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Jaeger’s clients adhere to the data model described in the OpenTracing standard. Reading the specificationexternal link will help you understand this section better.


Let’s start with a quick refresher on the terminology defined by the OpenTracing Specificationexternal link .


A span represents a logical unit of work in Jaeger that has an operation name, the start time of the operation, and the duration. Spans may be nested and ordered to model causal relationships.

Traces And Spans


A trace is a data/execution path through the system, and can be thought of as a directed acyclic graph of spans .


Jaeger can be deployed either as all-in-one binary, where all Jaeger backend components run in a single process, or as a scalable distributed system, discussed below. There are two main deployment options:

  1. Collectors are writing directly to storage.
  2. Collectors are writing to Kafka as a preliminary buffer.

Architecture Illustration of direct-to-storage architecture

Architecture Illustration of architecture with Kafka as intermediate buffer

This section details the constituent parts of Jaeger and how they relate to each other. It is arranged by the order in which spans from your application interact with them.

Jaeger client libraries

Jaeger clients are language specific implementations of the OpenTracing APIexternal link . They can be used to instrument applications for distributed tracing either manually or with a variety of existing open source frameworks, such as Flask, Dropwizard, gRPC, and many more, that are already integrated with OpenTracing.

An instrumented service creates spans when receiving new requests and attaches context information (trace id, span id, and baggage) to outgoing requests. Only the ids and baggage are propagated with requests; all other profiling data, like operation name, timing, tags and logs, is not propagated. Instead, it is transmitted out of process to the Jaeger backend asynchronously, in the background.

The instrumentation is designed to be always on in production. To minimize the overhead, Jaeger clients employ various sampling strategies. When a trace is sampled, the profiling span data is captured and transmitted to the Jaeger backend. When a trace is not sampled, no profiling data is collected at all, and the calls to the OpenTracing APIs are short-circuited to incur the minimal amount of overhead. By default, Jaeger clients sample 0.1% of traces (1 in 1000), and have the ability to retrieve sampling strategies from the Jaeger backend. For more information, please refer to Sampling .

Context propagation explained Illustration of context propagation


The Jaeger agent is a network daemon that listens for spans sent over UDP, which it batches and sends to the collector. It is designed to be deployed to all hosts as an infrastructure component. The agent abstracts the routing and discovery of the collectors away from the client.


The Jaeger collector receives traces from Jaeger agents and runs them through a processing pipeline. Currently our pipeline validates traces, indexes them, performs any transformations, and finally stores them.

Jaeger’s storage is a pluggable component which currently supports Cassandra , Elasticsearch and Kafka .


Query is a service that retrieves traces from storage and hosts a UI to display them.


Ingester is a service that reads from Kafka topic and writes to another storage backend (Cassandra, Elasticsearch).