dianfu commented on a change in pull request #9890: [FLINK-14272][python][table-planner-blink] Support Blink planner for Python UDF URL: https://github.com/apache/flink/pull/9890#discussion_r336300424
########## File path: flink-table/flink-table-planner-blink/src/main/scala/org/apache/flink/table/planner/codegen/PythonFunctionCodeGenerator.scala ########## @@ -0,0 +1,129 @@ +/* + * 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.flink.table.planner.codegen + +import org.apache.flink.api.common.typeinfo.TypeInformation +import org.apache.flink.table.functions.python.{PythonEnv, PythonFunction} +import org.apache.flink.table.functions.{FunctionLanguage, ScalarFunction, UserDefinedFunction} +import org.apache.flink.table.planner.codegen.CodeGenUtils.{newName, primitiveDefaultValue, primitiveTypeTermForType} +import org.apache.flink.table.planner.codegen.Indenter.toISC +import org.apache.flink.table.runtime.generated.GeneratedFunction +import org.apache.flink.table.runtime.types.TypeInfoLogicalTypeConverter +import org.apache.flink.table.utils.EncodingUtils + +/** + * A code generator for generating Python [[UserDefinedFunction]]s. + */ +object PythonFunctionCodeGenerator { + + private val PYTHON_SCALAR_FUNCTION_NAME = "PythonScalarFunction" + + /** + * Generates a [[ScalarFunction]] for the specified Python user-defined function. + * + * @param name class name of the user-defined function. Must be a valid Java class identifier + * @param serializedScalarFunction serialized Python scalar function + * @param inputTypes input data types + * @param resultType expected result type + * @param deterministic the determinism of the function's results + * @param pythonEnv the Python execution environment + * @return instance of generated ScalarFunction + */ + def generateScalarFunction( + name: String, + serializedScalarFunction: Array[Byte], + inputTypes: Array[TypeInformation[_]], + resultType: TypeInformation[_], + deterministic: Boolean, + pythonEnv: PythonEnv): ScalarFunction = { + val funcName = newName(PYTHON_SCALAR_FUNCTION_NAME) + val resultLogicType = TypeInfoLogicalTypeConverter.fromTypeInfoToLogicalType(resultType) + val resultTypeTerm = primitiveTypeTermForType(resultLogicType) + val defaultResultValue = primitiveDefaultValue(resultLogicType) + val inputParamCode = inputTypes.zipWithIndex.map { case (inputType, index) => + s"${primitiveTypeTermForType( + TypeInfoLogicalTypeConverter.fromTypeInfoToLogicalType(inputType))} in$index" + }.mkString(", ") + + val encodingUtilsTypeTerm = classOf[EncodingUtils].getCanonicalName + val typeInfoTypeTerm = classOf[TypeInformation[_]].getCanonicalName + val inputTypesCode = inputTypes.map(EncodingUtils.encodeObjectToString).map { inputType => + s""" + |($typeInfoTypeTerm) $encodingUtilsTypeTerm.decodeStringToObject( + | "$inputType", $typeInfoTypeTerm.class) + |""".stripMargin + }.mkString(", ") + + val encodedResultType = EncodingUtils.encodeObjectToString(resultType) + val encodedScalarFunction = EncodingUtils.encodeBytesToBase64(serializedScalarFunction) + val encodedPythonEnv = EncodingUtils.encodeObjectToString(pythonEnv) + val pythonEnvTypeTerm = classOf[PythonEnv].getCanonicalName + + val funcCode = j""" + |public class $funcName extends ${classOf[ScalarFunction].getCanonicalName} + | implements ${classOf[PythonFunction].getCanonicalName} { + | + | private static final long serialVersionUID = 1L; + | + | public $resultTypeTerm eval($inputParamCode) { + | return $defaultResultValue; + | } + | + | @Override + | public $typeInfoTypeTerm[] getParameterTypes(Class<?>[] signature) { + | return new $typeInfoTypeTerm[]{$inputTypesCode}; + | } + | + | @Override + | public $typeInfoTypeTerm getResultType(Class<?>[] signature) { + | return ($typeInfoTypeTerm) $encodingUtilsTypeTerm.decodeStringToObject( + | "$encodedResultType", $typeInfoTypeTerm.class); + | } + | + | @Override + | public ${classOf[FunctionLanguage].getCanonicalName} getLanguage() { + | return ${classOf[FunctionLanguage].getCanonicalName}.PYTHON; + | } + | + | @Override + | public byte[] getSerializedPythonFunction() { + | return $encodingUtilsTypeTerm.decodeBase64ToBytes("$encodedScalarFunction"); + | } + | + | @Override + | public $pythonEnvTypeTerm getPythonEnv() { + | return ($pythonEnvTypeTerm) $encodingUtilsTypeTerm.decodeStringToObject( + | "$encodedPythonEnv", $pythonEnvTypeTerm.class); + | } + | + | @Override + | public boolean isDeterministic() { + | return $deterministic; + | } + | + | @Override + | public String toString() { + | return "$name"; + | } + |} + |""".stripMargin + val clazz = new GeneratedFunction(funcName, funcCode, Array()) Review comment: Could make use of the CodeGeneratorContext.references defined in blink planner here. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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