Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/13653#discussion_r67023216
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala
---
@@ -0,0 +1,401 @@
+/*
+ * 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.streaming
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.sql.{AnalysisException, DataFrame, Dataset,
ForeachWriter}
+import org.apache.spark.sql.execution.datasources.DataSource
+import org.apache.spark.sql.execution.streaming.{ForeachSink, MemoryPlan,
MemorySink}
+
+/**
+ * :: Experimental ::
+ * Interface used to write a streaming [[Dataset]] to external storage
systems (e.g. file systems,
+ * key-value stores, etc). Use [[Dataset.write]] to access this.
+ *
+ * @since 2.0.0
+ */
+@Experimental
+final class DataStreamWriter[T] private[sql](ds: Dataset[T]) {
+
+ private val df = ds.toDF()
+
+ /**
+ * :: Experimental ::
+ * Specifies how data of a streaming DataFrame/Dataset is written to a
streaming sink.
+ * - `OutputMode.Append()`: only the new rows in the streaming
DataFrame/Dataset will be
+ * written to the sink
+ * - `OutputMode.Complete()`: all the rows in the streaming
DataFrame/Dataset will be written
+ * to the sink every time these is some
updates
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def outputMode(outputMode: OutputMode): DataStreamWriter[T] = {
+ this.outputMode = outputMode
+ this
+ }
+
+
+ /**
+ * :: Experimental ::
+ * Specifies how data of a streaming DataFrame/Dataset is written to a
streaming sink.
+ * - `append`: only the new rows in the streaming DataFrame/Dataset
will be written to
+ * the sink
+ * - `complete`: all the rows in the streaming DataFrame/Dataset will
be written to the sink
+ * every time these is some updates
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def outputMode(outputMode: String): DataStreamWriter[T] = {
+ this.outputMode = outputMode.toLowerCase match {
+ case "append" =>
+ OutputMode.Append
+ case "complete" =>
+ OutputMode.Complete
+ case _ =>
+ throw new IllegalArgumentException(s"Unknown output mode
$outputMode. " +
+ "Accepted output modes are 'append' and 'complete'")
+ }
+ this
+ }
+
+ /**
+ * :: Experimental ::
+ * Set the trigger for the stream query. The default value is
`ProcessingTime(0)` and it will run
+ * the query as fast as possible.
+ *
+ * Scala Example:
+ * {{{
+ * df.write.trigger(ProcessingTime("10 seconds"))
+ *
+ * import scala.concurrent.duration._
+ * df.write.trigger(ProcessingTime(10.seconds))
+ * }}}
+ *
+ * Java Example:
+ * {{{
+ * df.write.trigger(ProcessingTime.create("10 seconds"))
+ *
+ * import java.util.concurrent.TimeUnit
+ * df.write.trigger(ProcessingTime.create(10, TimeUnit.SECONDS))
+ * }}}
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def trigger(trigger: Trigger): DataStreamWriter[T] = {
+ this.trigger = trigger
+ this
+ }
+
+
+ /**
+ * :: Experimental ::
+ * Specifies the name of the [[ContinuousQuery]] that can be started
with `startStream()`.
+ * This name must be unique among all the currently active queries in
the associated SQLContext.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def queryName(queryName: String): DataStreamWriter[T] = {
+ this.extraOptions += ("queryName" -> queryName)
+ this
+ }
+
+ /**
+ * :: Experimental ::
+ * Specifies the underlying output data source. Built-in options include
"parquet", "json", etc.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def format(source: String): DataStreamWriter[T] = {
+ this.source = source
+ this
+ }
+
+ /**
+ * Partitions the output by the given columns on the file system. If
specified, the output is
+ * laid out on the file system similar to Hive's partitioning scheme. As
an example, when we
+ * partition a dataset by year and then month, the directory layout
would look like:
+ *
+ * - year=2016/month=01/
+ * - year=2016/month=02/
+ *
+ * Partitioning is one of the most widely used techniques to optimize
physical data layout.
+ * It provides a coarse-grained index for skipping unnecessary data
reads when queries have
+ * predicates on the partitioned columns. In order for partitioning to
work well, the number
+ * of distinct values in each column should typically be less than tens
of thousands.
+ *
+ * This was initially applicable for Parquet but in 1.5+ covers JSON,
text, ORC and avro as well.
+ *
+ * @since 1.4.0
+ */
+ @scala.annotation.varargs
+ def partitionBy(colNames: String*): DataStreamWriter[T] = {
+ this.partitioningColumns = Option(colNames)
+ this
+ }
+
+ /**
+ * :: Experimental ::
+ * Adds an output option for the underlying data source.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def option(key: String, value: String): DataStreamWriter[T] = {
+ this.extraOptions += (key -> value)
+ this
+ }
+
+ /**
+ * :: Experimental ::
+ * Adds an output option for the underlying data source.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def option(key: String, value: Boolean): DataStreamWriter[T] =
option(key, value.toString)
+
+ /**
+ * :: Experimental ::
+ * Adds an output option for the underlying data source.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def option(key: String, value: Long): DataStreamWriter[T] = option(key,
value.toString)
+
+ /**
+ * :: Experimental ::
+ * Adds an output option for the underlying data source.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def option(key: String, value: Double): DataStreamWriter[T] =
option(key, value.toString)
+
+ /**
+ * :: Experimental ::
+ * (Scala-specific) Adds output options for the underlying data source.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def options(options: scala.collection.Map[String, String]):
DataStreamWriter[T] = {
+ this.extraOptions ++= options
+ this
+ }
+
+ /**
+ * :: Experimental ::
+ * Adds output options for the underlying data source.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def options(options: java.util.Map[String, String]): DataStreamWriter[T]
= {
+ this.options(options.asScala)
+ this
+ }
+
+
+ /**
+ * :: Experimental ::
+ * Saves the content of the streaming [[DataFrame]] in Parquet format at
the specified path.
+ * This is equivalent to:
+ * {{{
+ * format("parquet").save(path)
--- End diff --
this should probably start with `df.writeStream.`
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