Possibility to force repartition on read/select

It will be nice and usefull in certain situations to have the capacity to do repartition like in spark (https://spark.apache.org/docs/latest/rdd-programming-guide.html)

either an automatically repartition in certain limit or possibility to indicate the desired repartition or both options.

The only way (that I know now) to do that with Drill is to change store.parquet.block-size and regenerate the input file then do the request (but it will be nice to have the ability to do that on read)

illustration : with 2 Parquets files file1 of 50Mo (1 million rows) and file2 of 1Mo (5000 rows)

CREATE TABLE dfs.test.`result_from_1_parquet` AS 
(SELECT * FROM dfs.test.`file2` INNER JOIN dfs.test.`file1` ON ...)
=> ~ 50min

-- Today we have to change the parquet block size to force multiple parquet files
ALTER SESSION SET `store.parquet.block-size` = 1048576;
-- Repartition data
CREATE TABLE dfs.test.`file1_bis` AS (SELECT * FROM dfs.test.`file1`);
-- then Launch the request
CREATE TABLE dfs.test.`result_from_1_parquet` AS 
(SELECT * FROM dfs.test.`file2` INNER JOIN dfs.test.`file1_bis` ON ...)
=> ~ 1min

So it's possible to save a lot of time (depending of configuration of cluster) by simply forcing more input file.
it would be useful not to have to regenerate the files with the ideal fragmentation before request.

This situation easily appears when making inequality JOIN (to lookup ip in ip range for example) on not so big dataset:

ALTER SESSION SET `planner.enable_nljoin_for_scalar_only` = false;
SELECT  * 
FROM dfs.test.`a_pqt` AS a
INNER JOIN dfs.test.`b_pqt` AS b
ON inet_aton(b.ip) >= inet_aton(a.ip_first) AND inet_aton(b.ip) <= inet_aton(a.ip_last);