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https://issues.apache.org/jira/browse/FLINK-7465?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16179237#comment-16179237
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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_r140823213
--- Diff: docs/dev/table/sql.md ---
@@ -2020,7 +2020,16 @@ COUNT(*)
<p>Returns the number of input rows.</p>
</td>
</tr>
-
+<tr>
+ <td>
+ {% highlight text %}
+CARDINALITY_COUNT(rsd, value)
--- End diff --
Would it be clearer to the user to have the function have the word
"approximate" in it such that the user understands the count is an estimate? I
see Apache Spark calls it
`approx_count_distinct`(https://spark.apache.org/docs/2.2.0/api/java/org/apache/spark/sql/functions.html#approx_count_distinct-org.apache.spark.sql.Column-double-)
and Redshift has it as `APPROXIMATE COUNT(DISTINCT column)`
(http://docs.aws.amazon.com/redshift/latest/dg/r_COUNT.html).
> 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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