call_function
Call a SQL function. Supports Spark Connect.
Syntax
Python
from pyspark.databricks.sql import functions as dbf
dbf.call_function(funcName=<funcName>, *cols)
Parameters
Parameter | Type | Description |
|---|---|---|
|
| Function name that follows the SQL identifier syntax (can be quoted, can be qualified). |
|
| Column names or Columns to be used in the function. |
Returns
pyspark.sql.Column: result of executed function.
Examples
Example 1: Calling a function with an integer column
Python
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()
Output
+---------+
|intX2(id)|
+---------+
| 2|
| 4|
| 6|
+---------+
Example 2: Calling a function with a string column
Python
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()
Output
+-----------+
|strX2(name)|
+-----------+
| aa|
| bb|
| cc|
+-----------+
Example 3: Calling a built-in function
Python
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()
Output
+-------+
|avg(id)|
+-------+
| 2.0|
+-------+
Example 4: Calling a custom SQL function
Python
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()
Output
+------------------------------------+
|spark_catalog.default.custom_avg(id)|
+------------------------------------+
| 102.0|
+------------------------------------+
Example 5: Calling a custom SQL function with fully qualified name
Python
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()
Output
+------------------------------------+
|spark_catalog.default.custom_avg(id)|
+------------------------------------+
| 102.0|
+------------------------------------+