[jira] [Comment Edited] (SPARK-20162) Reading data from MySQL - Cannot up cast from decimal(30,6) to decimal(38,18)
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https://issues.apache.org/jira/browse/SPARK-20162?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16387661#comment-16387661
]
Caio Quirino da Silva edited comment on SPARK-20162 at 3/6/18 12:08 PM:
Yes! And I can say that it started to fail from version 2.2.x.
For Spark 2.1.2 it's fine.
I have updated my last code snippet to create a cleaner stacktrace:
{code:java}
18/03/06 11:51:10 INFO DAGScheduler: Job 0 finished: save at package.scala:26,
took 0.941392 s
18/03/06 11:51:10 INFO FileFormatWriter: Job null committed.
Cannot up cast `field` from string to decimal(38,18) as it may truncate
The type path of the target object is:
- field (class: "scala.math.BigDecimal", name: "field")
- root class: "org.farfetch.bigdata.streaming.MyEntity"
You can either add an explicit cast to the input data or choose a higher
precision type of the field in the target object;
org.apache.spark.sql.AnalysisException: Cannot up cast `field` from string to
decimal(38,18) as it may truncate
The type path of the target object is:
- field (class: "scala.math.BigDecimal", name: "field")
- root class: "org.farfetch.bigdata.streaming.MyEntity"
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$$fail(Analyzer.scala:2123)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34$$anonfun$applyOrElse$14.applyOrElse(Analyzer.scala:2153)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34$$anonfun$applyOrElse$14.applyOrElse(Analyzer.scala:2140)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:268)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:268)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:267)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4$$anonfun$apply$11.apply(TreeNode.scala:336)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:334)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:245)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:245)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:266)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:276)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$6.apply(QueryPlan.scala:285)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:285)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:245)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:236)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34.applyOrElse(Analyzer.scala:2140)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34.applyOrElse(Analyzer.scala:2136)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
at
[jira] [Comment Edited] (SPARK-20162) Reading data from MySQL - Cannot up cast from decimal(30,6) to decimal(38,18)
[
https://issues.apache.org/jira/browse/SPARK-20162?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16383471#comment-16383471
]
Caio Quirino da Silva edited comment on SPARK-20162 at 3/6/18 11:53 AM:
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
>
