The Power of the Log from Ben Stopford This talk is about the beauty of sequential access and append only data structures. We’ll do this in the context of a little known paper entitled “Log Structured Merge Trees”. LSM describes a surprisingly counterintuitive approach to storing and accessing data in a sequential fashion. It …
Networks are unreliable. We’ve all experienced trouble connecting to Wi-Fi, or had a phone call drop on us abruptly.
The networks connecting our servers are, on average, more reliable than consumer-level last miles like cellular or home ISPs, but given enough information moving across the wire, they’re still going to fail in exotic ways. Outages, routing problems, and other intermittent failures may be statistically unusual on the whole, but still bound to be happening all the time at some ambient background rate.
To overcome this sort of inherently unreliable environment, it’s important to design APIs and clients that will be robust in the event of failure, and will predictably bring a complex integration to a consistent state despite them. Let’s take a look at a few ways to do that.
This article concludes the description of a way to develop microservices using Domain Driven Design, Event Sourcing and Command Query Responsibility Segregation (CQRS). The practical considerations and benefits of this approach are compared with other options.
NoSQL data storage systems lack the security measures and awareness that are required for data protection. Because code analysis alone is insufficient to prevent attacks in today's typical large-scale deployment, certain mitigations should be done throughout the entire software life cycle.
The Twitter Engineering team has recently provided an insight into the evolution and scaling of the core technologies behind their in-house infrastructure that powers the social media service. Core lessons shared included: Architect beyond the original specifications; there is no such a thing as a “temporary change or workaround”; and documenting best practices has been a “force multiplier”.
Modern applications are increasingly using probabilistic machine-learned models.: A big difference between human-written code and learned models is that the latter are usually not represented by text and hence are not understandable by human developers or manipulable by existing tools. The consequence is that none of the traditional software engineering techniques for conventional programs (such as code reviews, source control, and debugging) are applicable anymore. Since incomprehensibility is not unique to learned code, these aspects are not of concern here.
Continuing on with my series about microservices implementations (see “Why Microservices Should Be Event Driven”, “Three things to make your microservices more resilient”, “Carving the Java EE Monolith: Prefer Verticals, not Layers” for background) we’re going to explore probably the hardest problem when creating and developing microservices. Your data. Using Spring Boot/Dropwizard/Docker doesn’t mean you’re doing microservices. Taking a hard look at your domain and your data will help you get to microservices.
A problem with Domain-Driven Design (DDD) since the beginning has been the too common hunt for perfect designs, but DDD is not for perfectionists. In order to stop that hunt you need to have some idea of how to create software that is well designed, yet not perfect, Eric Evans noted in his presentation at the recent DDD Europe Conference in Amsterdam.
"I've seen more than one answer, from other people, explaining how partial function application is equivalent to dependency injection. In a small series of articles, I'll explain both why this is true, but also why it's not functional. I'll conclude by showing a functional alternative to decoupling logic and (side) effects."
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