Software Engineering Daily

The unit of computation has evolved from on premise servers to virtual machines in the cloud to containers running in those virtual machines. Serverless computation is another stage in the evolution of computational unit management. With a serverless architecture, a function call to the cloud spins up a transient container, calls the function on that container, and then spins down the container.

Ryan Scott Brown joins the show today to discuss the benefits and consequences of serverless computing. With containers and VMs, we still have to worry that the resources we are spinning up in the cloud will run without being utilized. Serverless computing gives us more control over these compute resources, so that we don’t have unused servers that we are paying for.

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Google’s site reliability engineers are responsible for maintaining the highly available services that power the Google software that we all use on a regular basis. O’Reilly recently published the book “Site Reliability Engineering: How Google Runs Production Systems”, and the book provides a comprehensive window into how the site reliability engineering role works.

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Dropbox has been storing files on Amazon Web Services for 8 years, and Dropbox’s core business is storing files. For the past three years, Dropbox has been working on a project to migrate its file storage from Amazon Web Services to its own custom-built infrastructure. Magic Pocket is the name of Dropbox’s new infrastructure layer, and it gives Dropbox more control and improved economics.

James Cowling leads the storage team at Dropbox. In today’s episode, James takes us into the architecture of Dropbox and explains how the team moved all of the user file storage from Amazon S3 to Dropbox’s Magic Pocket infrastructure. Dropbox’s architecture is built with a focus on simplicity–and there are numerous challenges to maintaining that simplicity in the face of an extremely complex problem like this.

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Distributed systems products are often marketed with terms like “real-time data” and “hassle-free scaling”, but what do those terms actually mean? Is data in a distributed system ever reliably “real time”? Do we ever have strong enough plans about our scalability strategy to say that scaling will be “hassle free”?

Camille Fournier joins us today to discuss distributed systems in practice. Like everything in else in computer science, distributed systems are all about tradeoffs–and picking the right sets of tradeoffs in our distributed system will affect the entire organization that is building that system.

We also discuss the Cloud Native Computing Foundation, which is similar to the Apache Foundation, but specifically for cloud technologies. The CNCF is likely to have strong impact on the way we build software for a long time to come.

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Exception monitoring services and log management services are two sides of a gradient. Exception monitoring services capture and aggregate the problems that occur on your application. Log management services aggregate all of your logs, so that you can decide for yourself what constitutes a problem.

Brian Rue from Rollbar joins the show today to talk about Rollbar’s exception monitoring architecture, and the competitive landscape of these technology products. Every software engineer wants track the problems with an application, but some developers need more information than others–and that ends up changing how these error aggregation services are architected. This is an interesting conversation on the business of SaaS products for developers, and the architecture of a distributed system designed to monitor and aggregate errors.

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