Distributed Systems
Distributed computing is a field of computer science that studies distributed systems. A distributed system is a system whose components are located on different networked computers, which then communicate and coordinate their actions by passing messages to each other. The components interact with each other in order to achieve a common goal. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock, and independent failure of components. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications.
A computer program that runs within a distributed system is called a distributed program (and distributed programming is the process of writing such programs). There are many different types of implementations for the message passing mechanism, including pure HTTP, RPC-like connectors and message queues.
A goal or challenge pursued by some computer scientists and practitioners in distributed systems is location transparency; however, this goal has fallen out of favor in industry. As distributed systems are a different concept from conventional non-distributed systems, the differences, such as network partitions, partial system failures, and partial upgrades, cannot simply be "papered over" by attempts at "transparency" (see CAP theorem).
Source: https://en.wikipedia.org/wiki/Distributed_computing
Position on the Adoption Curve
Presentations about Distributed Systems
No Microservice Is an Island
Debugging Microservices: How Google SREs Resolve Outages
Debugging Microservices: How Google SREs Resolve Outages
Chick-Fil-A: Milking the Most Out of 1000's of K8s Clusters
Chick-Fil-A: Milking the Most Out of 1000's of K8s Clusters
Forced Evolution: Shopify's Journey to Kubernetes
Complex Event Flows in Distributed Systems
Efficient Fault Tolerant Java with Aeron Clustering
Canopy: Scalable Distributed Tracing & Analysis @Facebook
Canopy: Scalable Distributed Tracing & Analysis @Facebook
Modeling the Real World With Elixir/OTP
Next Gen Networking Infrastructure With Rust
Large Scale Architectures Panel
Large Scale Architectures Panel
Large Scale Architectures Panel
Large Scale Architectures Panel
Large Scale Architectures Panel
Large Scale Architectures Panel
Large Scale Architectures Panel
Heretical Resilience: To Repair is Human
Help! I Accidentally Distributed My System!
Help! I Accidentally Distributed My System!
Platforms at Twilio: Unlocking Developer Effectiveness
Interviews
Chick-Fil-A: Milking the Most Out of 1000's of K8s Clusters
How does the Kubernetes implementation at Chick-Fil-A differ from more common approaches to Kubernetes deployments?
Brian: Most of the people that we see in the industry that are running Kubernetes clusters at any kind of scale (or really running and big container platforms) generally have a cloud-based infrastructure that they run in AWS (or Google). Most companies have a single or a few sizeable Kubernetes clusters with a large number of nodes (on the order of hundreds or even thousands). Each of these clusters then run tens to hundreds of thousands of containers across them. We have a similar infrastructure in our cloud environment from a control plane perspective.
What problems does this use case present?
Brian: The Kubernetes ecosystem is awesome. But there are some challenges we have it does not address. One example is deployments. We want to be able to provide a technology platform that lets us move at the speed of our business in our restaurants. This means being able to roll out highly distributed changes to production very frequently. During the talk, we will talk about a tool we build called “Fleet” that we use to manage deployments to our restaurants. There are some kubernetes native tools to help with deployments, but we found they came up short for the type of environment we had.
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