This module provides Apache Druid aggregators for distinct counting based on HLL sketch from [Apache DataSketches](https://datasketches.apache.org/) library. At ingestion time, this aggregator creates the HLL sketch objects to be stored in Druid segments. At query time, sketches are read and merged together. In the end, by default, you receive the estimate of the number of distinct values presented to the sketch. Also, you can use post aggregator to produce a union of sketch columns in the same row.
|`type`|This String should be [`HLLSketchBuild`](#hllsketchbuild-aggregator) or [`HLLSketchMerge`](#hllsketchmerge-aggregator)|yes|
|`name`|A String for the output (result) name of the calculation.|yes|
|`fieldName`|A String for the name of the input field.|yes|
|`lgK`|log2 of K that is the number of buckets in the sketch, parameter that controls the size and the accuracy. Must be a power of 2 from 4 to 21 inclusively.|no, defaults to `12`|
|`tgtHllType`|The type of the target HLL sketch. Must be `HLL_4`, `HLL_6` or `HLL_8` |no, defaults to `HLL_4`|
|`round`|Round off values to whole numbers. Only affects query-time behavior and is ignored at ingestion-time.|no, defaults to `false`|
> The default `lgK` value has proven to be sufficient for most use cases; expect only very negligible improvements in accuracy with `lgK` values over `16` in normal circumstances.
> It is very common to use `HLLSketchBuild` in combination with [rollup](../../ingestion/rollup.md) to create a [metric](../../ingestion/ingestion-spec.html#metricsspec) on high-cardinality columns. In this example, a metric called `userid_hll` is included in the `metricsSpec`. This will perform a HLL sketch on the `userid` field at ingestion time, allowing for highly-performant approximate `COUNT DISTINCT` query operations and improving roll-up ratios when `userid` is then left out of the `dimensionsSpec`.