HyukjinKwon commented on a change in pull request #27278:
[SPARK-30569][SQL][PYSPARK][SPARKR] Add percentile_approx DSL functions.
URL: https://github.com/apache/spark/pull/27278#discussion_r368780883
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/functions.scala
##########
@@ -652,6 +652,122 @@ object functions {
*/
def min(columnName: String): Column = min(Column(columnName))
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(e: Column, percentage: Array[Double], accuracy: Long):
Column = {
+ withAggregateFunction {
+ new ApproximatePercentile(
+ e.expr, typedLit(percentage).expr, lit(accuracy).expr
+ )
+ }
+ }
+
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(columnName: String, percentage: Array[Double],
accuracy: Long): Column = {
+ percentile_approx(Column(columnName), percentage, accuracy)
+ }
+
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(e: Column, percentage: Seq[Double], accuracy: Long):
Column = {
+ percentile_approx(e, percentage.toArray, accuracy)
+ }
+
+ /**
+ * Aggregate function: Returns and array of the approximate percentile values
+ * of numeric column col at the given percentages.
+ *
+ * Each value of the percentage array must be between 0.0 and 1.0.
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(columnName: String, percentage: Seq[Double], accuracy:
Long): Column = {
+ percentile_approx(Column(columnName), percentage.toArray, accuracy)
+ }
+
+ /**
+ * Aggregate function: Returns the approximate percentile value of numeric
+ * column col at the given percentage.
+ *
+ * The value of percentage must be between 0.0 and 1.0.\
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(e: Column, percentage: Double, accuracy: Long): Column
= {
+ withAggregateFunction {
+ new ApproximatePercentile(
+ e.expr, lit(percentage).expr, lit(accuracy).expr
+ )
+ }
+ }
+
+ /**
+ * Aggregate function: Returns the approximate percentile value of numeric
+ * column col at the given percentage.
+ *
+ * The value of percentage must be between 0.0 and 1.0.\
+ *
+ * The accuracy parameter is a positive numeric literal
+ * which controls approximation accuracy at the cost of memory.
+ * Higher value of accuracy yields better accuracy, 1.0/accuracy
+ * is the relative error of the approximation.
+ *
+ * @group agg_funcs
+ * @since 3.0.0
+ */
+ def percentile_approx(columnName: String, percentage: Double, accuracy:
Long): Column = {
Review comment:
This is what I meant `(Column, Column, Column) -> Column` to have a single
method to cover all other cases.
What we need to do might be just wrap it by `lit` or `col:
`percentile_approx(col(...), lit(...), lit(...))` so won't be too difficult. We
don't need to duplicate docs with less maintenance.
I did quick test and seems working fine in general:
Scala:
```scala
scala> spark.range(1).select(lit(Array(1, 2, 3))).show()
```
```
+---------+
| [1,2,3]|
+---------+
|[1, 2, 3]|
+---------+
```
Java:
```java
spark.range(1).select(lit(new String[]{"a", "b"})).show();
```
```
+------+
| [a,b]|
+------+
|[a, b]|
+------+
```
Python:
```python
>>> from pyspark.sql.functions import lit
>>> import array
>>> spark.range(1).select(lit(["a", "b"]))
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/functions.py", line 54, in _
jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column)
else col)
File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py",
line 1286, in __call__
File "/.../spark/python/pyspark/sql/utils.py", line 98, in deco
return f(*a, **kw)
File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/protocol.py", line
328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling
z:org.apache.spark.sql.functions.lit.
: java.lang.RuntimeException: Unsupported literal type class
java.util.ArrayList [a, b]
at
org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:85)
at
org.apache.spark.sql.catalyst.expressions.Literal$.$anonfun$create$2(literals.scala:145)
at scala.util.Failure.getOrElse(Try.scala:222)
at
org.apache.spark.sql.catalyst.expressions.Literal$.create(literals.scala:145)
at org.apache.spark.sql.functions$.typedLit(functions.scala:131)
at org.apache.spark.sql.functions$.lit(functions.scala:114)
at org.apache.spark.sql.functions.lit(functions.scala)
```
R:
```R
> collect(select(createDataFrame(mtcars), lit(list('a', 'b', 'c'))))
[a,b,c]
1 a, b, c
2 a, b, c
```
Seems we should fix PySpark's `lit` to take `list` as an array. It won't be
too difficult - we can check if this is a list with numeric elements, and
create an Array manually.
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