The book “Designing Data-Intensive Applications” by Martin Kleppman is a fantastic read with such a concise train of thought. It builds up from basics, adds another thing, and another thing.

I kept asking myself, what would happen if I were to extend on the feature currently presented in the chapter I was reading, only to find out my answers in the next chapter.

Brilliant book

This was truly one of the greatest, if not the greatest, book on software that I have ever read. I have read it twice at this point and fully intend to read it many more times. It is packed full of incredibly interesting information and written in a way that keeps you interested.

Not only is the main content great, but the references are numerous and open up entirely new sets of material as you progress.

And just to extend on the numorous references, this book reminded me what a time saver a good book is compared to lots of googling and blog posts.

I struggled with blog posts on raft, Byzantine fault tolerance, CAP theorem, transactions, serializable, and this book was my enlightenment

Problem with this book is: what to read after. The book is really good but leaves you with the feeling "there are some many topics I don't know yet... but all the other books out there suck". Any recommendation for someone that has already read DDIA?

From a previous question re: "Ask HN: CS papers for software architecture and design?" (https://news.ycombinator.com/item?id=15778396 and distributed systems we eventually realize were needed in the first place:

Bulk Synchronous Parallel: https://en.wikipedia.org/wiki/Bulk_synchronous_parallel .

Many/most (?) distributed systems can be described in terms of BSP primitives.

Paxos: https://en.wikipedia.org/wiki/Paxos_(computer_science) .

Raft: https://en.wikipedia.org/wiki/Raft_(computer_science) #Safety

CAP theorem: https://en.wikipedia.org/wiki/CAP_theorem .

Papers-we-love > Distributed Systems: https://github.com/papers-we-love/papers-we-love/tree/master...

awesome-distributed-systems also has many links to theory: https://github.com/theanalyst/awesome-distributed-systems

- Byzantine fault: https://en.wikipedia.org/wiki/Byzantine_fault :

A [Byzantine fault] is a condition of a computer system, particularly distributed computing systems, where components may fail and there is imperfect information on whether a component has failed. The term takes its name from an allegory, the "Byzantine Generals Problem",[2] developed to describe a situation in which, in order to avoid catastrophic failure of the system, the system's actors must agree on a concerted strategy, but some of these actors are unreliable.

awesome-bigdata lists a number of tools: https://github.com/onurakpolat/awesome-bigdata

Practically, dask.distributed (joblib -> SLURM,), dask ML, dask-labextension (a JupyterLab extension for dask), and the Rapids.ai tools (e.g. cuDF) scale from one to many nodes.

I posted this recently, but MIT's 6.824: Distributed Systems (taught by Robert Morris, of both Morris worm and Viaweb/Y Combinator fame) is completely open and available online, and it includes video lectures, notes, readings, and programming assignments from as recent as Spring 2020 (including half of the lectures recorded from home as the pandemic strikes). The assignments even include auto-graded testing scripts, so you can verify your solution to the assignments.

https://pdos.csail.mit.edu/6.824/