pg_timetable VS groupcache

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

As shown in the title, DREAMEMO is a distributed cache with out-of-the-box, high-scalability, modular-design features.The groupcache implementation is referenced, and re-structured, specific module differentiation is as follows:

I remember one of the first real-world uses of Go being the groupcache package: https://github.com/golang/groupcache (to serve Chrome downloads, IIRC?)

> comes with a cache filling mechanism. Whereas memcached just says "Sorry, cache miss", often resulting in a thundering herd of database (or whatever) loads from an unbounded number of clients (which has resulted in several fun outages), groupcache coordinates cache fills such that only one load in one process of an entire replicated set of processes populates the cache, then multiplexes the loaded value to all callers.

I read that group cache (https://github.com/golang/groupcache) can be used to sync servers around a key.

I've not tried this myself, but I've seen it suggested to use groupcache (https://github.com/golang/groupcache) to sync your servers.

https://github.com/golang/groupcache is managing distributed caching that addresses thundering herd problem of memcache.

How it goes sometimes. Check out https://github.com/golang/groupcache and of course the AWS golang SDK.

There is also groupcache, written by the same author as memcached, but better.