partitioning concept

Hi Jeanxyz ,

Yes, you’re absolutely right. If your Delta table is partitioned by department but your merge condition is based on employee_id, then the partitioning doesn’t really help during the merge. Spark will still need to scan all partitions to find the matching employee_id values, because the partition column isn’t part of the predicate.

Partitioning only speeds things up when your filter, join, or merge condition includes the partition column itself. For example, a merge or query that filters on department = 'HR' would benefit immediately, but a merge on employee_id won’t take advantage of department partitions.

So the general rule is:
Pick a partition column only if your downstream workloads actually filter or process data using that column. Otherwise, partitioning doesn’t provide much benefit.

Hope this helps — and glad the earlier correction was useful. If this answers your question, please mark it as Accepted Solution ✔️

– Gopi Krishna