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Liang-Chi Hsieh edited comment on SPARK-22137 at 9/29/17 2:56 PM: ------------------------------------------------------------------ Actually that is because we only allow to cast between {{UserDefinedType}} s and disallow {{UserDefinedType}} be casted to/from other data types. was (Author: viirya): Actually that is because we only allow to cast between {{UserDefinedType}}s and disallow {{UserDefinedType}} be casted to/from other data types. > 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} -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org