Jaeger is an open-source, CNCF-graduated distributed tracing platform. It was originally created by Uber Technologies and is designed specifically to monitor, profile, and troubleshoot complex transactions inside microservices architectures.

When a user clicks a button on a modern app, that single action might trigger a cascade of dozens of hidden API calls across various internal microservices (e.g., Frontend → Auth Service → Payment Gateway → Database). If that click takes 10 seconds to load, standard logs and metrics will only tell you that it was slow. Jaeger tells you exactly where it was slow.

1. Core Concepts: Traces and Spans

To understand Jaeger, you need to understand the structural data it visualizes:

• Span: A span is the fundamental building block of a trace. It represents a single, isolated unit of work done by a specific service (e.g., an HTTP request, a database query, or an internal function execution). It contains an operation name, a start and stop timestamp, metadata tags, and logs.
• Trace: A trace is a collection of spans organized in a parent-child hierarchy (a tree-like structure). It represents the entire end-to-end journey of a single transactional request as it navigates your distributed system.

2. Jaeger Architecture Components

Jaeger operates via a modular microservices architecture to process and display data at enterprise scale:

• Instrumentation Layer (OpenTelemetry): Historically, Jaeger provided its own language-specific SDK client libraries. The modern standard natively adopts OpenTelemetry (OTel). Your application code uses OTel to inject a unique Trace ID into request headers (Distributed Context Propagation) so services can pass the tracking token along down the line.
• Collector: The collector is the ingestion gateway. It receives spans sent from the applications, validates them, packages them in batches, and writes them to a persistent storage backend.
• Storage Backend: Jaeger features a pluggable storage layer. For small or local development, it can store traces in-memory. For production workloads, it relies on highly scalable databases like Elasticsearch, OpenSearch, or ClickHouse.
• Query Service & Web UI: The Query service retrieves data from your storage database and serves it to the built-in Jaeger Web Console. The UI provides intuitive graphs showing how requests traveled and where latency spikes or application errors originated.

3. What Problem Does Jaeger Solve?

Without distributed tracing, debugging microservices is like looking for a needle in a haystack of disjointed logs. Jaeger provides several core capabilities:

• Distributed Transaction Monitoring: View the live execution path of an active user transaction across completely different programming languages and physical servers.
• Performance & Latency Optimization: See a visual timeline of your API calls. If a page load is slow because a specific database query is executing sequentially 50 times instead of concurrently, Jaeger flags that visual bottleneck instantly.
• Root Cause Analysis: If a frontend request fails with an HTTP 500 error, Jaeger lets you drill down to the exact downstream internal microservice and function call that crashed, exposing the error logs embedded directly inside that specific span layer.
• Service Dependency Analysis: Jaeger analyzes your live trace streams to dynamically generate dependency graphs, showing you exactly how your microservices interface with one another.

4. Modern Context: Jaeger and OpenTelemetry

In the current cloud-native ecosystem, OpenTelemetry (OTel) has emerged as the universal standard framework for generating observability data (metrics, logs, and traces).

Instead of competing with OTel, Jaeger has fully embraced it. The architecture natively ingests the OpenTelemetry Protocol (OTLP). The modern best practice is to use OpenTelemetry to instrument your application code, and route that tracing data to Jaeger as the storage engine and visualization dashboard.