[ https://issues.apache.org/jira/browse/FLINK-7465?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
sunjincheng updated FLINK-7465: ------------------------------- Description: 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 was: 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! > 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 -- This message was sent by Atlassian JIRA (v6.4.14#64029)