- Comparing SQL to React weakens the argument. SQL is the language, React is a piece of software. You certainly can run 30 year old JS today in modern browsers.
Although SQL is of course not relational Algebra (and others like Datalog and D4M are better), it's still cool. It inspired kSQL like Lil uses https://beyondloom.com/decker/lil.html#lilthequerylanguage , which inspired the code I'm most proud of: https://codeberg.org/veqq/declarative-dsls A common query language, a common idiom, for many data structures (arrays, hashmaps, datafremas) is liberating, permitting you to e.g. solve sudoku, make mandelbrot sets or calculate primes directly:
(def n 40) # to reach primes up to, left is sqr of n, right n/2, then multiply them for rows
(def composites
(df/select :from (range 2 (+ 1 (math/floor (math/sqrt n))))
:cross (range 2 (+ 1 (/ n 2)))
:where |(<= (* ($ :value_left) ($ :value_right)) n)
[[:value_left :value_right] :value
|(* ($ :value_left) ($ :value_right))]))
(df/select :from (range 2 (+ 1 n)) :exclude composites)
Or e.g. (import declarative-dsls/dataframes :as df)
(def people (df/dataframe :name :age :job))
(df/dataframe? people)
(df/insert! {:name "Bob" :age 30 :job "Developer"} :into people)
(df/insert! {:name "Alice" :age 27 :job "Sales"} :into people)
(df/update! :set {:job "Engineer"}
:where |(= ($ :job) "Developer")
:from people)
(df/save-csv people "people.csv" :sep "\\t")
(def people2 (df/load-csv "people.csv" :sep "\\t"))
(-> people2
df/dataframe->rows
df/rows->dataframe
df/print-as-table)
The tests file has many such things (like the sudoku solver) and even datalog and minikanren implemented on top of this!That and SQLite seems to be able to scale to almost any problem, is disgustingly fast and with litestream incredibly resilient.
Javascript is actually fully backwards-compatible, to not break the Web. Any javascript from 10 years ago works in the browser. This is good but also a bit of a burden, since the language can only expand but not shrink. React is a library, and like all libraries it has breaking versions. Not understanding the basic difference between the two kinda undermines the credibility of the article.
Also, in a similar way, core, ANSI SQL is largely backwards compatible, but all the SQL dialects linked to various DBMS implementation are generally incompatible. Obviously that's not mentioned in the article.
> Not a tutorial. Not an ORM. Actual SQL: joins, subqueries, window functions, query plans.
Not text written by a human. Not a style that an real writer would ever use. Actual AI slop: Short sentences. Incorrect facts. Not X, Y.
teleforce•2d ago
As a modern array language D4M is the natural successor for SQL [1].
D4M is based on mathematics like SQL, specifically associative array algebra but not relational unlike SQL. It's more generic since can it caters to most modern data abstractions including spreadsheets, database tables, matrices, and graphs [2].
You can achieve 100M database inserts per second with D4M and Accumulo more than a decade ago back in 2014 [3].
[1] D4M: Dynamic Distributed Dimensional Data Model:
https://d4m.mit.edu/
[2] Mathematics of Big Data: Spreadsheets, Databases, Matrices, and Graphs:
https://direct.mit.edu/books/monograph/5691/Mathematics-of-B...
[3] Achieving 100M database inserts per second using Apache Accumulo and D4M (2017 - 46 comments):
https://news.ycombinator.com/item?id=13465141