diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
index 29afe5751bc16..ecfcafe69c1f2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
+++ b/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))))
@@ -434,26 +424,6 @@ case class DataSource(
     val caseSensitive = sparkSession.sessionState.conf.caseSensitiveAnalysis
     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.