jarulraj

:)

Agreed, I just updated it to "Elements of System Design".

I added it now :)

Yes :)

Exactly. It is intentionally open-ended: new "principles" can emerge, split, or retire as the taxonomy evolves. This is just version 1. The grid is a visual index and the fun part is mappin systems to "molecules" in different domains like OS, databases, computer architecture, distributed systems, programming languages, networking, and more..

Yes, I just borrowed the periodic table metaphor. "Elements of System Design" is a better name.

Author here, great question :) If principles are the elements, we can think of each system as a "molecule" with some imagination. For example, an SQL database system has many principles:

1. Abstraction Lifting (Al) + Policy/Mechanism Separation (Pm): SQL states high-level intent with precise semantics, and logical operators are decoupled from physical operators.

2. Equivalence-based Planning (Ep) + Invariant-Guided Transformation (Ig): We apply algebraic rewrites that preserve semantics (e.g., join reordering, predicate pushdown) under stated invariants.

3. Cost-based Planning (Cm): We choose concrete physical operators and join orders using a cost model and so on..

Totally fair.. I am not claiming periodicity here :) I just wanted to use the "periodic table" as a visual metaphor. The goal is to outline a mostly orthogonal set of system design principles and illustrate cross-domain connections to students so that it is easier to compare trade-offs and discuss designs more precisely.

Author here, appreciate the share :) I was not expecting this to get so much attention.

To clarify: this is indeed just a taxonomy of classic system-design principles. The periodic-table styling is a familiar metaphor; there is no claim that principles repeat periodically. The goal was to outline a mostly orthogonal set of design principles and highlight cross-domain connections across computer systems so it is easier to discuss designs precisely. Thanks for all the thoughtful feedback!

It is based on the feedback shared by an undergraduate student -- pretty subjective. For instance, it is possible to graduate with a CS degree without even taking an introductory course on systems.

It is currently not possible to get rigorous summaries of paper chunks using GPT-4.

Wouldn't more semantically related neighbors be retrieved by just increasing K?