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Kubernetes is a Container Orchestration Engine designed to host containerized applications on a set of nodes, commonly referred to as a cluster. Using a systems modeling approach, this series aims to advance the understanding of Kubernetes and its underlying concepts. The Kubernetes API is an Http API that provides Create/Read/Update/Delete access to query and modify the Kubernetes Object Store. Kubernetes supports multiple authentication and authorization strategies to control the access to the API.

Back in 2015, a revolution was under way. We were moving from manual, handcrafted infrastructures, to container-based, industrial, and human-free platforms. In those dark ages of orchestration, edge traffic was remarkably difficult to manage. On one side, we had traditional reverse-proxies that were built for static infrastructures, on the other side, we were building dynamic clusters made to deploy and manage thousands of microservices. The idea of having a simple and automatic edge router, all in one binary, was appealing, but also idealistic.

First introduced by Uber in November 2018, Peloton, a unified resource scheduler, manages resources across distinct workloads, combining separate compute clusters. Peloton is designed for web-scale companies like Uber with millions of containers and tens of thousands of nodes. Peloton features advanced resource management capabilities such as elastic resource sharing, hierarchical max-min fairness, resource overcommits, and workload preemption. As a cloud-agnostic system, Peloton can be run in on-premise data centers or in the cloud.

Reliability engineering teams at Uber build the tools, libraries, and infrastructure that enable engineers to operate our thousands of microservices reliably at scale. At its essence, reliability engineering boils down to actively preventing outages that affect the mean time between failures (MTBF). As Uber’s global mobility platform grows, our global scale and complex network of microservice call patterns have made capacity requirements for individual services difficult to predict. When we’re unable to predict service-level capacity requirements, capacity-related outages can occur.

How I wasted hours of my life because of an unfixed 2016 Kubernetes’s bug that didn’t want me to expose a service over both UDP and TCP on a same port. Long story short, I wasted hours of my life because of an unfixed 2016 Kubernetes’s bug that didn’t want me to expose a service over both UDP and TCP on a same port. May this article come up in your Google search and save you hours of suffering.

In a Cloud Native world, microservices are running with ephemeral containers that are regularly deployed to multiple availability zones, regions, and even multiple clouds. As these cloud native applications become more complex, our supporting solutions like monitoring, have also had to become more complex. Today, more traditional monitoring responsibilities are being automated, and monitoring has become less human centric. In the first part of this series, we’ve summarized some of the most popular monitoring solutions with Ambassador.

At the inaugural EnvoyCon, which ran alongside KubeCon in Seattle last December, several large organisations discussed how they have recently begun using Envoy as an edge proxy, such as eBay, Pinterest and Groupon. Moving away from hardware-based load balancers and other edge appliances towards the software-based “programmable edge” provided by Envoy clearly has many benefits, particularly in regard to dynamism and automation. However, one of the core challenges presented was the need to create an effective control plane that integrates well with the existing engineering workflow or developer experience.

Today we’ve announced that we’ve officially added eBPF instrumentation to extend container observability with Sysdig monitoring, security and forensics solutions. eBPF – extended Berkeley Packet Filter – is a Linux-native in-kernel virtual machine that enables secure, low-overhead tracing for application performance and event observability and analysis. Don’t let the name fool you – eBPF delivers a lot more than network packet information (more on that below). Sysdig now taps into eBPF to offer the deep visibility for cloud-native and container environments – from host and network data to container processes, resource utilization, and more.

One of the advantage of deploying a microservice-based application in an Istio service mesh is to allow one to externally control service monitoring, tracing, request (version) routing, resiliency testing, security and policy enforcement, etc., in a consistent way across those services, for the application as a whole. In this blog we will focus on the in-depth telemetry side of the house and see how can we configure mixer to collect a uniform set of metrics across all services.

Grafana v6.0 brings many new and exciting features and enhancements and at the same time includes a new React platform and architecture for panels & plugins that will fundamentally change Grafana’s future as a platform. Grafana’s dashboard UI is all about building dashboards for visualization. Explore strips away all the dashboard and panel options so that you can focus on the query & metric exploration. Iterate until you have a working query and then think about building a dashboard.