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Posted to issues@spark.apache.org by "Liang-Chi Hsieh (JIRA)" <ji...@apache.org> on 2017/10/05 03:15:00 UTC
[jira] [Commented] (SPARK-22137) Failed to insert VectorUDT to hive
table with DataFrameWriter.insertInto(tableName: String)
[ https://issues.apache.org/jira/browse/SPARK-22137?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16192414#comment-16192414 ]
Liang-Chi Hsieh commented on SPARK-22137:
-----------------------------------------
Ideally I think a UDT should be able casted to/from corresponding SQL type ({{UserDefinedType.sqlType}}). Practically I'm not sure if there are any reason we prevent such casting for now.
> Failed to insert VectorUDT to hive table with DataFrameWriter.insertInto(tableName: String)
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-22137
> URL: https://issues.apache.org/jira/browse/SPARK-22137
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.1
> Reporter: yzheng616
>
> Failed to insert VectorUDT to hive table with DataFrameWriter.insertInto(tableName: String). The issue seems similar with SPARK-17765 which have been resolved in 2.1.0.
> Error message:
> {color:red}Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot resolve '`features`' due to data type mismatch: cannot cast org.apache.spark.ml.linalg.VectorUDT@3bfc3ba7 to StructType(StructField(type,ByteType,true), StructField(size,IntegerType,true), StructField(indices,ArrayType(IntegerType,true),true), StructField(values,ArrayType(DoubleType,true),true));;
> 'InsertIntoTable Relation[id#21,features#22] parquet, OverwriteOptions(false,Map()), false
> +- 'Project [cast(id#13L as int) AS id#27, cast(features#14 as struct<type:tinyint,size:int,indices:array<int>,values:array<double>>) AS features#28]
> +- LogicalRDD [id#13L, features#14]{color}
> Reproduce code:
> {code:java}
> import scala.annotation.varargs
> import org.apache.spark.ml.linalg.SQLDataTypes
> import org.apache.spark.sql.Row
> import org.apache.spark.sql.SparkSession
> import org.apache.spark.sql.types.LongType
> import org.apache.spark.sql.types.StructField
> import org.apache.spark.sql.types.StructType
> case class UDT(`id`: Long, `features`: org.apache.spark.ml.linalg.Vector)
> object UDTTest {
> def main(args: Array[String]): Unit = {
> val tb = "table_udt"
> val spark = SparkSession.builder().master("local[4]").appName("UDTInsertInto").enableHiveSupport().getOrCreate()
> spark.sql("drop table if exists " + tb)
>
> /*
> * VectorUDT sql type definition:
> *
> * override def sqlType: StructType = {
> * StructType(Seq(
> * StructField("type", ByteType, nullable = false),
> * StructField("size", IntegerType, nullable = true),
> * StructField("indices", ArrayType(IntegerType, containsNull = false), nullable = true),
> * StructField("values", ArrayType(DoubleType, containsNull = false), nullable = true)))
> * }
> */
>
> //Create Hive table base on VectorUDT sql type
> spark.sql("create table if not exists "+tb+"(id int, features struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)" +
> " row format serde 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'"+
> " stored as"+
> " inputformat 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'"+
> " outputformat 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'")
> var seq = new scala.collection.mutable.ArrayBuffer[UDT]()
> for (x <- 1 to 2) {
> seq += (new UDT(x, org.apache.spark.ml.linalg.Vectors.dense(0.2, 0.21, 0.44)))
> }
> val rowRDD = (spark.sparkContext.makeRDD[UDT](seq)).map { x => Row.fromSeq(Seq(x.id,x.features)) }
> val schema = StructType(Array(StructField("id", LongType,false),StructField("features", SQLDataTypes.VectorType,false)))
> val df = spark.createDataFrame(rowRDD, schema)
>
> //insert into hive table
> df.write.insertInto(tb)
> }
> }
> {code}
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