调用 SQL 函数。 支持 Spark Connect。
Syntax
from pyspark.databricks.sql import functions as dbf
dbf.call_function(funcName=<funcName>, *cols)
参数
| 参数 | 类型 | Description |
|---|---|---|
funcName |
str |
SQL 标识符语法后面的函数名称（可以引用，可以限定）。 |
cols |
pyspark.sql.Column 或 str |
要在函数中使用的列名或列。 |
退货
pyspark.sql.Column：执行函数的结果。
例子
示例 1：调用包含整数列的函数
from pyspark.databricks.sql import functions as dbf
from pyspark.sql.types import IntegerType, StringType
df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "c")],["id", "name"])
_ = spark.udf.register("intX2", lambda i: i * 2, IntegerType())
df.select(dbf.call_function("intX2", "id")).show()
+---------+
|intX2(id)|
+---------+
| 2|
| 4|
| 6|
+---------+
示例 2：使用字符串列调用函数
from pyspark.databricks.sql import functions as dbf
from pyspark.sql.types import StringType
df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "c")],["id", "name"])
_ = spark.udf.register("strX2", lambda s: s * 2, StringType())
df.select(dbf.call_function("strX2", dbf.col("name"))).show()
+-----------+
|strX2(name)|
+-----------+
| aa|
| bb|
| cc|
+-----------+
示例 3：调用内置函数
from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "c")],["id", "name"])
df.select(dbf.call_function("avg", dbf.col("id"))).show()
+-------+
|avg(id)|
+-------+
| 2.0|
+-------+
示例 4：调用自定义 SQL 函数
from pyspark.databricks.sql import functions as dbf
_ = spark.sql("CREATE FUNCTION custom_avg AS 'test.org.apache.spark.sql.MyDoubleAvg'")
df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "c")],["id", "name"])
df.select(dbf.call_function("custom_avg", dbf.col("id"))).show()
+------------------------------------+
|spark_catalog.default.custom_avg(id)|
+------------------------------------+
| 102.0|
+------------------------------------+
示例 5：调用具有完全限定名称的自定义 SQL 函数
from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "c")],["id", "name"])
df.select(dbf.call_function("spark_catalog.default.custom_avg", dbf.col("id"))).show()
+------------------------------------+
|spark_catalog.default.custom_avg(id)|
+------------------------------------+
| 102.0|
+------------------------------------+