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How about using "pandas" as the default when it's installed and only to fall back to "astropy" if pandas is not available. In fact, you might not add a keyword at all and just take what's available.

you might not add a keyword at all and just take what's available.

that's the approach we've been following with bottleneck-powered accelerations but I would recommend we avoid it in the future, as it also creates confusion when the "faster" implementation is silently selected but returns incorrect results (or causes a crash).

How about using "pandas" as the default when it's installed and only to fall back to "astropy" if pandas is not available. In fact, you might not add a keyword at all and just take what's available.

I'm pretty keen on maintaining explicit control. It might be that astropy is faster for small tables, or the user wants the sort order from astropy to maintain stability in their pipeline products. So I would propose an option engine="auto" to automatically select the engine based on some heuristics.

According to gemini, Polars join is even faster than pandas by factors of 4-40x for medium to large datasets. So we want both explicit control and some logic to decide on the fastest join engine.