[jira] [Comment Edited] (FLINK-7465) Add build-in BloomFilterCount on TableAPI&SQL

["sunjincheng (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-7465?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16136062#comment-16136062 ] 

sunjincheng edited comment on FLINK-7465 at 8/22/17 9:01 AM:
-------------------------------------------------------------

[~fhueske] I want add accuracy and maxElement as function parameter，the function signature looks like: 
{code}
count-bf(accuracy:Double, maxKeyCount, col:Any)
{code}
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]




was (Author: sunjincheng121):
[~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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