Comment by lfittl
1 month ago
The article has a section where it estimates index bloat based on comparing the number of index reltuples * 40 bytes (?), compared to the size of the file on disk.
This is problematic, first of all because I don't think the math is right (see [0] for a more comprehensive query that takes into account column sizes), and second because it ignores the effects of B-Tree index deduplication in Postgres 13+: [1]
In my experience, fast bloat estimation queries can work okay for table bloat, but for index bloat I'd recommend instead looking at the change in page density over time (i.e. track relpages divided by reltuples), or just go direct to running pgstatindex outside business hours.
[0]: https://github.com/pgexperts/pgx_scripts/blob/master/bloat/i... [1]: https://www.postgresql.org/docs/current/btree.html#BTREE-DED...
Every time Postgres advice says to “schedule [important maintenance] during low traffic period” (OP) or “outside business hours”, it reinforces my sense that it’s not suitable for performance-sensitive data path on a 24/7/365 service and I’m not sure it really aims to be. (To be fair, running it like that for several years and desperately trying to make it work also gave me that feeling. But I’m kind of aghast that necessary operational maintenance still carries these caveats.)
> Every time Postgres advice says to “schedule [important maintenance] during low traffic period” (OP) or “outside business hours”, it reinforces my sense that it’s not suitable for performance-sensitive data path on a 24/7/365 service and I’m not sure it really aims to be.
It's a question of resource margins. If you have regular and predictable windows of low resource utilization, you can afford to run closer to the sun during busy periods, deferring (and amortizing, to some degree) maintenance costs till later. If you have a 24/7/365 service, you need considerably higher safety margins.
Also, there's a lot of terrible advice on the internet, if you haven't noticed.
> (To be fair, running it like that for several years and desperately trying to make it work also gave me that feeling. But I’m kind of aghast that necessary operational maintenance still carries these caveats.)
To be fair, I find oxides' continual low-info griping against postgres a bit tedious. There's plenty weaknesses in postgres, but criticizing postgres based on 10+ year old experiences of running an, at the time, outdated postgres, on an outdated OS is just ... not useful? Like, would it useful to criticize oxides lack of production hardware availability in 2021 or so?
Edit: duplicated word removed
> It's a question of resource margins.
What you describe is true and very important (more margin lets you weather more disruption), but it's not the whole story. The problem we had was queueing delays mainly due to I/O contention. The disks had the extra IOPS for the maintenance operation, but the resulting latency for all operations was higher. This meant overall throughput decreased when the maintenance was going on. The customer, finally accepting the problem, thought: "we'll just build enough extra shards to account for the degradation". But it just doesn't work like that. If the degradation is 30%, and you reduce the steady-state load on the database by 30%, that doesn't change the fact that when the maintenance is ongoing, even if the disks have the IOPS for the extra load, latency goes up. Throughput will still degrade. What they wanted was predictability but we just couldn't give that to them.
> To be fair, I find oxides' continual low-info griping against postgres a bit tedious. There's plenty weaknesses in postgres, but criticizing postgres based on 10+ year old experiences of running an, at the time, outdated postgres, on an outdated OS is just ... not useful?
First, although I work at Oxide, please don't think I speak for Oxide. None of this happened at Oxide. It informed some of the choices we made at Oxide and we've talked about that publicly. I try to remember to include the caveat that this information is very dated (and I made that edit immediately after my initial comment above).
I admit that some of this has been hard for me personally to let go. These issues dominated my professional life for three very stressful years. For most of that time (and several years earlier), the community members we reached out to were very dismissive, saying either these weren't problems, or they were known problems and we were wrong for not avoiding them, etc. And we certainly did make mistakes! But many of those problems were later acknowledged by the community. And many have been improved -- which is great! What remains is me feeling triggered when it feels like users' pain is being casually dismissed.
I'm sorry I let my crankiness slip into the comment above. I try to leave out the emotional baggage. Nonetheless, I do feel like it's a problem that, intentionally or otherwise, a lot of the user base has absorbed the idea that it's okay for necessary database maintenance to significantly degrade performance because folks will have some downtime in which to run it.*
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Regarding pgstattuple specifically: If this was a 24/7/365 service and you would be concerned by the I/O impact of loading the full table or index at any time, you could run this on a replica too. For tables there is pgstattuple_approx which is much better at managing its impact, but there is no equivalent for indexes today.
The REINDEX CONCURRENTLY mentioned in OP could also be run at other times of the day - the main issue is again I/O impact (with potentially some locking concerns at the very end of the reindex concurrently to swap out the index).
There are no magic solutions here - other databases have to deal with the same practical limitations, though Postgres sometimes is a bit slow to adopt operational best practices in core (e.g. the mentioned pg_squeeze from OP may finally get an in-core "REPACK CONCURRENTLY" equivalent in Postgres 19, but its been a long time to get there)
OP here - thank you for mentioning replica, forgot to mention it. Unless it would hit it pretty hard and `hot_standby_feedback` is on
Postgres-compatible cloud databases like AlloyDB address this issue.