Lesson 149 - Caching and CAP Theorem

In lesson 111 Mark Richards talked about CAP Theorem and illustrated what it meant. He also talked about in-memory replicated caching in lesson 78 and distributed caching in lesson 77. In this lesson Mark will talk about the combination of these lessons, and how CAP Theorem comes into play when using caching. He’ll show the CAP options you have with in-memory replicated caching and distributed caching, but also show some complications involving CAP Theorem when it comes to distributed caching.

Reference Links:

[alexsharma]

Eager to watch Lesson no. 150.

As a regular audience, we are waiting for something amazing for lesson 150.

[codeit]

This is probably one of the most mind-blowing and amazing lessons. Loved it!

[vikramchaudhary]

This is one of the most underrated channel. Hope your audience grows soon.

[joaodasilva]

Hi Mark, thanks for the great lesson. However, I do beleive that in the case of using a 'highly available' REDIS cache topology, where only the PRIMARY node is the one where requests are always arriving at, and where a PRIMARY node failure results in the next most-up-to-date node being chosen as the PRIMARY, means you almost eliminate lack of availability. I would say that in order to cross-out availability entirely would require that the entire distributed node layout be down, ie all nodes would be down. 🙂

[chitthiaayeehai]

When we don't share Data in Microservices why would we share Cache ?
We are showing Microservices in example hence the question.