Murder Mystery

Cloud Native: More Services, More Layers, More Challenges

Before Kubernetes

Before the era of cloud native, diagnosing and debugging systems was relatively straightforward. There was typically a monolithic application (or small number of applications) and so the search space for the problem was constrained primarily by the size of a single code base. Applications were also typically executed as a standalone process on a virtual machine. Viewing logs or attaching a remote debugger was often as simple as SSHing into the machine.

After Kubernetes

All of this changed with the adoption of microservices, containers, and Kubernetes. These new architectures and technologies enable rapid evolution of systems, but the cost is often related to increased complexity and reduced understandability. This can lead to the “whodunnit” style of bug hunting!

Locating an issue in a system composed of ten microservices now means that there are ten code bases where a potential bug could be lurking. And this isn’t counting the integrations and gaps between services where bugs can also hide. Containerizing applications and running them via Kubernetes also adds additional layers of complexity for viewing logs and debugging, and the applications can be rescheduled at a moment's notice.

Traditional
Cloud Native
Number of Services1 (or a small number)Many
Infrastructure LayersVMVM, Kubernetes, containers
Log AccessTail single processTail multiple processes, or logs shipped to centralized location
Correlation of User RequestsSingle/multiple threads in single processMultiple threads in multiple processes separate by network boundaries
DebuggingDebug locally running instances, or open ports in VM to enable remote debuggingOpen ports in firewall, VPC, K8s and containers to enable remote debugging
Traditional
Cloud Native
Number of Services
1 (or a small number)Many
Infrastructure Layers
VMVM, Kubernetes, containers
Log Access
Tail single processTail multiple processes, or logs shipped to centralized location
Correlation of User Requests
Single/multiple threads in single processMultiple threads in multiple processes separate by network boundaries
Debugging
Debug locally running instances, or open ports in VM to enable remote debuggingOpen ports in firewall, VPC, K8s and containers to enable remote debugging

Become a Cloud Native Sherlock Holmes

Being able to effectively debug services in Kubernetes is not dependent on a single tool or technique. A combination of approaches is required (and made easier by free, community tools):

  • Service Insight
  • System Visualization
  • Correlating User Requests
  • Centralized Log Management
  • Kubernetes-Native Debugging
Wheel
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Learnig Journey

Ready to investigate and debug your own Kubernetes woes?

This learning journey walks you through the primary concepts and hands-on activities required to debug issues across your cluster and multi-service applications.

Skill level Kubernetes beginner or experienced userTime to complete40 minutes • 10 lessonsWhat you'll learn
  • Annotating services to quickly identify key debugging information
  • How distributed tracing helps follow requests across multiple services
  • Debugging your cluster when things go wrong
  • Using Telepresence to debug services locally
What you needNothing, we’ll walk through learning the concepts and installing the tools you’ll need as we goWhat you'll use
Start Now

Part 1: Service insight with a service catalog

Incident response in the cloud-native world4 min • readAnnotating Kubernetes Services for humans4 min • readUsing a service catalog3 min • videoService Catalog quick quick-start4 min • read

Part 2: Correlate requests with distributed tracing

Context propagation3 min • readDistributed tracing with Zipkin4 min • readExplore distributed tracing and Kubernetes monitoring4 min • read

Part 3: Debugging K8s and microservices

Debugging issues in Kubernetes6 min • readMaking the remote local: Faster feedback, collaboration and debugging5 min • readTelepresence quick start8 min • tutorial

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