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Dongjoon Hyun commented on SPARK-22267: --------------------------------------- [~cloud_fan]. This issue comes from old Hive reader path. > Spark SQL incorrectly reads ORC file when column order is different > ------------------------------------------------------------------- > > Key: SPARK-22267 > URL: https://issues.apache.org/jira/browse/SPARK-22267 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.2.0 > Reporter: Dongjoon Hyun > > For a long time, Apache Spark SQL returns incorrect results when ORC file > schema is different from metastore schema order. > {code} > scala> Seq(1 -> 2).toDF("c1", > "c2").write.format("parquet").mode("overwrite").save("/tmp/p") > scala> Seq(1 -> 2).toDF("c1", > "c2").write.format("orc").mode("overwrite").save("/tmp/o") > scala> sql("CREATE EXTERNAL TABLE p(c2 INT, c1 INT) STORED AS parquet > LOCATION '/tmp/p'") > scala> sql("CREATE EXTERNAL TABLE o(c2 INT, c1 INT) STORED AS orc LOCATION > '/tmp/o'") > scala> spark.table("p").show // Parquet is good. > +---+---+ > | c2| c1| > +---+---+ > | 2| 1| > +---+---+ > scala> spark.table("o").show // This is wrong. > +---+---+ > | c2| c1| > +---+---+ > | 1| 2| > +---+---+ > scala> spark.read.orc("/tmp/o").show // This is correct. > +---+---+ > | c1| c2| > +---+---+ > | 1| 2| > +---+---+ > {code} > *TESTCASE* > {code} > test("SPARK-22267 Spark SQL incorrectly reads ORC files when column order > is different") { > withTempDir { dir => > val path = dir.getCanonicalPath > Seq(1 -> 2).toDF("c1", > "c2").write.format("orc").mode("overwrite").save(path) > checkAnswer(spark.read.orc(path), Row(1, 2)) > Seq("true", "false").foreach { value => > withTable("t") { > withSQLConf(HiveUtils.CONVERT_METASTORE_ORC.key -> value) { > sql(s"CREATE EXTERNAL TABLE t(c2 INT, c1 INT) STORED AS ORC > LOCATION '$path'") > checkAnswer(spark.table("t"), Row(2, 1)) > } > } > } > } > } > {code} -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org