To be a little more clear on what the detail of the benchmark in question is: it’s a benchmark that explicitly exercises a pathological use case for postgresqls

To be a little more clear on what the detail of the benchmark in question is: it’s a benchmark that explicitly exercises a pathological use case for postgresqls current design, one that nonetheless functions, and demonstrates that the advertised engine does not have that pathology. A key takeaway should probably be, if you’re a Postgres user: if your workload looks exactly like this (sparse upserts into a large data set at a high rate) then you might want to evaluate your the runway of your architecture before the geometric costs or latency stalls become relevant - just as for cost analysis of any other system. What is somewhat interesting in this article, and not super clearly presented, is that this workload is actually fairly pathological for most existing engines offering this set of structural and query facilities, and that’s interesting, if this is the niche you need. Most people do some amount of this, but not always at a super high rate, and there are ways to get the same effective writable/readable data using a different schema, while avoiding it. Nice thing here is you can do the one-liner version.