Cloud computing, microservices, open-source tools, and container-based delivery have made applications more distributed across an increasingly complex landscape. As a result, distributed tracing has become crucial to maintaining situational awareness and responding quickly to issues.
But what is distributed tracing exactly? We’ll answer that question and look at how you can gain adequate observability into a highly distributed cloud-native architecture to effectively trace transactions and analyze their significance in real time.
What is distributed tracing?
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 monolithic legacy 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.
The evolution of distributed tracing
Back when businesses primarily built monolithic applications, it was relatively straightforward to see what transpired within them. With the rise of service-oriented architectures, however, it became harder to understand how specific transactions traveled through the various tiers of an application. This, in turn, made it difficult to pinpoint the root causes behind latency and delays in execution time.
This complexity also created internal collaboration challenges. If the organization was unable to identify the affected microservice, then it could not determine which team was responsible for addressing the issue. With so little visibility into what was actually going on, it was easy for troubleshooting sessions to devolve into war rooms where teams blamed one another.
Businesses knew they needed better observability into their application environments. But creating a solution from scratch using internal development resources was too costly and time-consuming, and would slow down the pace of innovation. Distributed tracing now meets this need, allowing companies to better understand the performance issues affecting their microservices environments.
The benefits of distributed tracing
Distributed tracing helps teams get to the bottom of application performance issues faster, often before users even notice anything is wrong. Upon discovering an issue, the organization can rapidly identify the root cause and address it. Observability also provides teams with early warnings when microservices are in poor health, and can spot performance bottlenecks anywhere in the software stack, and highlight code that should be optimized. Through observability, the organization can also maintain a high-quality user experience and improve its compliance with service level agreements (SLAs). This minimizes potential impacts to the bottom line and helps the business maintain a steady flow of revenue.
Because distributed tracing pinpoints the exact areas where issues lie, it also helps boost collaboration and communication across teams. This improves the working relationships that are crucial for both timely troubleshooting and delivering innovations that grow the business. As a result, organizations can get to market with new products and services much more quickly, gaining a competitive advantage as a result.
How distributed tracing works and why we need it
Distributed tracing is essential to monitoring, debugging, and optimizing distributed software architecture, such as microservices–especially in dynamic microservices architectures. More specifically, it tracks the path of a single request by collecting and analyzing data on every interaction with every service the request touches.
Each activity — called a segment or span — triggered by a request is recorded as it moves both through and across services. Information collected includes a name, start and end timestamps, and other metadata. When one activity — a “parent” span — is completed, the next activity passes to its “child” span. The distributed trace places these spans in their correct order.
Businesses need distributed tracing to help streamline the complexity of their modern application environments. With distributed applications, there are more potential points of failure across the entire application stack, and it can take far more time to identify root causes when issues arise. This complexity has a direct effect on a company’s ability to maintain its SLAs and provide a stellar user experience.
Distributed tracing helps teams understand more quickly how each microservice is performing, so they can resolve issues without delay, increase customer satisfaction, ensure steady revenue, and preserve precious time for IT staff to innovate. This way, businesses can take full advantage of the benefits modern application environments offer while minimizing the challenges that their inherent complexity can also create.
Join us at the on-demand Performance Clinic, Distributed Tracing with Dynatrace, to see how Dynatrace automatically traces transactions between services and technologies.
The difference between distributed tracing and logging
So how is distributed tracing different from logging? For starters, logging is the process of using logs generated by an app to centrally track error reporting and related data. The focus with logging is specifically what happens with the application. System administrators use logging so they can take action to ensure applications function properly. Log file data can be for humans to use so they can respond to conditions such as alerts and changes in key performance indicators, or it can be machine data that can trigger automated responses. Writing log files is as much an art as a science. Logs need to contain enough information to trigger the appropriate action, but be lightweight so they don’t bog down system resources.
In comparison, distributed tracing is the process of following a single transaction from endpoint to endpoint in context. The focus of distributed tracing is to pinpoint exactly where a problem occurred. To provide this insight, distributed tracing needs the context about the flow of an application and data within it. Distributed tracing provides comprehensive visibility into application performance across microservices and containers, highlighting the transactions taking place between various services so teams can better understand the relationships between them. This reduces mean time to detection and mean time to resolution, greatly improving an organization’s ability to resolve application performance issues before they degrade the user experience.
Logging and tracing can be used in parallel. Organizations often begin with logging and may add distributed tracing as their application environment becomes more complex, for example when microservices are involved.
Where traditional monitoring methods struggle
To achieve its goal of enabling data-driven decision making, distributed tracing relies on observability data from across all environments. Traditional software monitoring platforms collect observability data in three main formats, which are often referred to as the three pillars of observability:
- 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. Before the advent of containers, Kubernetes, and microservices, gaining visibility into monolithic systems was simple. But in today’s vastly more complex and distributed 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, specifically, distributed tracing come in.
As opposed to simple monitoring, observability is the standard for understanding and gaining visibility into apps and services. It helps to explore the properties of and patterns within an environment that are not defined in advance. Distributed tracing is one of several capabilities key to achieving the observability that modern enterprises demand.
Open-source distributed tracing standards
There are now several open-source approaches to distributed tracing, including OpenTelemetry, Open Census, OpenTracking, OpenTracing, Jaeger, and Zipkin. OpenTelemetry, for example, is a widely popular observability framework for cloud-native software, created by combining OpenTracing and Open Census, and is one of the most widely used distributed tracing tools. It ultimately aims to support the three pillars of observability we mentioned earlier: metrics, traces, and logs. Currently, organizations can use OpenTelemetry to send collected telemetry data to a third-party system for analysis.
The impact of tracing through distributed systems
Distributed tracing can easily follow a request through hundreds of separate system components, and it does more than just record the end-to-end journey of a request. It can also provide real-time insight into system health. This enables IT, DevSecOps, and SRE teams to:
- Report on the health of applications and microservices to identify degraded states before a failure occurs.
- Detect unforeseen behavior that results from automated scaling, making it easier to prevent and recover from failures.
- Analyze how end-users experience the system in terms of average response times, error rates, and other digital experience metrics.
- Monitor key performance metrics with interactive visual dashboards.
- Debug systems, isolate bottlenecks, and resolve code-level performance issues.
- Identify and troubleshoot the root cause of unseen problems.
Cloud intelligence for the distributed world
Distributed tracing gives organizations crucial insight into application performance by uncovering the complete journey of a request as it travels throughout the application stack. Now that organizations are increasingly relying on modern cloud native applications to transform faster, it is critical for them to be able to gain comprehensive observability into the application environment. Distributed tracing allows them to quickly identify the root causes of application performance issues — often before users even notice them — and ensure a high quality user experience.
Dynatrace, a pioneer of distributed tracing since 2006 with PurePath, our patented distributed tracing technology, integrates metrics, logs, and distributed traces with code-level analysis, user experience data, and metrics from the latest open-source standards. This expansion gives you full contextual observability into your entire environment of apps and services and the underlying cloud infrastructure. With an all-in-one, AI-driven software intelligence platform, your BizDevOps teams have a single source of truth for all your data, which means less time troubleshooting and more time innovating.
Check out our Power Demo on PurePath and discover how it embraces open-source and cloud-native technologies.