juliuszsompolski commented on code in PR #40160:
URL: https://github.com/apache/spark/pull/40160#discussion_r1172607370
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/planner/SparkConnectPlanner.scala:
##########
@@ -1450,10 +1458,79 @@ class SparkConnectPlanner(val session: SparkSession) {
handleWriteOperationV2(command.getWriteOperationV2)
case proto.Command.CommandTypeCase.EXTENSION =>
handleCommandPlugin(command.getExtension)
+ case proto.Command.CommandTypeCase.SQL_COMMAND =>
+ handleSqlCommand(command.getSqlCommand, clientId, responseObserver)
case _ => throw new UnsupportedOperationException(s"$command not
supported.")
}
}
+ def handleSqlCommand(
+ getSqlCommand: SqlCommand,
+ clientId: String,
+ responseObserver: StreamObserver[ExecutePlanResponse]): Unit = {
+ // Eagerly execute commands of the provided SQL string.
+ val df = session.sql(getSqlCommand.getSql, getSqlCommand.getArgsMap)
+ // Check if commands have been executed.
+ val isCommand =
df.queryExecution.commandExecuted.isInstanceOf[CommandResult]
+ val rows = df.logicalPlan match {
+ case lr: LocalRelation => lr.data
+ case cr: CommandResult => cr.rows
+ case _ => Seq.empty
+ }
+
+ // Convert the results to Arrow.
+ val schema = df.schema
+ val maxRecordsPerBatch = session.sessionState.conf.arrowMaxRecordsPerBatch
+ val maxBatchSize =
(SparkEnv.get.conf.get(CONNECT_GRPC_ARROW_MAX_BATCH_SIZE) * 0.7).toLong
+ val timeZoneId = session.sessionState.conf.sessionLocalTimeZone
+
+ // Convert the data.
+ val bytes = if (rows.isEmpty) {
+ ArrowConverters.createEmptyArrowBatch(schema, timeZoneId)
+ } else {
+ val batches = ArrowConverters.toBatchWithSchemaIterator(
+ rows.iterator,
+ schema,
+ maxRecordsPerBatch,
+ maxBatchSize,
+ timeZoneId)
+ assert(batches.size == 1)
+ batches.next()
+ }
+
+ // To avoid explicit handling of the result on the client, we build the
expected input
+ // of the relation on the server. The client has to simply forward the
result.
+ val result = SqlCommandResult.newBuilder()
+ if (isCommand) {
+ result.setRelation(
+ proto.Relation
+ .newBuilder()
+ .setLocalRelation(
+ proto.LocalRelation
+ .newBuilder()
+ .setData(ByteString.copyFrom(bytes))))
+ } else {
+ result.setRelation(
+ proto.Relation
+ .newBuilder()
+ .setSql(
+ proto.SQL
+ .newBuilder()
+ .setQuery(getSqlCommand.getSql)
+ .putAllArgs(getSqlCommand.getArgsMap)))
+ }
+ // Exactly one SQL Command Result Batch
+ responseObserver.onNext(
+ ExecutePlanResponse
+ .newBuilder()
+ .setClientId(clientId)
+ .setSqlCommandResult(result)
+ .build())
+
+ // Send Metrics
+ SparkConnectStreamHandler.sendMetricsToResponse(clientId, df)
Review Comment:
@grundprinzip note: this creates the ExecutePlansResponse proto object, but
doesnt send it anywhere.
Should be
`responseObserver.onNext(SparkConnectStreamHandler.sendMetricsToResponse(clientId,
df))`
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##########
@@ -163,45 +195,17 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[ExecutePlanResp
response.setArrowBatch(batch)
responseObserver.onNext(response.build())
}
-
- responseObserver.onNext(sendMetricsToResponse(clientId, dataframe))
- responseObserver.onCompleted()
}
}
- private def sendMetricsToResponse(clientId: String, rows: DataFrame):
ExecutePlanResponse = {
+ def sendMetricsToResponse(clientId: String, rows: DataFrame):
ExecutePlanResponse = {
// Send a last batch with the metrics
Review Comment:
maybe best rename this function, since it doesn't send anything?
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