dbatomic commented on code in PR #46801: URL: https://github.com/apache/spark/pull/46801#discussion_r1628998632
########## sql/core/src/test/scala/org/apache/spark/sql/CollationExpressionWalkerSuite.scala: ########## @@ -0,0 +1,294 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.expressions.{EmptyRow, EvalMode, ExpectsInputTypes, Expression, GenericInternalRow, Literal} +import org.apache.spark.sql.internal.types.{AbstractArrayType, StringTypeAnyCollation, StringTypeBinaryLcase} +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types.{AbstractDataType, AnyDataType, AnyTimestampType, ArrayType, BinaryType, BooleanType, DataType, DatetimeType, Decimal, DecimalType, IntegerType, LongType, MapType, NumericType, StringType, StructType, TypeCollection} +import org.apache.spark.unsafe.types.UTF8String +import org.apache.spark.util.Utils + +/** + * This suite is introduced in order to test a bulk of expressions and functionalities related to + * collations + */ +class CollationExpressionWalkerSuite extends SparkFunSuite with SharedSparkSession { + + // Trait to distinguish different cases for generation + sealed trait CollationType + + case object Utf8Binary extends CollationType + + case object Utf8BinaryLcase extends CollationType + + /** + * Helper function to generate all necesary parameters + * + * @param inputEntry - List of all input entries that need to be generated + * @param collationType - Flag defining collation type to use + * @return + */ + def generateData( + inputEntry: Seq[Any], + collationType: CollationType): Seq[Any] = { + inputEntry.map(generateSingleEntry(_, collationType)) + } + + /** + * Helper function to generate single entry of data. + * @param inputEntry - Single input entry that requires generation + * @param collationType - Flag defining collation type to use + * @return + */ + def generateSingleEntry( + inputEntry: Any, + collationType: CollationType): Any = + inputEntry match { + case e: Class[_] if e.isAssignableFrom(classOf[Expression]) => + generateLiterals(StringTypeAnyCollation, collationType) + case se: Class[_] if se.isAssignableFrom(classOf[Seq[Expression]]) => + Seq(generateLiterals(StringTypeAnyCollation, collationType), + generateLiterals(StringTypeAnyCollation, collationType)) + case oe: Class[_] if oe.isAssignableFrom(classOf[Option[Any]]) => None + case b: Class[_] if b.isAssignableFrom(classOf[Boolean]) => false + case dt: Class[_] if dt.isAssignableFrom(classOf[DataType]) => StringType + case st: Class[_] if st.isAssignableFrom(classOf[StructType]) => StructType + case em: Class[_] if em.isAssignableFrom(classOf[EvalMode.Value]) => EvalMode.LEGACY + case m: Class[_] if m.isAssignableFrom(classOf[Map[_, _]]) => Map.empty + case c: Class[_] if c.isAssignableFrom(classOf[Char]) => '\\' + case i: Class[_] if i.isAssignableFrom(classOf[Int]) => 0 + case l: Class[_] if l.isAssignableFrom(classOf[Long]) => 0 + case adt: AbstractDataType => generateLiterals(adt, collationType) + case Nil => Seq() + case (head: AbstractDataType) :: rest => generateData(head :: rest, collationType) + } + + /** + * Helper function to generate single literal from the given type. + * + * @param inputType - Single input literal type that requires generation + * @param collationType - Flag defining collation type to use + * @return + */ + def generateLiterals( + inputType: AbstractDataType, + collationType: CollationType): Expression = + inputType match { + // TODO: Try to make this a bit more random. + case AnyTimestampType => Literal("2009-07-30 12:58:59") + case BinaryType => Literal(new Array[Byte](5)) + case BooleanType => Literal(true) + case _: DatetimeType => Literal(1L) + case _: DecimalType => Literal(new Decimal) + case IntegerType | NumericType => Literal(1) + case LongType => Literal(1L) + case _: StringType | StringTypeAnyCollation | StringTypeBinaryLcase | AnyDataType => + collationType match { + case Utf8Binary => + Literal.create("dummy string", StringType("UTF8_BINARY")) + case Utf8BinaryLcase => + Literal.create("DuMmY sTrInG", StringType("UTF8_BINARY_LCASE")) + } + case TypeCollection(typeCollection) => + val strTypes = typeCollection.filter(hasStringType) + if (strTypes.isEmpty) { + // Take first type + generateLiterals(typeCollection.head, collationType) + } else { + // Take first string type + generateLiterals(strTypes.head, collationType) + } + case AbstractArrayType(elementType) => + generateLiterals(elementType, collationType).map( + lit => Literal.create(Seq(lit.asInstanceOf[Literal].value), ArrayType(lit.dataType)) + ).head + case ArrayType(elementType, _) => + generateLiterals(elementType, collationType).map( + lit => Literal.create(Seq(lit.asInstanceOf[Literal].value), ArrayType(lit.dataType)) + ).head + case ArrayType => + generateLiterals(StringTypeAnyCollation, collationType).map( + lit => Literal.create(Seq(lit.asInstanceOf[Literal].value), ArrayType(lit.dataType)) + ).head + case MapType => + val key = generateLiterals(StringTypeAnyCollation, collationType) + val value = generateLiterals(StringTypeAnyCollation, collationType) + Literal.create(Map(key -> value)) + case MapType(keyType, valueType, _) => + val key = generateLiterals(keyType, collationType) + val value = generateLiterals(valueType, collationType) + Literal.create(Map(key -> value)) + case StructType => + Literal.create((generateLiterals(StringTypeAnyCollation, collationType), + generateLiterals(StringTypeAnyCollation, collationType))) + case StructType(fields) => + Literal.create(new GenericInternalRow( + fields.map(f => generateLiterals(f.dataType, collationType).asInstanceOf[Any])), + StructType(fields)) + } + + /** + * Helper function to extract types of relevance + * @param inputType + * @return + */ + def hasStringType(inputType: AbstractDataType): Boolean = { + inputType match { + case _: StringType | StringTypeAnyCollation | StringTypeBinaryLcase | AnyDataType => + true + case ArrayType => true + case MapType => true + case MapType(keyType, valueType, _) => hasStringType(keyType) || hasStringType(valueType) + case ArrayType(elementType, _) => hasStringType(elementType) + case AbstractArrayType(elementType) => hasStringType(elementType) + case TypeCollection(typeCollection) => + typeCollection.exists(hasStringType) + case StructType => true + case StructType(fields) => fields.exists(sf => hasStringType(sf.dataType)) + case _ => false + } + } + + def replaceExpressions(inputTypes: Seq[AbstractDataType], params: Seq[Class[_]]): Seq[Any] = { + (inputTypes, params) match { + case (Nil, mparams) => mparams + case (_, Nil) => Nil + case (minputTypes, mparams) if mparams.head.isAssignableFrom(classOf[Expression]) => + minputTypes.head +: replaceExpressions(inputTypes.tail, mparams.tail) + case (minputTypes, mparams) => + mparams.head +: replaceExpressions(minputTypes.tail, mparams.tail) + } + } + + test("SPARK-48280: Expression Walker for Test") { + // This test does following: + // 1) Take all expressions + // 2) Find the ones that have at least one argument of StringType + // 3) Use reflection to create an instance of the expression using first constructor + // (test other as well). + // 4) Check if the expression is of type ExpectsInputTypes (should make this a bit broader) + // 5) Run eval against literals with strings under: + // a) UTF8_BINARY, "dummy string" as input. + // b) UTF8_BINARY_LCASE, "DuMmY sTrInG" as input. + // 6) Check if both expressions throw an exception. + // 7) If no exception, check if the result is the same. + // 8) There is a list of allowed expressions that can differ (e.g. hex) + var expressionCounter = 0 + var expectsExpressionCounter = 0; + val funInfos = spark.sessionState.functionRegistry.listFunction().map { funcId => + spark.sessionState.catalog.lookupFunctionInfo(funcId) + }.filter(funInfo => { + // make sure that there is a constructor. + val cl = Utils.classForName(funInfo.getClassName) + !cl.getConstructors.isEmpty + }).filter(funInfo => { + expressionCounter = expressionCounter + 1 + val cl = Utils.classForName(funInfo.getClassName) + // dummy instance + // Take first constructor. + val headConstructor = cl.getConstructors.head + + val params = headConstructor.getParameters.map(p => p.getType) + + val args = generateData(params.toSeq, Utf8Binary) + // Find all expressions that have string as input + try { + val expr = headConstructor.newInstance(args: _*) + expr match { + case types: ExpectsInputTypes => + expectsExpressionCounter = expectsExpressionCounter + 1 + val inputTypes = types.inputTypes + inputTypes.exists(hasStringType) || inputTypes.isEmpty + } + } catch { + case _: Throwable => false + } + }).toArray + + val toSkip = List( + "parse_url", // Parse URL is using wrong concepts, not related to ExpectsInputTypes + "hex" // this is fine + ) + // scalastyle:off println + println("Total number of expression: " + expressionCounter) Review Comment: Can you use logInfo instead of println? -- This is an automated message from the Apache Git Service. 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