These are complex decisions that need to be made on a case-by-case basis and need to be made quickly, as they are generally caught in production and have a wide impact radius. But what if it has dependencies? What if it is more mission-critical to the business than all of the applications it is impacting? Straightforward logic says you should turn the misbehaving application off. Let's say there is a misbehaving client that is impacting the health of a Kafka cluster and, by association, the health of other applications connected to the cluster. However, on detection of something irregular, the question becomes "what's next?" Corresponding thresholds and alerts are created on these metrics, which are triggered when they are breached. Reactive automation takes the safeguarding by habit process discussed above and attempts to represent them in metrics that can be collected from your Kafka cluster. The obvious technology solution to any human-based process is to automate. These generally suffer from all the issues detailed above and as such carry a bad reputation. Just before a new application is released to production you develop, test, and a week before you go live, you are presented with several boxes to tick. These are typically applied right at the end of a development cycle. "Do we have to?" and "What can we get away with?" are common phrases associated with safeguarding by habit actions.Ī perfect example of this is the "production readiness checklist". Perception - Mostly due to the two factors above, these checks are seen as a necessary evil. Timeliness - When in the development cycle is safeguarding being applied? Safeguarding by habit is often applied right at the end of the cycle when changes are difficult and costly to make. Relevance - How well does the safeguarding rule fit the use case? Safeguarding by habit practice tends to be "one size fits all" and is not tailored or relevant for a particular technology/domain.
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