Overview Table:Tracing in Kubernetes

Overview Table:Tracing in Kubernetes .md

Level	Definition	Issues	Key Metrics
Application Level Tracing	Application-level tracing involves capturing and analyzing the flow of requests and responses within a Kubernetes application. It helps identify bottlenecks, latency issues, and performance optimizations	- Lack of visibility into request/response flow within the application. - Difficulty in identifying the root cause of performance issues. - Inefficient resource allocation and scaling decisions.	- Request latency - Number of requests per second - Error rates - Response time distribution
Distributed Tracing	Distributed tracing focuses on capturing and analyzing the flow of requests and responses across multiple services and components involved in a distributed system within a Kubernetes cluster. It helps identify performance bottlenecks, latency issues, and dependencies between services	- Lack of visibility into end-to-end request flow across distributed systems. - Difficulty in correlating events and traces across multiple services. - Inefficient service orchestration and communication.	- Trace latency - Span duration - Service dependencies - Error rates
End-to-End Tracing	End-to-End Tracing (e2e Tracing) is a diagnostic method that allows developers to follow the execution of code in the infrastructure to investigate why a code path has failed, or to provide detailed tracing for capacity planning and performance analysis. It is particularly useful in distributed systems where a request passes through multiple services and databases	- Lack of end-to-end visibility from the user interface to the underlying infrastructure	- Response time from user interface to backend services - Error rate across the entire stack - Overall system throughput
Infrastructure Tracing	Infrastructure tracing involves monitoring and analyzing the performance and behavior of the underlying infrastructure components in a Kubernetes cluster, such as nodes, pods, and network. It helps identify resource bottlenecks, network latency, and infrastructure issues	- Lack of visibility into resource utilization and performance at the infrastructure level. - Difficulty in identifying infrastructure-related bottlenecks and performance issues. - Inefficient resource allocation and capacity planning.	- CPU and memory utilization - Network latency - Disk I/O rates - Pod scheduling and eviction
Service Mesh Tracing	Service mesh tracing focuses on capturing and analyzing the flow of requests and responses between services within a Kubernetes cluster. It helps monitor service-to-service communication, identify latency issues, and optimize network traffic	- Lack of visibility into service-to-service communication. - Difficulty in debugging and troubleshooting microservices. - Inefficient load balancing and routing decisions.	- Latency between services - Error rates - Traffic distribution - Service dependencies
Service Tracing	Provides diagnostic information about the operation of services and modules. It can be used to diagnose and verify issues with services	- Difficulty in understanding the flow of requests across multiple services - Challenges in identifying latency and errors between services	- Response time between services - Error rate between services - Request throughput between services