Github user dragos commented on a diff in the pull request:
https://github.com/apache/spark/pull/7648#discussion_r35862291
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
---
@@ -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
--- End diff --
Carrying over conversation from previous thread that got lost due to rebase
>Its hard to understand what sumError mean in terms of the rates and all?
Can you write down the physical interpretation of this sumError? And also make
the name better accordingly?
cc @huitseeker
@tdas
tdas added a note 14 hours ago
So I am trying to understand this.
(scheduling delay / batch interval) = approx the number of batches the
system is delayed. Lets call it numDelayedBatches.
Now you are multiplying numDelayedBatches X processingSpeed. So you are
scaling the current processing rate with number of batches that are delayed.
Right?
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