Github user rdblue commented on a diff in the pull request:
https://github.com/apache/spark/pull/19269#discussion_r144097061
--- Diff:
sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/DataSourceV2Writer.java
---
@@ -0,0 +1,88 @@
+/*
+ * 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;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SaveMode;
+import org.apache.spark.sql.sources.v2.DataSourceV2Options;
+import org.apache.spark.sql.sources.v2.WriteSupport;
+import org.apache.spark.sql.types.StructType;
+
+/**
+ * A data source writer that is returned by
+ * {@link WriteSupport#createWriter(StructType, SaveMode,
DataSourceV2Options)}.
+ * It can mix in various writing optimization interfaces to speed up the
data saving. The actual
+ * writing logic is delegated to {@link DataWriter}.
+ *
+ * The writing procedure is:
+ * 1. Create a writer factory by {@link #createWriterFactory()},
serialize and send it to all the
+ * partitions of the input data(RDD).
+ * 2. For each partition, create the data writer, and write the data of
the partition with this
+ * writer. If all the data are written successfully, call {@link
DataWriter#commit()}. If
+ * exception happens during the writing, call {@link
DataWriter#abort()}.
+ * 3. If all writers are successfully committed, call {@link
#commit(WriterCommitMessage[])}. If
+ * some writers are aborted, or the job failed with an unknown
reason, call
+ * {@link #abort(WriterCommitMessage[])}.
+ *
+ * Spark won't retry failed writing jobs, users should do it manually in
their Spark applications if
+ * they want to retry.
+ *
+ * Please refer to the document of commit/abort methods for detailed
specifications.
+ *
+ * Note that, this interface provides a protocol between Spark and data
sources for transactional
+ * data writing, but the transaction here is Spark-level transaction,
which may not be the
+ * underlying storage transaction. For example, Spark successfully writes
data to a Cassandra data
+ * source, but Cassandra may need some more time to reach consistency at
storage level.
+ */
[email protected]
+public interface DataSourceV2Writer {
+
+ /**
+ * Creates a writer factory which will be serialized and sent to
executors.
+ */
+ DataWriterFactory<Row> createWriterFactory();
+
+ /**
+ * Commits this writing job with a list of commit messages. The commit
messages are collected from
+ * successful data writers and are produced by {@link
DataWriter#commit()}. If this method
+ * fails(throw exception), this writing job is considered to be failed,
and
+ * {@link #abort(WriterCommitMessage[])} will be called. The written
data should only be visible
+ * to data source readers if this method successes.
+ *
+ * Note that, one partition may have multiple committed data writers
because of speculative tasks.
+ * Spark will pick the first successful one and get its commit message.
Implementations should be
+ * aware of this and handle it correctly, e.g., have a mechanism to make
sure only one data writer
+ * can commit successfully, or have a way to clean up the data of
already-committed writers.
+ */
+ void commit(WriterCommitMessage[] messages);
+
+ /**
+ * Aborts this writing job because some data writers are failed to write
the records and aborted,
+ * or the Spark job fails with some unknown reasons, or {@link
#commit(WriterCommitMessage[])}
+ * fails. If this method fails(throw exception), the underlying data
source may have garbage that
+ * need to be cleaned manually, but these garbage should not be visible
to data source readers.
+ *
+ * Unless the abortion is triggered by the failure of commit, the given
messages should have some
--- End diff --
I think the correct noun is simply "abort".
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
