Software Engineering Daily

Chat bots are your newest co-worker. Slack, HipChat, and other chat clients allow developers and other team members to communicate more dynamically than the limits of email. Companies have started to add bots to their chat rooms. These bots can give you technical information, restart a server, or notify you that a build has finished.

Jason Hand is the author of ChatOps: Managing Infrastructure in Group Chat. He joins the show today to discuss how ChatOps improves development and operations by centralizing lots of functionality in group chat. Edaena Salinas is the host for today’s show. She also hosts the excellent Women In Tech Show–a podcast we highly recommend.

Since we are on the subject of bots–we want to thank O’Reilly Media for recently providing Software Engineering Daily a ticket to Bot Day.

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Kafka is a distributed log for producers and consumers to publish messages to each other. We’ve done many shows about Kafka as a key building block for distributed systems, but we often leave out the discussion of the complexities of setting up Kafka and monitoring it. Kafka deployments can be a complex piece of software to manage.

Tom Crayford is an engineer at Heroku, where he helped engineer the recent Heroku Kafka product, which is a managed version of Apache Kafka. Our conversation explored the use cases of Kafka and how to build Kafka as a cloud service at scale. For more information about Heroku Kafka, check out an upcoming webinar.

Full disclosure: Heroku is a sponsor of Software Engineering Daily. That said, this is a topic I am genuinely interested in–it is often difficult to get cloud providers to talk in detail about how they are architecting their services.

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Google Compute Engine is the public cloud built by Google. It provides infrastructure- and platform-as-a-service capabilities that rival Amazon Web Services. Today’s guest Joe Beda was there from the beginning of GCE, and he was also one of the early engineers on the Kubernetes project.

Google’s internal systems have made it easy for employees to spin up compute resources, but it was not a simple task to make this internal cloud consumable by the public–not to mention competitive with AWS.  In order for a cloud provider to be successful, it needs to offer self-healing, self-managing infrastructure that can run microservices.

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Cloud computing was something much different in 2011, when Brian Gracely and Aaron Delp started The Cloudcast, a podcast I listen to on a regular basis. The Cloudcast features technical discussions about cloud infrastructure technology, and one of the most recent shows was a monologue by Brian Gracely where he explained his perspective on the industry rumblings about a Docker fork.

The utility of a container for so many different purposes leads to different organizations having differing preferences for what use case is optimized for. The impression that I took away from this conversation, as well as the next episode that will air with Joe Beda, is that the diverse opinions and products in the container and orchestration ecosystem is quite healthy.

Brian does a great job explaining his perspective on The Cloudcast, and he discusses his beliefs further in this episode.

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When a user of a social network updates her profile, that profile update needs to propagate to several databases that want to know about such an update–search indexes, user databases, caches, and other services. When Neha Narkhede was at LinkedIn, she helped develop Kafka, which was deployed at LinkedIn to help solve this very problem. Using Kafka as an event queue, LinkedIn adopted the CQRS architectural pattern together with event sourcing.

Event sourcing is an architectural pattern that allows changes to our application model to be represented as events. Each event is published to an event queue, and is pulled off of the queue by each of the various services that need to consume that event. Event sourcing and the related architectural pattern CQRS allow for a flow of information through an application that is easy to reason about, and has several other desirable properties.

In today’s episode, Neha explains how to use Kafka for event sourcing and how related software patterns are improving the architectures of companies like Netflix and Uber.

For more information, check out this Confluent blog entry.

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