It's responsible for allocating and scheduling containers, providing then with abstracted functionality like internal networking and file storage, and then monitoring the health of all of these elements and stepping in to repair or adjust them as necessary.In short, it's all about abstracting how, when and where containers are run.

You’ll see pressure to push towards “Cloud neutral” solutions using Kubernetes in various places

Heroku? App Services? App Engine?

Kubernetes (often irritatingly abbreviated to k8s, along with it’s wonderful ecosystem of esoterically named additions like helm, and flux) requires a full time ops team to operate, and even in “managed vendor mode” on EKS/AKS/GKS the learning curve is far steeper than the alternatives.

Azure App Services, Google App Engine and AWS Lambda will be several orders of magnitude more productive for you as a programmer. They’ll be easier to operate in production, and more explicable and supported.

With the popularisation of docker and containers, there’s a lot of hype gone into things that provide “almost platform like” abstractions over Infrastructure-as-a-Service. These are all very expensive and hard work.

By using events that are buffered in queues, your system can support outage, scaling up and scaling down, and rolling upgrades without any special consideration. It’s normal mode of operation is “read from a queue”, and this doesn’t change in exceptional circumstances.

Reserved resources, capacity, or physical hardware can be protected for pieces of your software, so that an outage in one part of your system doesn’t ripple down to another.

The complimentary design pattern for all your circuit breakers – you need to make sure that you wrap all outbound connections in a retry policy, and a back-off.

Circuit breaking is a useful distributed system pattern where you model out-going connections as if they’re an electrical circuit. By measuring the success of calls over any given circuit, if calls start failing, you “blow the fuse”, queuing outbound requests rather than sending requests you know will fail.

“scaling out is the only cost-effective thing”, but plenty of successful companies managed to scale up with a handful of large machines or VMs

Scaling is hard if you try do it yourself, so absolutely don’t try do it yourself. Use vendor provided, cloud abstractions like Google App Engine, Azure Web Apps or AWS Lambda with autoscaling support enabled if you can possibly avoid it.

Hexagonal architectures, also known as “the ports and adapters” pattern

Good microservice design follows a few simple rules

What Microservices are supposed to be: Small, independently useful, independently versionable, independently shippable services that execute a specific domain function or operation. What Microservices often are: Brittle, co-dependent, myopic services that act as data access objects over HTTP that often fail in a domino like fashion.

In the mid-90s, “COM+” (Component Services) and SOAP were popular because they reduced the risk of deploying things, by splitting them into small components

all a design pattern is, is the answer to a problem that people solve so often, there’s an accepted way to solve it

Let’s do a quick run through of some very common ones:

CDNs are web servers run by other people, all over the world. You upload your data to them, and they will replicate your data across all of their “edges” (a silly term that just means “to all the servers all over the world that they run”) so that when someone requests your content, the DNS response will return a server that’s close to them, and the time it takes them to fetch that content will be much quicker.

Understanding how a distributed cache works is remarkably simple – when an item is added, the key (the thing you use to retrieve that item) that is generated includes the address or name of the computer that’s storing that data in the cache. Generating keys on any of the computers that are part of the distributed cache cluster will result in the same key. This means that when the client libraries that interact with the cache are used, they understand which computer they must call to retrieve the data.

Memory caches are used to store the result of something that is “heavy” to calculate, takes time, or just needs to be consistent across all the different computers running your server software. In exchange for a little bit of network latency, it makes the total amount of memory available to your application the sum of all the memory available across all your servers.

All a load balancer does, is accept HTTP requests for your application (or from it), pick a server that isn’t very busy, and forward the request.

GraphQL gels especially well with modern JavaScript driven front ends, with excellent tools like Apollo and Apollo-Server that help optimise these calls by batching requests.

BFF is an API that serves one, and specifically only one application

GraphQL really is just a smart and effective way to schema your APIs, and provide a BFF – that’s backend for frontend

What sets GraphQL apart a little from previous approaches (notably Microsofts’ OData) is the idea that Types and Queries are implemented with Resolver code on the server side, rather than just mapping directly to some SQL storage.

GraphQL is confusingly, a Query Language, a standard for HTTP APIs and a Schema tool all at once.

Use a swagger or OpenAPI library to generate a schema and you’re pretty much doing what most people are doing.

JSON-RPC is “level 0” of the Richardson Maturity Model – a model that describes the qualities of a REST design.

REST is a good architectural style (and it is, a lot of the modern naysaying about REST is relatively uninformed and not too dissimilar to the treatment SOAP had before it)

there was a push for standardisation of “SOAP” (simple object access protocol) APIs

The basics of HTTP are easy to grasp – there’s a mandatory “request line”

GET http://www.davidwhitney.co.uk/ HTTP/1.1
Host: www.davidwhitney.co.uk
Connection: keep-alive
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64…
Accept: text/html,application/xhtml+xml,application/xml;q=0.9
Accept-Encoding: gzip, deflate
Accept-Language: en-GB,en-US;q=0.9,en;q=0.8

HTTP/1.1 200 OK
Cache-Control: public,max-age=1
Content-Type: text/html; charset=utf-8
Vary: Accept-Encoding
Server: Kestrel
X-Powered-By: ASP.NET
Date: Wed, 11 Dec 2019 21:52:23 GMT
Content-Length: 8479


<!DOCTYPE html>
<html lang="en">...

web is an implementation of the design pattern REST – which stands for “Representational State Transfer”. You’ll hear people talk about REST a lot – it was originally defined by Roy Fielding in his PhD dissertation, but more importantly was a description of the way HTTP/1.0 worked at the time

So the web is RESTful by default. REST describes the way HTTP works.

Most APIs you use, or build will be “REST-ish”.

Honestly for the most part it’s a matter of taste, and they’re all perfectly appropriate ways to build web applications.

static site generators became increasingly popular – normally allowing you to use your normal front-end web dev stack, but then generating all the files using a build tool to bundle and distribute to dumb web servers or CDNs

MVVM is equally common in single page apps where there’s two way bindings between something that provides data (the model) and the UI (which the view model serves).

SPAs are incredibly common, popularised by client-side web frameworks like Angular, React and Vue.js.

http://www.mycoolsite.com/Home/Index

when most people say “MVC” they’re really describing “Rails-style” MVC apps where your code is organised into a few different directories

most people stick to a handful of common patterns

There are tonne of general purpose web servers out there

“web servers”, implement the “HTTP protocol” – a series of commands you can send to remote computers – that let you say “hey, computer, send me that document”