pg_timetable VS bolt

That's easily done with a cron job or something similar (e.g. pg_timetable)

When you are working on Oracle to PostgreSQL migrations, one of the Oracle packages that cause conversion issues is DBMS_JOB. Traditionally, we used extensions like : pg_agent, pg_cron or more recently pg_timetable for scheduling jobs. All of these tools or extensions use a cronjob like scheduling method which does not give a full compatibility of the features provided by Oracle DBMS_JOB. Translating calls to DBMS_JOB.SUBMIT() into a cron setting is more painful than being impossible when there is an execution interval lesser than a minute. There are several such DBMS_JOB compatibility issues including asynchronous scheduling which is not possible with the already existing extensions in PostgreSQL. MigOps hates to stay without Open Source solutions. So, we are announcing PG_DBMS_JOB extension for Oracle DBMS_JOB compatibility, released under PostgreSQL License. An interesting point here is that we have included full compatibility of Oracle DBMS_JOB in PG_DBMS_JOB PostgreSQL extension.

If you don't want to (or can't) use the Windows Scheduler, you might want to try out pg_timetable instead, which also offers Windows binaries.

Try pg_timetable

You better send emails periodically with cron-like scheduler. This one can do both cron and periodical jobs: https://github.com/cybertec-postgresql/pg_timetable

This crate started out as just a way for me to learn how boltdb works, while learning Rust at the same time. But somehow people started finding and using it and seem to like the simple API, so I figured I might as well share it in case someone else finds it useful too. If you want to know more about my motivations and the history of this crate, you can read the release notes on version 0.8.0!

Some example of embeddable database could be genji, badger and boltdb

Designing Data Intensive applications- specifically chapter 3 and 4 which deal with strategies and algorithms for storing and encoding data to be stored on disk and their pros and cons.

Once you read that, I'll suggest reading the source of a simple embedded key-value database, I wouldn't bother with RDBMs as they are complex beasts and contain way more than you need. BoltDB is a good project to read the source of https://github.com/boltdb/bolt, the whole thing is <10k lines of code and is a full blown production grade system with ACID semantics so packs a lot in those 10k and isn't just merely a toy.

Bolt db and Bolt db's author post to go with it.

https://github.com/boltdb/bolt is a ACID B+ tree key-value store

For a single server, SQLite, or boltdb[0]

I've never had to scale horizontally. I develop in Go and you can get very far along with just vertical scaling (aka beefier hardware).

Therefore I can't give concrete examples of a distributed db-as-a-library.

But all that you need is to extend the functions that fetch data to not just fetch from disk but from "peers" as well. For this to work you need servers (instances) to know about each other, and as you add more they also get added to their peers - sort of like a bittorrent network. I don't think it's difficult to do.

SQLite might not be suited for being distributed (although RQlite[1] claims to have done it).

Making a distributed data storage based on boltdb[0] is probably more feasible.

Whatever the case, there's no reason why a data storage engine can't be a library, even if it's distributed.

[0]: https://github.com/boltdb/bolt

[1]: https://github.com/rqlite/rqlite