cloud-fan commented on a change in pull request #29767:
URL: https://github.com/apache/spark/pull/29767#discussion_r491826956
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala
##########
@@ -300,54 +301,54 @@ final class DataStreamWriter[T] private[sql](ds:
Dataset[T]) {
"write files of Hive data source directly.")
}
- if (source == "memory") {
- assertNotPartitioned("memory")
+ if (source == SOURCE_NAME_TABLE) {
+ assertNotPartitioned(SOURCE_NAME_TABLE)
+
+ import df.sparkSession.sessionState.analyzer.CatalogAndIdentifier
+
+ import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+ val originalMultipartIdentifier = df.sparkSession.sessionState.sqlParser
+ .parseMultipartIdentifier(tableName)
+ val CatalogAndIdentifier(catalog, identifier) =
originalMultipartIdentifier
+
+ // Currently we don't create a logical streaming writer node in logical
plan, so cannot rely
+ // on analyzer to resolve it. Directly lookup only for temp view to
provide clearer message.
+ // TODO (SPARK-27484): we should add the writing node before the plan is
analyzed.
+ if (isTempView(df.sparkSession, originalMultipartIdentifier)) {
+ throw new AnalysisException(s"Temporary view $tableName doesn't
support streaming write")
+ }
+
+ val tableInstance = catalog.asTableCatalog.loadTable(identifier)
+
+ import
org.apache.spark.sql.execution.datasources.v2.DataSourceV2Implicits._
+ val sink = tableInstance match {
+ case t: SupportsWrite if t.supports(STREAMING_WRITE) => t
+ case t => throw new AnalysisException(s"Table $tableName doesn't
support streaming " +
+ s"write - $t")
+ }
+
+ startQuery(sink, extraOptions)
+ } else if (source == SOURCE_NAME_MEMORY) {
+ assertNotPartitioned(SOURCE_NAME_MEMORY)
if (extraOptions.get("queryName").isEmpty) {
throw new AnalysisException("queryName must be specified for memory
sink")
}
val sink = new MemorySink()
val resultDf = Dataset.ofRows(df.sparkSession, new MemoryPlan(sink,
df.schema.toAttributes))
- val chkpointLoc = extraOptions.get("checkpointLocation")
val recoverFromChkpoint = outputMode == OutputMode.Complete()
- val query =
df.sparkSession.sessionState.streamingQueryManager.startQuery(
- extraOptions.get("queryName"),
- chkpointLoc,
- df,
- extraOptions.toMap,
- sink,
- outputMode,
- useTempCheckpointLocation = true,
- recoverFromCheckpointLocation = recoverFromChkpoint,
- trigger = trigger)
- resultDf.createOrReplaceTempView(query.name)
- query
- } else if (source == "foreach") {
- assertNotPartitioned("foreach")
+ startQuery(sink, extraOptions, Some(resultDf), recoverFromCheckpoint =
recoverFromChkpoint)
+ } else if (source == SOURCE_NAME_FOREACH) {
+ assertNotPartitioned(SOURCE_NAME_FOREACH)
val sink = ForeachWriterTable[T](foreachWriter, ds.exprEnc)
- df.sparkSession.sessionState.streamingQueryManager.startQuery(
- extraOptions.get("queryName"),
- extraOptions.get("checkpointLocation"),
- df,
- extraOptions.toMap,
- sink,
- outputMode,
- useTempCheckpointLocation = true,
- trigger = trigger)
- } else if (source == "foreachBatch") {
- assertNotPartitioned("foreachBatch")
+ startQuery(sink, extraOptions)
+ } else if (source == SOURCE_NAME_FOREACH_BATCH) {
+ assertNotPartitioned(SOURCE_NAME_FOREACH_BATCH)
if (trigger.isInstanceOf[ContinuousTrigger]) {
- throw new AnalysisException("'foreachBatch' is not supported with
continuous trigger")
+ throw new AnalysisException(s"'$SOURCE_NAME_FOREACH_BATCH' is not
supported with " +
Review comment:
nit: we can use `$source` to shorten the code.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala
##########
@@ -380,17 +381,44 @@ final class DataStreamWriter[T] private[sql](ds:
Dataset[T]) {
createV1Sink(optionsWithPath)
}
- df.sparkSession.sessionState.streamingQueryManager.startQuery(
- extraOptions.get("queryName"),
- extraOptions.get("checkpointLocation"),
- df,
- optionsWithPath.originalMap,
- sink,
- outputMode,
- useTempCheckpointLocation = source == "console" || source == "noop",
- recoverFromCheckpointLocation = true,
- trigger = trigger)
+ startQuery(sink, optionsWithPath.originalMap)
+ }
+ }
+
+ private def startQuery(
+ sink: Table,
+ newOptions: Map[String, String],
+ resultDf: Option[DataFrame] = None,
+ recoverFromCheckpoint: Boolean = true): StreamingQuery = {
+ val queryName = extraOptions.get("queryName")
+ val checkpointLocation = extraOptions.get("checkpointLocation")
+ val useTempCheckpointLocation =
SOURCES_ALLOW_ONE_TIME_QUERY.contains(source)
+
+ val query = df.sparkSession.sessionState.streamingQueryManager.startQuery(
+ queryName,
+ checkpointLocation,
+ df,
+ newOptions,
+ sink,
+ outputMode,
+ useTempCheckpointLocation = useTempCheckpointLocation,
+ recoverFromCheckpointLocation = recoverFromCheckpoint,
+ trigger = trigger)
+
+ resultDf.foreach { resDf => resDf.createOrReplaceTempView(query.name) }
Review comment:
only the in-memory sink passes in `resultDf`. Shall we register temp
view only in the `else if (source == "memory")` branch?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala
##########
@@ -380,17 +381,44 @@ final class DataStreamWriter[T] private[sql](ds:
Dataset[T]) {
createV1Sink(optionsWithPath)
}
- df.sparkSession.sessionState.streamingQueryManager.startQuery(
- extraOptions.get("queryName"),
- extraOptions.get("checkpointLocation"),
- df,
- optionsWithPath.originalMap,
- sink,
- outputMode,
- useTempCheckpointLocation = source == "console" || source == "noop",
- recoverFromCheckpointLocation = true,
- trigger = trigger)
+ startQuery(sink, optionsWithPath.originalMap)
+ }
+ }
+
+ private def startQuery(
+ sink: Table,
+ newOptions: Map[String, String],
+ resultDf: Option[DataFrame] = None,
+ recoverFromCheckpoint: Boolean = true): StreamingQuery = {
+ val queryName = extraOptions.get("queryName")
+ val checkpointLocation = extraOptions.get("checkpointLocation")
+ val useTempCheckpointLocation =
SOURCES_ALLOW_ONE_TIME_QUERY.contains(source)
+
+ val query = df.sparkSession.sessionState.streamingQueryManager.startQuery(
+ queryName,
+ checkpointLocation,
+ df,
+ newOptions,
+ sink,
+ outputMode,
+ useTempCheckpointLocation = useTempCheckpointLocation,
+ recoverFromCheckpointLocation = recoverFromCheckpoint,
+ trigger = trigger)
+
+ resultDf.foreach { resDf => resDf.createOrReplaceTempView(query.name) }
+ query
+ }
+
+ private def isTempView(sparkSession: SparkSession, multiPartIdentifier:
Seq[String]): Boolean = {
+ val globalTempDBName = df.sparkSession.conf.get(
+ org.apache.spark.sql.internal.StaticSQLConf.GLOBAL_TEMP_DATABASE)
+ val identifierForTempView = multiPartIdentifier match {
+ case Seq(dbName, tempViewName) if dbName.equals(globalTempDBName) =>
+ Seq(dbName, tempViewName)
+ case Seq(_, tempViewName) => Seq(tempViewName)
+ case ident => ident
}
+ df.sparkSession.sessionState.catalog.isTempView(identifierForTempView)
Review comment:
shall we simply call `...catalog.isTempView(multiPartIdentifier)`?
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