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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:11:03 UTC
[jira] [Resolved] (SPARK-19477) [SQL] Datasets created from a
Dataframe with extra columns retain the extra columns
[ https://issues.apache.org/jira/browse/SPARK-19477?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-19477.
----------------------------------
Resolution: Incomplete
> [SQL] Datasets created from a Dataframe with extra columns retain the extra columns
> -----------------------------------------------------------------------------------
>
> Key: SPARK-19477
> URL: https://issues.apache.org/jira/browse/SPARK-19477
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Don Drake
> Priority: Major
> Labels: bulk-closed
>
> In 1.6, when you created a Dataset from a Dataframe that had extra columns, the columns not in the case class were dropped from the Dataset.
> For example in 1.6, the column c4 is gone:
> {code}
> scala> case class F(f1: String, f2: String, f3:String)
> defined class F
> scala> import sqlContext.implicits._
> import sqlContext.implicits._
> scala> val df = Seq(("a","b","c","x"), ("d", "e", "f","y"), ("h", "i", "j","z")).toDF("f1", "f2", "f3", "c4")
> df: org.apache.spark.sql.DataFrame = [f1: string, f2: string, f3: string, c4: string]
> scala> val ds = df.as[F]
> ds: org.apache.spark.sql.Dataset[F] = [f1: string, f2: string, f3: string]
> scala> ds.show
> +---+---+---+
> | f1| f2| f3|
> +---+---+---+
> | a| b| c|
> | d| e| f|
> | h| i| j|
> {code}
> This seems to have changed in Spark 2.0 and also 2.1:
> Spark 2.1.0:
> {code}
> scala> case class F(f1: String, f2: String, f3:String)
> defined class F
> scala> import spark.implicits._
> import spark.implicits._
> scala> val df = Seq(("a","b","c","x"), ("d", "e", "f","y"), ("h", "i", "j","z")).toDF("f1", "f2", "f3", "c4")
> df: org.apache.spark.sql.DataFrame = [f1: string, f2: string ... 2 more fields]
> scala> val ds = df.as[F]
> ds: org.apache.spark.sql.Dataset[F] = [f1: string, f2: string ... 2 more fields]
> scala> ds.show
> +---+---+---+---+
> | f1| f2| f3| c4|
> +---+---+---+---+
> | a| b| c| x|
> | d| e| f| y|
> | h| i| j| z|
> +---+---+---+---+
> scala> import org.apache.spark.sql.Encoders
> import org.apache.spark.sql.Encoders
> scala> val fEncoder = Encoders.product[F]
> fEncoder: org.apache.spark.sql.Encoder[F] = class[f1[0]: string, f2[0]: string, f3[0]: string]
> scala> fEncoder.schema == ds.schema
> res2: Boolean = false
> scala> ds.schema
> res3: org.apache.spark.sql.types.StructType = StructType(StructField(f1,StringType,true), StructField(f2,StringType,true), StructField(f3,StringType,true), StructField(c4,StringType,true))
> scala> fEncoder.schema
> res4: org.apache.spark.sql.types.StructType = StructType(StructField(f1,StringType,true), StructField(f2,StringType,true), StructField(f3,StringType,true))
> {code}
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