Re: [PR] [SPARK-55689] Skip unsupported column changes during schema evolution [spark]

["aokolnychyi (via GitHub)" <gi...@apache.org>]

aokolnychyi commented on code in PR #54658:
URL: https://github.com/apache/spark/pull/54658#discussion_r3020418948


##########
sql/catalyst/src/main/java/org/apache/spark/sql/connector/catalog/SupportsSchemaEvolution.java:
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@@ -0,0 +1,45 @@
+/*
+ * 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.connector.catalog;
+
+import org.apache.spark.annotation.Experimental;
+import org.apache.spark.sql.connector.catalog.TableChange.ColumnChange;
+
+/**
+ * A mix-in interface for {@link Table} schema evolution support. Data sources can implement this
+ * interface to indicate the schema changes they support during write operations. Tables must
+ * report capability {@link TableCapability#AUTOMATIC_SCHEMA_EVOLUTION} for schema evolution to
+ * happen.
+ * <p>
+ * During automatic schema evolution, Spark computes the set of column changes (e.g. adding a new
+ * column, widening a column type) needed to make the target table schema compatible with the
+ * source data schema. Each candidate change is passed to {@link #supportsColumnChange} to
+ * determine whether the data source can apply it. Changes that are not supported are skipped.

Review Comment:
   I agree, it would be helpful to note that Spark will attempt to resolve such changes using casts and if that fails, then it will result in a failed query.



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