[SPARK-18917][SC-5624] Remove schema check in appending data

In append mode, we check whether the schema of the write is compatible with the schema of the existing data. It can be a significant performance issue in cloud environment to find the existing schema for files. This patch removes the check.

Note that for catalog tables, we always do the check, as discussed in apache#16339 (comment)

backport apache#16622 to our internal 2.1 branch.

Author: Reynold Xin <[email protected]>

Closes apache#178 from cloud-fan/backport.

Author: rxin

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

@@ -104,20 +104,12 @@ case class DataSource(
    *     dataTypes in `userSpecifiedSchema`. All subsequent triggers for this stream will re-use
    *     this information, therefore calls to this method should be very cheap, i.e. there won't
    *     be any further inference in any triggers.
-   *   4. `df.saveAsTable(tableThatExisted)`: In this case, we call this method to resolve the
-   *     existing table's partitioning scheme. This is achieved by not providing
-   *     `userSpecifiedSchema`. For this case, we add the boolean `justPartitioning` for an early
-   *     exit, if we don't care about the schema of the original table.
    *
    * @param format the file format object for this DataSource
-   * @param justPartitioning Whether to exit early and provide just the schema partitioning.
    * @return A pair of the data schema (excluding partition columns) and the schema of the partition
-   *         columns. If `justPartitioning` is `true`, then the dataSchema will be provided as
-   *         `null`.
+   *         columns.
    */
-  private def getOrInferFileFormatSchema(
-      format: FileFormat,
-      justPartitioning: Boolean = false): (StructType, StructType) = {
+  private def getOrInferFileFormatSchema(format: FileFormat): (StructType, StructType) = {
     // the operations below are expensive therefore try not to do them if we don't need to, e.g.,
     // in streaming mode, we have already inferred and registered partition columns, we will
     // never have to materialize the lazy val below
@@ -174,9 +166,7 @@ case class DataSource(
         StructType(partitionFields)
       }
     }
-    if (justPartitioning) {
-      return (null, partitionSchema)
-    }
+
     val dataSchema = userSpecifiedSchema.map { schema =>
       val equality = sparkSession.sessionState.conf.resolver
       StructType(schema.filterNot(f => partitionSchema.exists(p => equality(p.name, f.name))))
@@ -435,26 +425,6 @@ case class DataSource(
     PartitioningUtils.validatePartitionColumn(
       data.schema, partitionColumns, caseSensitive)
 
-    // If we are appending to a table that already exists, make sure the partitioning matches
-    // up.  If we fail to load the table for whatever reason, ignore the check.
-    if (mode == SaveMode.Append) {
-      val existingPartitionColumns = Try {
-        getOrInferFileFormatSchema(format, justPartitioning = true)._2.fieldNames.toList
-      }.getOrElse(Seq.empty[String])
-      // TODO: Case sensitivity.
-      val sameColumns =
-        existingPartitionColumns.map(_.toLowerCase()) == partitionColumns.map(_.toLowerCase())
-      if (existingPartitionColumns.nonEmpty && !sameColumns) {
-        throw new AnalysisException(
-          s"""Requested partitioning does not match existing partitioning.
-             |Existing partitioning columns:
-             |  ${existingPartitionColumns.mkString(", ")}
-             |Requested partitioning columns:
-             |  ${partitionColumns.mkString(", ")}
-             |""".stripMargin)
-      }
-    }
-
     // SPARK-17230: Resolve the partition columns so InsertIntoHadoopFsRelationCommand does
     // not need to have the query as child, to avoid to analyze an optimized query,
     // because InsertIntoHadoopFsRelationCommand will be optimized first.