9d39eedbf",
  "runId" : "79cb037e-0f28-4faf-a03e-2572b4301afe",
  "name" : null,
  "timestamp" : "2017-08-21T06:57:26.719Z",
  "batchId" : ,
  "numInputRows" : ,
  "processedRowsPerSecond" : 0.0,
  "durationMs" : {
    "addBatch" : ,
    "getBatch" : ,
    "getOffset" : ,
    "queryPlanning" : ,
    "triggerExecution" : ,
    "walCommit" :
  },
  "stateOperators" : [ {
    "numRowsTotal" : ,
    "numRowsUpdated" : ,
    "memoryUsedBytes" :
  } ],
  "sources" : [ {
    "description" : "RateSource[rowsPerSecond=1, rampUpTimeSeconds=0, numPartitions=8]",
    "startOffset" : null,
    "endOffset" : ,
    "numInputRows" : ,
    "processedRowsPerSecond" : 0.0
  } ],
  "sink" : {
    "description" : "ConsoleSink[numRows=20, truncate=false]"
  }
}
// :: DEBUG StreamExecution: batch  committed
...
-------------------------------------------
Batch:
-------------------------------------------
Device:
Signals ():
. Signal(-- ::27.682,,)
State: GroupState(<undefined>)
Device:
Signals ():
. Signal(-- ::26.682,,)
State: GroupState(<undefined>)
Device:
Signals ():
. Signal(-- ::28.682,,)
State: GroupState(<undefined>)
+--------+-----+
|deviceId|count|
+--------+-----+
|       |    |
|       |    |
|       |    |
+--------+-----+
...
// :: INFO StreamExecution: Streaming query made progress: {
  "id" : "a43822a6-500b-4f02-9133-53e9d39eedbf",
  "runId" : "79cb037e-0f28-4faf-a03e-2572b4301afe",
  "name" : null,
  "timestamp" : "2017-08-21T06:57:30.004Z",
  "batchId" : ,
  "numInputRows" : ,
  "inputRowsPerSecond" : 0.91324200913242,
  "processedRowsPerSecond" : 2.2388059701492535,
  "durationMs" : {
    "addBatch" : ,
    "getBatch" : ,
    "getOffset" : ,
    "queryPlanning" : ,
    "triggerExecution" : ,
    "walCommit" :
  },
  "stateOperators" : [ {
    "numRowsTotal" : ,
    "numRowsUpdated" : ,
    "memoryUsedBytes" :
  } ],
  "sources" : [ {
    "description" : "RateSource[rowsPerSecond=1, rampUpTimeSeconds=0, numPartitions=8]",
    "startOffset" : ,
    "endOffset" : ,
    "numInputRows" : ,
    "inputRowsPerSecond" : 0.91324200913242,
    "processedRowsPerSecond" : 2.2388059701492535
  } ],
  "sink" : {
    "description" : "ConsoleSink[numRows=20, truncate=false]"
  }
}
// :: DEBUG StreamExecution: batch  committed
...
-------------------------------------------
Batch:
-------------------------------------------
Device:
Signals ():
. Signal(-- ::36.682,,)
State: GroupState(<undefined>)
Device:
Signals ():
. Signal(-- ::32.682,,)
. Signal(-- ::35.682,,)
State: GroupState(Map( -> ))
Device:
Signals ():
. Signal(-- ::34.682,,)
State: GroupState(<undefined>)
Device:
Signals ():
. Signal(-- ::29.682,,)
State: GroupState(<undefined>)
Device:
Signals ():
. Signal(-- ::31.682,,)
. Signal(-- ::33.682,,)
State: GroupState(Map( -> ))
Device:
Signals ():
. Signal(-- ::30.682,,)
. Signal(-- ::37.682,,)
State: GroupState(Map( -> ))
Device:
Signals ():
. Signal(-- ::38.682,,)
State: GroupState(<undefined>)
+--------+-----+
|deviceId|count|
+--------+-----+
|       |    |
|       |    |
|       |    |
|       |    |
|       |    |
|       |    |
|       |    |
+--------+-----+
...
// :: INFO StreamExecution: Streaming query made progress: {
  "id" : "a43822a6-500b-4f02-9133-53e9d39eedbf",
  "runId" : "79cb037e-0f28-4faf-a03e-2572b4301afe",
  "name" : null,
  "timestamp" : "2017-08-21T06:57:40.005Z",
  "batchId" : ,
  "numInputRows" : ,
  "inputRowsPerSecond" : 0.9999000099990002,
  "processedRowsPerSecond" : 9.242144177449168,
  "durationMs" : {
    "addBatch" : ,
    "getBatch" : ,
    "getOffset" : ,
    "queryPlanning" : ,
    "triggerExecution" : ,
    "walCommit" :
  },
  "stateOperators" : [ {
    "numRowsTotal" : ,
    "numRowsUpdated" : ,
    "memoryUsedBytes" :
  } ],
  "sources" : [ {
    "description" : "RateSource[rowsPerSecond=1, rampUpTimeSeconds=0, numPartitions=8]",
    "startOffset" : ,
    "endOffset" : ,
    "numInputRows" : ,
    "inputRowsPerSecond" : 0.9999000099990002,
    "processedRowsPerSecond" : 9.242144177449168
  } ],
  "sink" : {
    "description" : "ConsoleSink[numRows=20, truncate=false]"
  }
}
// :: DEBUG StreamExecution: batch  committed

// In the end...
sq.stop

// Use stateOperators to access the stats
scala> println(sq.lastProgress.stateOperators().prettyJson)
{
  "numRowsTotal" : ,
  "numRowsUpdated" : ,
  "memoryUsedBytes" :
}