Github user jose-torres commented on a diff in the pull request:
https://github.com/apache/spark/pull/22009#discussion_r209021329
--- Diff:
sql/core/src/main/java/org/apache/spark/sql/sources/v2/writer/streaming/StreamingDataWriterFactory.java
---
@@ -0,0 +1,58 @@
+/*
+ * 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 java.io.Serializable;
+
+import org.apache.spark.annotation.InterfaceStability;
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.sources.v2.writer.DataWriter;
+
+/**
+ * A factory of {@link DataWriter} returned by
+ * {@link StreamingWriteSupport#createStreamingWriterFactory()}, which is
responsible for creating
+ * and initializing the actual data writer at executor side.
+ *
+ * Note that, the writer factory will be serialized and sent to executors,
then the data writer
+ * will be created on executors and do the actual writing. So this
interface must be
+ * serializable and {@link DataWriter} doesn't need to be.
+ */
[email protected]
+public interface StreamingDataWriterFactory extends Serializable {
+
+ /**
+ * Returns a data writer to do the actual writing work. Note that, Spark
will reuse the same data
+ * object instance when sending data to the data writer, for better
performance. Data writers
+ * are responsible for defensive copies if necessary, e.g. copy the data
before buffer it in a
+ * list.
+ *
+ * If this method fails (by throwing an exception), the action will fail
and no Spark job will be
+ * 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 taskId A unique identifier for a task that is performing the
write of the partition
+ * data. Spark may run multiple tasks for the same
partition (due to speculation
+ * or task failures, for example).
--- End diff --
Is it the ID of the task or the ID of one particular attempt of the task?
(The target audience here is people who know a reasonable amount about Spark -
I think we should just say TaskContext.taskAttemptId() if that's what this is.)
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