Floating-point Number Comparison

Spark normalizes NaN and zero for floating point numbers for several cases. See NormalizeFloatingNumbers optimization rule in Spark. However, one exception is comparison. Spark does not normalize NaN and zero when comparing values because they are handled well in Spark (e.g., SQLOrderingUtil.compareFloats). But the comparison functions of arrow-rs used by DataFusion do not normalize NaN and zero (e.g., arrow::compute::kernels::cmp::eq). So Comet adds additional normalization expression of NaN and zero for comparisons, and may still have differences to Spark in some cases, especially when the data contains both positive and negative zero. This is likely an edge case that is not of concern for many users. If it is a concern, setting spark.comet.exec.strictFloatingPoint=true will make relevant operations fall back to Spark.