What Is Jaeger?

Jaeger is an open-source, end-to-end distributed tracing system originally developed by Uber and now a graduated CNCF project. It tracks requests as they flow through microservices, recording timing data and metadata at each step. This allows teams to visualize the complete path of a request, identify bottlenecks, and understand dependencies between services.

Why Jaeger Matters

In microservices architectures, a single user request might touch ten or more services. When latency increases or errors occur, finding the root cause without tracing is extremely difficult. Jaeger provides the visibility needed to pinpoint exactly which service or operation is causing the problem, reducing mean time to resolution and helping teams optimize performance.

Teams that understand and adopt jaeger gain a significant operational advantage, reducing manual effort and improving the reliability and scalability of their infrastructure. As cloud-native adoption accelerates, familiarity with jaeger has become a core competency for DevOps engineers, platform teams, and site reliability engineers working in production Kubernetes and cloud environments.

How Jaeger Works

When a request enters the system, a trace is created with a unique ID. As the request passes through each service, a span is recorded containing the service name, operation, duration, and metadata. These spans are collected and sent to the Jaeger backend, which stores them and makes them searchable. The Jaeger UI displays traces as timelines showing every service involved, how long each operation took, and where time was spent.

Understanding how jaeger fits into the broader cloud-native ecosystem is important for making informed architecture decisions. It works alongside other tools and practices in the DevOps and platform engineering space, and choosing the right combination depends on your team's specific requirements, scale, and operational maturity.

Key Features

Trace Visualization

View the complete journey of a request across all services in a waterfall timeline, making it easy to spot bottlenecks.

Root Cause Analysis

Identify the exact service and operation causing latency or errors by examining individual spans within a trace.

Service Dependency Graph

Automatically generate a map of service dependencies based on actual traffic patterns observed in traces.

Adaptive Sampling

Dynamically adjust the sampling rate to balance between data completeness and storage efficiency.

Common Use Cases

Debugging slow API responses by tracing requests across backend microservices to find the bottleneck.

Understanding service dependencies in complex architectures by analyzing real traffic patterns.

Monitoring the latency impact of new deployments by comparing trace data before and after releases.

Identifying intermittent failures that only occur in specific request paths through the system.

How Obsium Helps

Obsium's managed observability team helps organizations implement and optimize jaeger as part of production-grade infrastructure. Whether you are adopting jaeger for the first time or looking to improve an existing implementation, our engineers bring hands-on experience across cloud platforms and Kubernetes environments. Learn more about our managed observability services →