approx_distinct function using the HyperLogLog data structure.
Data structures
Trino implements HyperLogLog data sketches as a set of 32-bit buckets which store a maximum hash. They can be stored sparsely (as a map from bucket ID to bucket), or densely (as a contiguous memory block). The HyperLogLog data structure starts as the sparse representation, switching to dense when it is more efficient. The P4HyperLogLog structure is initialized densely and remains dense for its lifetime.hyperloglog_type implicitly casts to
p4hyperloglog_type, while one can
explicitly cast HyperLogLog to P4HyperLogLog:
Serialization
Data sketches can be serialized to and deserialized fromvarbinary.
This allows them to be stored for later use. Combined with the ability
to merge multiple sketches, this allows one to calculate
approx_distinct of the elements of a partition of a query, then for the entirety of a query with very little
cost.
For example, calculating the HyperLogLog for daily unique users will
allow weekly or monthly unique users to be calculated incrementally by
combining the dailies. This is similar to computing weekly revenue by
summing daily revenue. Uses of approx_distinct with GROUPING SETS can be converted to use HyperLogLog.Examples:
Functions
approx_set()
approx_set(x) → HyperLogLog
Returns the HyperLogLog sketch of the input data set of x. This data
sketch underlies approx_distinct and can be stored and used later by calling cardinality().
cardinality()
cardinality(hll) → bigint
This will perform approx_distinct on
the data summarized by the hll HyperLogLog data sketch.
empty_hll()
empty_hll() → HyperLogLog
Returns an empty HyperLogLog.
merge()
merge(hyperloglog) → HyperLogLog
Returns the HyperLogLog of the aggregate union of the individual hll
HyperLogLog structures.
