Implement the table tolerance function about qbeast-spark HOT 2 OPEN

Here are some issues found while working on the tolerance feature.

1. Right now the tolerance is defined for the mean (or avg) function only. A similar concept for other types of aggregate functions like min, max, etc can have a different name and a different range of admissible values (for tolerance it is [0, 1]).
2. The tolerance is defined as sampleDeviation * zScore / mean / sqrt(sampleSize). It is not clear if the tolerance is still efficient if the mean is 0 or is close to 0.
3. Current implementation just extracts the column with avg function and calculate the mean using samples of the whole table. Suppose the user specified val df = spark.read.format("qbeast").load("...").where("value > 100").agg(avg("value")).tolerance(0.01). The sampling should apply the specified where condition otherwise the returned average can be wrong.
4. zScore is hardcoded, should it be a parameter specified by user?

from qbeast-spark.

We should also define more precisely what kind of queries we want to support, so the user can have a clear understanding whether a given query is supported or not. Can we define it in terms of SQL syntax tree or alike, maybe a bit informal?

from qbeast-spark.