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ASF GitHub Bot commented on FLINK7465:

Github user jparkie commented on a diff in the pull request:
https://github.com/apache/flink/pull/4652#discussion_r140832701
 Diff: flinklibraries/flinktable/src/main/java/org/apache/flink/table/runtime/functions/aggfunctions/cardinality/HyperLogLog.java

@@ 0,0 +1,333 @@
+/*
+ * 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/LICENSE2.0
+ *
+ * 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.table.runtime.functions.aggfunctions.cardinality;
+
+import java.io.ByteArrayInputStream;
+import java.io.ByteArrayOutputStream;
+import java.io.DataInput;
+import java.io.DataInputStream;
+import java.io.DataOutput;
+import java.io.DataOutputStream;
+import java.io.Externalizable;
+import java.io.IOException;
+import java.io.ObjectInput;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutput;
+import java.io.Serializable;
+
+/**
+ * Java implementation of HyperLogLog (HLL) algorithm from this paper:
+ * <p/>
+ * http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf
+ * <p/>
+ * HLL is an improved version of LogLog that is capable of estimating
+ * the cardinality of a set with accuracy = 1.04/sqrt(m) where
+ * m = 2^b. So we can control accuracy vs space usage by increasing
+ * or decreasing b.
+ * <p/>
+ * The main benefit of using HLL over LL is that it only requires 64%
+ * of the space that LL does to get the same accuracy.
+ * <p/>
+ * <p>
+ * Note that this implementation does not include the long range correction function
+ * defined in the original paper. Empirical evidence shows that the correction
+ * function causes more harm than good.
+ * </p>
+ */
+public class HyperLogLog implements ICardinality, Serializable {
 End diff 
I see this class is adapted from https://github.com/addthis/streamlib. I think you should
comment that this class was adapted from the link, so people can track differences.
> Add buildin BloomFilterCount on TableAPI&SQL
> 
>
> Key: FLINK7465
> URL: https://issues.apache.org/jira/browse/FLINK7465
> Project: Flink
> Issue Type: Subtask
> Components: Table API & SQL
> Reporter: sunjincheng
> Assignee: sunjincheng
> Attachments: bloomfilter.png
>
>
> In this JIRA. use BloomFilter to implement counting functions.
> BloomFilter Algorithm description:
> An empty Bloom filter is a bit array of m bits, all set to 0. There must also be k different
hash functions defined, each of which maps or hashes some set element to one of the m array
positions, generating a uniform random distribution. Typically, k is a constant, much smaller
than m, which is proportional to the number of elements to be added; the precise choice of
k and the constant of proportionality of m are determined by the intended false positive rate
of the filter.
> To add an element, feed it to each of the k hash functions to get k array positions.
Set the bits at all these positions to 1.
> To query for an element (test whether it is in the set), feed it to each of the k hash
functions to get k array positions. If any of the bits at these positions is 0, the element
is definitely not in the set – if it were, then all the bits would have been set to 1 when
it was inserted. If all are 1, then either the element is in the set, or the bits have by
chance been set to 1 during the insertion of other elements, resulting in a false positive.
> An example of a Bloom filter, representing the set {x, y, z}. The colored arrows show
the positions in the bit array that each set element is mapped to. The element w is not in
the set {x, y, z}, because it hashes to one bitarray position containing 0. For this figure,
m = 18 and k = 3. The sketch as follows:
> !bloomfilter.png!
> Reference:
> 1. https://en.wikipedia.org/wiki/Bloom_filter
> 2. https://github.com/apache/hive/blob/master/storageapi/src/java/org/apache/hive/common/util/BloomFilter.java
> Hi [~fhueske] [~twalthr] I appreciated if you can give me some advice. :)

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