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[GitHub] [druid] vogievetsky opened a new pull request #12091: Standardizing SQL function docs
vogievetsky opened a new pull request #12091:
URL: https://github.com/apache/druid/pull/12091
Just making sure that the little details are consistent among the function docs.
Specifically
- Consistent formatting for optional arguments
- Consistent code quoting for `expr`
- Consistent space after `,`
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779764079
##########
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`|
Review comment:
```suggestion
|`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`|
```
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[GitHub] [druid] clintropolis merged pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
clintropolis merged pull request #12091:
URL: https://github.com/apache/druid/pull/12091
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779762076
##########
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".|
Review comment:
```suggestion
|`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, a number, or a prebuilt hyperUnique column. This is always approximate, regardless of the value of `useApproximateCountDistinct`.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779761535
##########
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.|
Review comment:
```suggestion
|`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 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, you can set it 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 runs on prebuilt hyperUnique columns, but not on prebuilt HLLSketchBuild columns.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779753264
##########
File path: docs/querying/sql.md
##########
@@ -431,37 +431,37 @@ String functions accept strings, and return a type appropriate to the function.
|Function|Notes|
|--------|-----|
-|<code>x || y</code>|Concat strings x and y.|
+|<code>x || y</code>|Concat strings `x` and `y`.|
|`CONCAT(expr, expr...)`|Concats a list of expressions.|
-|`TEXTCAT(expr, expr)`|Two argument version of CONCAT.|
-|`STRING_FORMAT(pattern[, args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
-|`LENGTH(expr)`|Length of expr in UTF-16 code units.|
+|`TEXTCAT(expr, expr)`|Two argument version of `CONCAT`.|
+|`STRING_FORMAT(pattern, [args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
+|`LENGTH(expr)`|Length of `expr` in UTF-16 code units.|
|`CHAR_LENGTH(expr)`|Synonym for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Synonym for `LENGTH`.|
|`STRLEN(expr)`|Synonym for `LENGTH`.|
-|`LOOKUP(expr, lookupName)`|Look up expr in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
-|`LOWER(expr)`|Returns expr in all lowercase.|
-|`PARSE_LONG(string[, radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
-|`POSITION(needle IN haystack [FROM fromIndex])`|Returns the index of needle within haystack, with indexes starting from 1. The search will begin at fromIndex, or 1 if fromIndex is not specified. If the needle is not found, returns 0.|
+|`LOOKUP(expr, lookupName)`|Look up `expr` in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
+|`LOWER(expr)`|Returns `expr` in all lowercase.|
+|`PARSE_LONG(string, [radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
+|`POSITION(needle IN haystack [FROM fromIndex])`|Returns the index of needle within haystack, with indexes starting from 1. The search will begin at `fromIndex`, or 1 if `fromIndex` is not specified. If the needle is not found, returns 0.|
|`REGEXP_EXTRACT(expr, pattern, [index])`|Apply regular expression `pattern` to `expr` and extract a capture group, or `NULL` if there is no match. If index is unspecified or zero, returns the first substring that matched the pattern. The pattern may match anywhere inside `expr`; if you want to match the entire string instead, use the `^` and `$` markers at the start and end of your pattern. Note: when `druid.generic.useDefaultValueForNull = true`, it is not possible to differentiate an empty-string match from a non-match (both will return `NULL`).|
|`REGEXP_LIKE(expr, pattern)`|Returns whether `expr` matches regular expression `pattern`. The pattern may match anywhere inside `expr`; if you want to match the entire string instead, use the `^` and `$` markers at the start and end of your pattern. Similar to [`LIKE`](#comparison-operators), but uses regexps instead of LIKE patterns. Especially useful in WHERE clauses.|
-|`CONTAINS_STRING(<expr>, str)`|Returns true if the `str` is a substring of `expr`.|
-|`ICONTAINS_STRING(<expr>, str)`|Returns true if the `str` is a substring of `expr`. The match is case-insensitive.|
-|`REPLACE(expr, pattern, replacement)`|Replaces pattern with replacement in expr, and returns the result.|
-|`STRPOS(haystack, needle)`|Returns the index of needle within haystack, with indexes starting from 1. If the needle is not found, returns 0.|
-|`SUBSTRING(expr, index, [length])`|Returns a substring of expr starting at index, with a max length, both measured in UTF-16 code units.|
-|`RIGHT(expr, [length])`|Returns the rightmost length characters from expr.|
-|`LEFT(expr, [length])`|Returns the leftmost length characters from expr.|
+|`CONTAINS_STRING(expr, str)`|Returns true if the `str` is a substring of `expr`.|
+|`ICONTAINS_STRING(expr, str)`|Returns true if the `str` is a substring of `expr`. The match is case-insensitive.|
+|`REPLACE(expr, pattern, replacement)`|Replaces pattern with replacement in `expr`, and returns the result.|
+|`STRPOS(haystack, needle)`|Returns the index of `needle` within `haystack`, with indexes starting from 1. If the `needle` is not found, returns 0.|
+|`SUBSTRING(expr, index, [length])`|Returns a substring of `expr` starting at index, with a max length, both measured in UTF-16 code units.|
+|`RIGHT(expr, [length])`|Returns the rightmost length characters from `expr`.|
+|`LEFT(expr, [length])`|Returns the leftmost length characters from `expr`.|
|`SUBSTR(expr, index, [length])`|Synonym for SUBSTRING.|
-|`TRIM([BOTH `<code>|</code>` LEADING `<code>|</code>` TRAILING] [<chars> FROM] expr)`|Returns expr with characters removed from the leading, trailing, or both ends of "expr" if they are in "chars". If "chars" is not provided, it defaults to " " (a space). If the directional argument is not provided, it defaults to "BOTH".|
-|`BTRIM(expr[, chars])`|Alternate form of `TRIM(BOTH <chars> FROM <expr>)`.|
-|`LTRIM(expr[, chars])`|Alternate form of `TRIM(LEADING <chars> FROM <expr>)`.|
-|`RTRIM(expr[, chars])`|Alternate form of `TRIM(TRAILING <chars> FROM <expr>)`.|
-|`UPPER(expr)`|Returns expr in all uppercase.|
-|`REVERSE(expr)`|Reverses expr.|
-|`REPEAT(expr, [N])`|Repeats expr N times|
-|`LPAD(expr, length[, chars])`|Returns a string of `length` from `expr` left-padded with `chars`. If `length` is shorter than the length of `expr`, the result is `expr` which is truncated to `length`. The result will be null if either `expr` or `chars` is null. If `chars` is an empty string, no padding is added, however `expr` may be trimmed if necessary.|
-|`RPAD(expr, length[, chars])`|Returns a string of `length` from `expr` right-padded with `chars`. If `length` is shorter than the length of `expr`, the result is `expr` which is truncated to `length`. The result will be null if either `expr` or `chars` is null. If `chars` is an empty string, no padding is added, however `expr` may be trimmed if necessary.|
+|`TRIM([BOTH `<code>|</code>` LEADING `<code>|</code>` TRAILING] [chars FROM] expr)`|Returns `expr` with characters removed from the leading, trailing, or both ends of "expr" if they are in "chars". If "chars" is not provided, it defaults to " " (a space). If the directional argument is not provided, it defaults to "BOTH".|
+|`BTRIM(expr, [chars])`|Alternate form of `TRIM(BOTH chars FROM expr)`.|
+|`LTRIM(expr, [chars])`|Alternate form of `TRIM(LEADING chars FROM expr)`.|
+|`RTRIM(expr, [chars])`|Alternate form of `TRIM(TRAILING chars FROM expr)`.|
+|`UPPER(expr)`|Returns `expr` in all uppercase.|
+|`REVERSE(expr)`|Reverses `expr`.|
+|`REPEAT(expr, [N])`|Repeats `expr` N times|
+|`LPAD(expr, length, [chars])`|Returns a string of `length` from `expr` left-padded with `chars`. If `length` is shorter than the length of `expr`, the result is `expr` which is truncated to `length`. The result will be null if either `expr` or `chars` is null. If `chars` is an empty string, no padding is added, however `expr` may be trimmed if necessary.|
+|`RPAD(expr, length, [chars])`|Returns a string of `length` from `expr` right-padded with `chars`. If `length` is shorter than the length of `expr`, the result is `expr` which is truncated to `length`. The result will be null if either `expr` or `chars` is null. If `chars` is an empty string, no padding is added, however `expr` may be trimmed if necessary.|
### Time functions
Review comment:
```suggestion
### Date and time functions
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779749356
##########
File path: docs/querying/sql.md
##########
@@ -431,37 +431,37 @@ String functions accept strings, and return a type appropriate to the function.
|Function|Notes|
|--------|-----|
-|<code>x || y</code>|Concat strings x and y.|
+|<code>x || y</code>|Concat strings `x` and `y`.|
|`CONCAT(expr, expr...)`|Concats a list of expressions.|
-|`TEXTCAT(expr, expr)`|Two argument version of CONCAT.|
-|`STRING_FORMAT(pattern[, args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
-|`LENGTH(expr)`|Length of expr in UTF-16 code units.|
+|`TEXTCAT(expr, expr)`|Two argument version of `CONCAT`.|
+|`STRING_FORMAT(pattern, [args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
+|`LENGTH(expr)`|Length of `expr` in UTF-16 code units.|
|`CHAR_LENGTH(expr)`|Synonym for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Synonym for `LENGTH`.|
|`STRLEN(expr)`|Synonym for `LENGTH`.|
Review comment:
```suggestion
|`CHAR_LENGTH(expr)`|Alias for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Alias for `LENGTH`.|
|`STRLEN(expr)`|Alias for `LENGTH`.|
```
```suggestion
|`CHAR_LENGTH(expr)`|Synonym for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Synonym for `LENGTH`.|
|`STRLEN(expr)`|Synonym for `LENGTH`.|
```
##########
File path: docs/querying/sql.md
##########
@@ -431,37 +431,37 @@ String functions accept strings, and return a type appropriate to the function.
|Function|Notes|
|--------|-----|
-|<code>x || y</code>|Concat strings x and y.|
+|<code>x || y</code>|Concat strings `x` and `y`.|
|`CONCAT(expr, expr...)`|Concats a list of expressions.|
-|`TEXTCAT(expr, expr)`|Two argument version of CONCAT.|
-|`STRING_FORMAT(pattern[, args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
-|`LENGTH(expr)`|Length of expr in UTF-16 code units.|
+|`TEXTCAT(expr, expr)`|Two argument version of `CONCAT`.|
+|`STRING_FORMAT(pattern, [args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
+|`LENGTH(expr)`|Length of `expr` in UTF-16 code units.|
|`CHAR_LENGTH(expr)`|Synonym for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Synonym for `LENGTH`.|
|`STRLEN(expr)`|Synonym for `LENGTH`.|
Review comment:
```suggestion
|`CHAR_LENGTH(expr)`|Alias for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Alias for `LENGTH`.|
|`STRLEN(expr)`|Alias for `LENGTH`.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779749642
##########
File path: docs/querying/sql.md
##########
@@ -431,37 +431,37 @@ String functions accept strings, and return a type appropriate to the function.
|Function|Notes|
|--------|-----|
-|<code>x || y</code>|Concat strings x and y.|
+|<code>x || y</code>|Concat strings `x` and `y`.|
|`CONCAT(expr, expr...)`|Concats a list of expressions.|
-|`TEXTCAT(expr, expr)`|Two argument version of CONCAT.|
-|`STRING_FORMAT(pattern[, args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
-|`LENGTH(expr)`|Length of expr in UTF-16 code units.|
+|`TEXTCAT(expr, expr)`|Two argument version of `CONCAT`.|
+|`STRING_FORMAT(pattern, [args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
+|`LENGTH(expr)`|Length of `expr` in UTF-16 code units.|
|`CHAR_LENGTH(expr)`|Synonym for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Synonym for `LENGTH`.|
|`STRLEN(expr)`|Synonym for `LENGTH`.|
-|`LOOKUP(expr, lookupName)`|Look up expr in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
-|`LOWER(expr)`|Returns expr in all lowercase.|
-|`PARSE_LONG(string[, radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
-|`POSITION(needle IN haystack [FROM fromIndex])`|Returns the index of needle within haystack, with indexes starting from 1. The search will begin at fromIndex, or 1 if fromIndex is not specified. If the needle is not found, returns 0.|
+|`LOOKUP(expr, lookupName)`|Look up `expr` in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
+|`LOWER(expr)`|Returns `expr` in all lowercase.|
+|`PARSE_LONG(string, [radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
+|`POSITION(needle IN haystack [FROM fromIndex])`|Returns the index of needle within haystack, with indexes starting from 1. The search will begin at `fromIndex`, or 1 if `fromIndex` is not specified. If the needle is not found, returns 0.|
|`REGEXP_EXTRACT(expr, pattern, [index])`|Apply regular expression `pattern` to `expr` and extract a capture group, or `NULL` if there is no match. If index is unspecified or zero, returns the first substring that matched the pattern. The pattern may match anywhere inside `expr`; if you want to match the entire string instead, use the `^` and `$` markers at the start and end of your pattern. Note: when `druid.generic.useDefaultValueForNull = true`, it is not possible to differentiate an empty-string match from a non-match (both will return `NULL`).|
|`REGEXP_LIKE(expr, pattern)`|Returns whether `expr` matches regular expression `pattern`. The pattern may match anywhere inside `expr`; if you want to match the entire string instead, use the `^` and `$` markers at the start and end of your pattern. Similar to [`LIKE`](#comparison-operators), but uses regexps instead of LIKE patterns. Especially useful in WHERE clauses.|
-|`CONTAINS_STRING(<expr>, str)`|Returns true if the `str` is a substring of `expr`.|
-|`ICONTAINS_STRING(<expr>, str)`|Returns true if the `str` is a substring of `expr`. The match is case-insensitive.|
-|`REPLACE(expr, pattern, replacement)`|Replaces pattern with replacement in expr, and returns the result.|
-|`STRPOS(haystack, needle)`|Returns the index of needle within haystack, with indexes starting from 1. If the needle is not found, returns 0.|
-|`SUBSTRING(expr, index, [length])`|Returns a substring of expr starting at index, with a max length, both measured in UTF-16 code units.|
-|`RIGHT(expr, [length])`|Returns the rightmost length characters from expr.|
-|`LEFT(expr, [length])`|Returns the leftmost length characters from expr.|
+|`CONTAINS_STRING(expr, str)`|Returns true if the `str` is a substring of `expr`.|
+|`ICONTAINS_STRING(expr, str)`|Returns true if the `str` is a substring of `expr`. The match is case-insensitive.|
+|`REPLACE(expr, pattern, replacement)`|Replaces pattern with replacement in `expr`, and returns the result.|
+|`STRPOS(haystack, needle)`|Returns the index of `needle` within `haystack`, with indexes starting from 1. If the `needle` is not found, returns 0.|
+|`SUBSTRING(expr, index, [length])`|Returns a substring of `expr` starting at index, with a max length, both measured in UTF-16 code units.|
+|`RIGHT(expr, [length])`|Returns the rightmost length characters from `expr`.|
+|`LEFT(expr, [length])`|Returns the leftmost length characters from `expr`.|
|`SUBSTR(expr, index, [length])`|Synonym for SUBSTRING.|
Review comment:
```suggestion
|`SUBSTR(expr, index, [length])`|Alias for SUBSTRING.|
```
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[GitHub] [druid] FrankChen021 commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
FrankChen021 commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r773563661
##########
File path: docs/querying/sql.md
##########
@@ -328,13 +328,13 @@ 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(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)|
Review comment:
```suggestion
|`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)|
```
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[GitHub] [druid] FrankChen021 commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
FrankChen021 commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r773564295
##########
File path: docs/querying/sql.md
##########
@@ -431,37 +431,37 @@ String functions accept strings, and return a type appropriate to the function.
|Function|Notes|
|--------|-----|
-|<code>x || y</code>|Concat strings x and y.|
+|<code>x || y</code>|Concat strings `x` and `y`.|
|`CONCAT(expr, expr...)`|Concats a list of expressions.|
-|`TEXTCAT(expr, expr)`|Two argument version of CONCAT.|
-|`STRING_FORMAT(pattern[, args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
-|`LENGTH(expr)`|Length of expr in UTF-16 code units.|
+|`TEXTCAT(expr, expr)`|Two argument version of `CONCAT`.|
+|`STRING_FORMAT(pattern, [args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
+|`LENGTH(expr)`|Length of `expr` in UTF-16 code units.|
|`CHAR_LENGTH(expr)`|Synonym for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Synonym for `LENGTH`.|
|`STRLEN(expr)`|Synonym for `LENGTH`.|
-|`LOOKUP(expr, lookupName)`|Look up expr in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
-|`LOWER(expr)`|Returns expr in all lowercase.|
-|`PARSE_LONG(string[, radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
+|`LOOKUP(expr, lookupName)`|Look up `expr` in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
+|`LOWER(expr)`|Returns `expr` in all lowercase.|
+|`PARSE_LONG(string, [radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
|`POSITION(needle IN haystack [FROM fromIndex])`|Returns the index of needle within haystack, with indexes starting from 1. The search will begin at fromIndex, or 1 if fromIndex is not specified. If the needle is not found, returns 0.|
|`REGEXP_EXTRACT(expr, pattern, [index])`|Apply regular expression `pattern` to `expr` and extract a capture group, or `NULL` if there is no match. If index is unspecified or zero, returns the first substring that matched the pattern. The pattern may match anywhere inside `expr`; if you want to match the entire string instead, use the `^` and `$` markers at the start and end of your pattern. Note: when `druid.generic.useDefaultValueForNull = true`, it is not possible to differentiate an empty-string match from a non-match (both will return `NULL`).|
|`REGEXP_LIKE(expr, pattern)`|Returns whether `expr` matches regular expression `pattern`. The pattern may match anywhere inside `expr`; if you want to match the entire string instead, use the `^` and `$` markers at the start and end of your pattern. Similar to [`LIKE`](#comparison-operators), but uses regexps instead of LIKE patterns. Especially useful in WHERE clauses.|
-|`CONTAINS_STRING(<expr>, str)`|Returns true if the `str` is a substring of `expr`.|
-|`ICONTAINS_STRING(<expr>, str)`|Returns true if the `str` is a substring of `expr`. The match is case-insensitive.|
-|`REPLACE(expr, pattern, replacement)`|Replaces pattern with replacement in expr, and returns the result.|
+|`CONTAINS_STRING(expr, str)`|Returns true if the `str` is a substring of `expr`.|
+|`ICONTAINS_STRING(expr, str)`|Returns true if the `str` is a substring of `expr`. The match is case-insensitive.|
+|`REPLACE(expr, pattern, replacement)`|Replaces pattern with replacement in `expr`, and returns the result.|
|`STRPOS(haystack, needle)`|Returns the index of needle within haystack, with indexes starting from 1. If the needle is not found, returns 0.|
Review comment:
```suggestion
|`STRPOS(haystack, needle)`|Returns the index of `needle` within `haystack`, with indexes starting from 1. If the `needle` is not found, returns 0.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779758145
##########
File path: docs/querying/sql.md
##########
@@ -619,22 +619,22 @@ All 'array' references in the multi-value string function documentation can refe
|Function|Notes|
|--------|-----|
-| `ARRAY[expr1,expr ...]` | constructs a SQL ARRAY literal from the expression arguments, using the type of the first argument as the output array type |
-| `MV_FILTER_ONLY(expr, arr)` | filters multi-value `expr` to include only values contained in array `arr` |
-| `MV_FILTER_NONE(expr, arr)` | filters multi-value `expr` to include no values contained in array `arr` |
-| `MV_LENGTH(arr)` | returns length of array expression |
-| `MV_OFFSET(arr,long)` | returns the array element at the 0 based index supplied, or null for an out of range index|
-| `MV_ORDINAL(arr,long)` | returns the array element at the 1 based index supplied, or null for an out of range index |
-| `MV_CONTAINS(arr,expr)` | returns 1 if the array contains the element specified by expr, or contains all elements specified by expr if expr is an array, else 0 |
-| `MV_OVERLAP(arr1,arr2)` | returns 1 if arr1 and arr2 have any elements in common, else 0 |
-| `MV_OFFSET_OF(arr,expr)` | returns the 0 based index of the first occurrence of expr in the array, or `-1` or `null` if `druid.generic.useDefaultValueForNull=false` if no matching elements exist in the array. |
-| `MV_ORDINAL_OF(arr,expr)` | returns the 1 based index of the first occurrence of expr in the array, or `-1` or `null` if `druid.generic.useDefaultValueForNull=false` if no matching elements exist in the array. |
-| `MV_PREPEND(expr,arr)` | adds expr to arr at the beginning, the resulting array type determined by the type of the array |
-| `MV_APPEND(arr1,expr)` | appends expr to arr, the resulting array type determined by the type of the first array |
-| `MV_CONCAT(arr1,arr2)` | concatenates 2 arrays, the resulting array type determined by the type of the first array |
-| `MV_SLICE(arr,start,end)` | return the subarray of arr from the 0 based index start(inclusive) to end(exclusive), or `null`, if start is less than 0, greater than length of arr or less than end|
-| `MV_TO_STRING(arr,str)` | joins all elements of arr by the delimiter specified by str |
-| `STRING_TO_MV(str1,str2)` | splits str1 into an array on the delimiter specified by str2 |
+|`ARRAY[expr1, expr2, ...]`|Constructs a SQL ARRAY literal from the expression arguments, using the type of the first argument as the output array type.|
+|`MV_FILTER_ONLY(expr, arr)`|Filters multi-value `expr` to include only values contained in array `arr`.|
+|`MV_FILTER_NONE(expr, arr)`|Filters multi-value `expr` to include no values contained in array `arr`.|
+|`MV_LENGTH(arr)`|Returns length of array expression.|
+|`MV_OFFSET(arr, long)`|Returns the array element at the 0 based index supplied, or null for an out of range index.|
+|`MV_ORDINAL(arr, long)`|Returns the array element at the 1 based index supplied, or null for an out of range index.|
+|`MV_CONTAINS(arr, expr)`|Returns 1 if the array contains the element specified by `expr`, or contains all elements specified by `expr` if `expr` is an array, else 0.|
+|`MV_OVERLAP(arr1, arr2)`|Returns 1 if arr1 and arr2 have any elements in common, else 0|
Review comment:
```suggestion
|`MV_OVERLAP(arr1, arr2)`|Returns 1 if arr1 and arr2 have any elements in common, else 0.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779754611
##########
File path: docs/querying/sql.md
##########
@@ -609,8 +609,8 @@ The [DataSketches extension](../development/extensions-core/datasketches-extensi
|`CASE WHEN boolean_expr1 THEN result1 \[ WHEN boolean_expr2 THEN result2 ... \] \[ ELSE resultN \] END`|Searched CASE.|
|`NULLIF(value1, value2)`|Returns NULL if value1 and value2 match, else returns value1.|
|`COALESCE(value1, value2, ...)`|Returns the first value that is neither NULL nor empty string.|
-|`NVL(expr,expr-for-null)`|Returns 'expr-for-null' if 'expr' is null (or empty string for string type).|
-|`BLOOM_FILTER_TEST(<expr>, <serialized-filter>)`|Returns true if the value is contained in a Base64-serialized bloom filter. See the [Bloom filter extension](../development/extensions-core/bloom-filter.md) documentation for additional details.|
+|`NVL(expr, expr-for-null)`|Returns `expr-for-null` if `expr` is null (or empty string for string type).|
+|`BLOOM_FILTER_TEST(expr, serialized-filter)`|Returns true if the value of `expr` is contained in the Base64-serialized bloom filter. See the [Bloom filter extension](../development/extensions-core/bloom-filter.md) documentation for additional details.|
Review comment:
```suggestion
|`BLOOM_FILTER_TEST(expr, serialized-filter)`|Returns true if the value of `expr` is contained in the Base64-serialized Bloom filter. See the [Bloom filter extension](../development/extensions-core/bloom-filter.md) documentation for additional details.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [druid] FrankChen021 commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
FrankChen021 commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r773563503
##########
File path: docs/querying/sql.md
##########
@@ -328,13 +328,13 @@ 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(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`|
Review comment:
```suggestion
|`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`|
```
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[GitHub] [druid] FrankChen021 commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
FrankChen021 commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r773564086
##########
File path: docs/querying/sql.md
##########
@@ -431,37 +431,37 @@ String functions accept strings, and return a type appropriate to the function.
|Function|Notes|
|--------|-----|
-|<code>x || y</code>|Concat strings x and y.|
+|<code>x || y</code>|Concat strings `x` and `y`.|
|`CONCAT(expr, expr...)`|Concats a list of expressions.|
-|`TEXTCAT(expr, expr)`|Two argument version of CONCAT.|
-|`STRING_FORMAT(pattern[, args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
-|`LENGTH(expr)`|Length of expr in UTF-16 code units.|
+|`TEXTCAT(expr, expr)`|Two argument version of `CONCAT`.|
+|`STRING_FORMAT(pattern, [args...])`|Returns a string formatted in the manner of Java's [String.format](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-).|
+|`LENGTH(expr)`|Length of `expr` in UTF-16 code units.|
|`CHAR_LENGTH(expr)`|Synonym for `LENGTH`.|
|`CHARACTER_LENGTH(expr)`|Synonym for `LENGTH`.|
|`STRLEN(expr)`|Synonym for `LENGTH`.|
-|`LOOKUP(expr, lookupName)`|Look up expr in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
-|`LOWER(expr)`|Returns expr in all lowercase.|
-|`PARSE_LONG(string[, radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
+|`LOOKUP(expr, lookupName)`|Look up `expr` in a registered [query-time lookup table](lookups.md). Note that lookups can also be queried directly using the [`lookup` schema](#from).|
+|`LOWER(expr)`|Returns `expr` in all lowercase.|
+|`PARSE_LONG(string, [radix])`|Parses a string into a long (BIGINT) with the given radix, or 10 (decimal) if a radix is not provided.|
|`POSITION(needle IN haystack [FROM fromIndex])`|Returns the index of needle within haystack, with indexes starting from 1. The search will begin at fromIndex, or 1 if fromIndex is not specified. If the needle is not found, returns 0.|
Review comment:
```suggestion
|`POSITION(needle IN haystack [FROM fromIndex])`|Returns the index of needle within haystack, with indexes starting from 1. The search will begin at `fromIndex`, or 1 if `fromIndex` is not specified. If the needle is not found, returns 0.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779750621
##########
File path: docs/querying/sql.md
##########
@@ -386,27 +386,27 @@ to FLOAT. At runtime, Druid will widen 32-bit floats to 64-bit for most expressi
|`PI`|Constant Pi.|
|`ABS(expr)`|Absolute value.|
|`CEIL(expr)`|Ceiling.|
-|`EXP(expr)`|e to the power of expr.|
+|`EXP(expr)`|e to the power of `expr`.|
|`FLOOR(expr)`|Floor.|
|`LN(expr)`|Logarithm (base e).|
|`LOG10(expr)`|Logarithm (base 10).|
-|`POWER(expr, power)`|expr to a power.|
+|`POWER(expr, power)`|`expr` raised to the power of `power`.|
|`SQRT(expr)`|Square root.|
-|`TRUNCATE(expr[, digits])`|Truncate expr to a specific number of decimal digits. If digits is negative, then this truncates that many places to the left of the decimal point. Digits defaults to zero if not specified.|
-|`TRUNC(expr[, digits])`|Synonym for `TRUNCATE`.|
-|`ROUND(expr[, digits])`|`ROUND(x, y)` would return the value of the x rounded to the y decimal places. While x can be an integer or floating-point number, y must be an integer. The type of the return value is specified by that of x. y defaults to 0 if omitted. When y is negative, x is rounded on the left side of the y decimal points. If `expr` evaluates to either `NaN`, `expr` will be converted to 0. If `expr` is infinity, `expr` will be converted to the nearest finite double. |
+|`TRUNCATE(expr, [digits])`|Truncate `expr` to a specific number of decimal digits. If digits is negative, then this truncates that many places to the left of the decimal point. Digits defaults to zero if not specified.|
+|`TRUNC(expr, [digits])`|Synonym for `TRUNCATE`.|
+|`ROUND(expr, [digits])`|`ROUND(x, y)` would return the value of the x rounded to the y decimal places. While x can be an integer or floating-point number, y must be an integer. The type of the return value is specified by that of x. y defaults to 0 if omitted. When y is negative, x is rounded on the left side of the y decimal points. If `expr` evaluates to either `NaN`, `expr` will be converted to 0. If `expr` is infinity, `expr` will be converted to the nearest finite double. |
|`x + y`|Addition.|
|`x - y`|Subtraction.|
|`x * y`|Multiplication.|
|`x / y`|Division.|
|`MOD(x, y)`|Modulo (remainder of x divided by y).|
-|`SIN(expr)`|Trigonometric sine of an angle expr.|
-|`COS(expr)`|Trigonometric cosine of an angle expr.|
-|`TAN(expr)`|Trigonometric tangent of an angle expr.|
-|`COT(expr)`|Trigonometric cotangent of an angle expr.|
-|`ASIN(expr)`|Arc sine of expr.|
-|`ACOS(expr)`|Arc cosine of expr.|
-|`ATAN(expr)`|Arc tangent of expr.|
+|`SIN(expr)`|Trigonometric sine of an angle `expr`.|
+|`COS(expr)`|Trigonometric cosine of an angle `expr`.|
+|`TAN(expr)`|Trigonometric tangent of an angle `expr`.|
+|`COT(expr)`|Trigonometric cotangent of an angle `expr`.|
+|`ASIN(expr)`|Arc sine of `expr`.|
+|`ACOS(expr)`|Arc cosine of `expr`.|
+|`ATAN(expr)`|Arc tangent of `expr`.|
|`ATAN2(y, x)`|Angle theta from the conversion of rectangular coordinates (x, y) to polar * coordinates (r, theta).|
|`DEGREES(expr)`|Converts an angle measured in radians to an approximately equivalent angle measured in degrees|
|`RADIANS(expr)`|Converts an angle measured in degrees to an approximately equivalent angle measured in radians|
Review comment:
```suggestion
|`DEGREES(expr)`|Converts an angle measured in radians to an approximately equivalent angle measured in degrees.|
|`RADIANS(expr)`|Converts an angle measured in degrees to an approximately equivalent angle measured in radians.|
```
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779756921
##########
File path: docs/querying/sql.md
##########
@@ -619,22 +619,22 @@ All 'array' references in the multi-value string function documentation can refe
|Function|Notes|
|--------|-----|
-| `ARRAY[expr1,expr ...]` | constructs a SQL ARRAY literal from the expression arguments, using the type of the first argument as the output array type |
-| `MV_FILTER_ONLY(expr, arr)` | filters multi-value `expr` to include only values contained in array `arr` |
-| `MV_FILTER_NONE(expr, arr)` | filters multi-value `expr` to include no values contained in array `arr` |
-| `MV_LENGTH(arr)` | returns length of array expression |
-| `MV_OFFSET(arr,long)` | returns the array element at the 0 based index supplied, or null for an out of range index|
-| `MV_ORDINAL(arr,long)` | returns the array element at the 1 based index supplied, or null for an out of range index |
-| `MV_CONTAINS(arr,expr)` | returns 1 if the array contains the element specified by expr, or contains all elements specified by expr if expr is an array, else 0 |
-| `MV_OVERLAP(arr1,arr2)` | returns 1 if arr1 and arr2 have any elements in common, else 0 |
-| `MV_OFFSET_OF(arr,expr)` | returns the 0 based index of the first occurrence of expr in the array, or `-1` or `null` if `druid.generic.useDefaultValueForNull=false` if no matching elements exist in the array. |
-| `MV_ORDINAL_OF(arr,expr)` | returns the 1 based index of the first occurrence of expr in the array, or `-1` or `null` if `druid.generic.useDefaultValueForNull=false` if no matching elements exist in the array. |
-| `MV_PREPEND(expr,arr)` | adds expr to arr at the beginning, the resulting array type determined by the type of the array |
-| `MV_APPEND(arr1,expr)` | appends expr to arr, the resulting array type determined by the type of the first array |
-| `MV_CONCAT(arr1,arr2)` | concatenates 2 arrays, the resulting array type determined by the type of the first array |
-| `MV_SLICE(arr,start,end)` | return the subarray of arr from the 0 based index start(inclusive) to end(exclusive), or `null`, if start is less than 0, greater than length of arr or less than end|
-| `MV_TO_STRING(arr,str)` | joins all elements of arr by the delimiter specified by str |
-| `STRING_TO_MV(str1,str2)` | splits str1 into an array on the delimiter specified by str2 |
+|`ARRAY[expr1, expr2, ...]`|Constructs a SQL ARRAY literal from the expression arguments, using the type of the first argument as the output array type.|
+|`MV_FILTER_ONLY(expr, arr)`|Filters multi-value `expr` to include only values contained in array `arr`.|
+|`MV_FILTER_NONE(expr, arr)`|Filters multi-value `expr` to include no values contained in array `arr`.|
+|`MV_LENGTH(arr)`|Returns length of array expression.|
+|`MV_OFFSET(arr, long)`|Returns the array element at the 0 based index supplied, or null for an out of range index.|
+|`MV_ORDINAL(arr, long)`|Returns the array element at the 1 based index supplied, or null for an out of range index.|
+|`MV_CONTAINS(arr, expr)`|Returns 1 if the array contains the element specified by `expr`, or contains all elements specified by `expr` if `expr` is an array, else 0.|
+|`MV_OVERLAP(arr1, arr2)`|Returns 1 if arr1 and arr2 have any elements in common, else 0|
+|`MV_OFFSET_OF(arr, expr)`|Returns the 0 based index of the first occurrence of `expr` in the array, or `-1` or `null` if `druid.generic.useDefaultValueForNull=false` if no matching elements exist in the array.|
+|`MV_ORDINAL_OF(arr, expr)`|Returns the 1 based index of the first occurrence of `expr` in the array, or `-1` or `null` if `druid.generic.useDefaultValueForNull=false` if no matching elements exist in the array.|
+|`MV_PREPEND(expr, arr)`|Adds `expr` to `arr` at the beginning, the resulting array type determined by the type of the array.|
+|`MV_APPEND(arr1, expr)`|Appends `expr` to `arr`, the resulting array type determined by the type of the first array.|
+|`MV_CONCAT(arr1, arr2)`|Concatenates 2 arrays, the resulting array type determined by the type of the first array.|
+|`MV_SLICE(arr, start, end)`|Return the subarray of `arr` from the 0 based index start(inclusive) to end(exclusive), or `null`, if start is less than 0, greater than length of arr or less than end.|
Review comment:
```suggestion
|`MV_SLICE(arr, start, end)`|Returns the subarray of `arr` from the 0 based index start(inclusive) to end(exclusive), or `null`, if start is less than 0, greater than length of arr or less than end.|
```
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[GitHub] [druid] FrankChen021 commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
FrankChen021 commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r773563582
##########
File path: docs/querying/sql.md
##########
@@ -328,13 +328,13 @@ 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(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`|
Review comment:
```suggestion
|`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`|
```
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[GitHub] [druid] clintropolis merged pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
clintropolis merged pull request #12091:
URL: https://github.com/apache/druid/pull/12091
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779164651
##########
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 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
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[GitHub] [druid] vtlim commented on a change in pull request #12091: Standardizing SQL function docs
Posted by GitBox <gi...@apache.org>.
vtlim commented on a change in pull request #12091:
URL: https://github.com/apache/druid/pull/12091#discussion_r779748879
##########
File path: docs/querying/sql.md
##########
@@ -386,27 +386,27 @@ to FLOAT. At runtime, Druid will widen 32-bit floats to 64-bit for most expressi
|`PI`|Constant Pi.|
|`ABS(expr)`|Absolute value.|
|`CEIL(expr)`|Ceiling.|
-|`EXP(expr)`|e to the power of expr.|
+|`EXP(expr)`|e to the power of `expr`.|
|`FLOOR(expr)`|Floor.|
|`LN(expr)`|Logarithm (base e).|
|`LOG10(expr)`|Logarithm (base 10).|
-|`POWER(expr, power)`|expr to a power.|
+|`POWER(expr, power)`|`expr` raised to the power of `power`.|
|`SQRT(expr)`|Square root.|
-|`TRUNCATE(expr[, digits])`|Truncate expr to a specific number of decimal digits. If digits is negative, then this truncates that many places to the left of the decimal point. Digits defaults to zero if not specified.|
-|`TRUNC(expr[, digits])`|Synonym for `TRUNCATE`.|
-|`ROUND(expr[, digits])`|`ROUND(x, y)` would return the value of the x rounded to the y decimal places. While x can be an integer or floating-point number, y must be an integer. The type of the return value is specified by that of x. y defaults to 0 if omitted. When y is negative, x is rounded on the left side of the y decimal points. If `expr` evaluates to either `NaN`, `expr` will be converted to 0. If `expr` is infinity, `expr` will be converted to the nearest finite double. |
+|`TRUNCATE(expr, [digits])`|Truncate `expr` to a specific number of decimal digits. If digits is negative, then this truncates that many places to the left of the decimal point. Digits defaults to zero if not specified.|
+|`TRUNC(expr, [digits])`|Synonym for `TRUNCATE`.|
Review comment:
```suggestion
|`TRUNC(expr, [digits])`|Alias for `TRUNCATE`.|
```
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