Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/13342#discussion_r66537102
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala ---
@@ -401,6 +381,53 @@ final class DataFrameWriter private[sql](df:
DataFrame) {
}
/**
+ * :: Experimental ::
+ * Starts the execution of the streaming query, which will continually
send results to the given
+ * [[ForeachWriter]] as new data arrives. The returned
[[ContinuousQuery]] object can be used to
+ * interact with the stream.
+ *
+ * @since 2.0.0
+ */
+ @Experimental
+ def foreach(writer: ForeachWriter[T]): ContinuousQuery = {
+ assertNotBucketed("foreach")
+ assertStreaming(
+ "foreach() on streaming Datasets and DataFrames can only be called
on continuous queries")
+
+ val queryName = extraOptions.getOrElse("queryName",
StreamExecution.nextName)
+ val sink = new
ForeachSink[T](ds.sparkSession.sparkContext.clean(writer))(ds.exprEnc)
+ df.sparkSession.sessionState.continuousQueryManager.startQuery(
+ queryName,
+ getCheckpointLocation(queryName, required = false),
+ df,
+ sink,
+ outputMode,
+ trigger)
+ }
+
+ /**
+ * Returns the checkpointLocation for a query. If `required` is `true`
but the checkpoint
+ * location is not set, [[AnalysisException]] will be thrown. If
`required` is `false`, a temp
+ * folder will be created if the checkpoint location is not set.
+ */
+ private def getCheckpointLocation(queryName: String, required: Boolean):
String = {
+ extraOptions.get("checkpointLocation").map { userSpecified =>
+ new Path(userSpecified).toUri.toString
+ }.orElse {
+ df.sparkSession.conf.get(SQLConf.CHECKPOINT_LOCATION).map { location
=>
+ new Path(location, queryName).toUri.toString
+ }
+ }.getOrElse {
+ if (required) {
+ throw new AnalysisException("checkpointLocation must be specified
either " +
+ "through option() or SQLConf")
--- End diff --
SQLConf is actually not a user facing concept.
Just say `SparkSession.conf.set`
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