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https://issues.apache.org/jira/browse/FLINK-7465?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16156251#comment-16156251
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ASF GitHub Bot commented on FLINK-7465:
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GitHub user sunjincheng121 opened a pull request:
https://github.com/apache/flink/pull/4652
[FLINK-7465][table]Add cardinality count for tableAPI and SQL.
## What is the purpose of the change
*In this PR. we want add add CARDINALITY_COUNT for tableAPI and SQL.(Using
`HyperLogLog` algorithm).
The implementation of HyperLogLog (HLL) algorithm from this paper:
http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf
As we know there are still some improved algorithms, such as:
HyperLogLog++, HyperBitBit etc.
But `HyperLogLog` is a classic algorithm that has been massively verified,
so I chose to use the `HyperLogLog` algorithm as the first version of
cardinality to achieve. And we can improve the algorithm at any time If we need.
*
## Brief change log
- *Add Java implementation of `HyperLogLog`(base on stream-lib)*
- *Add MURMURHASH See more: http://murmurhash.googlepages.com/*
- *Add build-in `CardinalityCountAggFunction`*
- *Add some test case for the validation*
- *Add documentation for TableAPI&SQL*
## Verifying this change
This change added tests and can be verified as follows:
- *Added SQL/TableAPI integration tests for `cardinality_count`*
- *Added `CardinalityCountAggFunctionTest` test case for verify the AGG
logic.*
## Does this pull request potentially affect one of the following parts:
- Dependencies (does it add or upgrade a dependency): (no)
- The public API, i.e., is any changed class annotated with
`@Public(Evolving)`: (no)
- The serializers: (no)
- The runtime per-record code paths (performance sensitive): (no)
- Anything that affects deployment or recovery: JobManager (and its
components), Checkpointing, Yarn/Mesos, ZooKeeper: (no)
## Documentation
- Does this pull request introduce a new feature? (yes)
- If yes, how is the feature documented? (docs / JavaDocs)
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/sunjincheng121/flink FLINK-7465-PR
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/flink/pull/4652.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #4652
----
commit bc1166ad88538bdcdd6df685c750359aadff3950
Author: ้็ซน <[email protected]>
Date: 2017-09-05T10:21:10Z
[FLINK-7465][table]Add cardinality count for tableAPI and SQL.
----
> 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 โ 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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