Distributed Tracing

Distributed tracing is a key part of any observability implementation. Incorporating distributed trace data helps power the three phases of observability.

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Distributed tracing is the process of mapping and analyzing requests as they flow through various services.

This helps you understand where errors or performance issues occur, even in a complex and distributed microservices architecture. A single request may touch thousands of services in a large environment, so the ability to capture and analyze each operation can help you quickly get to the root cause of issues. 

Distributed tracing, logging, and cloud monitoring

Distributed tracing is one of three legs of a great observability strategy, when it comes to effective and holistic data monitoring, tracking and observing. Each play an important role and help users collect, track, and analyze data.

The challenges with distributed tracing

There is nearly universal agreement that distributed tracing data solves problems that metrics and logs cannot. However, distributed traces are not widely adopted — and where it is deployed, it is often underutilized. Some of the challenges associated with distributed tracing include:

  • It’s too hard to get full distributed tracing coverage
  • Incumbent solutions are too complex and not intuitive
  • The existing tooling is too siloed and lack context
  • It’s too expensive to maintain across entire system

Distributed traces are extremely powerful tools for solving problems across large and/or complex systems. However until now, no one has found a way to harness this power to deliver a solution that unlocks distributed tracing’s potential and delivers the return on the time and monetary investment.

The result delivers limited value to customers.

Chronosphere’s solution for distributed tracing

Chronosphere’s observability platform allows customers to more rapidly triage and understand the root cause of problems. These capabilities are powered by the ability to ingest distributed traces at scale, seamlessly along with metrics. Chronosphere is the first observability platform that enables customers to capture, store, and analyze every single distributed trace, even at scale, without being cost-prohibitive.

These new capabilities enable customers to:

  • Extend existing alert and triage workflows with root cause analysis. Start with the broader context from alerts and dashboards and hone in on more granular distributed trace data to quickly understand the root cause of a problem.
  • Make better decisions with complete data. Capture, store and analyze every single distributed trace (even at scale), allowing you to make more accurate decisions based on the full distributed trace data set. Stop making decisions based on statistics, guesses, and samples.
  • Empower both advanced users and beginners. Distributed tracing has suffered from complex tools for too long. To help bridge the gap, Chronosphere offers a guided experience for beginners while still giving power users the freedom to explore their data.

How does distributed tracing fit into the three phases of observability?

One popular definition of observability is known as the three phases: know, triage, and understand. During each phase, the focus is on alleviating the customer impact — or remediating the problem — as fast as possible. Distributed traces play an important role in all three of the phases:


While metrics are the primary data source that powers this phase, due to their speed and real-time nature, distributed traces have an important role to play as well. Chronosphere can generate metrics from distributed trace data that can be used to augment existing alerts and generate new highly contextual alerts.


To help engineers triage issues, they need to be able to quickly put the alert into context of understanding how many customers or systems are impacted, and to what degree. Chronosphere can aggregate and analyze sets of distributed traces to quickly discover and dissect problematic requests.


At this phase is when distributed traces truly shine. Distributed traces have the unique ability to help engineers identify the direct upstream and downstream dependencies of the service experiencing the active issue. When metrics and distributed traces are tightly linked, engineers can easily hone in on the relevant distributed traces that are associated with the alert and instantly uncover the root cause of an issue.

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Distributed Tracing FAQs

Distributed tracing is the process of mapping and analyzing requests as they flow through various services. This helps you understand where errors or performance issues occur in a complex and distributed architecture. Distributed traces are often underutilized, but when captured, stored and analyzed distributed traces can enable root cause analysis, improve the accuracy of your data decisions and provide a deeper understanding of upstream and downstream dependencies.

Traces, also known as distributed tracing, is the process of mapping and analyzing requests as they flow through various services. This helps you understand where errors or performance issues occur in a complex and distributed architecture. Events let you know an action has happened. They can be used to validate the occurrence of something.

Exemplars are references to data outside of the metrics published by an application. In addition to the metrics an application pushes, it also publishes a reference to some other data that relates to what we are measuring. While exemplars give us an easy way to jump from metrics to a relevant distributed trace, there are limitations. See why our platform doesn’t rely on exemplars to link metric data to traces here.

A span is work done by an individual service or component. Spans may also be seen as a reference point.