advancedxy commented on a change in pull request #25863:
[SPARK-28945][SPARK-29037][CORE][SQL] Fix the issue that spark gives duplicate
result and support concurrent file source write operations write to different
partitions in the same table.
URL: https://github.com/apache/spark/pull/25863#discussion_r329336071
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
##########
@@ -156,4 +189,66 @@ class PartitionedWriteSuite extends QueryTest with
SharedSparkSession {
}
}
}
+
+ test("Output path should be a staging output dir, whose last level path name
is jobId," +
+ " when dynamicPartitionOverwrite is enabled") {
+ withSQLConf(SQLConf.PARTITION_OVERWRITE_MODE.key ->
PartitionOverwriteMode.DYNAMIC.toString) {
+ withTable("t") {
+ withSQLConf(SQLConf.FILE_COMMIT_PROTOCOL_CLASS.key ->
+ classOf[DetectCorrectOutputPathFileCommitProtocol].getName) {
+ Seq((1, 2)).toDF("a", "b")
+ .write
+ .partitionBy("b")
+ .mode("overwrite")
+ .saveAsTable("t")
+ }
+ }
+ }
+ }
+
+ test("Concurrent write to the same table with different partitions should be
possible") {
+ withSQLConf(SQLConf.PARTITION_OVERWRITE_MODE.key ->
PartitionOverwriteMode.DYNAMIC.toString) {
+ withTable("t") {
+ val sem = new Semaphore(0)
+ Seq((1, 2)).toDF("a", "b")
+ .write
+ .partitionBy("b")
+ .mode("overwrite")
+ .saveAsTable("t")
+
+ val df1 = spark.range(0, 10).map(x => (x, 1)).toDF("a", "b")
+ val df2 = spark.range(0, 10).map(x => (x, 2)).toDF("a", "b")
+ val dfs = Seq(df1, df2)
+
+ var throwable: Option[Throwable] = None
+ for (i <- 0 until 2) {
+ new Thread {
+ override def run(): Unit = {
+ try {
+ dfs(i)
+ .write
+ .mode("overwrite")
+ .insertInto("t")
+ } catch {
+ case t: Throwable =>
+ throwable = Some(t)
+ } finally {
+ sem.release()
+ }
+ }
+ }.start()
+ }
+ // make sure writing table in two threads are executed.
+ sem.acquire(2)
+ throwable.foreach { t => throw improveStackTrace(t) }
+ checkAnswer(spark.sql("select a, b from t where b = 1"), df1)
+ checkAnswer(spark.sql("select a, b from t where b = 2"), df2)
+ }
+ }
Review comment:
Ah, that's a limitation of `DataFrameWriter`, we may need to extend
`DataFrameWriter` to support that.
But currently, i think we can simply use the SQL syntax since we can use
`spark.sql` and get the same behaviour.
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