andygrove commented on issue #4024:
URL:
https://github.com/apache/arrow-datafusion/issues/4024#issuecomment-1299357227
I am investigating more and there are potentially multiple issues here.
I am comparing Spark and DataFusion behavior against a Parquet file:
## Spark
```
scala>
spark.read.parquet("/mnt/bigdata/tpch/sf1-parquet/lineitem").createTempView("lineitem")
scala> spark.sql("SELECT count(*) from lineitem where l_discount between
0.05 and 0.07").show
+--------+
|count(1)|
+--------+
|16361562|
+--------+
scala> spark.sql("SELECT count(*) from lineitem where l_discount between
0.06-0.01 and 0.06+0.01").show
+--------+
|count(1)|
+--------+
|16361562|
+--------+
```
## DataFusion
```
❯ create external table lineitem stored as parquet location
'/mnt/bigdata/tpch/sf1-parquet/lineitem';
0 rows in set. Query took 0.015 seconds.
❯ select count(*) from lineitem where l_discount between 0.05 and 0.07;
+-----------------+
| COUNT(UInt8(1)) |
+-----------------+
| 16361562 |
+-----------------+
1 row in set. Query took 0.487 seconds.
❯ select count(*) from lineitem where l_discount between 0.06-0.01 and
0.06+0.01;
+-----------------+
| COUNT(UInt8(1)) |
+-----------------+
| 10908630 |
+-----------------+
1 row in set. Query took 0.394 seconds.
```
So `between 0.05 and 0.07` is consistent between Spark and DataFusion at
least
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