Github user jose-torres commented on a diff in the pull request:
https://github.com/apache/spark/pull/20752#discussion_r172617421
--- Diff:
sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/streaming/StreamingDataWriterFactory.java
---
@@ -0,0 +1,51 @@
+/*
+ * 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.writer.streaming;
+
+import org.apache.spark.annotation.InterfaceStability;
+import org.apache.spark.sql.sources.v2.writer.DataWriter;
+import org.apache.spark.sql.sources.v2.writer.DataWriterFactory;
+
+@InterfaceStability.Evolving
+public interface StreamingDataWriterFactory<T> extends
DataWriterFactory<T> {
+ /**
+ * Returns a data writer to do the actual writing work.
+ *
+ * If this method fails (by throwing an exception), the action would
fail and no Spark job was
+ * submitted.
+ *
+ * @param partitionId A unique id of the RDD partition that the returned
writer will process.
+ * Usually Spark processes many RDD partitions at the
same time,
+ * implementations should use the partition id to
distinguish writers for
+ * different partitions.
+ * @param attemptNumber Spark may launch multiple tasks with the same
task id. For example, a task
+ * failed, Spark launches a new task wth the same
task id but different
+ * attempt number. Or a task is too slow, Spark
launches new tasks wth the
+ * same task id but different attempt number, which
means there are multiple
+ * tasks with the same task id running at the same
time. Implementations can
+ * use this attempt number to distinguish writers
of different task attempts.
+ * @param epochId A monotonically increasing id for streaming queries
that are split in to
+ * discrete periods of execution. For non-streaming
queries,
+ * this ID will always be 0.
+ */
+ DataWriter<T> createDataWriter(int partitionId, int attemptNumber, long
epochId);
+
+ @Override default DataWriter<T> createDataWriter(int partitionId, int
attemptNumber) {
+ throw new IllegalStateException("Streaming data writer factory cannot
create data writers without epoch.");
--- End diff --
If there's no common interface, DataSourceRDD would need to take a
java.util.List[Any] instead of java.util.List[DataWriterFactory[T]]. This kind
of pattern is present in a lot of DataSourceV2 interfaces, and I think it's
endemic to the general design.
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