[ 
https://issues.apache.org/jira/browse/HIVE-21823?focusedWorklogId=254257&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-254257
 ]

ASF GitHub Bot logged work on HIVE-21823:
-----------------------------------------

                Author: ASF GitHub Bot
            Created on: 05/Jun/19 08:29
            Start Date: 05/Jun/19 08:29
    Worklog Time Spent: 10m 
      Work Description: pvary commented on pull request #660: HIVE-21823: New 
metrics to get the average queue length / free executor number for a given time 
window
URL: https://github.com/apache/hive/pull/660#discussion_r290630646
 
 

 ##########
 File path: 
llap-server/src/java/org/apache/hadoop/hive/llap/metrics/LlapDaemonExecutorMetrics.java
 ##########
 @@ -397,4 +425,106 @@ public JvmMetrics getJvmMetrics() {
   public String getName() {
     return name;
   }
+
+  /**
+   * Generate time aware average for data points.
+   * For example if we have 3s when the queue size is 1, and 1s when the queue 
size is 2 then the
+   * calculated average should be (3*1+1*2)/4 = 1.25.
+   */
+  @VisibleForTesting
+  static class TimedAverageMetrics {
+    private final int windowDataSize;
+    private final long windowTimeSize;
+    private final Data[] data;
+    private int nextPos = 0;
+
+    /**
+     * Creates and initializes the metrics object.
+     * @param windowDataSize The maximum number of samples stored
+     * @param windowTimeSize The time window used to generate the average in 
nanoseconds
+     */
+    TimedAverageMetrics(int windowDataSize, long windowTimeSize) {
+      this(windowDataSize, windowTimeSize, System.nanoTime());
+    }
+
+    @VisibleForTesting
+    TimedAverageMetrics(int windowDataSize, long windowTimeSize,
+        long defaultTime) {
+      assert(windowDataSize > 0);
+      this.windowDataSize = windowDataSize;
+      this.windowTimeSize = windowTimeSize;
+      this.data = new Data[windowDataSize];
+      Arrays.setAll(data, i -> new Data(defaultTime, 0L));
+    }
+
+    /**
+     * Adds a new sample value to the metrics.
+     * @param value The new sample value
+     */
+    public synchronized void add(long value) {
+      add(System.nanoTime(), value);
+    }
+
+    /**
+     * Calculates the average for the last windowTimeSize window.
+     * @return The average
+     */
+    public synchronized long value() {
+      return value(System.nanoTime());
+    }
+
+    @VisibleForTesting
+    void add(long time, long value) {
+      data[nextPos].nanoTime = time;
+      data[nextPos].value = value;
+      nextPos++;
 
 Review comment:
   Synchronization is mainly here to make sure that we do not mess up with 
getting the value().
   Do you think we need to have a finer grained locking? My hope is that this 
stuff is so fast for the amount of data we have that calculating the results 
should not cause problem. If we find that this is a problem I think the real 
solution could be a queue and an extra thread for adding the changes. Do you 
think we need this?
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 254257)
    Time Spent: 2h 20m  (was: 2h 10m)

> New metrics to get the average queue length / free executor number for a 
> given time window
> ------------------------------------------------------------------------------------------
>
>                 Key: HIVE-21823
>                 URL: https://issues.apache.org/jira/browse/HIVE-21823
>             Project: Hive
>          Issue Type: Sub-task
>          Components: llap
>            Reporter: Peter Vary
>            Assignee: Peter Vary
>            Priority: Major
>              Labels: pull-request-available
>         Attachments: HIVE-21823.patch
>
>          Time Spent: 2h 20m
>  Remaining Estimate: 0h
>
> We need to calculate the average queue size / free executor size for a window 
> to have good data for making routing decisions.
> Interesting things to consider:
>  * The timeĀ gap between arriving request can be different, so simple average 
> is not enough to have correct data
>  * We need to have 2 parameters
>  ** Time window length
>  ** Maximum data point numbers - so we will not collect "infinite" amount of 
> data at high load



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