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
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@@ -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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