[jira] [Resolved] (SPARK-14231) JSON data source fails to infer floats as decimal when precision is bigger than 38 or scale is bigger than precision.

["Davies Liu (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-14231?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Davies Liu resolved SPARK-14231.
--------------------------------
       Resolution: Fixed
    Fix Version/s: 2.0.0

Issue resolved by pull request 12030
[https://github.com/apache/spark/pull/12030]

> JSON data source fails to infer floats as decimal when precision is bigger than 38 or scale is bigger than precision.
> ---------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-14231
>                 URL: https://issues.apache.org/jira/browse/SPARK-14231
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>            Reporter: Hyukjin Kwon
>            Priority: Minor
>             Fix For: 2.0.0
>
>
> Currently, JSON data source supports {{floatAsBigDecimal}} option, which reads floats as {{DecimalType}}.
> I noticed there are several restrictions in Spark {{DecimalType}} below:
> 1. The precision cannot be bigger than 38.
> 2. scale cannot be bigger than precision. 
> However, with the option above, it reads {{BigDecimal}} which does not follow the conditions above.
> This could be observed as below:
> {code}
> def simpleFloats: RDD[String] =
>   sqlContext.sparkContext.parallelize(
>     """{"a": 0.01}""" ::
>     """{"a": 0.02}""" :: Nil)
> val jsonDF = sqlContext.read
>   .option("floatAsBigDecimal", "true")
>   .json(simpleFloats)
> jsonDF.printSchema()
> {code}
> throws an exception below:
> {code}
> org.apache.spark.sql.AnalysisException: Decimal scale (2) cannot be greater than precision (1).;
> 	at org.apache.spark.sql.types.DecimalType.<init>(DecimalType.scala:44)
> 	at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:144)
> 	at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:108)
> 	at org.apache.spark.sql.execution.datasources.json.InferSchema$$anonfun$1$$anonfun$apply$1$$anonfun$apply$3.apply(InferSchema.scala:59)
> 	at org.apache.spark.sql.execution.datasources.json.InferSchema$$anonfun$1$$anonfun$apply$1$$anonfun$apply$3.apply(InferSchema.scala:57)
> 	at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2249)
> 	at org.apache.spark.sql.execution.datasources.json.InferSchema$$anonfun$1$$anonfun$apply$1.apply(InferSchema.scala:57)
> 	at org.apache.spark.sql.execution.datasources.json.InferSchema$$anonfun$1$$anonfun$apply$1.apply(InferSchema.scala:55)
> 	at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:396)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:742)
> ...
> {code}
> Since JSON data source infers {{DataType}} as {{StringType}} when it fails to infer, it might have to be inferred as {{StringType}} or maybe just simply {{DoubleType}}



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org