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https://issues.apache.org/jira/browse/FLINK-7465?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16179276#comment-16179276
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ASF GitHub Bot commented on FLINK-7465:
---------------------------------------
Github user jparkie commented on a diff in the pull request:
https://github.com/apache/flink/pull/4652#discussion_r140828795
--- Diff:
flink-libraries/flink-table/src/main/java/org/apache/flink/table/runtime/functions/aggfunctions/cardinality/ICardinality.java
---
@@ -0,0 +1,80 @@
+/*
+ * 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.table.runtime.functions.aggfunctions.cardinality;
+
+import java.io.IOException;
+
+/**
+ * An interface definition for implementation of cardinality.
+ */
+public interface ICardinality {
+
+ /**
+ * Check whether the element is impact estimate.
+ *
+ * @param o stream element
+ * @return false if the value returned by cardinality() is unaffected
by the appearance of o in the stream.
+ */
+ boolean offer(Object o);
+
+ /**
+ * Offer the value as a hashed long value.
+ *
+ * @param hashedLong - the hash of the item to offer to the estimator
+ * @return false if the value returned by cardinality() is unaffected
by the appearance of hashedLong in the stream
+ */
+ boolean offerHashed(long hashedLong);
+
+ /**
+ * Offer the value as a hashed long value.
+ *
+ * @param hashedInt - the hash of the item to offer to the estimator
+ * @return false if the value returned by cardinality() is unaffected
by the appearance of hashedInt in the stream
+ */
+ boolean offerHashed(int hashedInt);
+
+ /**
+ * @return the number of unique elements in the stream or an estimate
thereof.
+ */
+ long cardinality();
+
+ /**
+ * @return size in bytes needed for serialization.
+ */
+ int sizeof();
+
+ /**
+ * Get the byte array used for the calculation.
+ *
+ * @return The byte array used for the calculation
+ * @throws IOException
+ */
+ byte[] getBytes() throws IOException;
+
+ /**
+ * Merges estimators to produce a new estimator for the combined streams
+ * of this estimator and those passed as arguments.
+ * <p/>
+ * Nor this estimator nor the one passed as parameters are modified.
+ *
+ * @param estimators Zero or more compatible estimators
+ * @throws Exception If at least one of the estimators is not
compatible with this one
+ */
+ ICardinality merge(ICardinality... estimators) throws Exception;
--- End diff --
Wouldn't it be nicer to have a more specific Exception?
> Add build-in BloomFilterCount on TableAPI&SQL
> ---------------------------------------------
>
> Key: FLINK-7465
> URL: https://issues.apache.org/jira/browse/FLINK-7465
> Project: Flink
> Issue Type: Sub-task
> 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
> bit-array 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/storage-api/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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