Github user sujith71955 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22575#discussion_r238336135
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/SQLStreamingSink.scala
---
@@ -0,0 +1,115 @@
+/*
+ * 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.execution.streaming
+
+import java.util.concurrent.TimeUnit
+
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.catalog.CatalogTable
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.streaming.InternalOutputModes
+import org.apache.spark.sql.execution.command.RunnableCommand
+import org.apache.spark.sql.execution.datasources.DataSource
+import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Utils
+import org.apache.spark.sql.sources.v2.StreamingWriteSupportProvider
+import org.apache.spark.sql.streaming.Trigger
+import org.apache.spark.util.Utils
+
+/**
+ * The basic RunnableCommand for SQLStreaming, using Command.run to start
a streaming query.
+ *
+ * @param sparkSession
+ * @param extraOptions
+ * @param partitionColumnNames
+ * @param child
+ */
+case class SQLStreamingSink(sparkSession: SparkSession,
+ table: CatalogTable,
+ child: LogicalPlan)
+ extends RunnableCommand {
+
+ private val sqlConf = sparkSession.sqlContext.conf
+
+ /**
+ * The given column name may not be equal to any of the existing column
names if we were in
+ * case-insensitive context. Normalize the given column name to the real
one so that we don't
+ * need to care about case sensitivity afterwards.
+ */
+ private def normalize(df: DataFrame, columnName: String, columnType:
String): String = {
+ val validColumnNames = df.logicalPlan.output.map(_.name)
+ validColumnNames.find(sparkSession.sessionState.analyzer.resolver(_,
columnName))
+ .getOrElse(throw new AnalysisException(s"$columnType column
$columnName not found in " +
+ s"existing columns (${validColumnNames.mkString(", ")})"))
+ }
+
+ /**
+ * Parse spark.sqlstreaming.trigger.seconds to Trigger
+ */
+ private def parseTrigger(): Trigger = {
+ val trigger = Utils.timeStringAsMs(sqlConf.sqlStreamTrigger)
+ Trigger.ProcessingTime(trigger, TimeUnit.MICROSECONDS)
--- End diff --
do we require micro seconds unit here? milliseconds/seconds will do i
guess.the lowest latency supported by structured stream is 100 ms.
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