Michel Lemay created SPARK-21021: ------------------------------------ Summary: Reading partitioned parquet does not respect specified schema column order Key: SPARK-21021 URL: https://issues.apache.org/jira/browse/SPARK-21021 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 2.1.0 Reporter: Michel Lemay Priority: Minor
When reading back a partitioned parquet folder, column order gets messed up. Consider the following example: {code:scala} case class Event(f1: String, f2: String, f3: String) val df = Seq(Event("v1", "v2", "v3")).toDF df.write.partitionBy("f1", "f2").parquet("out") val schema: StructType = StructType(StructField("f1", StringType, true) :: StructField("f2", StringType, true) :: StructField("f3", StringType, true) :: Nil) val dfRead = spark.read.schema(schema).parquet("out") dfRead.show +---+---+---+ | f3| f1| f2| +---+---+---+ | v3| v1| v2| +---+---+---+ dfRead.columns Array[String] = Array(f3, f1, f2) schema.fields Array(StructField(f1,StringType,true), StructField(f2,StringType,true), StructField(f3,StringType,true)) {code} This makes it really hard to have compatible schema when reading from multiple sources. -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org