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Write, debug and test your back-end in your front-end. Get the best of both worlds. Develop with your full stack in one runtime for increased productivity. Then deploy to the cloud for security and performance. Bonus, get an automatically-defined, clean REST API for free.
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README.md

ArtEry - Client Development > Cloud Deployment

ArtEry conceptually allows you to develop, test and debug applications 100% as client-code, but with the security and performance of cloud-code. When you deploy to production, it's trivial to control which code goes in the Client, Server or Both.

  • developing, testing and maintaining server-side code is 10x harder than client-side code
  • so... don't write a single line of server-side code

Why ArtEry?

The basic idea of ArtEry is to develop your entire app in one runtime, client-side. Then, for production, ArtEry manages deploying some of your code to the cloud and some to the client-app. ArtEry gives you full control over what code runs cloud-side, client-side or both.

Benefits:

  • Fastest possible development (testing, debugging, build cycle)
  • Security
  • Performance
  • Eliminate code duplication
  • Together with ArtEryServer, generate a full REST API automatically

Fastest Possible Development

Client-code has many advantages over cloud-code:

  • easier to test
  • easier to debug
  • dramatically shorter build cycle

In short, it's much faster to develop.

The key observation is code is easier to develop, test and debug when it's all in one runtime. Stack traces span your full stack. You can hot-reload your full stack for rapid development.

Security and Performance

There are some things that can't be done safely client-side:

  • Authentication
  • Authorization
  • Validation
  • TimeStamps
  • Update Counts

Pipeline filters make it easy to apply any security rules you need.

Performance

Some requests are more efficient to process in the cloud:

  • requests with require multiple cloud-side requests
    • client-to-cloud requests typically cost much more than cloud-to-cloud request
  • requests which consume a bunch of data, reduce it, and output much less data
    • cloud-to-client data-transfer typically costs much more than cloud-to-cloud
  • requests with a lot of computation

Adding custom handler-types for complex requests make this easy to do. A robust "sub-request" system makes synthesizing complex requests easy in a scalable, performant way.

Eliminate Code Duplication

Some code should run both on the cloud and client. Specifically, validation should happen client-side for the fastest possible response to client actions, /but it needs to be verified in the cloud for security. ArtEry makes it trivial to re-use code across your full stack.

Example

# language: CaffeineScript
import &ArtEry

# simple in-memory CRUD data-store
class Post extends Pipeline

  constructor: -> @data = {}

  # crud-api
  @handlers
    get:    ({key})       -> @data[key]
    create: ({data})      -> @data[key] = merge data, id: key = randomString()
    update: ({key, data}) -> @data[key] = merge @data[key], data
    delete: ({key})       -> delete @data[key]

  # text-trimming filter
  @before
    update: trimText = (request) -> request.withMergedData text: request.data.text?.trim()
    create: trimText

Use:

pipelines.post.create data: text: "Hello world!"
.then ({id})   -> pipelines.post.get id
.then ({text}) -> console.log text  # Hello world!

How it Works

  • Declare one or more pipelines with handlers and filters
  • Make client-side requests to those pipelines
  • The pipeline takes care of routing the request wherever it needs to go.

All requests are handled with promises in a (nearly) pure-functional way. Each filter and handler returns a NEW request or response, passing the information down the pipeline. Since each step of the pipeline is handled as a promise, you can get maximum scalable performance out of node.js.

Pipelines

Pipelines are the main structural unit for ArtEry. A pipeline consists of:

  • name: - derived from the pipeline's class-name
  • handlers: a map from request-types to handlers: [<String>]: <Handler>
  • filters:

A pipeline is a named grouping of request-types and filters. When designing an API for a database-backed backend, it's usually best to have one pipeline per table in your database.

Handlers

Handlers are just functions:

(<Request>) -> <[optional Promise] Response, null, plain-Object, or other response-compatible-data-types>

Requests and Responses

At their most simple, requests and responses both look like this:

type: <String>
props:
  key:  <any, but usually String>
  data: <any, but usually Object>
  <any, but usually nothing>

For convenience, there are some common getters:

# getters
key:  -> @props.key
data: -> @props.data

Filters

At their simplest, functions are almost exactly the same as handlers:

# before-handler
(<Request>) -> <[optional Promise] Request, Response, plain-Object, or other response-compatible-data-types>

# after-handler
(<Response>) -> <[optional Promise] Response, plain-Object, or other response-compatible-data-types>

In general, each filter applies to the whole pipeline. It can filter any before and after any request-type. In practice, you'll write filters which only filter certain request-types and perhaps only before or after the handler.

Filters can also be configured to run client-side, server-side or both:

class MyFilter extends Filter

  @location :client # :server or :both

  @before create: (request) -> ...
  @after  create: (request) -> ...

FAQ

  • Should I put my request-fields in request.props or request.data?

    • NOTE: request.data == request.props.data - in other words, data is actually a props-field with a convenient accessor.
    • You can put anything in props.data and almost anything in props itself.
    • Recommendation: If the field is one of the defined @fields, it goes in data. Else, put it in props.
    • Why? To make filters as re-usable as possible, they need to make assumptions about your request-props and response-props.
  • What do you mean by "Nearly" pure-functional?

    • To facilitate more complex sub-request patterns, each root-request has a mutable context object for shared state across all sub-requests. This is useful for functionality such as caching sub-requests. Keeping all context attached to the root request rather than somewhere else ensures we can have any number of request in flight simultaneously. In general, only advanced filter-developers should touch the context object. Almost everything you'll ever need to do can be done in a pure-functional way with normal filters and handlers.
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