TimescaleDB VS temporal_tables

> based on the time period

Is it the kind of thing where the TimescaleDB extension would make sense?

https://github.com/timescale/timescaledb

Timescale — Open-source time-series SQL database optimized for fast ingest and complex queries.

Compression and other features use the non-Apache license:

https://github.com/timescale/timescaledb/tree/main/tsl

Don't forget PostgreSQL extensions. For something like a chat log, TimescaleDB (https://www.timescale.com/) can be surprisingly efficient. It will handle partitioning for you, with additional features like data reordering, compression, and retention policies.

Offboard it to Postgres specialists like https://www.timescale.com/

TimescaleDB{:target="_blank"}: an extension of PostgreSQL that adds time-series capabilities to the relational database model. It provides scalability and performance optimizations for handling large volumes of time-stamped data while maintaining the flexibility of a relational database.

Have some experience with https://www.timescale.com in this context

There is also the temporal_tables extension.

[0] https://github.com/arkhipov/temporal_tables

- https://github.com/arkhipov/temporal_tables

I haven't used any of these but I work on https://xtdb.com which is also implementing SQL:2011's temporal features :)

It seems like PG has this extension here anyone ever use it?

You could look at that or ask me questions about it (disclaimer, I am the author). Also there is https://github.com/arkhipov/temporal_tables/

All good. Note there is also https://github.com/arkhipov/temporal_tables/ (which is also type 4 as a postgres extension - pretty similar to what ebean orm is doing)

The Temporal Tables Postgres extension works well. https://github.com/arkhipov/temporal_tables

If you're doing this across regions, you really really should reconsider. If you're doing it in the same data center you might be able to get away with it (but then I'm not sure why you're doing it in the first place, if the system fits in one DC then you probably can just scale up). It might be worth considering a sharded & passively combined approach -- i.e. every country has it's own data, and there's some huge public schema which consists of all the data that is drip fed in to materialized views or tables at regular intervals. You could also combine this with temporal_tables to get a very delayed but theoretically time-consistent (well, aside from clock skew across regions of course...) view of your DB to query... Really depends on the use case.

One of postgres's most underrated features. RLS is amazing, can be unseen/basically work silently if your programming language-side tools are good enough, and is documented well (like everything else):

https://www.postgresql.org/docs/current/ddl-rowsecurity.html

But the power of PG is that it doesn't stop there, if you combine this with a plugin like temporal_tables and you can segment by user and time:

https://github.com/arkhipov/temporal_tables

All of this mostly unknown to the thing that's accessing the DB. If that's not enough for you, why not add some auditing with pgaudit:

https://www.pgaudit.org/#section_three

I think it might not actually be hyperbole to say that Postgres is the greatest RDBMS database that has ever existed.