Github user marmbrus commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15307#discussion_r81873108
  
    --- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamMetrics.scala
 ---
    @@ -0,0 +1,244 @@
    +/*
    + * 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.sql.execution.streaming
    +
    +import scala.collection.mutable
    +
    +import com.codahale.metrics.{Gauge, MetricRegistry}
    +
    +import org.apache.spark.internal.Logging
    +import org.apache.spark.metrics.source.{Source => CodahaleSource}
    +import org.apache.spark.sql.DataFrame
    +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
    +import org.apache.spark.sql.execution.SparkPlan
    +import org.apache.spark.util.Clock
    +
    +class StreamMetrics(sources: Set[Source], triggerClock: Clock, 
codahaleSourceName: String)
    +  extends CodahaleSource with Logging {
    +
    +  import StreamMetrics._
    +
    +  // Trigger infos
    +  private val triggerStatus = new mutable.HashMap[String, String]
    +  private val sourceTriggerStatus = new mutable.HashMap[Source, 
mutable.HashMap[String, String]]
    +
    +  // Rate estimators for sources and sinks
    +  private val inputRates = new mutable.HashMap[Source, RateCalculator]
    +  private val processingRates = new mutable.HashMap[Source, RateCalculator]
    +  private val outputRate = new RateCalculator
    +
    +  // Number of input rows in the current trigger
    +  private val numInputRows = new mutable.HashMap[Source, Long]
    +  private var numOutputRows: Option[Long] = None
    +  private var currentTriggerStartTimestamp: Long = -1
    +  private var previousTriggerStartTimestamp: Long = -1
    +  private var latency: Option[Double] = None
    +
    +  override val sourceName: String = codahaleSourceName
    +  override val metricRegistry: MetricRegistry = new MetricRegistry
    +
    +  // =========== Initialization ===========
    +
    +  // Metric names should not have . in them, so that all the metrics of a 
query are identified
    +  // together in Ganglia as a single metric group
    +  registerGauge("inputRate-total", currentInputRate)
    +  registerGauge("processingRate-total", () => currentProcessingRate)
    +  registerGauge("outputRate", () => currentOutputRate)
    +  registerGauge("latency", () => currentLatency().getOrElse(-1.0))
    +
    +  sources.foreach { s =>
    +    inputRates.put(s, new RateCalculator)
    +    processingRates.put(s, new RateCalculator)
    +    sourceTriggerStatus.put(s, new mutable.HashMap[String, String])
    +
    +    registerGauge(s"inputRate-${s.toString}", () => 
currentSourceInputRate(s))
    +    registerGauge(s"processingRate-${s.toString}", () => 
currentSourceProcessingRate(s))
    +  }
    +
    +  // =========== Setter methods ===========
    +
    +  def reportTriggerStarted(triggerId: Long): Unit = synchronized {
    +    numInputRows.clear()
    +    numOutputRows = None
    +    triggerStatus.clear()
    +    sourceTriggerStatus.values.foreach(_.clear())
    +
    +    reportTriggerStatus(TRIGGER_ID, triggerId)
    +    sources.foreach(s => reportSourceTriggerStatus(s, TRIGGER_ID, 
triggerId))
    +    reportTriggerStatus(ACTIVE, true)
    +    currentTriggerStartTimestamp = triggerClock.getTimeMillis()
    +    reportTriggerStatus(START_TIMESTAMP, currentTriggerStartTimestamp)
    +  }
    +
    +  def reportTriggerStatus[T](key: String, value: T): Unit = synchronized {
    +    triggerStatus.put(key, value.toString)
    +  }
    +
    +  def reportSourceTriggerStatus[T](source: Source, key: String, value: T): 
Unit = synchronized {
    +    sourceTriggerStatus(source).put(key, value.toString)
    +  }
    +
    +  def reportNumRows(inputRows: Map[Source, Long], outputRows: 
Option[Long]): Unit = synchronized {
    +    numInputRows ++= inputRows
    +    numOutputRows = outputRows
    +  }
    +
    +  def reportTriggerFinished(): Unit = synchronized {
    +    require(currentTriggerStartTimestamp >= 0)
    +    val currentTriggerFinishTimestamp = triggerClock.getTimeMillis()
    +    reportTriggerStatus(FINISH_TIMESTAMP, currentTriggerFinishTimestamp)
    +    reportTriggerStatus(STATUS_MESSAGE, "")
    +    reportTriggerStatus(ACTIVE, false)
    +
    +    // Report number of rows
    +    val totalNumInputRows = numInputRows.values.sum
    +    reportTriggerStatus(NUM_INPUT_ROWS, totalNumInputRows)
    +    reportTriggerStatus(NUM_OUTPUT_ROWS, numOutputRows.getOrElse(0))
    +    numInputRows.foreach { case (s, r) =>
    +      reportSourceTriggerStatus(s, NUM_SOURCE_INPUT_ROWS, r)
    +    }
    +
    +    val currentTriggerDuration = currentTriggerFinishTimestamp - 
currentTriggerStartTimestamp
    +    val previousInputIntervalOption = if (previousTriggerStartTimestamp >= 
0) {
    +      Some(currentTriggerStartTimestamp - previousTriggerStartTimestamp)
    +    } else None
    +
    +    // Update input rate = num rows received by each source during the 
previous trigger interval
    +    // Interval is measures as interval between start times of previous 
and current trigger.
    +    //
    +    // TODO: Instead of trigger start, we should use time when getOffset 
was called on each source
    +    // as this may be different for each source if there are many sources 
in the query plan
    +    // and getOffset is called serially on them.
    +    if (previousInputIntervalOption.nonEmpty) {
    +      sources.foreach { s =>
    +        inputRates(s).update(numInputRows.getOrElse(s, 0), 
previousInputIntervalOption.get)
    +      }
    +    }
    +
    +    // Update processing rate = num rows processed for each source in 
current trigger duration
    +    sources.foreach { s =>
    +      processingRates(s).update(numInputRows.getOrElse(s, 0), 
currentTriggerDuration)
    +    }
    +
    +    // Update output rate = num rows output to the sink in current trigger 
duration
    +    outputRate.update(numOutputRows.getOrElse(0), currentTriggerDuration)
    +    logDebug("Output rate updated to " + outputRate.currentRate)
    +
    +    // Update latency = if data present, 0.5 * previous trigger interval + 
current trigger duration
    +    if (previousInputIntervalOption.nonEmpty && totalNumInputRows > 0) {
    +      latency = Some((previousInputIntervalOption.get.toDouble / 2) + 
currentTriggerDuration)
    +    } else {
    +      latency = None
    +    }
    +
    +    previousTriggerStartTimestamp = currentTriggerStartTimestamp
    +    currentTriggerStartTimestamp = -1
    +  }
    +
    +  // =========== Getter methods ===========
    +
    +  def currentInputRate(): Double = synchronized {
    +    // Since we are calculating source input rates using the same time 
interval for all sources
    +    // it is fine to calculate total input rate as the sum of per source 
input rate.
    +    inputRates.map(_._2.currentRate).sum
    +  }
    +
    +  def currentSourceInputRate(source: Source): Double = synchronized {
    +    inputRates(source).currentRate
    +  }
    +
    +  def currentProcessingRate(): Double = synchronized {
    +    // Since we are calculating source processing rates using the same 
time interval for all sources
    +    // it is fine to calculate total processing rate as the sum of per 
source processing rate.
    +    processingRates.map(_._2.currentRate).sum
    +  }
    +
    +  def currentSourceProcessingRate(source: Source): Double = synchronized {
    +    processingRates(source).currentRate
    +  }
    +
    +  def currentOutputRate(): Double = synchronized { outputRate.currentRate }
    +
    +  def currentLatency(): Option[Double] = synchronized { latency }
    +
    +  def currentTriggerStatus(): Map[String, String] = synchronized { 
triggerStatus.toMap }
    +
    +  def currentSourceTriggerStatus(source: Source): Map[String, String] = 
synchronized {
    +    sourceTriggerStatus(source).toMap
    +  }
    +
    +  // =========== Other methods ===========
    +
    +  private def registerGauge[T](name: String, f: () => T)(implicit num: 
Numeric[T]): Unit = {
    +    synchronized {
    +      metricRegistry.register(name, new Gauge[T] {
    +        override def getValue: T = f()
    +      })
    +    }
    +  }
    +
    +  def stop(): Unit = synchronized {
    +    inputRates.valuesIterator.foreach { _.stop() }
    +    processingRates.valuesIterator.foreach { _.stop() }
    +    outputRate.stop()
    +    latency = None
    +  }
    +}
    +
    +object StreamMetrics extends Logging {
    +
    +  class RateCalculator {
    --- End diff --
    
    I would avoid inner classes unless there is a reason.


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