[
https://issues.apache.org/jira/browse/SPARK-18484?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Damian Momot updated SPARK-18484:
---------------------------------
Description:
Currently when using decimal type (BigDecimal in scala case class) there's no
way to enforce precision and scale. This is quite critical when saving data -
regarding space usage and compatibility with external systems (for example Hive
table) because spark saves data as Decimal(38,18)
{code}
case class TestClass(id: String, money: BigDecimal)
val testDs = spark.createDataset(Seq(
TestClass("1", BigDecimal("22.50")),
TestClass("2", BigDecimal("500.66"))
))
testDs.printSchema()
{code}
{code}
root
|-- id: string (nullable = true)
|-- money: decimal(38,18) (nullable = true)
{code}
Workaround is to convert dataset to dataframe before saving and manually cast
to specific decimal scale/precision:
{code}
import org.apache.spark.sql.types.DecimalType
val testDf = testDs.toDF()
testDf
.withColumn("money", testDf("money").cast(DecimalType(10,2)))
.printSchema()
{code}
{code}
root
|-- id: string (nullable = true)
|-- money: decimal(10,2) (nullable = true)
{code}
was:
Currently when using decimal type (BigDecimal in scala case class) there's no
way to enforce precision and scale. This is quite critical when saving data -
regarding space usage and compatibility with external systems (for example Hive
table) because spark saves data as Decimal(38,18)
{code:scala}
val spark: SparkSession = ???
case class TestClass(id: String, money: BigDecimal)
val testDs = spark.createDataset(Seq(
TestClass("1", BigDecimal("22.50")),
TestClass("2", BigDecimal("500.66"))
))
testDs.printSchema()
{code}
{code}
root
|-- id: string (nullable = true)
|-- money: decimal(38,18) (nullable = true)
{code}
Workaround is to convert dataset to dataframe before saving and manually cast
to specific decimal scale/precision:
{code:scala}
import org.apache.spark.sql.types.DecimalType
val testDf = testDs.toDF()
testDf
.withColumn("money", testDf("money").cast(DecimalType(10,2)))
.printSchema()
{code}
{code}
root
|-- id: string (nullable = true)
|-- money: decimal(10,2) (nullable = true)
{code}
> case class datasets - ability to specify decimal precision and scale
> --------------------------------------------------------------------
>
> Key: SPARK-18484
> URL: https://issues.apache.org/jira/browse/SPARK-18484
> Project: Spark
> Issue Type: Improvement
> Affects Versions: 2.0.0, 2.0.1
> Reporter: Damian Momot
>
> Currently when using decimal type (BigDecimal in scala case class) there's no
> way to enforce precision and scale. This is quite critical when saving data -
> regarding space usage and compatibility with external systems (for example
> Hive table) because spark saves data as Decimal(38,18)
> {code}
> case class TestClass(id: String, money: BigDecimal)
> val testDs = spark.createDataset(Seq(
> TestClass("1", BigDecimal("22.50")),
> TestClass("2", BigDecimal("500.66"))
> ))
> testDs.printSchema()
> {code}
> {code}
> root
> |-- id: string (nullable = true)
> |-- money: decimal(38,18) (nullable = true)
> {code}
> Workaround is to convert dataset to dataframe before saving and manually cast
> to specific decimal scale/precision:
> {code}
> import org.apache.spark.sql.types.DecimalType
> val testDf = testDs.toDF()
> testDf
> .withColumn("money", testDf("money").cast(DecimalType(10,2)))
> .printSchema()
> {code}
> {code}
> root
> |-- id: string (nullable = true)
> |-- money: decimal(10,2) (nullable = true)
> {code}
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