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Overall architecture

One of the objectives of this project is to be minimalistic: don't introduce components just for convenience, only introduce them when they usefully make a point. We also want to know security wise what the minimal permissions are that are required to make this work.

The overall architecture of the solution has the following components:

  • At the core of the architecture is a 'serverless' function that regularly pulls data from the Foobot API and sends it to an AdaFruit dashboard for viewing and a BigQuery database for later analysis. This is a Google Cloud Platform (GCP) function, though it could probably be implemented in a similar way in Amazon Web Services (AWS) Lambda.
  • The function is triggered on a regular basis. In Google Cloud this is done through a specific service (cron) that is related to the Google App Engine service, and communicates through PubSub, the GCP messaging service. In AWS all this is part of Lambda.
  • BigQuery data storage was selected because its cost is proportional to usage, and there is no hourly charge (in contrast to Google's BigTable).
  • Part of the operation is to monitor production and alert on anomalies.
  • In developing and updating the system we want the feature velocity to be high: from the push to the source code repository to live in production should be as frictionless as possible. In fact, it should be automatic, including testing.
  • Finally we aim to be able to go one step further and even automate the initial set up of the pipeline.

See the following sections for more details:

Alternative architectures

  • You could run code like this on a server. In fact, an earlier version of this ran on a Raspberry Pi. This implies an operational responsibility for that machine and all the software that runs on it. It remains to be seen if this is more or less work than running it on GCP. In terms of scalability however, the server approach is either vastly overprovisioned or underprovisioned. Both are a waste.
  • There may be SaaS/PaaS level solutions to do this API integration. Zapier and IFTTT come to mind. However, in general these cannot be relied upon to support the specific API services that you need. Beyond that, their cost model is based on number of invocations. The example project runs one every 10 minutes, and will burn through the paid starter plan monthly allotment in about 20 days.
  • Within GCP, we could consider logging our API JSON files into StackDriver, and then use its functionality to create our reports. However, we'd need to figure out how long term data retention works in StackDriver.
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