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_r384779037
 
 

 ##########
 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:
   > from the number of overflowing elements we can calculate the rate at which 
they would be cleared by dividing it by the batch interval
   
   @dragos Why? I still don't understand why historical error = # overflowing 
elements / batch interval. The explanation that if # overflowing elements = 0, 
then historical error = 0 does not explain the 1 / batch interval coefficient.
   
   I feel the hard part is: It is easy to reason about the accumulated error if 
error is an absolute number, for example, the number of overflowing elements, 
but what is an accumulated error of a rate?
   
   Setting up 1 / batch interval as the coefficient may not be a big problem 
anyway, because at the end of the day it is nothing but a constant that you can 
tune the integral coefficient to compensate.

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