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ScalaUDF Expression

ScalaUDF is an Expression to manage the lifecycle of a user-defined function (and hook it to Catalyst execution path).

ScalaUDF is a NonSQLExpression (and so has no representation in SQL).

ScalaUDF is a UserDefinedExpression.

Creating Instance

ScalaUDF takes the following to be created:

ScalaUDF is created when:


deterministic: Boolean

deterministic is part of the Expression abstraction.

ScalaUDF is deterministic when all the following hold:

  1. udfDeterministic is enabled
  2. All the children are deterministic

Text Representation

toString: String

toString is part of the TreeNode abstraction.

toString uses the name and the [children] for the text representation:

[name]([comma-separated children])


name: String

name is part of the UserDefinedExpression abstraction.

name is the udfName (if defined) or UDF.

Generating Java Source Code for Code-Generated Expression Evaluation

  ctx: CodegenContext,
  ev: ExprCode): ExprCode

doGenCode is part of the Expression abstraction.

doGenCode requests the given CodegenContext to register a reference (that gives a udf reference):

Input Argument Value
objName udf
obj The given function
className scala.FunctionN (where N is the number of the given children)

Since Scala functions are executed using apply method, doGenCode creates a string with the following source code:

[udf].apply([comma-separated funcArgs])


There is more in doGenCode.

In the end, doGenCode generates a block of a Java code in the following format:

[boxedType] [resultTerm] = null;
try {
} catch (Throwable e) {
  throw QueryExecutionErrors.failedExecuteUserDefinedFunctionError(
    "[funcCls]", "[inputTypesString]", "[outputType]", e);

boolean [isNull] = [resultTerm] == null;
[dataType] [value] = [defaultValue];
if (![isNull]) {
  [value] = [resultTerm];

Interpreted Expression Evaluation

  input: InternalRow): Any

eval is part of the Expression abstraction.


Node Patterns

nodePatterns: Seq[TreePattern]

nodePatterns is part of the TreeNode abstraction.

nodePatterns is SCALA_UDF.


Logical Analyzer uses HandleNullInputsForUDF and ResolveEncodersInUDF logical evaluation rules to analyze queries with ScalaUDF expressions.


Zero-Argument UDF

Let's define a zero-argument UDF.

val myUDF = udf { () => "Hello World" }
// "Execute" the UDF
// Attach it to an "execution environment", i.e. a Dataset
// by specifying zero columns to execute on (since the UDF is no-arg)
import org.apache.spark.sql.catalyst.expressions.ScalaUDF
val scalaUDF = myUDF().expr.asInstanceOf[ScalaUDF]


Let's execute the UDF (on every row in a Dataset). We simulate it relying on the EmptyRow that is the default InternalRow of eval.

scala> scalaUDF.eval()
res2: Any = Hello World

Whole-Stage Code Gen

import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
val ctx = new CodegenContext
val code = scalaUDF.genCode(ctx).code
scala> println(code)
UTF8String result_1 = null;
try {
  result_1 = (UTF8String)((scala.Function1[]) references[2] /* converters */)[0].apply(((scala.Function0) references[3] /* udf */).apply());
} catch (Throwable e) {
  throw QueryExecutionErrors.failedExecuteUserDefinedFunctionError(
    "$read$$iw$$Lambda$2020/0x000000080153ad78", "", "string", e);

boolean isNull_1 = result_1 == null;
UTF8String value_1 = null;
if (!isNull_1) {
  value_1 = result_1;

One-Argument UDF

Let's define a UDF of one argument.

val lengthUDF = udf { s: String => s.length }.withName("lengthUDF")
val c = lengthUDF($"name")
scala> println(c.expr.treeString)
+- 'name
import org.apache.spark.sql.catalyst.expressions.ScalaUDF

Let's define another UDF of one argument.

val hello = udf { s: String => s"Hello $s" }

// Binding the hello UDF to a column name
import org.apache.spark.sql.catalyst.expressions.ScalaUDF
val helloScalaUDF = hello($"name").expr.asInstanceOf[ScalaUDF]

assert(helloScalaUDF.resolved == false)
// Resolve helloScalaUDF, i.e. the only `name` column reference

scala> helloScalaUDF.children
res4: Seq[org.apache.spark.sql.catalyst.expressions.Expression] = ArrayBuffer('name)
// The column is free (i.e. not bound to a Dataset)
// Define a Dataset that becomes the rows for the UDF
val names = Seq("Jacek", "Agata").toDF("name")
scala> println(names.queryExecution.analyzed.numberedTreeString)
00 Project [value#1 AS name#3]
01 +- LocalRelation [value#1]

Resolve the references using the Dataset.

val plan = names.queryExecution.analyzed
val resolver = spark.sessionState.analyzer.resolver
import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
val resolvedUDF = helloScalaUDF.transformUp { case a @ UnresolvedAttribute(names) =>
  // we're in controlled environment
  // so get is safe
  plan.resolve(names, resolver).get
scala> println(resolvedUDF.numberedTreeString)
00 UDF(name#3)
01 +- name#3: string
import org.apache.spark.sql.catalyst.expressions.BindReferences
val attrs = names.queryExecution.sparkPlan.output
val boundUDF = BindReferences.bindReference(resolvedUDF, attrs)

// Create an internal binary row, i.e. InternalRow
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
val stringEncoder = ExpressionEncoder[String]
val row = stringEncoder.toRow("world")

Yay! It works!

scala> boundUDF.eval(row)
res8: Any = Hello world

Just to show the regular execution path (i.e. how to execute an UDF in a context of a Dataset).

val q =$"name"))
|  UDF(name)|
|Hello Jacek|
|Hello Agata|