Github user sureshthalamati commented on a diff in the pull request:
https://github.com/apache/spark/pull/19136#discussion_r137453919
--- Diff:
sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/DataSourceV2Reader.java
---
@@ -0,0 +1,126 @@
+/*
+ * 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.sources.v2.reader;
+
+import java.io.IOException;
+import java.util.List;
+import java.util.stream.Collectors;
+
+import org.apache.spark.annotation.Experimental;
+import org.apache.spark.annotation.InterfaceStability;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder;
+import org.apache.spark.sql.catalyst.encoders.RowEncoder;
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
+import org.apache.spark.sql.types.StructType;
+
+/**
+ * The main interface and minimal requirement for a data source reader.
The implementations should
+ * at least implement the full scan logic, users can mix in more
interfaces to implement scan
+ * optimizations like column pruning, filter push down, etc.
+ *
+ * There are mainly 2 kinds of scan optimizations:
+ * 1. push operators downward to the data source, e.g., column pruning,
filter push down, etc.
+ * 2. propagate information upward to Spark, e.g., report statistics,
report ordering, etc.
+ * Spark first applies all operator push down optimizations which this
data source supports. Then
+ * Spark collects information this data source provides for further
optimizations. Finally Spark
+ * issues the scan request and does the actual data reading.
+ */
+public abstract class DataSourceV2Reader {
+
+ /**
+ * Returns the actual schema of this data source reader, which may be
different from the physical
+ * schema of the underlying storage, as column pruning or other
optimizations may happen.
+ */
+ public abstract StructType readSchema();
+
+ /**
+ * Returns a list of read tasks, each task is responsible for outputting
data for one RDD
+ * partition, which means the number of tasks returned here is same as
the number of RDD
+ * partitions this scan outputs.
+ *
+ * Note that, this may not be a full scan if the data source reader
mixes in other optimization
+ * interfaces like column pruning, filter push down, etc. These
optimizations are applied before
+ * Spark issues the scan request.
+ */
+ protected abstract List<ReadTask<Row>> createReadTasks();
+
+ /**
+ * Inside Spark, the input rows will be converted to `UnsafeRow`s before
processing. To avoid
+ * this conversion, implementations can overwrite this method and output
`UnsafeRow`s directly.
+ * Note that, this is an experimental and unstable interface, as
`UnsafeRow` is not public and
+ * may get changed in future Spark versions.
+ *
+ * If implementations overwrite this method, `createReadTasks` will
never be called and they can
+ * just throw an exception in `createReadTasks`.
+ */
+ @Experimental
+ @InterfaceStability.Unstable
+ public List<ReadTask<UnsafeRow>> createUnsafeRowReadTasks() {
--- End diff --
I really like the new API's flexibility to implement the different types of
support. Considering UnsafeRow is unstable , Would it be possible to move
createUnsafeRowReadTasks to a different interface ? That might make data
source implement two types of data sources one with Row , and another one with
UnsafeRow and make it easily configurable based on the spark version.
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