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https://issues.apache.org/jira/browse/SPARK-21246?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16068553#comment-16068553
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Monica Raj commented on SPARK-21246:
------------------------------------
Thanks for your response. I also tried with Seq(3) as Seq(3L), however I had
changed this back during the course of trying other options. I should also
mention that we are running Zeppelin 0.6.0. I tried running the code you
provided and still got the following output:
import org.apache.spark.sql.types._
import org.apache.spark.sql.Row
schemaString: String = name
lstVals: Seq[Long] = List(3)
rowRdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] =
MapPartitionsRDD[30] at map at <console>:59
res20: Array[org.apache.spark.sql.Row] = Array([3])
fields: Array[org.apache.spark.sql.types.StructField] =
Array(StructField(name,LongType,true))
schema: org.apache.spark.sql.types.StructType =
StructType(StructField(name,LongType,true))
StructType(StructField(name,LongType,true))peopleDF:
org.apache.spark.sql.DataFrame = [name: bigint]
+----+
|name|
+----+
| 3|
+----+
> Unexpected Data Type conversion from LONG to BIGINT
> ---------------------------------------------------
>
> Key: SPARK-21246
> URL: https://issues.apache.org/jira/browse/SPARK-21246
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.1
> Environment: Using Zeppelin Notebook or Spark Shell
> Reporter: Monica Raj
>
> The unexpected conversion occurred when creating a data frame out of an
> existing data collection. The following code can be run in zeppelin notebook
> to reproduce the bug:
> import org.apache.spark.sql.types._
> import org.apache.spark.sql.Row
> val schemaString = "name"
> val lstVals = Seq(3)
> val rowRdd = sc.parallelize(lstVals).map(x => Row( x ))
> rowRdd.collect()
> // Generate the schema based on the string of schema
> val fields = schemaString.split(" ")
> .map(fieldName => StructField(fieldName, LongType, nullable = true))
> val schema = StructType(fields)
> print(schema)
> val peopleDF = sqlContext.createDataFrame(rowRdd, schema)
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