Denis Yakovenko yakovenkodenis

Приёмы оптимизации кода по скорости

Организационные

Культура разработки performance-first: https://tonsky.me/blog/performance-first/
Бюджет скорости: https://wp-rocket.me/blog/performance-budgets/
Performance mantra: http://www.brendangregg.com/blog/2018-06-30/benchmarking-checklist.html

Не зависящие от языка и его реализации

Rosetta Code - реализации задач на разных языках: http://rosettacode.org/wiki/Collections
Статьи по основным структурам данных:

React context vs Redux in 2020

The [React docs][condoc] give some example use cases for context:

Context is designed to share data that can be considered “global” for a tree of React components, such as the current authenticated user, theme, or preferred language.

The common property of these use cases is that data like the current theme doesn't change often and needs to be shared deep down the component tree, which would be cumbersome with "[prop drilling][drill]". Something else that needs to be shared everywhere is the application state when using a "single source of truth" pattern, so it would follow that the context API would help with that as well, but there's a catch: components that use context will rerender every time that the provided value changes, so sharing the whole application state through context would cause excessive render lifecycles.

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

General rules

Follow standard conventions.
Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
Boy scout rule. Leave the campground cleaner than you found it.
Always find root cause. Always look for the root cause of a problem.

	type M = [any, any];
	type Delete<k, m extends M> = k extends m[0]
	? m extends [k, any]
	? never
	: m
	: m[0] extends k
	? [unknown, unknown]
	: m;
	type Add<k, v, m extends M> = [k, v] \| Delete<k, m>;
	type Get<k, m extends M> = k extends m[0]

	import pandas as pd
	from collections import Counter
	import tensorflow as tf
	from tffm import TFFMRegressor
	from sklearn.metrics import mean_squared_error
	from sklearn.model_selection import train_test_split
	import numpy as np

	# Loading datasets'

	var str = 'class ಠ_ಠ extends Array {constructor(j = "a", ...c) {const q = (({u: e}) => {return { [`s${c}`]: Symbol(j) };})({});super(j, q, ...c);}}' +
	'new Promise((f) => {const a = function* (){return "\u{20BB7}".match(/./u)[0].length === 2 \|\| true;};for (let vre of a()) {' +
	'const [uw, as, he, re] = [new Set(), new WeakSet(), new Map(), new WeakMap()];break;}f(new Proxy({}, {get: (han, h) => h in han ? han[h] ' +
	': "42".repeat(0o10)}));}).then(bi => new ಠ_ಠ(bi.rd));';

	try {
	eval(str);
	} catch(e) {
	alert('Your browser does not support ES6!')
	}


	// Very inefficient but purely declarative approach
	const findAnagrams = words => {
	const pairIsAnagram = (word1, word2) =>
	word1.split('').sort()
	.reduce(
	(acc, el, i) => { acc[i] = el == acc[i]; return acc; },
	word2.split('').sort()
	)
	.reduce((acc, el, i) => acc && el, true);

	import os

	import gym
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt

	from collections import defaultdict

	import numpy as np
	import pandas as pd
	from statistics import mean
	from sklearn.grid_search import ParameterGrid
	from sklearn.preprocessing import StandardScaler


	def getPredict(theta, x):
	return np.dot(x, theta)