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https://issues.apache.org/jira/browse/SPARK-20162?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16383471#comment-16383471
]
Caio Quirino da Silva edited comment on SPARK-20162 at 3/6/18 11:53 AM:
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I have reproduced the problem using Spark 2.2.0 with that snippet:
{code:java}
case class MyEntity(field: BigDecimal)
private val avroFileDir = "abc.avro"
def test(): Unit = {
val sp = sparkSession
import sp.implicits._
val rdd =
sparkSession.sparkContext.parallelize(List(MyEntity(BigDecimal(1.23))))
val df = sp.createDataFrame(rdd)
df.write.mode(SaveMode.Append).avro(avroFileDir)
sp.read.avro(avroFileDir).as[MyEntity].head
}{code}
So I think that we can reopen this issue...
org.apache.spark.sql.AnalysisException: Cannot up cast lambdavariable ........
from string to decimal(38,18) as it may truncate
was (Author: caioquirino):
I have reproduced the problem using Spark 2.2.0 with that snippet:
{code:java}
case class MyEntity(field: BigDecimal)
val df = ss.createDataframe(Seq(MyEntity(BigDecimal(1.23))))
df.write.mode(SaveMode.Append).avro("dir.avro")
ss.read.avro("dir.avro").as[MyEntity].head
{code}
So I think that we can reopen this issue...
org.apache.spark.sql.AnalysisException: Cannot up cast lambdavariable ........
from string to decimal(38,18) as it may truncate
> Reading data from MySQL - Cannot up cast from decimal(30,6) to decimal(38,18)
> -----------------------------------------------------------------------------
>
> Key: SPARK-20162
> URL: https://issues.apache.org/jira/browse/SPARK-20162
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Miroslav Spehar
> Priority: Major
>
> While reading data from MySQL, type conversion doesn't work for Decimal type
> when the decimal in database is of lower precision/scale than the one spark
> expects.
> Error:
> Exception in thread "main" org.apache.spark.sql.AnalysisException: Cannot up
> cast `DECIMAL_AMOUNT` from decimal(30,6) to decimal(38,18) as it may truncate
> The type path of the target object is:
> - field (class: "org.apache.spark.sql.types.Decimal", name: "DECIMAL_AMOUNT")
> - root class: "com.misp.spark.Structure"
> You can either add an explicit cast to the input data or choose a higher
> precision type of the field in the target object;
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveUpCast$$fail(Analyzer.scala:2119)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34$$anonfun$applyOrElse$14.applyOrElse(Analyzer.scala:2141)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34$$anonfun$applyOrElse$14.applyOrElse(Analyzer.scala:2136)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5$$anonfun$apply$11.apply(TreeNode.scala:360)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
> at scala.collection.immutable.List.map(List.scala:285)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:358)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionDown$1(QueryPlan.scala:248)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:258)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$6.apply(QueryPlan.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:267)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:236)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34.applyOrElse(Analyzer.scala:2136)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34.applyOrElse(Analyzer.scala:2132)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.apply(Analyzer.scala:2132)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.apply(Analyzer.scala:2117)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
> at
> scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
> at scala.collection.immutable.List.foldLeft(List.scala:84)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
> at
> org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolveAndBind(ExpressionEncoder.scala:258)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:209)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:167)
> at org.apache.spark.sql.Dataset$.apply(Dataset.scala:58)
> at org.apache.spark.sql.Dataset.as(Dataset.scala:376)
> at com.misp.spark.CalculationEngine$.main(CalculationEngine.scala:109)
> at com.misp.spark.CalculationEngine.main(CalculationEngine.scala)
> Process finished with exit code 1
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