Improve performance of odata expand operations

Hello Ramesh and Steven,
this is a follow up regarding an observation in the discussion from TEIID-5643. I thought I open an extra issue for the topic as this seems not to be related to TEIID-5500.
As you already know, I am using SAPUI5 as frontend for ODATA requests. SAPUI5 supports binding of a user interface control group (like a list with its list items) to a single ODATA path at a time only. If the control group items require additional information which is stored in a different table in the database, I have to expand those parameters in the odata query.
When doing so, I am running in a serious performance issue with TEIID, which would render the approach of using sapui5 with Teiid infeasible if we cannot find a way to speedup the issue. At the moment I have a small table with entries (table Diary with about 20 records) for which the query extracts several items (just a single one in the example given below). Now the filtered item is expanded with data from a larger table in the database (FDBProducts with about 680.000 records). The whole query takes about 15s to be processed. The query is given as:

https://morpheus.fritz.box/odata4/svc/my_nutri_diary/Diary?$select=AmountInG,idDiaryEntry&$expand=fkDiaryToFDBProducts($select=brands,energy_100g,idCode,product_name)&$filter=AddedDateTime%20ge%202019-03-06T00:00:00%2B01:00%20and%20AddedDateTime%20le%202019-03-07T00:00:00%2B01:00%20and%20MealNumber%20eq%20%270%27&$skip=0&$top=100

I checked the output when using
<logger category="org.teiid.CONNECTOR"><level name="TRACE"/></logger>

This shows the problem. It seems the join operation is not pushed down to the database but the data are rather joined within Teiid. Teiid therefore downloads the entire dataset of the large FDBProducts table, which makes the expand approach infeasible for real world datasets with a certain size. So my question is, if you can modify Teiid to push down the entire join operation to the underlaying database (I assume this would be the most efficient approach), or alternatively query just the items from the table to be joined which where filtered from the first table if the first option is not possible?

Thanks for your help.
Christoph