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What is Distributed Tracing and Why Does It Matter?

distributed tracing

researchHQ’s Key Takeaways:

  • Distributed tracing is a method of observing requests as they propagate through distributed cloud environments.
  • While traditional software monitoring platforms are limited by their inability to offer an overarching view of system health, observability helps explore properties of and patterns within environments not defined in advance.
  • Distributed tracing helps companies to identify degraded states before failures occur, detect unforeseen behaviour caused by automated scaling, debug systems and troubleshoot the origin of unseen problems.

 

Learn how businesses use intelligent observability platforms with distributed tracing to manage your app and service architecture.

Distributed tracing is a method of observing requests as they propagate through distributed cloud environments. Distributed tracing follows an interaction by tagging it with a unique identifier, which stays with it as it interacts with microservices, containers, and infrastructure. It can also offer real-time visibility into user experience, from the top of the stack right down to the application layer and the large-scale infrastructure beneath.

As legacy monolithic applications give way to more nimble and portable services, the tools once used to monitor their performance are unable to serve the complex cloud-native architectures that now host them. This complexity makes distributed tracing critical to attaining observability into these modern environments.

In fact, a recent global survey of 700 CIOs found that 86% of companies are now using cloud-native technologies and platforms, such as Kubernetes, microservices, and containers, to accelerate innovation and stay competitive. With this shift comes the need for effective observability into these complex and dynamic environments.

Where traditional methods struggle

The goal of monitoring is to enable data-driven decision-making. Traditional software monitoring platforms collect observability data in three main formats:

  • Logs: Timestamped records of an event or events.
  • Metrics: Numeric representation of data measured over a set period.
  • Traces: A record of events that occur along the path of a single request.

In the past, platforms made good use of this data, such as following a request through a single application domain. Gaining visibility into monolithic systems before containers, Kubernetes, and microservices was simple. However, in today’s vastly more complex environments, such data offers no overarching view of system health.

Log aggregation, the practice of combining logs from many different services, is a good example. It may give a snapshot of the activity within a collection of individual services, but the logs lack contextual metadata to provide the full picture of a request as it travels downstream through possibly millions of application dependencies. On its own, this method simply isn’t sufficient for troubleshooting in distributed systems. This is where observability, and distributed tracing specifically, come in.

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Business Challenge:We've curated the most common business challenges Observing dyanmic cloud environments
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