3 ms·

It’s hard to generalize on these points. In a situation where the throughput was inverted but the proportional system usage was the same, you would instead say

by raggi 3y ago

It’s hard to generalize on these points. In a situation where the throughput was inverted but the proportional system usage was the same, you would instead say “you can still vertically scale by adding more disks”, rather than saying adding bigger cpu. It’s not meaningful in isolation. It may be reasonable to suggest that for a new code base that is cpu bound there’s a good chance there is low hanging fruit for cpu optimizations that may further increase the throughput gap. It’s also the case however that the prior engines tuning starting life on much older computer architectures, drastically different proportional syscall costs and so on, it very often means that there’s low hanging fruit in configuration to improve baseline benchmarks such as these. High io time suggests poor caching which in many scenarios you’d consider a suboptimal deployed configuration. It’s not just the devil that’s in the details, it’s everything.

acjohnson55 3y ago
That makes sense. I'm mostly just trying to explain the counterintuitive reason that the high CPU usage shouldn't be interpreted as a flaw.
raggi 3y ago
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.
gary_0 3y ago
> you can still vertically scale by adding more disks Parallelizing IO is a lot different from scaling up CPU power, though. I'd imagine DB server IO performance has a lot less lower-hanging fruit than CPU/software performance.
- raggi 3y ago
  That depends, the ratio of free bus capacity for data fetch, and free capacity for inter-CPU synchronization is skewed _massively_ in favor of capacity for data fetch. An x86 system is already under-capacity at the cpu/bus interface, which is why we keep throwing more and more cache at the problem and it works. Similarly in the cloud on AWS fro example, you have publicly available scalability options starting from 5k IOPS up to 2M IOPS, >400x or 3 orders of magnitude. By contrast you're going from 1vcpu to 192 cores, about half the raise, and a lower performance scaling due to the increased cost of cross-package shootdowns. Yup, they're different, for sure, but the implication that CPU is easier is not all that clear. In either case, with a database style workload, and with either of these engines in practice you're going to hit a limit at the bus in practice long before you hit a limit on compute or disk io, for any sustained workload - bursts are different.

by raggi 3y ago

acjohnson55 3y ago

That makes sense. I'm mostly just trying to explain the counterintuitive reason that the high CPU usage shouldn't be interpreted as a flaw.

raggi 3y ago

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.

gary_0 3y ago

> you can still vertically scale by adding more disks Parallelizing IO is a lot different from scaling up CPU power, though. I'd imagine DB server IO performance has a lot less lower-hanging fruit than CPU/software performance.

raggi 3y ago
That depends, the ratio of free bus capacity for data fetch, and free capacity for inter-CPU synchronization is skewed _massively_ in favor of capacity for data fetch. An x86 system is already under-capacity at the cpu/bus interface, which is why we keep throwing more and more cache at the problem and it works. Similarly in the cloud on AWS fro example, you have publicly available scalability options starting from 5k IOPS up to 2M IOPS, >400x or 3 orders of magnitude. By contrast you're going from 1vcpu to 192 cores, about half the raise, and a lower performance scaling due to the increased cost of cross-package shootdowns. Yup, they're different, for sure, but the implication that CPU is easier is not all that clear. In either case, with a database style workload, and with either of these engines in practice you're going to hit a limit at the bus in practice long before you hit a limit on compute or disk io, for any sustained workload - bursts are different.