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https://issues.apache.org/jira/browse/HDFS-16949?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17708042#comment-17708042
]
ASF GitHub Bot commented on HDFS-16949:
---------------------------------------
mkuchenbecker commented on code in PR #5495:
URL: https://github.com/apache/hadoop/pull/5495#discussion_r1156191111
##########
hadoop-common-project/hadoop-common/src/test/java/org/apache/hadoop/metrics2/util/TestSampleQuantiles.java:
##########
@@ -91,28 +93,70 @@ public void testClear() throws IOException {
@Test
public void testQuantileError() throws IOException {
final int count = 100000;
- Random r = new Random(0xDEADDEAD);
- Long[] values = new Long[count];
+ Random rnd = new Random(0xDEADDEAD);
+ int[] values = new int[count];
Review Comment:
Why int vs long?
##########
hadoop-common-project/hadoop-common/src/main/java/org/apache/hadoop/metrics2/lib/MutableInverseQuantiles.java:
##########
@@ -0,0 +1,102 @@
+/**
+ * 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.hadoop.metrics2.lib;
+
+import org.apache.commons.lang3.StringUtils;
+import org.apache.hadoop.classification.InterfaceAudience;
+import org.apache.hadoop.classification.InterfaceStability;
+import org.apache.hadoop.classification.VisibleForTesting;
+import org.apache.hadoop.metrics2.MetricsInfo;
+import org.apache.hadoop.metrics2.util.Quantile;
+import org.apache.hadoop.metrics2.util.SampleQuantiles;
+import
org.apache.hadoop.thirdparty.com.google.common.util.concurrent.ThreadFactoryBuilder;
+import java.util.concurrent.Executors;
+import java.util.concurrent.ScheduledExecutorService;
+import java.util.concurrent.ScheduledFuture;
+import java.util.concurrent.TimeUnit;
+import static org.apache.hadoop.metrics2.lib.Interns.info;
+
+/**
+ * Watches a stream of long values, maintaining online estimates of specific
+ * quantiles with provably low error bounds. Inverse quantiles are meant for
+ * highly accurate low-percentile (e.g. 1st, 5th) latency metrics.
+ * InverseQuantiles are used for metrics where higher the value better it is.
+ * ( eg: data transfer rate ).
+ * The 1st percentile here corresponds to the 99th inverse percentile metric,
+ * 5th percentile to 95th and so on.
+ */
[email protected]
[email protected]
+public class MutableInverseQuantiles extends MutableQuantiles{
+
+ @VisibleForTesting
+ public static final Quantile[] INVERSE_QUANTILES = { new Quantile(0.50,
0.050),
Review Comment:
+1
> 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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