[cryptica]

If you want to have a truly parallel system, you cannot share resources between processes. If you need a lock, then that implies sharing of resources... Which implies limited scalability according to Amdahl's law.

To achieve unlimited scalability, you need to make sure that no single data store is shared between all processes and that the amount of interprocess communication (on a per-process basis) doesn't increase as you add more processes.

The more sharing you have, the less you can scale your system.

[infinite8s]

This. Sometimes it is better to duplicate computation than to wait for shared state (this is in the context of analytics and not a single-source of truth).

[TallGuyShort]

Although no one starts out wanting to have a truly parallel system. They start out wanting to solve a problem and when that requires scalability or redundancy a parallel system is a means to that end. Not every problem can or should be solved with no shared state, and when that's the case, you should know how to do it right.

[jondubois]

I find the code of highly parallel systems much more elegant and easy to read.

Personally, I find it easier to write highly parallel code than parallel-up-to-a-certain-point code that involves locks likes mutexes and/or semaphores. Highly parallel code just takes a little bit of extra planning and thinking up-front but it saves lots of time and stress down the line.

[eklavya]

What if you have a shared resource? Is there any way around distributed locking?