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Franco Bonazza commented on SPARK-18484: ---------------------------------------- What if you have a DataFrame with higher precision e.g. 38, 17, this effectively breaks df.as[TestClass], it busts on truncation because I can't specify the schema of the resulting Dataset. Am I missing something? Doesn't seem like a non issue to me. The only work around I see is not using Datasets. > 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 > Priority: Major > > 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} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org