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https://issues.apache.org/jira/browse/SPARK-22320?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16212624#comment-16212624
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zhengruifeng commented on SPARK-22320:
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I test on spark 2.2.0.
parquet works fine.
I have not try MatrixUDT.
------------------ 原始邮件 ------------------
发件人: "Dongjoon Hyun (JIRA)" <[email protected]>;
发送时间: 2017年10月20日(星期五) 19:34
收件人: "ruifengz" <[email protected]>;
主题: [jira] [Commented] (SPARK-22320) ORC should supportVectorUDT/MatrixUDT
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https://issues.apache.org/jira/browse/SPARK-22320?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16212507#comment-16212507
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Dongjoon Hyun commented on SPARK-22320:
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Thank you for reporting, [~podongfeng].
- Is this ORC only issue? What about Parquet?
- Is this a regression in master branch? Could you check official Spark 2.2.0?
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> 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
>
> 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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