Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/19269#discussion_r145763287
--- Diff:
sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataWriter.java
---
@@ -0,0 +1,92 @@
+/*
+ * 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;
+
+import org.apache.spark.annotation.InterfaceStability;
+
+/**
+ * A data writer returned by {@link DataWriterFactory#createWriter(int,
int)} and is
+ * responsible for writing data for an input RDD partition.
+ *
+ * One Spark task has one exclusive data writer, so there is no
thread-safe concern.
+ *
+ * {@link #write(Object)} is called for each record in the input RDD
partition. If one record fails
+ * the {@link #write(Object)}, {@link #abort()} is called afterwards and
the remaining records will
+ * not be processed. If all records are successfully written, {@link
#commit()} is called.
+ *
+ * If this data writer succeeds(all records are successfully written and
{@link #commit()}
+ * succeeds), a {@link WriterCommitMessage} will be sent to the driver
side and pass to
+ * {@link DataSourceV2Writer#commit(WriterCommitMessage[])} with commit
messages from other data
+ * writers. If this data writer fails(one record fails to write or {@link
#commit()} fails), an
+ * exception will be sent to the driver side, and Spark will retry this
writing task for some times,
+ * each time {@link DataWriterFactory#createWriter(int, int)} gets a
different `attemptNumber`,
+ * and finally call {@link
DataSourceV2Writer#abort(WriterCommitMessage[])} if all retry fail.
+ *
+ * Besides the retry mechanism, Spark may launch speculative tasks if the
existing writing task
+ * takes too long to finish. Different from retried tasks, which are
launched one by one after the
+ * previous one fails, speculative tasks are running simultaneously. It's
possible that one input
+ * RDD partition has multiple data writers with different `attemptNumber`
running at the same time,
+ * and data sources should guarantee that these data writers don't
conflict and can work together.
+ * Implementations can coordinate with driver during {@link #commit()} to
make sure only one of
+ * these data writers can commit successfully. Or implementations can
allow all of them to commit
+ * successfully, and have a way to revert committed data writers without
the commit message, because
+ * Spark only accepts the commit message that arrives first and ignore
others.
--- End diff --
In the test case, could we implement the above logics?
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