KubeCon + CloudNativeCon Europe 2021 Virtual

Chaos engineering is becoming a standard to test the resiliency and performance of cloud native applications. It allows you to validate assumptions, catch loopholes, and generally improve resiliency in your cluster. This talk will focus on the specific experiments that will improve cluster and application performance and cover tools in the ecosystem, including Litmuschaos, Kraken, and Chaos Mesh. At the end of the talk, the audience will understand the basics of experiments, apply them in their orgs, and code to run in their infrastructures. Breakdown: The idea of an experiment Application Performance Ideas Identifying Cluster Latency Issues (k8s, k3s) Improving App SLO Fixing issues with app latency Detecting latency in Service Meshes Rightsizing cluster auto-scaling issues Ways we use Chaos Experiments in our companies Demo: Running a latency Chaos Experiment continuously in a CI pipeline on k3s Chaos Tooling review FAQs about Chaos Engineering Conclusions

In Logging, there is a common fact: more applications means more data to handle. Running services at scale in a distributed environment brings exciting challenges for data management, but with the volume of data increasing there is a necessity to ship this data faster, but a few ones realize the side effect: high resource consumption. On implementing a logging pipeline, pre-processing of the data is mandatory, a simple example of this is Kubernetes metadata enrichment for every log record, but more data means more computing time, the same cost applies when delivering to the final storage or cloud service. In this session, we will do a deep dive into our journey of performance challenges that we faced in the Fluent Bit project around Network I/O + TLS, filesystem buffers, routing, and multiplexing for high throughput. We will share how did we go from 5k/sec to more than 30k/sec using a single-core CPU using purely design improvements and taking the most of Linux OS interfaces.

Autoscaling is a powerful technique to improve reliability and save costs. However, configuration of node autoscaling systems is a complex art. Karpenter is an open source, vendor neutral project that enables node autoscaling without the need for configuration and discovery for node groups in any cloud provider. With direct provider integration and out-of-the-box support for common node parameters like architecture, operating systems etc., taints, and labels, Karpenter gets users up and running quickly, while extending cleanly to more complex use cases.

In this session, we'll explore the history of autoscaling and our latest approach to low latency node autoscaling.

When we started our Kubernetes journey, manual responses to traffic spikes were the norm, causing significant brown-outs greatly impacting the customer experience. Graduating into an autoscaling implementation for both workloads and cluster nodes took time but yielded immense results. With this talk, we’ll explore how StockX has grown to solve these challenges operationally and developmentally, even enabling a proactive solution in addition to our reactive methods. Attendees will gain advanced clarity on autoscaling tools like the HorizontalPodAutoscaler and Cluster-Autoscaler, difficulties managing at scale, challenges in educating a rapidly growing team, and the unique intersection of e-commerce marketing events with infrastructure reliability efforts. Finally, we’ll announce an in-house tool we’re Open Sourcing to help aid others in their proactive autoscaling efforts!