Cardinality estimation has a wide range of applications and is of particular
importance in database systems. Various algorithms have been proposed in the past,
and the HyperLogLog algorithm is one of them. In this paper, we present a series of
improvements to this algorithm that reduce its memory requirements and
significantly increase its accuracy for an important range of cardinalities. We
have implemented our proposed algorithm for a system at Google and evaluated it
empirically, comparing it to the original HyperLogLog algorithm. Like HyperLogLog,
our improved algorithm parallelizes perfectly and computes the cardinality estimate
in a single pass.
