Major Description
select id, data FROM testcat.ns1.ns2.table where id =2 and md5(data) = '8cde774d6f7333752ed72cacddb05126' and trim(data) = 'a'
Based on the SQL, we currently get the filters in the following order:
// `(md5(cast(data#23 as binary)) = 8cde774d6f7333752ed72cacddb05126)) AND (trim(data#23, None) = a))` comes before `(id#22L = 2)` == Physical Plan == *(1) Project [id#22L, data#23] +- *(1) Filter ((((isnotnull(data#23) AND isnotnull(id#22L)) AND (md5(cast(data#23 as binary)) = 8cde774d6f7333752ed72cacddb05126)) AND (trim(data#23, None) = a)) AND (id#22L = 2)) +- BatchScan[id#22L, data#23] class org.apache.spark.sql.connector.InMemoryTable$InMemoryBatchScan
In this predicate order, all data needs to participate in the evaluation, even if some data does not meet the later filtering criteria and it may causes spark tasks to execute slowly.
So i think that filtering predicates that need to be evaluated should automatically be placed to the far right to avoid data that does not meet the criteria being evaluated.
As shown below:
// `(id#22L = 2)` comes before `(md5(cast(data#23 as binary)) = 8cde774d6f7333752ed72cacddb05126)) AND (trim(data#23, None) = a))` == Physical Plan == *(1) Project [id#22L, data#23] +- *(1) Filter ((((isnotnull(data#23) AND isnotnull(id#22L)) AND (id#22L = 2) AND (md5(cast(data#23 as binary)) = 8cde774d6f7333752ed72cacddb05126)) AND (trim(data#23, None) = a))) +- BatchScan[id#22L, data#23] class org.apache.spark.sql.connector.InMemoryTable$InMemoryBatchScan
