wuwenchi commented on code in PR #5120: URL: https://github.com/apache/iceberg/pull/5120#discussion_r905790660
########## flink/v1.15/flink/src/main/java/org/apache/iceberg/flink/sink/PartitionTransformUdf.java: ########## @@ -0,0 +1,50 @@ +/* + * 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 Licenet ideajoinet ideajoin for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.iceberg.flink.sink; + +import org.apache.flink.table.annotation.DataTypeHint; +import org.apache.flink.table.annotation.InputGroup; +import org.apache.flink.table.functions.ScalarFunction; +import org.apache.iceberg.expressions.Literals; +import org.apache.iceberg.transforms.Transform; +import org.apache.iceberg.transforms.Transforms; +import org.apache.iceberg.types.Type; +import org.apache.iceberg.types.TypeUtil; + +public class PartitionTransformUdf { + + public static class Truncate extends ScalarFunction { + public String eval(int num, @DataTypeHint(inputGroup = InputGroup.ANY) Object obj) { Review Comment: Because I used `InputGroup.ANY`, in terms of parameter types, flink will pass any data type and finally rely on the original process to check the data type. The specific check is in the `get` function of the corresponding transform, for example ``` java static <T> Truncate<T> get(Type type, int width) { Preconditions.checkArgument(width > 0, "Invalid truncate width: %s (must be > 0)", width); switch (type.typeId()) { case INTEGER: return (Truncate<T>) new TruncateInteger(width); case LONG: ....... default: throw new UnsupportedOperationException( "Cannot truncate type: " + type); } } ``` Both the bucket function and the truncate function can support many parameter types. If they are all split into functions with specific parameter types, it will be redundant, because only the input parameter types are different, and the function processing process is exactly the same. Of course, we can know which types these functions support through the case branch above. This may not seem very straightforward... -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
