Github user rdblue commented on a diff in the pull request:
https://github.com/apache/spark/pull/22547#discussion_r226789748
--- Diff: sql/core/src/main/java/org/apache/spark/sql/sources/v2/Table.java
---
@@ -15,37 +15,43 @@
* limitations under the License.
*/
-package org.apache.spark.sql.sources.v2.reader;
+package org.apache.spark.sql.sources.v2;
import org.apache.spark.annotation.InterfaceStability;
+import org.apache.spark.sql.execution.datasources.v2.NoopScanConfigBuilder;
+import org.apache.spark.sql.sources.v2.reader.ScanConfig;
+import org.apache.spark.sql.sources.v2.reader.ScanConfigBuilder;
+import org.apache.spark.sql.types.StructType;
/**
- * An interface that defines how to load the data from data source for
batch processing.
+ * An interface representing a logical structured data set of a data
source. For example, the
+ * implementation can be a directory on the file system, or a table in the
catalog, etc.
*
- * The execution engine will get an instance of this interface from a data
source provider
- * (e.g. {@link org.apache.spark.sql.sources.v2.BatchReadSupportProvider})
at the start of a batch
- * query, then call {@link #newScanConfigBuilder()} and create an instance
of {@link ScanConfig}.
- * The {@link ScanConfigBuilder} can apply operator pushdown and keep the
pushdown result in
- * {@link ScanConfig}. The {@link ScanConfig} will be used to create input
partitions and reader
- * factory to scan data from the data source with a Spark job.
+ * This interface can mixin the following interfaces to support different
operations:
+ * <ul>
+ * <li>{@link SupportsBatchRead}: this table can be read in batch
queries.</li>
+ * <li>{@link SupportsMicroBatchRead}: this table can be read in
streaming queries with
+ * micro-batch trigger.</li>
+ * <li>{@link SupportsContinuousRead}: this table can be read in
streaming queries with
+ * continuous trigger.</li>
+ * </ul>
*/
@InterfaceStability.Evolving
-public interface BatchReadSupport extends ReadSupport {
+public interface Table {
+
+ /**
+ * Returns the schema of this table.
+ */
+ StructType schema();
/**
* Returns a builder of {@link ScanConfig}. Spark will call this method
and create a
* {@link ScanConfig} for each data scanning job.
*
* The builder can take some query specific information to do operators
pushdown, and keep these
* information in the created {@link ScanConfig}.
- *
- * This is the first step of the data scan. All other methods in {@link
BatchReadSupport} needs
- * to take {@link ScanConfig} as an input.
- */
- ScanConfigBuilder newScanConfigBuilder();
-
- /**
- * Returns a factory, which produces one {@link PartitionReader} for one
{@link InputPartition}.
*/
- PartitionReaderFactory createReaderFactory(ScanConfig config);
+ default ScanConfigBuilder newScanConfigBuilder(DataSourceOptions
options) {
--- End diff --
I think it should be clear that these are scan-specific options. Maybe add
some documentation with an example of something that would be passed to
configure a scan, like a target split size for combining.
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