cadence VS groupcache

Uber | Software Engineers | Hybrid (Denmark) | https://www.uber.com/dk/en/careers/locations/aarhus/

Work with an amazing team responsible for the infrastructure software that makes Uber’s data centers around the world reliable and scalable. If you want to solve the toughest engineering challenges alongside some of the smartest people in the industry, Uber Aarhus is the right place for you.

The team in Aarhus build and operate the stateless and stateful compute platforms used by nearly every other engineer in the company (Up - https://www.uber.com/en-GB/blog/up-portable-microservices-re... and Odin - https://www.uber.com/en-GB/blog/how-uber-optimized-cassandra...) as well as other related infrastructure projects such as Cadence - https://github.com/uber/cadence.

May be this..https://cadenceworkflow.io/

a managed esb orchestration for example is exactly same as step functions and workflow engines like cadence - https://github.com/uber/cadence

Having done a reasonable amount of messaging code in my time, I would say the final form of this sort of thing might look more like Cadence[0] than anything like this.

[0] https://github.com/uber/cadence

Emit events from your primary DB (postgres, etc.) to something like kafka or rabbitmq and then catch that in your search engine. There's also some end-to-end solutions like temporal (temporal.io) or cadence (https://cadenceworkflow.io/)

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.