Scalar User Defined Functions (UDFs)
Description
User-Defined Functions (UDFs) are user-programmable routines that act on one row. This documentation lists the classes that are required for creating and registering UDFs. It also contains examples that demonstrate how to define and register UDFs and invoke them in Spark SQL.
UserDefinedFunction
To define the properties of a user-defined function, the user can use some of the methods defined in this class.
-
asNonNullable(): UserDefinedFunction
Updates UserDefinedFunction to non-nullable.
-
asNondeterministic(): UserDefinedFunction
Updates UserDefinedFunction to nondeterministic.
-
withName(name: String): UserDefinedFunction
Updates UserDefinedFunction with a given name.
Examples
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.udf
val spark = SparkSession
.builder()
.appName("Spark SQL UDF scalar example")
.getOrCreate()
// Define and register a zero-argument non-deterministic UDF
// UDF is deterministic by default, i.e. produces the same result for the same input.
val random = udf(() => Math.random())
spark.udf.register("random", random.asNondeterministic())
spark.sql("SELECT random()").show()
// +-------+
// |UDF() |
// +-------+
// |xxxxxxx|
// +-------+
// Define and register a one-argument UDF
val plusOne = udf((x: Int) => x + 1)
spark.udf.register("plusOne", plusOne)
spark.sql("SELECT plusOne(5)").show()
// +------+
// |UDF(5)|
// +------+
// | 6|
// +------+
// Define a two-argument UDF and register it with Spark in one step
spark.udf.register("strLenScala", (_: String).length + (_: Int))
spark.sql("SELECT strLenScala('test', 1)").show()
// +--------------------+
// |strLenScala(test, 1)|
// +--------------------+
// | 5|
// +--------------------+
// UDF in a WHERE clause
spark.udf.register("oneArgFilter", (n: Int) => { n > 5 })
spark.range(1, 10).createOrReplaceTempView("test")
spark.sql("SELECT * FROM test WHERE oneArgFilter(id)").show()
// +---+
// | id|
// +---+
// | 6|
// | 7|
// | 8|
// | 9|
// +---+
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/UserDefinedScalar.scala" in the Spark repo.
import org.apache.spark.sql.*;
import org.apache.spark.sql.api.java.UDF1;
import org.apache.spark.sql.expressions.UserDefinedFunction;
import static org.apache.spark.sql.functions.udf;
import org.apache.spark.sql.types.DataTypes;
SparkSession spark = SparkSession
.builder()
.appName("Java Spark SQL UDF scalar example")
.getOrCreate();
// Define and register a zero-argument non-deterministic UDF
// UDF is deterministic by default, i.e. produces the same result for the same input.
UserDefinedFunction random = udf(
() -> Math.random(), DataTypes.DoubleType
);
random.asNondeterministic();
spark.udf().register("random", random);
spark.sql("SELECT random()").show();
// +-------+
// |UDF() |
// +-------+
// |xxxxxxx|
// +-------+
// Define and register a one-argument UDF
spark.udf().register("plusOne",
(UDF1<Integer, Integer>) x -> x + 1, DataTypes.IntegerType);
spark.sql("SELECT plusOne(5)").show();
// +----------+
// |plusOne(5)|
// +----------+
// | 6|
// +----------+
// Define and register a two-argument UDF
UserDefinedFunction strLen = udf(
(String s, Integer x) -> s.length() + x, DataTypes.IntegerType
);
spark.udf().register("strLen", strLen);
spark.sql("SELECT strLen('test', 1)").show();
// +------------+
// |UDF(test, 1)|
// +------------+
// | 5|
// +------------+
// UDF in a WHERE clause
spark.udf().register("oneArgFilter",
(UDF1<Long, Boolean>) x -> x > 5, DataTypes.BooleanType);
spark.range(1, 10).createOrReplaceTempView("test");
spark.sql("SELECT * FROM test WHERE oneArgFilter(id)").show();
// +---+
// | id|
// +---+
// | 6|
// | 7|
// | 8|
// | 9|
// +---+
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedScalar.java" in the Spark repo.