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System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

Clarify and agree on the scope of the system

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

These are the Kickstarter Engineering and Data role definitions for both teams.

Technical	Data	People
Junior Software Engineer
Software Engineer	Data Analyst
Senior Software Engineer	Data Scientist	Engineering Manager
Staff Engineer	VP of Data	Engineering Director
Principal Engineer		CTO

Native app performance metrics

This is a draft list of what we're thinking about measuring in Etsy's native apps.

Currently we're looking at how to measure these things with Espresso and Kif (or if each metric is even possible to measure in an automated way). We'd like to build internal dashboards and alerts around regressions in these metrics using automated tests. In the future, we'll want to measure most of these things with RUM too.

Overall app metrics

App launch time - how long does it take between tapping the icon and being able to interact with the app?
Time to complete critical flows - using automated testing, how long does it take a user to finish the checkout flow, etc.?
Battery usage, including radio usage and GPS usage
Peak memory allocation

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

	/**
	* MacEditorTextView
	* Copyright (c) Thiago Holanda 2020-2021
	* https://twitter.com/tholanda
	*
	* MIT license
	* Modified by https://github.com/cjwcommuny for TextKit 2
	*/

	import Combine

	# heka.toml
	[hekad]
	base_dir = '/tmp/heka'

	[boot]
	type = "LogstreamerInput"
	log_directory = '/var/log'
	file_match = 'boot\.log'

	[pulse]

	" A minimal vimrc for new vim users to start with.
	"
	" Referenced here: http://vimuniversity.com/samples/your-first-vimrc-should-be-nearly-empty
	"
	" Original Author: Bram Moolenaar <[email protected]>
	" Made more minimal by: Ben Orenstein
	" Modified by : Ben McCormick
	" Last change: 2014 June 8
	"
	" To use it, copy it to

	master % scala ~/Projects/stripe-ctf-3/level1 > go tool pprof solve parallel.prof
	Welcome to pprof! For help, type 'help'.
	(pprof) topN
	Total: 3501 samples
	2696 77.0% 77.0% 2696 77.0% runtime.mach_semaphore_wait
	227 6.5% 83.5% 227 6.5% runtime.usleep
	95 2.7% 86.2% 95 2.7% crypto/sha1.block
	50 1.4% 87.6% 50 1.4% runtime.mach_semaphore_timedwait
	39 1.1% 88.7% 39 1.1% runtime.casp
	26 0.7% 89.5% 136 3.9% runtime.mallocgc