rdblue commented on a change in pull request #23266: [SPARK-26313][SQL] move 
read related methods from Table to read related mix-in traits
URL: https://github.com/apache/spark/pull/23266#discussion_r240695027
 
 

 ##########
 File path: 
sql/core/src/main/java/org/apache/spark/sql/sources/v2/SupportsBatchRead.java
 ##########
 @@ -20,14 +20,27 @@
 import org.apache.spark.annotation.Evolving;
 import org.apache.spark.sql.sources.v2.reader.Scan;
 import org.apache.spark.sql.sources.v2.reader.ScanBuilder;
+import org.apache.spark.sql.types.StructType;
 
 /**
- * An empty mix-in interface for {@link Table}, to indicate this table 
supports batch scan.
- * <p>
- * If a {@link Table} implements this interface, its {@link 
Table#newScanBuilder(DataSourceOptions)}
- * must return a {@link ScanBuilder} that builds {@link Scan} with {@link 
Scan#toBatch()}
- * implemented.
- * </p>
+ * A mix-in interface for {@link Table} to provide data reading ability of 
batch processing.
  */
 @Evolving
-public interface SupportsBatchRead extends Table { }
+public interface SupportsBatchRead extends Table {
+
+  /**
+   * Returns the schema of this table.
+   */
+  StructType schema();
 
 Review comment:
   @cloud-fan, in that case, we're talking about a change to how we apply 
validation rules. I've proposed a way to customize the validation rules for a 
data source, using capabilities.
   
   For example, we could add a capability that instructs Spark to not validate 
the schema for a write. I don't think that's very useful, so I think it is more 
likely that we would add a capability that instructs Spark to loosen the 
validation in some way -- like the one I've already suggested that allows 
omitting columns that are optional.

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