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Posted to issues@spark.apache.org by "Dongjoon Hyun (JIRA)" <ji...@apache.org> on 2018/01/22 02:17:15 UTC
[jira] [Updated] (SPARK-22320) ORC should support
VectorUDT/MatrixUDT
[ https://issues.apache.org/jira/browse/SPARK-22320?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dongjoon Hyun updated SPARK-22320:
----------------------------------
Priority: Minor (was: Major)
> ORC should support VectorUDT/MatrixUDT
> --------------------------------------
>
> Key: SPARK-22320
> URL: https://issues.apache.org/jira/browse/SPARK-22320
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2, 2.1.2, 2.2.0
> Reporter: zhengruifeng
> Priority: Minor
>
> I save dataframe containing vectors in ORC format, when I read it back, the format is changed.
> {code}
> scala> import org.apache.spark.ml.linalg._
> import org.apache.spark.ml.linalg._
> scala> val data = Seq((1,Vectors.dense(1.0,2.0)), (2,Vectors.sparse(8, Array(4), Array(1.0))))
> data: Seq[(Int, org.apache.spark.ml.linalg.Vector)] = List((1,[1.0,2.0]), (2,(8,[4],[1.0])))
> scala> val df = data.toDF("i", "vec")
> df: org.apache.spark.sql.DataFrame = [i: int, vec: vector]
> scala> df.schema
> res0: org.apache.spark.sql.types.StructType = StructType(StructField(i,IntegerType,false), StructField(vec,org.apache.spark.ml.linalg.VectorUDT@3bfc3ba7,true))
> scala> df.write.orc("/tmp/123")
> scala> val df2 = spark.sqlContext.read.orc("/tmp/123")
> df2: org.apache.spark.sql.DataFrame = [i: int, vec: struct<type: tinyint, size: int ... 2 more fields>]
> scala> df2.schema
> res3: org.apache.spark.sql.types.StructType = StructType(StructField(i,IntegerType,true), StructField(vec,StructType(StructField(type,ByteType,true), StructField(size,IntegerType,true), StructField(indices,ArrayType(IntegerType,true),true), StructField(values,ArrayType(DoubleType,true),true)),true))
> {code}
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