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https://issues.apache.org/jira/browse/HDFS-16949?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17706542#comment-17706542
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ASF GitHub Bot commented on HDFS-16949:
---------------------------------------
rdingankar commented on code in PR #5495:
URL: https://github.com/apache/hadoop/pull/5495#discussion_r1152330963
##########
hadoop-hdfs-project/hadoop-hdfs/src/test/java/org/apache/hadoop/hdfs/server/datanode/TestDataNodeMetrics.java:
##########
@@ -413,7 +410,7 @@ public Boolean get() {
final long endWriteValue = getLongCounter("TotalWriteTime", rbNew);
final long endReadValue = getLongCounter("TotalReadTime", rbNew);
assertCounter("ReadTransferRateNumOps", 1L, rbNew);
- assertQuantileGauges("ReadTransferRate" + "60s", rbNew, "Rate");
+ assertInverseQuantileGauges("ReadTransferRate" + "60s", rbNew,
"Rate");
Review Comment:
updated
##########
hadoop-hdfs-project/hadoop-hdfs/src/test/java/org/apache/hadoop/hdfs/server/datanode/TestDataNodeMetrics.java:
##########
@@ -18,10 +18,7 @@
package org.apache.hadoop.hdfs.server.datanode;
import static org.apache.hadoop.hdfs.DFSConfigKeys.DFS_HEARTBEAT_INTERVAL_KEY;
-import static org.apache.hadoop.test.MetricsAsserts.assertCounter;
-import static org.apache.hadoop.test.MetricsAsserts.assertQuantileGauges;
-import static org.apache.hadoop.test.MetricsAsserts.getLongCounter;
-import static org.apache.hadoop.test.MetricsAsserts.getMetrics;
+import static org.apache.hadoop.test.MetricsAsserts.*;
Review Comment:
done
> Update ReadTransferRate to ReadLatencyPerGB for effective percentile metrics
> ----------------------------------------------------------------------------
>
> Key: HDFS-16949
> URL: https://issues.apache.org/jira/browse/HDFS-16949
> Project: Hadoop HDFS
> Issue Type: Bug
> Components: datanode
> Reporter: Ravindra Dingankar
> Assignee: Ravindra Dingankar
> Priority: Minor
> Labels: pull-request-available
> Fix For: 3.3.0, 3.4.0
>
>
> HDFS-16917 added ReadTransferRate quantiles to calculate the rate which data
> is read per unit of time.
> With percentiles the values are sorted in ascending order and hence for the
> transfer rate p90 gives us the value where 90 percent rates are lower
> (worse), p99 gives us the value where 99 percent values are lower (worse).
> Note that value(p90) < p(99) thus p99 is a better transfer rate as compared
> to p90.
> However as the percentile increases the value should become worse in order to
> know how good our system is.
> Hence instead of calculating the data read transfer rate, we should calculate
> it's inverse. We will instead calculate the time taken for a GB of data to be
> read. ( seconds / GB )
> After this the p90 value will give us 90 percentage of total values where the
> time taken is less than value(p90), similarly for p99 and others.
> Also p(90) < p(99) and here p(99) will become a worse value (taking more time
> each byte) as compared to p(90)
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