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If the calculation of the lateral view function is completed, the result will be directly returned to the upper layer. It will cause a lot of memory copy and network transmission. The reason is that the original column that generally participates in the lateral view is very likely to be a very long value. If Doris still retain this column after calculating the lateral view, it need to perform a memory copy. However, in many cases, the upper plan node does not need the original columns of the lateral view, so it is necessary to perform column pruning after the calculation of the lateral view, so as to avoid useless memory copy and network transmission. For example, the following query can prune the original column v1 ```select k1, e1 from table lateral view explode_split(v1, ",") tmp as e1;``` The `outputSlotIds` in TableFunctionNode is used to store the columns that should be retained after pruning. * Support scalar function in lateral view The child 0 of explode_split function could be a scalar function such as: concat(k1, ",", k2) This pr mainly detects whether the lateral view with function satisfies the following specifications in semantics. 1. The columns in the function must all belong to the original table 2. The function must be a scalar function
# 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. # fe-common This module is used to store some common classes of other modules. # spark-dpp This module is Spark DPP program, used for Spark Load function. Depends: fe-common # fe-core This module is the main process module of FE. Depends: fe-common, spark-dpp