executor: add new agg function APPROX_COUNT_DISTINCT (#17175)
#18120
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Signed-off-by: ti-srebot <ti-srebot@pingcap.com>
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/run-all-tests |
ti-srebot
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sig/execution
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@solotzg please accept the invitation then you can push to the cherry-pick pull requests. |
Signed-off-by: Tong Zhigao <tongzhigao@pingcap.com>
Signed-off-by: Tong Zhigao <tongzhigao@pingcap.com>
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/run-all-tests |
2 similar comments
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/run-all-tests |
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/run-all-tests |
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/run-all-tests |
XuHuaiyu
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status/LGT2
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/merge |
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Sorry @XuHuaiyu, you don't have permission to trigger auto merge event on this branch. |
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cherry-pick #17175 to release-4.0
Signed-off-by: Tong Zhigao tongzhigao@pingcap.com
What problem does this PR solve?
Issue Number: close #14632
Problem Summary:
What is changed and how it works?
Add new agg function
APPROX_COUNT_DISTINCT.Use
BJKSTalgorithm to compute approximate result of distinct count.For the calculation state, it uses a sample of element hash values with a size up to 2^16. Compared with the widely known HyperLogLog algorithm, this algorithm is less effective in terms of accuracy and memory consumption (even up to proportionality), but it is adaptive. This means that with fairly high accuracy, it consumes less memory during simultaneous computation of cardinality for a large number of data sets whose cardinality has power law distribution (i.e. in cases when most of the data sets are small). This algorithm is also very accurate for data sets with small cardinality and very efficient on CPU.
For TiFlash, TiDB can push down cop request and merge all partial result. For other engine, TiDB needs to collect all original data and compute all by itself.
Tests
Release note
APPROX_COUNT_DISTINCTto support approximate count distinct.