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algebraic-
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96646455bd |
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@ -69,6 +69,19 @@ figcaption { text-align: right; font-style: italic; font-size: 0.875em; }
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#toc ul { padding-left: 2em; }
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#toc, blockquote, pre.example, .line-block { margin: 1rem; padding: 1rem; border-left: solid var(--gr); }
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.pubDate { display: inline-block; width: 7em; font-size: .825em; opacity: 0.4; }
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/* blockquote */
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blockquote {
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padding-top: 0;
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padding-bottom: 0;
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}
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blockquote > p:first-child {
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margin-top: 0;
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padding-top: 0;
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}
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blockquote > p:last-child {
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margin-bottom: 0;
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padding-bottom: 0;
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}
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/* COLORS */
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/* colortheme switch */
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146
src/posts/0022-even-more-stupid-than-you-think/index.org
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146
src/posts/0022-even-more-stupid-than-you-think/index.org
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@ -0,0 +1,146 @@
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#+title: Even more stupid than you think
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#+description:
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#+keywords: programming
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#+author: Yann Esposito
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#+email: yann@esposito.host
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#+date: [2021-11-02 Tue]
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#+lang: en
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#+options: auto-id:t
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#+startup: showeverything
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We all know about the [[https://en.wikipedia.org/wiki/KISS_principle][KISS (keep it simple, stupid)]] principle.
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In fact, unlike what it looks to suggest. Keeping things simple is more
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difficult than one imagine at first[fn:simple-vs-easy].
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The other adage from Einstein being:
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#+begin_quote
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Make everything as simple as possible, but not simpler.
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---Albert Einstein[fn:einstein]
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#+end_quote
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And what is hidden here, is the fact that finding a way to build something
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that is simple need an internal comprehension of the system.
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To me this is typically what algebra is all about.
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Find a system as simple as possible that still respect a set of properties.
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So after a few years of experience in software development everyone will
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agree with the principle.
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And this is a bit easy to agree with a principle.
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What is difficult is to really dig about how simple something could be.
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So here are a few evidences about my experience about how far you can go.
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And if you don't like the word "simple" think about the word /abstraction/.
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The sense of /abstraction/ is also quite large.
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If I wanted to be more precise, I would say, that a good programming
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abstraction is generally sustained by a good algebraic abstraction.
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What provide a good algebraic abstraction is the certainty that your system
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will be *composable* upon some properties.
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So the important word here is *composability* which is arguably superior to /modularity/.
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Here are a few example about how far we could / should go.
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[fn:simple-vs-easy] If you want a great overview of the subject, I highly
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suggest you to watch [[https://thestrangeloop.com/2011/simple-made-easy.html][Simple made Easy]] video from Rich Hickey.
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[fn:einstein] This is only attributed to Albert Einstein. He apparently
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said something similar but we don't have any strong evidence he wrote or
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said exactly that.
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* CRUD+Search
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:PROPERTIES:
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:CUSTOM_ID: crud-search
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:END:
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With this simple abstraction you could build successful API.
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#+begin_src clojure
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(defprotocol CRUDPS
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(create [this id value])
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(read [this id])
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(update [this id new-value])
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(delete [this id])
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(patch [this id new-partial-value])
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(search [this partial-value pagination-parameters]))
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#+end_src
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One important detail is that we could provide "partial values".
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So we should have a language that should support the notion of "sub-object".
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And if you are doing this in javascript, this should be easy.
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In Java or Haskell, it might be a bit more funky.
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So just that. It is even simpler than an ORM.
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You don't need more than that most of the time.
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If you force yourself to work within this restriction this give you
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immediately a few pretty good properties.
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1. *Portability*; This protocol is so simple to write it is easy to implement
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it for different DBs. Postgres, ES, MongoDB, etc...
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2. *Scalability*; as this only focus on a single table/index, you do not have
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to think about join.
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One of the most important property of my system is generally to never
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write join.
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And you can go surprisingly far without the need of any join.
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In fact, most of the time "fake join" are more efficient, et good enough
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for almost all user facing use case.
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3. *Simplicity*; by only using this API internally, it is easy to manipulate
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DB accesses.
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Use HTTP, with 90% CRUD + Search.
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This is enough for most use cases.
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And by search, don't think about something too fancy.
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A sub-JSON will be enough for most use case.
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Then later you will probably want to add text search/matching.
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Why sub-JSON is probably superior to your SQL query?
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Because it is composable, more precisely this is a morphism, which SQL
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queries aren't.
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What do I precisely mean by "sub-JSON"?
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Simply the ~<@~ operator in Postgres.
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Or the basic MongoDB/ES search.
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Not some system introducing complex structure.
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So you want to match:
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#+begin_src js
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{"foo":"bar"}
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// should match with
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{"foo": bar,
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"x": "y"}
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// but shouldn't match
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{"foo":"notbar",
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"x":"y"}
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//nor
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{"x":"y"}
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#+end_src
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So this is a morphism with the following property:
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#+begin_src
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(find x) AND (find y) <=> (find (x inter y))
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#+end_src
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Which is not possible to easily do with a SQL query.
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If you have a string representing a query Q1, and another string
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representing a query Q2.
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It is difficult to produce a query for Q1 AND Q2.
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Because, there are field selection for example.
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For most modern web application/API you don't need to enforce strong
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constant on that either.
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For example, you can loose so much time trying to support a great update or patch.
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Doing so in a concurrent system is hard.
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If you don't care much, most of the time this will be perfectly ok.
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So just with that and the correct data structure.
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No search by text, no join.
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Just 1 table, per data structure saving JSON + the ability to search by sub-json.
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We created, multiple time, a big reliable and nice to use API.
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No surprise, no discussion about query optimization, no problem about scalability.
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