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 store in Druid segments. By default, Druid reads and merges sketches at query time. The default result is
the estimate of the number of distinct values presented to the sketch. You can also use post aggregators to produce a union of sketch columns in the same row.
You can use the HLL sketch aggregator on any column to estimate its cardinality.
|`lgK`|log2 of K that is the number of buckets in the sketch, parameter that controls the size and the accuracy. Must be between 4 and 21 inclusively.|no, defaults to `12`|
|`shouldFinalize`|Return the final double type representing the estimate rather than the intermediate sketch type itself. In addition to controlling the finalization of this aggregator, you can control whether all aggregators are finalized with the query context parameters [`finalize`](../../querying/query-context.md) and [`sqlFinalizeOuterSketches`](../../querying/sql-query-context.md).|no, defaults to `true`|
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.
The `HLLSketchBuild` aggregator builds an HLL sketch object from the specified input column. When used during ingestion, Druid stores pre-generated HLL sketch objects in the datasource instead of the raw data from the input column.
When applied at query time on an existing dimension, you can use the resulting column as an intermediate dimension by the [post-aggregators](#post-aggregators).
It is very common to use `HLLSketchBuild` in combination with [rollup](../../ingestion/rollup.md) to create a [metric](../../ingestion/ingestion-spec.md#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`.
You can use the `HLLSketchMerge` aggregator to ingest pre-generated sketches from an input dataset. For example, you can set up a batch processing job to generate the sketches before sending the data to Druid. You must serialize the sketches in the input dataset to Base64-encoded bytes. Then, specify `HLLSketchMerge` for the input column in the native ingestion `metricsSpec`.
