What exactly is distributed tracing, and why is it important?

What is distributed tracing and why does it matter?

What is distributed tracing?

Observing requests as they flow over distributed cloud systems is known as distributed tracing. A unique identifier is assigned to each interaction in distributed tracing. As the transaction interacts with microservices, containers, and infrastructure, its identifier remains. The unique identifier gives real-time visibility into the user experience from the top of the stack to the application layer and infrastructure below.

As monolithic legacy programs give way to more flexible and portable services, the tools that historically monitored their performance are no longer capable of serving the sophisticated cloud-native architectures that now house them. Because of this complexity, distributed tracing is essential for achieving observability in today’s systems.

In fact, according to a recent global poll of 700 CIOs, 86 percent of firms are now utilizing cloud-native technologies and platforms to accelerate innovation and stay competitive, such as Kubernetes, microservices, and containers. As a result of this shift, effective observability in these complex and dynamic situations is required.

The evolution of distributed tracing

It was pretty easy to understand what happened inside monolithic programs when firms predominantly constructed them. However, with the emergence of service-oriented architectures, it became more difficult to comprehend how specific transactions moved through an application’s multiple layers. As a result, pinpointing the core causes of latency and execution time delays becomes more challenging.

The intricacy also hampered internal collaboration. If the organization couldn’t find the affected microservice, it couldn’t figure out which team was in charge of fixing the problem. It was easy for troubleshooting meetings to deteriorate into war rooms where groups blamed one another since there was so little visibility into what was going on.

Businesses were well aware that they needed more visibility into their application environments. Developing a solution from the bottom up with internal development resources, on the other hand, would be prohibitively expensive and time-consuming, impeding the pace of innovation. This need is now being met by distributed tracing, allowing businesses to identify better the performance issues affecting their microservices environments.

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The benefits of distributed tracing

Distributed tracing enables teams to diagnose application performance issues quickly, often before users notice anything is wrong. The organization can quickly identify and treat a problem if the root cause discover. Observability can also find performance bottlenecks anywhere in the software stack and flag code. That needs to be improved, alerting teams when microservices fail. The firm can also improve compliance with service level agreements and maintain a high-quality customer experience by using observability (SLAs). It helps the company carry a continuous flow of revenue while minimizing potential adverse effects on the bottom line.

Because distributed tracing pinpoints the exact location of problems, it improves team collaboration and communication. It strengthens working relationships, critical for quick troubleshooting and producing business-growing ideas. As a result, businesses may bring new products and services to market faster, giving them a competitive advantage.

How distributed tracing works and why we need it

Monitoring, debugging, and optimizing distributed application architectures like microservices–especially in dynamic micro architectures–requires distributed tracing. It keeps track of a single request by collecting and evaluating data on every interaction the request has with each service.

Each request-triggered activity is tracked through and across services and referred to as a segment or span. A name, start and end timestamps, and other metadata are among the information gathered. After completing a “parent” span of activity, the following action shifts to its “child” span. The distributed trace places these spans in the correct order.

Businesses need distributing tracing to help them manage the complexity of their present application infrastructures. There are additional potential sources of failure across the entire application stack with distributed programs. As a result, identifying core reasons when problems develop can take a long time. The added complexity directly impacts a company’s ability to meet SLAs and provide a great user experience.

Distributed tracing allows teams to understand better how each microservice is working. This knowledge aids them in promptly resolving difficulties, increasing client satisfaction, ensuring consistent revenue, and allowing teams to innovate. Businesses may benefit fully from the advantages of modern application environments while reducing the issues that their inherent complexity might bring.

The difference between distributed tracing and logging

So, what distinguishes distributed tracing from logging? Logging is a method of tracking error reporting and related data in a central location using logs generated by an app. When it comes to logging, it’s all about what happens inside the application. System administrators can use logging to take steps to ensure that programs function smoothly. Humans can use log file data to respond to alerts and changes in critical performance measures. It can also log machine data and send out automated responses. Creating log files is both an art and a science. Records must contain enough information to initiate the required action while remaining lightweight not to saturate system resources.

Distributed tracing, on the other hand, is the process of following a single transaction in context from endpoint to endpoint. The goal of distributed tracing is to locate the specific location of an issue. Distributed tracing requires context about an application’s flow and data to deliver this insight. Distributed tracing gives you complete visibility into the performance of your application across microservices and containers. It highlights the interactions between different services so that teams can better understand their interdependencies. That minimizes both the time it takes to detect, and the time it takes to resolve the problem. Then it improves a company’s ability to deal with application performance issues before they hurt the user experience.

You can utilize both logging and tracing at the same time. It’s customary to start with logging and subsequently add distributed tracing when a company’s application environment becomes more complicated, such as when microservices involve.

Where traditional monitoring methods struggle

Distributed tracing relies on observability data from all settings to reach its goal of enabling data-driven decision-making. The three pillars of observability are the three basic formats in which traditional software monitoring solutions collect observability data:

• It keeps track of the timestamps of a single event or a series of connected occurrences.
• Metrics are a numerical representation of data gathered over some time.
• A trace is a description of events that occur along the path of a single request. 

Platforms have made good use of this information, such as following a request across a single application domain. Getting visibility into monolithic systems was simple before the advent of containers, Kubernetes, and microservices. However, such data provides no overarching view of system health in today’s significantly more complicated and distributed contexts.

An excellent example is a log aggregation, which is the technique of combining logs from multiple services. It may provide a snapshot of activity inside a group of individual services. Still, the records lack contextual metadata that would allow them to give a complete picture of a request as it travels downstream through potentially millions of application dependents. This strategy is insufficient for troubleshooting distributed systems on its own. It is where observability, mainly distributed tracing, comes into play.

Observability, rather than essential monitoring, is the gold standard for understanding and visibility into apps and services. It enables you to look into an environment’s traits and patterns that do not pre-define. Modern businesses expect a high level of observability, requiring a range of qualities, including distributed tracing.

Open-source distributed tracing standards

The impact of tracing through distributed systems

Distributed tracing can quickly track a request across hundreds of different system components, and it does more than record the request’s end-to-end path. It can also provide real-time information about the health of the system. IT, DevSecOps, and SRE teams can use this to:

• Report on the health of apps and microservices to detect degraded situations before they fail. 
• Detecting unanticipated behavior caused by automatic scaling makes problems easier to avoid and recover from. 
• Analyze the system’s average response times, error rates, and other digital experience data to see how end-users feel about it. 
• With dynamic visual dashboards, you can keep track of crucial performance parameters. 
• Debug systems, identify bottlenecks and fix performance issues at the code level. 
• Identify and solve the fundamental cause of problems that aren’t visible.

Cloud intelligence for the distributed world

By revealing the whole course of a request as it travels through the application stack, distributed tracing provides enterprises with critical information into application performance. Organizations must get broad observability into the application environment as they increasingly rely on modern cloud-native applications to adapt faster. Distributed tracing enables teams to swiftly detect the fundamental causes of application performance issues — often before users are even aware of them — and maintain a high-quality user experience.

With PurePath, our unique distributed tracing technology a pioneer of distributed tracing since 2006, blends metrics, logs, and distributed traces with code-level analysis, user experience data, and metrics from the most recent open-source standards. This upgrade gives you complete contextual visibility into your whole app and service environment and the underlying cloud architecture. Your BizDevOps teams will have a single source of truth for your data with an all-in-one AI-driven software intelligence platform, which means minor troubleshooting and more time developing.

Please take a look at our PurePath Power Demo to see how it integrates open-source and cloud-native technology.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.