New: Cookbooks and AI ExplanationsStep-by-Step recipes to solve problems connected to Roadmaps and Cheat Sheets. Need more details? Use AI buttons for structured and simple explanations with concrete examples throughout the whole platform.Take a look
A ten-million-row export from the source database crawls in on one JDBC connection, one thread doing all the work, while the rest of your cluster sits there doing nothing.
What you'll have at the end
The same export split across several parallel JDBC connections, timed against the single-connection version
You need
A PySpark job already reading a source table over a single JDBC connection, that table holding tens of millions of rows, and a way to ask the source database its own connection limit and whether a replica of it exists.
Not covered
Deciding whether this table needs a full pull every run or only its changed rows, and recovering from a connection that drops mid-read: both are separate jobs.
Leans on
Choose full reload or incremental for a new source
decide whether this table needs a full parallel pull like this one on every run, or only the rows that changed since the last one.
No gate stops bad data from reaching your Spark load
add a quality check on what this parallel read just pulled in before it reaches anything downstream.
Checked 25 Aug 2026
Part of the PySpark cookbook