API‑triggered Process Model using Write to Data Store (MySQL) takes 90+ seconds for ~25 inserts into a single table

90s for 25 rows usually means the loop + repeated reads/updates are the problem, not the fact that it is one table.

Write to Data Store Entity does not bulk insert, so if you call it once per row Appian pays that cost each time. I would test 3 things first:

1. Pass the full CDT array into one Write to Data Store Entity node instead of looping.
2. Time the insert path separately from the reads/updates, since you mentioned the same table is being retrieved, inserted, and updated multiple times in the same process model.
3. If the caller does not need the DB work finished before the response, return from a!startProcess() with isSynchronous: false and let the writes finish asynchronously.

If one-array write is still slow after that, I would move the insert-heavy part to Execute Stored Procedure. That is usually the next step when network round trips or per-node transaction overhead are the bottleneck.

90s for 25 rows usually means the loop + repeated reads/updates are the problem, not the fact that it is one table.

Write to Data Store Entity does not bulk insert, so if you call it once per row Appian pays that cost each time. I would test 3 things first:

1. Pass the full CDT array into one Write to Data Store Entity node instead of looping.
2. Time the insert path separately from the reads/updates, since you mentioned the same table is being retrieved, inserted, and updated multiple times in the same process model.
3. If the caller does not need the DB work finished before the response, return from a!startProcess() with isSynchronous: false and let the writes finish asynchronously.

If one-array write is still slow after that, I would move the insert-heavy part to Execute Stored Procedure. That is usually the next step when network round trips or per-node transaction overhead are the bottleneck.