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Warming Up
In the previous post Iāve proposed a new approach to the Serverless Architecture specification. In this post, I argued, that the current modeling practices use too limited vocabulary and produce artifacts without consistent semantics:
Serverless Architecture Language
As a running sample I chose the MakirOto application, presented by IL AWS Solution Architects Team at the recent AWS Tel AvivĀ Summit:
As a result of applying new modeling approach to the MakirOto Frontend component the following high-level Process Model was produced:
MakirOto Process Model soĀ far
In thisĀ diagram:
- dashed rectangles represent services, implemented as AWS Cloud Formation stacks
- arrows denote visibility and access permissions between computations and resources from variousĀ stacks
The first post was about modeling online services. In this post, I will take a closer look at consistent modeling of long-running background processes.
For that purpose I will use another MakirOto componentāāāData Collector. This component is responsible for crawling social networks in order to obtain important information about user connections and interests.
MakirOto Data Collector: StartingĀ Point
This is the DataCollector architecture presented during theĀ session:
In this diagram, some icons represent computations and resources:
, while others still represent AWS services:
Connection lines do not have any clear semantics.
Letās see if we could improve it without too muchĀ effort:
MakirOto Data Collector ProcessĀ Model
This is, indeed, an improvement. Here, every icon represents either
- computation process instance: AWS Lambda, Step Function, FargateĀ Service)
- or a fully manged re
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