Sketch Functions

Reference documentation for approximate distinct count and sketch-based aggregation functions in Apache Pinot.

Pinot supports several sketch-based algorithms for approximate distinct counting and summarization. These functions trade a small amount of accuracy for significant memory and performance savings at scale.

For exact distinct counting, see DISTINCTCOUNT.

CPC Sketch

The Compressed Probability Counting (CPC) Sketch enables extremely space-efficient cardinality estimation — about 40% less space than an HLL sketch of comparable accuracy.

Function	Description
DISTINCTCOUNTCPCSKETCH	Returns approximate distinct count using CPC sketch
DISTINCTCOUNTRAWCPCSKETCH	Returns raw CPC sketch as hex string

HyperLogLog Plus

HyperLogLogPlus (HLL++) provides approximate distinct counts with configurable precision (p, sp parameters).

Function	Description
DISTINCTCOUNTHLLPLUS	Approximate distinct count using HLL++
DISTINCTCOUNTHLLPLUSMV	HLL++ for multi-value columns
DISTINCTCOUNTRAWHLLPLUS	Returns serialized HLL++ sketch
DISTINCTCOUNTRAWHLLPLUSMV	Serialized HLL++ sketch for multi-value columns

UltraLogLog

The UltraLogLog Sketch from Dynatrace requires less space than HyperLogLog and provides a simpler, faster estimator. Implemented via Hash4j.

Function	Description
DISTINCTCOUNTULL	Approximate distinct count using ULL (default p=12)
DISTINCTCOUNTRAWULL	Returns serialized ULL sketch

Tuple Sketch

The Tuple Sketch extends the Theta Sketch with additional summary values per entry, ideal for summarizing attributes like impressions or clicks.

Function	Description
DISTINCTCOUNTTUPLESKETCH	Distinct count from tuple sketch
DISTINCTCOUNTRAWINTEGERSUMTUPLESKETCH	Raw tuple sketch as hex
AVGVALUEINTEGERSUMTUPLESKETCH	Average of summary values
SUMVALUESINTEGERSUMTUPLESKETCH	Sum of summary values

Frequency Sketches

Function	Description
FREQUENTLONGSSKETCH	Frequent items sketch for long values
FREQUENTSTRINGSSKETCH	Frequent items sketch for string values