spark技术分享UDFs — User-Defined Functions
User-Defined Functions (aka UDF) is a feature of Spark SQL to define new Column-based functions that extend the vocabulary of Spark SQL’s DSL for transforming Datasets.
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Important
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Use the higher-level standard Column-based functions (with Dataset operators) whenever possible before reverting to developing user-defined functions since UDFs are a blackbox for Spark SQL and it cannot (and does not even try to) optimize them. As Reynold Xin from the Apache Spark project has once said on Spark’s dev mailing list:
Check out UDFs are Blackbox — Don’t Use Them Unless You’ve Got No Choice if you want to know the internals. |
You define a new UDF by defining a Scala function as an input parameter of udf function. It accepts Scala functions of up to 10 input parameters.
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val dataset = Seq((0, "hello"), (1, "world")).toDF("id", "text") // Define a regular Scala function val upper: String => String = _.toUpperCase // Define a UDF that wraps the upper Scala function defined above // You could also define the function in place, i.e. inside udf // but separating Scala functions from Spark SQL's UDFs allows for easier testing import org.apache.spark.sql.functions.udf val upperUDF = udf(upper) // Apply the UDF to change the source dataset scala> dataset.withColumn("upper", upperUDF('text)).show +---+-----+-----+ | id| text|upper| +---+-----+-----+ | 0|hello|HELLO| | 1|world|WORLD| +---+-----+-----+ |
You can register UDFs to use in SQL-based query expressions via UDFRegistration (that is available through SparkSession.udf attribute).
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val spark: SparkSession = ... scala> spark.udf.register("myUpper", (input: String) => input.toUpperCase) |
You can query for available standard and user-defined functions using the Catalog interface (that is available through SparkSession.catalog attribute).
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val spark: SparkSession = ... scala> spark.catalog.listFunctions.filter('name like "%upper%").show(false) +-------+--------+-----------+-----------------------------------------------+-----------+ |name |database|description|className |isTemporary| +-------+--------+-----------+-----------------------------------------------+-----------+ |myupper|null |null |null |true | |upper |null |null |org.apache.spark.sql.catalyst.expressions.Upper|true | +-------+--------+-----------+-----------------------------------------------+-----------+ |
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Note
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UDFs play a vital role in Spark MLlib to define new Transformers that are function objects that transform DataFrames into DataFrames by introducing new columns.
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udf Functions (in functions object)
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udf[RT: TypeTag](f: Function0[RT]): UserDefinedFunction ... udf[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](f: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT]): UserDefinedFunction |
org.apache.spark.sql.functions object comes with udf function to let you define a UDF for a Scala function f.
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val df = Seq( (0, "hello"), (1, "world")).toDF("id", "text") // Define a "regular" Scala function // It's a clone of upper UDF val toUpper: String => String = _.toUpperCase import org.apache.spark.sql.functions.udf val upper = udf(toUpper) scala> df.withColumn("upper", upper('text)).show +---+-----+-----+ | id| text|upper| +---+-----+-----+ | 0|hello|HELLO| | 1|world|WORLD| +---+-----+-----+ // You could have also defined the UDF this way val upperUDF = udf { s: String => s.toUpperCase } // or even this way val upperUDF = udf[String, String](_.toUpperCase) scala> df.withColumn("upper", upperUDF('text)).show +---+-----+-----+ | id| text|upper| +---+-----+-----+ | 0|hello|HELLO| | 1|world|WORLD| +---+-----+-----+ |
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Tip
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Define custom UDFs based on “standalone” Scala functions (e.g. toUpperUDF) so you can test the Scala functions using Scala way (without Spark SQL’s “noise”) and once they are defined reuse the UDFs in UnaryTransformers.
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