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https://issues.apache.org/jira/browse/FLINK-1297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14499424#comment-14499424
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ASF GitHub Bot commented on FLINK-1297:
---------------------------------------
Github user rmetzger commented on a diff in the pull request:
https://github.com/apache/flink/pull/605#discussion_r28575850
--- Diff:
flink-core/src/test/java/org/apache/flink/statistics/heavyhitters/LossyCountingTest.java
---
@@ -0,0 +1,129 @@
+/*
+ * 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.flink.statistics.heavyhitters;
+
+import org.junit.Test;
+
+import java.util.Map;
+import java.util.Random;
+
+import static org.junit.Assert.assertTrue;
+
+/*
+* Test the structure implemented for Lossy Counting
+*/
+
+public class LossyCountingTest {
+
+ static final double fraction = 0.05;
+ static final double error = 0.005;
+ static final int seed = 7362181;
+ static final Random r = new Random(seed);
+
+ @Test
+ public void testAccuracy() {
+
+ int numItems = 1000000;
+ long frequency = (int)Math.ceil(numItems* fraction);
+ long minFrequency = (int)Math.ceil(numItems* (fraction-error));
+
+ int[] xs = new int[numItems];
+ int maxScale = 20;
+
+ for (int i = 0; i < numItems; i++) {
+ double p = r.nextDouble();
+ if (p<0.2){
+ xs[i] = r.nextInt(5);
+ }else {
+ int scale = r.nextInt(maxScale);
+ xs[i] = r.nextInt(1 << scale);
+ }
+ }
+
+ LossyCounting lossyCounting = new LossyCounting(fraction,error);
+
+ for (int x : xs) {
+ lossyCounting.addObject(x);
+ }
+
+ long[] actualFreq = new long[1 << maxScale];
+ for (int x : xs) {
+ actualFreq[x]++;
+ }
+
+ System.out.println("Size of heavy hitters:
"+lossyCounting.heavyHitters.size());
--- End diff --
can you use regular logging instead of system.outs for these messages?
We have different logging configurations for travis, local and IDE but we
can not control system.outs with our logging settings.
> Add support for tracking statistics of intermediate results
> -----------------------------------------------------------
>
> Key: FLINK-1297
> URL: https://issues.apache.org/jira/browse/FLINK-1297
> Project: Flink
> Issue Type: Improvement
> Components: Distributed Runtime
> Reporter: Alexander Alexandrov
> Assignee: Alexander Alexandrov
> Fix For: 0.9
>
> Original Estimate: 1,008h
> Remaining Estimate: 1,008h
>
> One of the major problems related to the optimizer at the moment is the lack
> of proper statistics.
> With the introduction of staged execution, it is possible to instrument the
> runtime code with a statistics facility that collects the required
> information for optimizing the next execution stage.
> I would therefore like to contribute code that can be used to gather basic
> statistics for the (intermediate) result of dataflows (e.g. min, max, count,
> count distinct) and make them available to the job manager.
> Before I start, I would like to hear some feedback form the other users.
> In particular, to handle skew (e.g. on grouping) it might be good to have
> some sort of detailed sketch about the key distribution of an intermediate
> result. I am not sure whether a simple histogram is the most effective way to
> go. Maybe somebody would propose another lightweight sketch that provides
> better accuracy.
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