[SPARK-18087][SQL] Optimize insert to not require REPAIR TABLE

## What changes were proposed in this pull request?

When inserting into datasource tables with partitions managed by the hive metastore, we need to notify the metastore of newly added partitions. Previously this was implemented via `msck repair table`, but this is more expensive than needed.

This optimizes the insertion path to add only the updated partitions.
## How was this patch tested?

Existing tests (I verified manually that tests fail if the repair operation is omitted).

Author: Eric Liang <ekl@databricks.com>

Closes #15633 from ericl/spark-18087.

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala

@@ -528,7 +528,7 @@ case class DataSource(
             columns,
             bucketSpec,
             format,
-            () => Unit, // No existing table needs to be refreshed.
+            _ => Unit, // No existing table needs to be refreshed.
             options,
             data.logicalPlan,
             mode)

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala

@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.{CatalystConf, CatalystTypeConverters, Inte
 import org.apache.spark.sql.catalyst.CatalystTypeConverters.convertToScala
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.catalog.{CatalogTable, SimpleCatalogRelation}
+import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
 import org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
@@ -34,7 +35,7 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project, Union}
 import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, UnknownPartitioning}
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.execution.{RowDataSourceScanExec, SparkPlan}
-import org.apache.spark.sql.execution.command.{AlterTableRecoverPartitionsCommand, DDLUtils, ExecutedCommandExec}
+import org.apache.spark.sql.execution.command.{AlterTableAddPartitionCommand, DDLUtils, ExecutedCommandExec}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -179,24 +180,30 @@ case class DataSourceAnalysis(conf: CatalystConf) extends Rule[LogicalPlan] {
           "Cannot overwrite a path that is also being read from.")
       }
 
+      def refreshPartitionsCallback(updatedPartitions: Seq[TablePartitionSpec]): Unit = {
+        if (l.catalogTable.isDefined &&
+            l.catalogTable.get.partitionColumnNames.nonEmpty &&
+            l.catalogTable.get.partitionProviderIsHive) {
+          val metastoreUpdater = AlterTableAddPartitionCommand(
+            l.catalogTable.get.identifier,
+            updatedPartitions.map(p => (p, None)),
+            ifNotExists = true)
+          metastoreUpdater.run(t.sparkSession)
+        }
+        t.location.refresh()
+      }
+
       val insertCmd = InsertIntoHadoopFsRelationCommand(
         outputPath,
         query.resolve(t.partitionSchema, t.sparkSession.sessionState.analyzer.resolver),
         t.bucketSpec,
         t.fileFormat,
-        () => t.location.refresh(),
+        refreshPartitionsCallback,
         t.options,
         query,
         mode)
 
-      if (l.catalogTable.isDefined && l.catalogTable.get.partitionColumnNames.nonEmpty &&
-          l.catalogTable.get.partitionProviderIsHive) {
-        // TODO(ekl) we should be more efficient here and only recover the newly added partitions
-        val recoverPartitionCmd = AlterTableRecoverPartitionsCommand(l.catalogTable.get.identifier)
-        Union(insertCmd, recoverPartitionCmd)
-      } else {
-        insertCmd
-      }
+      insertCmd
   }
 }
 

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelationCommand.scala

@@ -23,6 +23,7 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.catalog.BucketSpec
+import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.command.RunnableCommand
@@ -40,7 +41,7 @@ case class InsertIntoHadoopFsRelationCommand(
     partitionColumns: Seq[Attribute],
     bucketSpec: Option[BucketSpec],
     fileFormat: FileFormat,
-    refreshFunction: () => Unit,
+    refreshFunction: (Seq[TablePartitionSpec]) => Unit,
     options: Map[String, String],
     @transient query: LogicalPlan,
     mode: SaveMode)

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala

@@ -30,6 +30,7 @@ import org.apache.hadoop.util.Shell
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.Resolver
+import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
 import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
 import org.apache.spark.sql.types._
 
@@ -244,6 +245,17 @@ object PartitioningUtils {
     }
   }
 
+  /**
+   * Given a partition path fragment, e.g. `fieldOne=1/fieldTwo=2`, returns a parsed spec
+   * for that fragment, e.g. `Map(("fieldOne", "1"), ("fieldTwo", "2"))`.
+   */
+  def parsePathFragment(pathFragment: String): TablePartitionSpec = {
+    pathFragment.split("/").map { kv =>
+      val pair = kv.split("=", 2)
+      (unescapePathName(pair(0)), unescapePathName(pair(1)))
+    }.toMap
+  }
+
   /**
    * Normalize the column names in partition specification, w.r.t. the real partition column names
    * and case sensitivity. e.g., if the partition spec has a column named `monTh`, and there is a

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriteOutput.scala

@@ -19,6 +19,8 @@ package org.apache.spark.sql.execution.datasources
 
 import java.util.{Date, UUID}
 
+import scala.collection.mutable
+
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce._
@@ -30,6 +32,7 @@ import org.apache.spark.internal.Logging
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.sql.{Dataset, SparkSession}
 import org.apache.spark.sql.catalyst.catalog.BucketSpec
+import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
 import org.apache.spark.sql.catalyst.InternalRow
@@ -85,7 +88,7 @@ object WriteOutput extends Logging {
       hadoopConf: Configuration,
       partitionColumns: Seq[Attribute],
       bucketSpec: Option[BucketSpec],
-      refreshFunction: () => Unit,
+      refreshFunction: (Seq[TablePartitionSpec]) => Unit,
       options: Map[String, String],
       isAppend: Boolean): Unit = {
 
@@ -120,7 +123,7 @@ object WriteOutput extends Logging {
       val committer = setupDriverCommitter(job, outputPath.toString, isAppend)
 
       try {
-        sparkSession.sparkContext.runJob(queryExecution.toRdd,
+        val updatedPartitions = sparkSession.sparkContext.runJob(queryExecution.toRdd,
           (taskContext: TaskContext, iter: Iterator[InternalRow]) => {
             executeTask(
               description = description,
@@ -128,11 +131,11 @@ object WriteOutput extends Logging {
               sparkPartitionId = taskContext.partitionId(),
               sparkAttemptNumber = taskContext.attemptNumber(),
               iterator = iter)
-          })
+          }).flatten.distinct
 
         committer.commitJob(job)
         logInfo(s"Job ${job.getJobID} committed.")
-        refreshFunction()
+        refreshFunction(updatedPartitions.map(PartitioningUtils.parsePathFragment))
       } catch { case cause: Throwable =>
         logError(s"Aborting job ${job.getJobID}.", cause)
         committer.abortJob(job, JobStatus.State.FAILED)
@@ -147,7 +150,7 @@ object WriteOutput extends Logging {
       sparkStageId: Int,
       sparkPartitionId: Int,
       sparkAttemptNumber: Int,
-      iterator: Iterator[InternalRow]): Unit = {
+      iterator: Iterator[InternalRow]): Set[String] = {
 
     val jobId = SparkHadoopWriter.createJobID(new Date, sparkStageId)
     val taskId = new TaskID(jobId, TaskType.MAP, sparkPartitionId)
@@ -187,11 +190,12 @@ object WriteOutput extends Logging {
     try {
       Utils.tryWithSafeFinallyAndFailureCallbacks(block = {
         // Execute the task to write rows out
-        writeTask.execute(iterator)
+        val outputPaths = writeTask.execute(iterator)
         writeTask.releaseResources()
 
         // Commit the task
         SparkHadoopMapRedUtil.commitTask(committer, taskAttemptContext, jobId.getId, taskId.getId)
+        outputPaths
       })(catchBlock = {
         // If there is an error, release resource and then abort the task
         try {
@@ -213,7 +217,7 @@ object WriteOutput extends Logging {
    * automatically trigger task aborts.
    */
   private trait ExecuteWriteTask {
-    def execute(iterator: Iterator[InternalRow]): Unit
+    def execute(iterator: Iterator[InternalRow]): Set[String]
     def releaseResources(): Unit
 
     final def filePrefix(split: Int, uuid: String, bucketId: Option[Int]): String = {
@@ -240,11 +244,12 @@ object WriteOutput extends Logging {
       outputWriter
     }
 
-    override def execute(iter: Iterator[InternalRow]): Unit = {
+    override def execute(iter: Iterator[InternalRow]): Set[String] = {
       while (iter.hasNext) {
         val internalRow = iter.next()
         outputWriter.writeInternal(internalRow)
       }
+      Set.empty
     }
 
     override def releaseResources(): Unit = {
@@ -327,7 +332,7 @@ object WriteOutput extends Logging {
       newWriter
     }
 
-    override def execute(iter: Iterator[InternalRow]): Unit = {
+    override def execute(iter: Iterator[InternalRow]): Set[String] = {
       // We should first sort by partition columns, then bucket id, and finally sorting columns.
       val sortingExpressions: Seq[Expression] =
         description.partitionColumns ++ bucketIdExpression ++ sortColumns
@@ -375,6 +380,7 @@ object WriteOutput extends Logging {
 
       // If anything below fails, we should abort the task.
       var currentKey: UnsafeRow = null
+      val updatedPartitions = mutable.Set[String]()
       while (sortedIterator.next()) {
         val nextKey = getBucketingKey(sortedIterator.getKey).asInstanceOf[UnsafeRow]
         if (currentKey != nextKey) {
@@ -386,6 +392,10 @@ object WriteOutput extends Logging {
           logDebug(s"Writing partition: $currentKey")
 
           currentWriter = newOutputWriter(currentKey, getPartitionString)
+          val partitionPath = getPartitionString(currentKey).getString(0)
+          if (partitionPath.nonEmpty) {
+            updatedPartitions.add(partitionPath)
+          }
         }
         currentWriter.writeInternal(sortedIterator.getValue)
       }
@@ -393,6 +403,7 @@ object WriteOutput extends Logging {
         currentWriter.close()
         currentWriter = null
       }
+      updatedPartitions.toSet
     }
 
     override def releaseResources(): Unit = {