Those are great answers in this thread. In order to better show the difference, here is just an example. The reason I put it here b/c during my work the numbers are required to be NOT half-up :
import org.apache.spark.sql.types._
val values = List(1.2345,2.9998,3.4567,4.0099,5.1231)
val df = values.toDF
df.show()
+------+
| value|
+------+
|1.2345|
|2.9998|
|3.4567|
|4.0099|
|5.1231|
+------+
val df2 = df.withColumn("floor_val", floor(col("value"))).
withColumn("dec_val", col("value").cast(DecimalType(26,2))).
withColumn("floor2", (floor(col("value") * 100.0)/100.0).cast(DecimalType(26,2)))
df2.show()
+------+---------+-------+------+
| value|floor_val|dec_val|floor2|
+------+---------+-------+------+
|1.2345| 1| 1.23| 1.23|
|2.9998| 2| 3.00| 2.99|
|3.4567| 3| 3.46| 3.45|
|4.0099| 4| 4.01| 4.00|
|5.1231| 5| 5.12| 5.12|
+------+---------+-------+------+
floor
function floors to the largest interger less than current value. DecimalType
by default will enable HALF_UP
mode, not just cut to precision you want. If you want to cut to a certain precision without using HALF_UP
mode, you can use above solution instead ( or use scala.math.BigDecimal
(where you have to explicitly define rounding modes).