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https://issues.apache.org/jira/browse/HIVE-21823?focusedWorklogId=255743&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-255743
]
ASF GitHub Bot logged work on HIVE-21823:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 07/Jun/19 08:54
Start Date: 07/Jun/19 08:54
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_r291505729
##########
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:
As discussed offline run some tests:
Test case:
- 1m with a for loop iterations averaged out
- Adding 20k items
- Querying a value which uses 15k items
Results:
- With sync:
- Full: 0.155/0.152/0.152 ms - both adding and querying
- Add: 0.134/0.133/0.133
- Query: 0.016/0.016/0.017
- Without sync:
- Full: 0.113/0.108/0.108
- Add: 0.099/0.091/0.091
- Query: 0.017/0.016/0.016
So this means that synchronization adds an extra 2 nanosecond to a 4.7
nanosecond task. I think even though this is relative high overhead, but this
is small in absolute numbers. So I think we should not complicate this part of
the code more
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Issue Time Tracking
-------------------
Worklog Id: (was: 255743)
Time Spent: 2.5h (was: 2h 20m)
> 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-21823.2.patch, HIVE-21823.patch
>
> Time Spent: 2.5h
> 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 time 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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