[GitHub] [spark] Ngone51 commented on a change in pull request #31842: [SPARK-34748][SS] Create a rule of the analysis logic for streaming write

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Ngone51 commented on a change in pull request #31842:
URL: https://github.com/apache/spark/pull/31842#discussion_r596834842



##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ResolveWriteToStream.scala
##########
@@ -0,0 +1,99 @@
+/*
+ * 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.UUID
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.SQLConfHelper
+import org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.streaming.{WriteToStream, WriteToStreamStatement}
+import org.apache.spark.sql.connector.catalog.SupportsWrite
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.util.Utils
+
+/**
+ * Replaces logical [[WriteToStreamStatement]] operator with an [[WriteToStream]] operator.
+ */
+object ResolveWriteToStream extends Rule[LogicalPlan] with SQLConfHelper {
+  def apply(plan: LogicalPlan): LogicalPlan = plan.resolveOperators {
+    case WriteToStreamStatement(userSpecifiedName, userSpecifiedCheckpointLocation,
+        useTempCheckpointLocation, recoverFromCheckpointLocation, sink, outputMode, hadoopConf,
+        isContinuousTrigger, queryPlan) =>

Review comment:
       It recommends matching the type only instead of expanding all parameters according to [pattern-matching](https://github.com/databricks/scala-style-guide#pattern-matching).

##########
File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/streaming/WriteToStreamStatement.scala
##########
@@ -0,0 +1,61 @@
+/*
+ * 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.catalyst.streaming
+
+import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.connector.catalog.Table
+import org.apache.spark.sql.streaming.OutputMode
+
+/**
+ * A statement for Stream writing. It contains all neccessary param and will be address in the
+ * rule [[ResolveStreamWrite]].
+ *
+ * @param userSpecifiedName  Query name optionally specified by the user.
+ * @param userSpecifiedCheckpointLocation  Checkpoint location optionally specified by the user.
+ * @param useTempCheckpointLocation  Whether to use a temporary checkpoint location when the user
+ *                                   has not specified one. If false, then error will be thrown.
+ * @param recoverFromCheckpointLocation  Whether to recover query from the checkpoint location.
+ *                                       If false and the checkpoint location exists, then error
+ *                                       will be thrown.
+ * @param sink  Sink to write the streaming outputs.
+ * @param outputMode  Output mode for the sink.
+ * @param hadoopConf  The Hadoop Configuration to get a FileSystem instance
+ * @param isContinuousTrigger  Whether the statement is triggered by a continous query or not.
+ * @param queryPlan  The analyzed query plan from the streaming DataFrame.
+ */
+case class WriteToStreamStatement(
+    userSpecifiedName: Option[String],
+    userSpecifiedCheckpointLocation: Option[String],
+    useTempCheckpointLocation: Boolean,
+    recoverFromCheckpointLocation: Boolean,
+    sink: Table,
+    outputMode: OutputMode,
+    hadoopConf: Configuration,
+    isContinuousTrigger: Boolean,
+    queryPlan: LogicalPlan) extends LogicalPlan {
+
+  override def isStreaming: Boolean = true
+
+  override def output: Seq[Attribute] = Nil
+
+  override def children: Seq[LogicalPlan] = Seq.empty

Review comment:
       Why not extend the `LeafNode` for simplicity?

##########
File path: sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryManager.scala
##########
@@ -239,82 +237,39 @@ class StreamingQueryManager private[sql] (sparkSession: SparkSession) extends Lo
       recoverFromCheckpointLocation: Boolean,
       trigger: Trigger,
       triggerClock: Clock): StreamingQueryWrapper = {
-    var deleteCheckpointOnStop = false
-    val checkpointLocation = userSpecifiedCheckpointLocation.map { userSpecified =>
-      new Path(userSpecified).toString
-    }.orElse {
-      df.sparkSession.sessionState.conf.checkpointLocation.map { location =>
-        new Path(location, userSpecifiedName.getOrElse(UUID.randomUUID().toString)).toString
-      }
-    }.getOrElse {
-      if (useTempCheckpointLocation) {
-        deleteCheckpointOnStop = true
-        val tempDir = Utils.createTempDir(namePrefix = s"temporary").getCanonicalPath
-        logWarning("Temporary checkpoint location created which is deleted normally when" +
-          s" the query didn't fail: $tempDir. If it's required to delete it under any" +
-          s" circumstances, please set ${SQLConf.FORCE_DELETE_TEMP_CHECKPOINT_LOCATION.key} to" +
-          s" true. Important to know deleting temp checkpoint folder is best effort.")
-        tempDir
-      } else {
-        throw new AnalysisException(
-          "checkpointLocation must be specified either " +
-            """through option("checkpointLocation", ...) or """ +
-            s"""SparkSession.conf.set("${SQLConf.CHECKPOINT_LOCATION.key}", ...)""")
-      }
-    }
-
-    // If offsets have already been created, we trying to resume a query.
-    if (!recoverFromCheckpointLocation) {
-      val checkpointPath = new Path(checkpointLocation, "offsets")
-      val fs = checkpointPath.getFileSystem(df.sparkSession.sessionState.newHadoopConf())
-      if (fs.exists(checkpointPath)) {
-        throw new AnalysisException(
-          s"This query does not support recovering from checkpoint location. " +
-            s"Delete $checkpointPath to start over.")
-      }
-    }
-
     val analyzedPlan = df.queryExecution.analyzed
     df.queryExecution.assertAnalyzed()
 
-    val operationCheckEnabled = sparkSession.sessionState.conf.isUnsupportedOperationCheckEnabled
+    val dataStreamWritePlan = WriteToStreamStatement(
+      userSpecifiedName,
+      userSpecifiedCheckpointLocation,
+      useTempCheckpointLocation,
+      recoverFromCheckpointLocation,
+      sink,
+      outputMode,
+      df.sparkSession.sessionState.newHadoopConf(),
+      trigger.isInstanceOf[ContinuousTrigger],
+      analyzedPlan)
 
-    if (sparkSession.sessionState.conf.adaptiveExecutionEnabled) {
-      logWarning(s"${SQLConf.ADAPTIVE_EXECUTION_ENABLED.key} " +
-          "is not supported in streaming DataFrames/Datasets and will be disabled.")
-    }
+    val analyzedStreamWritePlan =
+      sparkSession.sessionState.executePlan(dataStreamWritePlan).analyzed

Review comment:
       This seems to introduce the analysis overhead here. Do we have a plan to add more rules? Or, maybe, something like: 
   ```scala
   val analyzedStreamWritePlan  = rules.foldLeft(dataStreamWritePlan) { case (p, r) => r.apply(p) }
   ```
   should be enough?
   
   




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