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https://issues.apache.org/jira/browse/FLINK-7465?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16143844#comment-16143844
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Jacob Park commented on FLINK-7465:
-----------------------------------
[~fhueske] [~sunjincheng121] Thanks for the context. :)
If HyperLogLogs are out, then how about Cuckoo Filters? They are similar to
Bloom Filters, but they are designed differently as inspired by cuckoo hashing,
supports deletion, and takes approximately the same space.
https://www.cs.cmu.edu/~dga/papers/cuckoo-conext2014.pdf See
https://bdupras.github.io/filter-tutorial/ for an interactive summary. You can
also estimate a count with Cuckoo Filters unlike standard Bloom Filters.
{noformat}
...for reasonably large sized sets, for the same false positive rate as a
corresponding Bloom filter, cuckoo filters use less space than Bloom filters,
are faster on lookups (but slower on insertions/to construct), and amazingly
also allow deletions of keys (which Bloom filters cannot do). -Michael
Mitzenmacher (2014)
{noformat}
> 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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