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<pre>> <span style="color:black">Workaround: 'pyodbc' or 'psycopg2'</span><br><br>Hey Marco, I probably should have specified I’m trying to keep all my logic in a plpgsql function so that all work can be done in a single transaction. dblink is basically the same concept as psycopg2 -I can create 7 connections to do the work and then wait for them to complete, but then I’d have to commit the source table so that the connections I spawn would be able to see it and also make sure those connections are cleaned up. <br><br>Postgres parallelism handles all of that -and I’m getting the max number of workers. The issue I appear to be having is that postgres seems to be allocating all of the rows to a single worker because all of the rows are under block size due to toast and the parallelism is blockwise rather than rowwise.<br><br>So I think what I need to do is figure out how to get the rows to be just a bit under the block size so that each worker is reading one row/block. <br><br>To this effect, I tried making a dummy text column and filling with lpad(‘’,2000,’0’) and then setting the storage parameter toast_tuple_target = 8160 (max) for the table assuming that would force reading of one row/block and cancel out the TOASTing of the geometry column -but this sadly did not succeed.<br><br>It is good to know that the psycopg2 type approach holds up on a vast number of cores though.<span style="color:black"><o:p></o:p></span></pre>
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