HyperLogLog Functions#
Presto implements the approx_distinct()
function using the
HyperLogLog data structure.
Data Structures#
Presto implements HyperLogLog data sketches as a set of 32bit 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 implicitly casts to P4HyperLogLog,
while one can explicitly cast HyperLogLog
to P4HyperLogLog
:
cast(hll AS P4HyperLogLog)
Serialization#
Data sketches can be serialized to and deserialized from varbinary
. 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:
CREATE TABLE visit_summaries (
visit_date date,
hll varbinary
);
INSERT INTO visit_summaries
SELECT visit_date, cast(approx_set(user_id) AS varbinary)
FROM user_visits
GROUP BY visit_date;
SELECT cardinality(merge(cast(hll AS HyperLogLog))) AS weekly_unique_users
FROM visit_summaries
WHERE visit_date >= current_date  interval '7' day;
Functions#

approx_set
(x) → HyperLogLog# Returns the
HyperLogLog
sketch of the input data set ofx
. The value of the maximum standard error is defaulted to0.01625
. This data sketch underliesapprox_distinct()
and can be stored and used later by callingcardinality()
.

approx_set
(x, e) → HyperLogLog# Returns the
HyperLogLog
sketch of the input data set ofx
, with a maximum standard error ofe
. The current implementation of this function requires thate
be in the range of[0.0040625, 0.26000]
. This data sketch underliesapprox_distinct()
and can be stored and used later by callingcardinality()
.

cardinality
(hll) → bigint This will perform
approx_distinct()
on the data summarized by thehll
HyperLogLog data sketch.

empty_approx_set
() → HyperLogLog# Returns an empty
HyperLogLog
. The value of the maximum standard error is defaulted to0.01625
.

empty_approx_set
(e) → HyperLogLog# Returns an empty
HyperLogLog
with a maximum standard error ofe
. The current implementation of this function requires thate
be in the range of[0.0040625, 0.26000]
.

merge
(HyperLogLog) → HyperLogLog# Returns the
HyperLogLog
of the aggregate union of the individualhll
HyperLogLog structures.

merge_hll
(array(HyperLogLog)) → HyperLogLog# Returns the
HyperLogLog
of the union of an arrayhll
HyperLogLog structures.