Details
Description
I noticed a bug when doing arithmetic on a dataframe containing decimal values with codegen enabled.
I tried to narrow it down on a small repro and got this (executed in spark-shell):
scala> val df = Seq(
| ("a", BigDecimal("12.0")),
| ("a", BigDecimal("12.0")),
| ("a", BigDecimal("11.9999999988")),
| ("a", BigDecimal("12.0")),
| ("a", BigDecimal("12.0")),
| ("a", BigDecimal("11.9999999988")),
| ("a", BigDecimal("11.9999999988"))
| ).toDF("text", "number")
df: org.apache.spark.sql.DataFrame = [text: string, number: decimal(38,18)]
scala> val df_grouped_1 = df.groupBy(df.col("text")).agg(functions.avg(df.col("number")).as("number"))
df_grouped_1: org.apache.spark.sql.DataFrame = [text: string, number: decimal(38,22)]
scala> df_grouped_1.collect()
res0: Array[org.apache.spark.sql.Row] = Array([a,11.9999999994857142857143])
scala> val df_grouped_2 = df_grouped_1.groupBy(df_grouped_1.col("text")).agg(functions.sum(df_grouped_1.col("number")).as("number"))
df_grouped_2: org.apache.spark.sql.DataFrame = [text: string, number: decimal(38,22)]
scala> df_grouped_2.collect()
res1: Array[org.apache.spark.sql.Row] = Array([a,1199999999948571.4285714285714285714286])
scala> val df_total_sum = df_grouped_1.agg(functions.sum(df_grouped_1.col("number")).as("number"))
df_total_sum: org.apache.spark.sql.DataFrame = [number: decimal(38,22)]
scala> df_total_sum.collect()
res2: Array[org.apache.spark.sql.Row] = Array([11.9999999994857142857143])
The results of df_grouped_1 and df_total_sum are correct, whereas the result of df_grouped_2 is clearly incorrect (it is the value of the correct result times 10^14).
When codegen is disabled all results are correct.
Attachments
Issue Links
- is duplicated by
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SPARK-25146 avg() returns null on some decimals
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- Resolved
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- links to
