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A Bloom filter is a space-efficient, probabilistic data structure that can be used to test set membership. Callers will sometimes incur false positives, but never false negatives. The rate of false positives is a function of the total number of elements and the amount of memory available for the Bloom filter. Two classic applications of Bloom filters are cache filtering, and data synchronization testing. Any user of Bloom filters must accept the possibility of false positives as a cost worth paying for the benefit in space efficiency. This commit adds a test harness extension module, test_bloomfilter. It can be used to get a sense of how the Bloom filter implementation performs under varying conditions. This is infrastructure for the upcoming "heapallindexed" amcheck patch, which verifies the consistency of a heap relation against one of its indexes. Author: Peter Geoghegan Reviewed-By: Andrey Borodin, Michael Paquier, Thomas Munro, Andres Freund Discussion: https://postgr.es/m/CAH2-Wzm5VmG7cu1N-H=nnS57wZThoSDQU+F5dewx3o84M+jY=g@mail.gmail.com |
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