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Posted to commits@druid.apache.org by GitBox <gi...@apache.org> on 2022/01/06 00:10:43 UTC
[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779183994
##########
File path: docs/querying/sql.md
##########
@@ -328,17 +328,17 @@ Only the COUNT, ARRAY_AGG, and STRING_AGG aggregations can accept the DISTINCT k
|Function|Notes|Default|
|--------|-----|-------|
|`COUNT(*)`|Counts the number of rows.|`0`|
-|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for APPROX_COUNT_DISTINCT. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can could strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, expr must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
+|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for `APPROX_COUNT_DISTINCT`. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can use strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, `expr` must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
|`SUM(expr)`|Sums numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `0`|
|`MIN(expr)`|Takes the minimum of numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `9223372036854775807` (maximum LONG value)|
|`MAX(expr)`|Takes the maximum of numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `-9223372036854775808` (minimum LONG value)|
|`AVG(expr)`|Averages numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `0`|
-|`APPROX_COUNT_DISTINCT(expr)`|Counts distinct values of expr using an approximate algorithm. The expr can be a regular column or a prebuilt sketch column.<br><br>The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). By default, this is `APPROX_COUNT_DISTINCT_BUILTIN`. If the [DataSketches extension](../development/extensions-core/datasketches-extension.md) is loaded, this can also be set to `APPROX_COUNT_DISTINCT_DS_HLL` or `APPROX_COUNT_DISTINCT_DS_THETA`.<br><br>When run on prebuilt sketch columns, the sketch column type must match the implementation of this function. For example: when `druid.sql.approxCountDistinct.function` is set to `APPROX_COUNT_DISTINCT_BUILTIN`, this function will be able to run on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.|
-|`APPROX_COUNT_DISTINCT_BUILTIN(expr)`|_Usage note:_ consider using `APPROX_COUNT_DISTINCT_DS_HLL` instead, which offers better accuracy in many cases.<br/><br/>Counts distinct values of expr using Druid's built-in "cardinality" or "hyperUnique" aggregators, which implement a variant of [HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf). The expr can be a string, number, or prebuilt hyperUnique column. This is always approximate, regardless of the value of "useApproximateCountDistinct".|
-|`APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])`|Counts distinct values of expr, which can be a regular column or an [HLL sketch](../development/extensions-core/datasketches-hll.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `lgK` and `tgtHllType` parameters here are, like the equivalents in the [aggregator](../development/extensions-core/datasketches-hll.md#aggregators), described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
-|`APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])`|Counts distinct values of expr, which can be a regular column or a [Theta sketch](../development/extensions-core/datasketches-theta.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
-|`DS_HLL(expr, [lgK, tgtHllType])`|Creates an [HLL sketch](../development/extensions-core/datasketches-hll.md) on the values of expr, which can be a regular column or a column containing HLL sketches. The `lgK` and `tgtHllType` parameters are described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0'` (STRING)|
-|`DS_THETA(expr, [size])`|Creates a [Theta sketch](../development/extensions-core/datasketches-theta.md) on the values of expr, which can be a regular column or a column containing Theta sketches. The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0.0'` (STRING)|
+|`APPROX_COUNT_DISTINCT(expr)`|Counts distinct values of `expr` using an approximate algorithm. The `expr` can be a regular column or a prebuilt sketch column.<br><br>The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). By default, this is `APPROX_COUNT_DISTINCT_BUILTIN`. If the [DataSketches extension](../development/extensions-core/datasketches-extension.md) is loaded, this can also be set to `APPROX_COUNT_DISTINCT_DS_HLL` or `APPROX_COUNT_DISTINCT_DS_THETA`.<br><br>When run on prebuilt sketch columns, the sketch column type must match the implementation of this function. For example: when `druid.sql.approxCountDistinct.function` is set to `APPROX_COUNT_DISTINCT_BUILTIN`, this function will be able to run on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.|
+|`APPROX_COUNT_DISTINCT_BUILTIN(expr)`|_Usage note:_ consider using `APPROX_COUNT_DISTINCT_DS_HLL` instead, which offers better accuracy in many cases.<br/><br/>Counts distinct values of `expr` using Druid's built-in "cardinality" or "hyperUnique" aggregators, which implement a variant of [HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf). The `expr` can be a string, number, or prebuilt hyperUnique column. This is always approximate, regardless of the value of "useApproximateCountDistinct".|
+|`APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])`|Counts distinct values of `expr`, which can be a regular column or an [HLL sketch](../development/extensions-core/datasketches-hll.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `lgK` and `tgtHllType` parameters here are, like the equivalents in the [aggregator](../development/extensions-core/datasketches-hll.md#aggregators), described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
+|`APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])`|Counts distinct values of `expr`, which can be a regular column or a [Theta sketch](../development/extensions-core/datasketches-theta.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
+|`DS_HLL(expr, [lgK, tgtHllType])`|Creates an [HLL sketch](../development/extensions-core/datasketches-hll.md) on the values of `expr`, which can be a regular column or a column containing HLL sketches. The `lgK` and `tgtHllType` parameters are described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0'` (STRING)|
+|`DS_THETA(expr, [size])`|Creates a [Theta sketch](../development/extensions-core/datasketches-theta.md) on the values of `expr`, which can be a regular column or a column containing Theta sketches. The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0.0'` (STRING)|
|`APPROX_QUANTILE(expr, probability, [resolution])`|_Deprecated._ Use `APPROX_QUANTILE_DS` instead, which provides a superior distribution-independent algorithm with formal error guarantees.<br/><br/>Computes approximate quantiles on numeric or [approxHistogram](../development/extensions-core/approximate-histograms.md#approximate-histogram-aggregator) exprs. The "probability" should be between 0 and 1 (exclusive). The "resolution" is the number of centroids to use for the computation. Higher resolutions will give more precise results but also have higher overhead. If not provided, the default resolution is 50. The [approximate histogram extension](../development/extensions-core/approximate-histograms.md) must be loaded to use this function.|`NaN`|
|`APPROX_QUANTILE_DS(expr, probability, [k])`|Computes approximate quantiles on numeric or [Quantiles sketch](../development/extensions-core/datasketches-quantiles.md) exprs. Allowable "probability" values are between 0 and 1, exclusive. The `k` parameter is described in the Quantiles sketch documentation. You must load [DataSketches extension](../development/extensions-core/datasketches-extension.md) to use this function.<br/><br/>See the [known issue](#a-known-issue-with-approximate-functions-based-on-data-sketches) with this function.|`NaN`|
Review comment:
```suggestion
|`APPROX_QUANTILE_DS(expr, probability, [k])`|Computes approximate quantiles on numeric or [Quantiles sketch](../development/extensions-core/datasketches-quantiles.md) expressions. Allowable `probability` values are between 0 and 1, exclusive. The `k` parameter is described in the Quantiles sketch documentation. You must load [DataSketches extension](../development/extensions-core/datasketches-extension.md) to use this function.<br/><br/>See the [known issue](#a-known-issue-with-approximate-functions-based-on-data-sketches) with this function.|`NaN`|
```
##########
File path: docs/querying/sql.md
##########
@@ -328,17 +328,17 @@ Only the COUNT, ARRAY_AGG, and STRING_AGG aggregations can accept the DISTINCT k
|Function|Notes|Default|
|--------|-----|-------|
|`COUNT(*)`|Counts the number of rows.|`0`|
-|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for APPROX_COUNT_DISTINCT. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can could strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, expr must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
+|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for `APPROX_COUNT_DISTINCT`. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can use strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, `expr` must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
|`SUM(expr)`|Sums numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `0`|
|`MIN(expr)`|Takes the minimum of numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `9223372036854775807` (maximum LONG value)|
|`MAX(expr)`|Takes the maximum of numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `-9223372036854775808` (minimum LONG value)|
|`AVG(expr)`|Averages numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `0`|
-|`APPROX_COUNT_DISTINCT(expr)`|Counts distinct values of expr using an approximate algorithm. The expr can be a regular column or a prebuilt sketch column.<br><br>The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). By default, this is `APPROX_COUNT_DISTINCT_BUILTIN`. If the [DataSketches extension](../development/extensions-core/datasketches-extension.md) is loaded, this can also be set to `APPROX_COUNT_DISTINCT_DS_HLL` or `APPROX_COUNT_DISTINCT_DS_THETA`.<br><br>When run on prebuilt sketch columns, the sketch column type must match the implementation of this function. For example: when `druid.sql.approxCountDistinct.function` is set to `APPROX_COUNT_DISTINCT_BUILTIN`, this function will be able to run on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.|
-|`APPROX_COUNT_DISTINCT_BUILTIN(expr)`|_Usage note:_ consider using `APPROX_COUNT_DISTINCT_DS_HLL` instead, which offers better accuracy in many cases.<br/><br/>Counts distinct values of expr using Druid's built-in "cardinality" or "hyperUnique" aggregators, which implement a variant of [HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf). The expr can be a string, number, or prebuilt hyperUnique column. This is always approximate, regardless of the value of "useApproximateCountDistinct".|
-|`APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])`|Counts distinct values of expr, which can be a regular column or an [HLL sketch](../development/extensions-core/datasketches-hll.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `lgK` and `tgtHllType` parameters here are, like the equivalents in the [aggregator](../development/extensions-core/datasketches-hll.md#aggregators), described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
-|`APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])`|Counts distinct values of expr, which can be a regular column or a [Theta sketch](../development/extensions-core/datasketches-theta.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
-|`DS_HLL(expr, [lgK, tgtHllType])`|Creates an [HLL sketch](../development/extensions-core/datasketches-hll.md) on the values of expr, which can be a regular column or a column containing HLL sketches. The `lgK` and `tgtHllType` parameters are described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0'` (STRING)|
-|`DS_THETA(expr, [size])`|Creates a [Theta sketch](../development/extensions-core/datasketches-theta.md) on the values of expr, which can be a regular column or a column containing Theta sketches. The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0.0'` (STRING)|
+|`APPROX_COUNT_DISTINCT(expr)`|Counts distinct values of `expr` using an approximate algorithm. The `expr` can be a regular column or a prebuilt sketch column.<br><br>The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). By default, this is `APPROX_COUNT_DISTINCT_BUILTIN`. If the [DataSketches extension](../development/extensions-core/datasketches-extension.md) is loaded, this can also be set to `APPROX_COUNT_DISTINCT_DS_HLL` or `APPROX_COUNT_DISTINCT_DS_THETA`.<br><br>When run on prebuilt sketch columns, the sketch column type must match the implementation of this function. For example: when `druid.sql.approxCountDistinct.function` is set to `APPROX_COUNT_DISTINCT_BUILTIN`, this function will be able to run on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.|
+|`APPROX_COUNT_DISTINCT_BUILTIN(expr)`|_Usage note:_ consider using `APPROX_COUNT_DISTINCT_DS_HLL` instead, which offers better accuracy in many cases.<br/><br/>Counts distinct values of `expr` using Druid's built-in "cardinality" or "hyperUnique" aggregators, which implement a variant of [HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf). The `expr` can be a string, number, or prebuilt hyperUnique column. This is always approximate, regardless of the value of "useApproximateCountDistinct".|
+|`APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])`|Counts distinct values of `expr`, which can be a regular column or an [HLL sketch](../development/extensions-core/datasketches-hll.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `lgK` and `tgtHllType` parameters here are, like the equivalents in the [aggregator](../development/extensions-core/datasketches-hll.md#aggregators), described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
+|`APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])`|Counts distinct values of `expr`, which can be a regular column or a [Theta sketch](../development/extensions-core/datasketches-theta.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
+|`DS_HLL(expr, [lgK, tgtHllType])`|Creates an [HLL sketch](../development/extensions-core/datasketches-hll.md) on the values of `expr`, which can be a regular column or a column containing HLL sketches. The `lgK` and `tgtHllType` parameters are described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0'` (STRING)|
+|`DS_THETA(expr, [size])`|Creates a [Theta sketch](../development/extensions-core/datasketches-theta.md) on the values of `expr`, which can be a regular column or a column containing Theta sketches. The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0.0'` (STRING)|
|`APPROX_QUANTILE(expr, probability, [resolution])`|_Deprecated._ Use `APPROX_QUANTILE_DS` instead, which provides a superior distribution-independent algorithm with formal error guarantees.<br/><br/>Computes approximate quantiles on numeric or [approxHistogram](../development/extensions-core/approximate-histograms.md#approximate-histogram-aggregator) exprs. The "probability" should be between 0 and 1 (exclusive). The "resolution" is the number of centroids to use for the computation. Higher resolutions will give more precise results but also have higher overhead. If not provided, the default resolution is 50. The [approximate histogram extension](../development/extensions-core/approximate-histograms.md) must be loaded to use this function.|`NaN`|
Review comment:
```suggestion
|`APPROX_QUANTILE(expr, probability, [resolution])`|_Deprecated._ Use `APPROX_QUANTILE_DS` instead, which provides a superior distribution-independent algorithm with formal error guarantees.<br/><br/>Computes approximate quantiles on numeric or [approxHistogram](../development/extensions-core/approximate-histograms.md#approximate-histogram-aggregator) expressions. `probability` should be between 0 and 1, exclusive. `resolution` is the number of centroids to use for the computation. Higher resolutions will give more precise results but also have higher overhead. If not provided, the default resolution is 50. Load the [approximate histogram extension](../development/extensions-core/approximate-histograms.md) to use this function.|`NaN`|
```
##########
File path: docs/querying/sql.md
##########
@@ -328,17 +328,17 @@ Only the COUNT, ARRAY_AGG, and STRING_AGG aggregations can accept the DISTINCT k
|Function|Notes|Default|
|--------|-----|-------|
|`COUNT(*)`|Counts the number of rows.|`0`|
-|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for APPROX_COUNT_DISTINCT. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can could strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, expr must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
+|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for `APPROX_COUNT_DISTINCT`. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can use strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, `expr` must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
Review comment:
```suggestion
|`COUNT(DISTINCT expr)`|Counts distinct values of `expr`.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for `APPROX_COUNT_DISTINCT`. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can use strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, `expr` must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|`0`|
```
restores the default value
##########
File path: docs/querying/sql.md
##########
@@ -328,17 +328,17 @@ Only the COUNT, ARRAY_AGG, and STRING_AGG aggregations can accept the DISTINCT k
|Function|Notes|Default|
|--------|-----|-------|
|`COUNT(*)`|Counts the number of rows.|`0`|
-|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for APPROX_COUNT_DISTINCT. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can could strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, expr must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
+|`COUNT(DISTINCT expr)`|Counts distinct values of expr.<br><br>When "useApproximateCountDistinct" is set to "true" (the default), this is an alias for `APPROX_COUNT_DISTINCT`. The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). In this mode, you can use strings, numbers, or prebuilt sketches. If counting prebuilt sketches, the prebuilt sketch type must match the selected algorithm.<br><br>When "useApproximateCountDistinct" is set to "false", the computation will be exact. In this case, `expr` must be string or numeric, since exact counts are not possible using prebuilt sketches. In exact mode, only one distinct count per query is permitted unless "useGroupingSetForExactDistinct" is enabled.|
|`SUM(expr)`|Sums numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `0`|
|`MIN(expr)`|Takes the minimum of numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `9223372036854775807` (maximum LONG value)|
|`MAX(expr)`|Takes the maximum of numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `-9223372036854775808` (minimum LONG value)|
|`AVG(expr)`|Averages numbers.|`null` if `druid.generic.useDefaultValueForNull=false`, otherwise `0`|
-|`APPROX_COUNT_DISTINCT(expr)`|Counts distinct values of expr using an approximate algorithm. The expr can be a regular column or a prebuilt sketch column.<br><br>The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). By default, this is `APPROX_COUNT_DISTINCT_BUILTIN`. If the [DataSketches extension](../development/extensions-core/datasketches-extension.md) is loaded, this can also be set to `APPROX_COUNT_DISTINCT_DS_HLL` or `APPROX_COUNT_DISTINCT_DS_THETA`.<br><br>When run on prebuilt sketch columns, the sketch column type must match the implementation of this function. For example: when `druid.sql.approxCountDistinct.function` is set to `APPROX_COUNT_DISTINCT_BUILTIN`, this function will be able to run on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.|
-|`APPROX_COUNT_DISTINCT_BUILTIN(expr)`|_Usage note:_ consider using `APPROX_COUNT_DISTINCT_DS_HLL` instead, which offers better accuracy in many cases.<br/><br/>Counts distinct values of expr using Druid's built-in "cardinality" or "hyperUnique" aggregators, which implement a variant of [HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf). The expr can be a string, number, or prebuilt hyperUnique column. This is always approximate, regardless of the value of "useApproximateCountDistinct".|
-|`APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])`|Counts distinct values of expr, which can be a regular column or an [HLL sketch](../development/extensions-core/datasketches-hll.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `lgK` and `tgtHllType` parameters here are, like the equivalents in the [aggregator](../development/extensions-core/datasketches-hll.md#aggregators), described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
-|`APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])`|Counts distinct values of expr, which can be a regular column or a [Theta sketch](../development/extensions-core/datasketches-theta.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
-|`DS_HLL(expr, [lgK, tgtHllType])`|Creates an [HLL sketch](../development/extensions-core/datasketches-hll.md) on the values of expr, which can be a regular column or a column containing HLL sketches. The `lgK` and `tgtHllType` parameters are described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0'` (STRING)|
-|`DS_THETA(expr, [size])`|Creates a [Theta sketch](../development/extensions-core/datasketches-theta.md) on the values of expr, which can be a regular column or a column containing Theta sketches. The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0.0'` (STRING)|
+|`APPROX_COUNT_DISTINCT(expr)`|Counts distinct values of `expr` using an approximate algorithm. The `expr` can be a regular column or a prebuilt sketch column.<br><br>The specific algorithm that will be used depends on the value of [`druid.sql.approxCountDistinct.function`](../configuration/index.md#sql). By default, this is `APPROX_COUNT_DISTINCT_BUILTIN`. If the [DataSketches extension](../development/extensions-core/datasketches-extension.md) is loaded, this can also be set to `APPROX_COUNT_DISTINCT_DS_HLL` or `APPROX_COUNT_DISTINCT_DS_THETA`.<br><br>When run on prebuilt sketch columns, the sketch column type must match the implementation of this function. For example: when `druid.sql.approxCountDistinct.function` is set to `APPROX_COUNT_DISTINCT_BUILTIN`, this function will be able to run on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.|
+|`APPROX_COUNT_DISTINCT_BUILTIN(expr)`|_Usage note:_ consider using `APPROX_COUNT_DISTINCT_DS_HLL` instead, which offers better accuracy in many cases.<br/><br/>Counts distinct values of `expr` using Druid's built-in "cardinality" or "hyperUnique" aggregators, which implement a variant of [HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf). The `expr` can be a string, number, or prebuilt hyperUnique column. This is always approximate, regardless of the value of "useApproximateCountDistinct".|
+|`APPROX_COUNT_DISTINCT_DS_HLL(expr, [lgK, tgtHllType])`|Counts distinct values of `expr`, which can be a regular column or an [HLL sketch](../development/extensions-core/datasketches-hll.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `lgK` and `tgtHllType` parameters here are, like the equivalents in the [aggregator](../development/extensions-core/datasketches-hll.md#aggregators), described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
+|`APPROX_COUNT_DISTINCT_DS_THETA(expr, [size])`|Counts distinct values of `expr`, which can be a regular column or a [Theta sketch](../development/extensions-core/datasketches-theta.md) column. Results are always approximate, regardless of the value of [`useApproximateCountDistinct`](#connection-context). The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function. See also `COUNT(DISTINCT expr)`. |`0`|
+|`DS_HLL(expr, [lgK, tgtHllType])`|Creates an [HLL sketch](../development/extensions-core/datasketches-hll.md) on the values of `expr`, which can be a regular column or a column containing HLL sketches. The `lgK` and `tgtHllType` parameters are described in the HLL sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0'` (STRING)|
+|`DS_THETA(expr, [size])`|Creates a [Theta sketch](../development/extensions-core/datasketches-theta.md) on the values of `expr`, which can be a regular column or a column containing Theta sketches. The `size` parameter is described in the Theta sketch documentation. The [DataSketches extension](../development/extensions-core/datasketches-extension.md) must be loaded to use this function.|`'0.0'` (STRING)|
|`APPROX_QUANTILE(expr, probability, [resolution])`|_Deprecated._ Use `APPROX_QUANTILE_DS` instead, which provides a superior distribution-independent algorithm with formal error guarantees.<br/><br/>Computes approximate quantiles on numeric or [approxHistogram](../development/extensions-core/approximate-histograms.md#approximate-histogram-aggregator) exprs. The "probability" should be between 0 and 1 (exclusive). The "resolution" is the number of centroids to use for the computation. Higher resolutions will give more precise results but also have higher overhead. If not provided, the default resolution is 50. The [approximate histogram extension](../development/extensions-core/approximate-histograms.md) must be loaded to use this function.|`NaN`|
|`APPROX_QUANTILE_DS(expr, probability, [k])`|Computes approximate quantiles on numeric or [Quantiles sketch](../development/extensions-core/datasketches-quantiles.md) exprs. Allowable "probability" values are between 0 and 1, exclusive. The `k` parameter is described in the Quantiles sketch documentation. You must load [DataSketches extension](../development/extensions-core/datasketches-extension.md) to use this function.<br/><br/>See the [known issue](#a-known-issue-with-approximate-functions-based-on-data-sketches) with this function.|`NaN`|
|`APPROX_QUANTILE_FIXED_BUCKETS(expr, probability, numBuckets, lowerLimit, upperLimit, [outlierHandlingMode])`|Computes approximate quantiles on numeric or [fixed buckets histogram](../development/extensions-core/approximate-histograms.md#fixed-buckets-histogram) exprs. The "probability" should be between 0 and 1 (exclusive). The `numBuckets`, `lowerLimit`, `upperLimit`, and `outlierHandlingMode` parameters are described in the fixed buckets histogram documentation. The [approximate histogram extension](../development/extensions-core/approximate-histograms.md) must be loaded to use this function.|`0.0`|
Review comment:
```suggestion
|`APPROX_QUANTILE_FIXED_BUCKETS(expr, probability, numBuckets, lowerLimit, upperLimit, [outlierHandlingMode])`|Computes approximate quantiles on numeric or [fixed buckets histogram](../development/extensions-core/approximate-histograms.md#fixed-buckets-histogram) expressions. `probability` should be between 0 and 1, exclusive. The `numBuckets`, `lowerLimit`, `upperLimit`, and `outlierHandlingMode` parameters are described in the fixed buckets histogram documentation. Load the [approximate histogram extension](../development/extensions-core/approximate-histograms.md) to use this function.|`0.0`|
```
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