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https://issues.apache.org/jira/browse/FLINK-7465?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16136062#comment-16136062
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sunjincheng commented on FLINK-7465:
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[~fhueske] I want add accuracy and maxElement as function parameter，the
function signature looks like:
{code}
count-bf(accuracy:Double, maxKeyCount, col:Any)
{code}
And we will use the following formula to calculate the bitarray size(bsize):
{code}
(-maxKeyCount * Math.log(accuracy) / (Math.log(2) * Math.log(2)))
{code}
And we will use the following formula to calculate the cont of hash function:
{code}
Math.max(1, Math.round(bsize.asInstanceOf[Double] / maxKeyCount * Math.log(2)))
{code}
The formula same as the reference of the JIRA. description.
That mean we configure the accuracy when the function is used. Is this make
sense for you? [~fhueske]
I think {{count-min}} is very useful in some certain cases. so does the
{{HyperLogLog}} (cardinality counting). After we complete the this JIRA. we can
discuss these implementations.
[~jark] The de/serialize of bitArray if very important in the implementation. I
think the best way is do the de/serialization at check point or in
{{open/close}} method, but currently we can not access the {{RuntimeContext}}
from {{FunctionContext}},we need do some change. OR using DataView. Currently
In my mind we have some choices as follows：
* de/serialization bitArray every call the {{accumulate}}(bitArray as member of
ACC)
* de/serialization bitArray in check point.( bitArray as member of AGG)
* de/serialization bitArray in {{open/close}} .( bitArray as member of AGG)
What do you think? [~jark] [~fhueske]
> 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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