[GitHub] [spark] YikSanChan commented on a change in pull request #7648: [SPARK-8979] Add a PID based rate estimator

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YikSanChan commented on a change in pull request #7648: [SPARK-8979] Add a PID based rate estimator
URL: https://github.com/apache/spark/pull/7648#discussion_r386648122
 
 

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 File path: streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
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 @@ -0,0 +1,100 @@
+/*
+ * 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.scheduler.rate
+
+/**
+ * Implements a proportional-integral-derivative (PID) controller which acts on
+ * the speed of ingestion of elements into Spark Streaming. A PID controller works
+ * by calculating an '''error''' between a measured output and a desired value. In the
+ * case of Spark Streaming the error is the difference between the measured processing
+ * rate (number of elements/processing delay) and the previous rate.
+ *
+ * @see https://en.wikipedia.org/wiki/PID_controller
+ *
+ * @param batchDurationMillis the batch duration, in milliseconds
+ * @param proportional how much the correction should depend on the current
+ *        error. This term usually provides the bulk of correction. A value too large would
+ *        make the controller overshoot the setpoint, while a small value would make the
+ *        controller too insensitive. The default value is -1.
+ * @param integral how much the correction should depend on the accumulation
+ *        of past errors. This term accelerates the movement towards the setpoint, but a large
+ *        value may lead to overshooting. The default value is -0.2.
+ * @param derivative how much the correction should depend on a prediction
+ *        of future errors, based on current rate of change. This term is not used very often,
+ *        as it impacts stability of the system. The default value is 0.
+ */
+private[streaming] class PIDRateEstimator(
+    batchIntervalMillis: Long,
+    proportional: Double = -1D,
+    integral: Double = -.2D,
+    derivative: Double = 0D)
+  extends RateEstimator {
+
+  private var firstRun: Boolean = true
+  private var latestTime: Long = -1L
+  private var latestRate: Double = -1D
+  private var latestError: Double = -1L
+
+  require(
+    batchIntervalMillis > 0,
+    s"Specified batch interval $batchIntervalMillis in PIDRateEstimator is invalid.")
+
+  def compute(time: Long, // in milliseconds
+      elements: Long,
+      processingDelay: Long, // in milliseconds
+      schedulingDelay: Long // in milliseconds
+    ): Option[Double] = {
+
+    this.synchronized {
+      if (time > latestTime && processingDelay > 0 && batchIntervalMillis > 0) {
+
+        // in seconds, should be close to batchDuration
+        val delaySinceUpdate = (time - latestTime).toDouble / 1000
+
+        // in elements/second
+        val processingRate = elements.toDouble / processingDelay * 1000
+
+        // in elements/second
+        val error = latestRate - processingRate
+
+        // in elements/second
+        val sumError = schedulingDelay.toDouble * processingRate / batchIntervalMillis
 
 Review comment:
   @dragos Thanks for responding!
   
   > What prompted you to look into this code? Do you see bad behavior on certain loads?
   
   I am not seeing bad behavior because I am not using spark streaming. The reason why I look into the code is I try to leverage the PID controller in an imaginary scenario I describe [here](https://robotics.stackexchange.com/questions/20248/how-to-determine-actuator-and-process-for-pid-and-how-to-intepret-the-output). I use Scala and Akka so I look for PID controller example in Scala on GitHub, which brings me here.
   
   Your implementation gives me a nice picture of how to turn a "rule of thumb" (that's how I feel about PID) into an actual implementation that is well explained and understood (with clear physical/system interpretation) and runs well in production. Thank you :)
   
   > If the overflowing elements are 0, doesn't this imply the schedulingDelay is zero, so this whole term is 0 as well (instead of 1/batchInterval)?
   
   Yes, you are right. But what I am asking is: in the equation `historicalError = schedulingDelay * processingRate / batchInterval`, why is the coefficient `1 / batchInterval`, rather than `1 / 2`, `1 / 100`, `1 / sum(batchInteval)`, or whatever else?
   
   > in other words, by how much is the existing rate "off", if we wanted to have no overflowing elements.
   
   If I understand correctly, the overflowing elements are accumulated since batch 0 (provided we're at batch n). If we all agree with the statement, then shouldn't the rate be `1 /(n * batchInterval)` instead?

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