How do you observe/monitor applications in a continuously growing distributed system while software development continues to evolve in complexity?
The answer (to a significant degree) lies in distributed tracing. But what is distributed tracing, why does it matter, and why are more and more enterprises leaning on it to keep up with the rapidly moving application development wave?
This blog is your essential guide to all things distributed tracing – from the what(s), the how (s), and the why(s). We’ll explain how it works, the best practices to make the most out of it, and how you can choose the best platform for your distributed tracing needs in 2025.
What is Distributed Tracing?
Distributed tracing is a method that enables developers to monitor and understand the flow of data requests across multiple microservices within a distributed system.
In contemporary microservices architectures, applications are composed of numerous independent components that interact through APIs to execute complex operations. Distributed tracing offers visibility into the path of these requests, allowing developers to detect and resolve errors, bugs, and performance issues more efficiently.
To that end, developers use distributed tracing to upscale observability and solve performance issues that conventional debugging and monitoring tools cannot do.
For instance, end-to-end distributed tracing can track the progression of a user action from the initial click in a frontend application through to the backend services and database interactions.
Alternatively, distributed tracing can focus on monitoring the network flow between backend service requests after initiation. The objective is to observe how requests propagate across single or multiple runtime environments, providing teams with a detailed view of each interaction and assisting in pinpointing potential issues.
Here’s a good visual representation of distributed tracing.
Types of Distributed Tracing
Understanding the different types of tracing can help select the appropriate method for specific debugging and performance analysis needs. Here are three different types of distributed tracing:
Code Tracing
Code tracing involves analyzing the execution flow of an application’s source code during runtime. This method helps developers understand the sequence of operations, identify logical errors, and verify that the application behaves as expected. Developers can inspect variables, control structures, and function calls by stepping through the code to pinpoint issues.
Program Tracing
Program tracing monitors an application’s execution at the instruction level. It captures detailed information about the program’s state, including memory addresses, register values, and system calls. This low-level tracing is essential for debugging complex issues such as memory leaks, race conditions, and performance bottlenecks.
End-to-End Tracing
End-to-end tracing, often synonymous with distributed tracing, tracks a request’s complete journey across multiple services in a distributed system. It assigns a unique identifier to each request, allowing for the correlation of logs and metrics across services. This comprehensive view helps identify the system’s latency issues, service dependencies, and failure points. It is helpful in microservices, where a single request traverses multiple services before completion.
How does distributed tracing work?
Distributed tracing enables tracking individual requests as they propagate through various distributed system components, providing visibility into the system’s behavior and performance.
The following are the core components and workflow:
- Trace and Span Identification
a. Every incoming request is assigned a unique Trace ID that serves as a global identifier for the request’s lifecycle.
b. As the request is processed, each operation within the system is represented as a Span, which includes metadata such as operation name, start and end timestamps, and contextual information. - Instrumentation
a. Applications are instrumented using libraries provided by frameworks like OpenTelemetry.
b. These libraries capture span data for operations and automatically handle context propagation. - Context Propagation
a. Context propagation mechanisms maintain the relationship between spans across service boundaries.
b. This involves transmitting trace context (e.g., Trace ID and Span ID) via headers in HTTP or messaging protocols, ensuring that each service can associate its spans with the overall trace. - Data Collection and Export
a. Collected spans are exported to a backend system for storage and analysis.
b. OpenTelemetry allows exporters to send trace data to various backends, including Jaeger, Zipkin, and others. Visualization and Analysis
Backend systems like Jaeger aggregate and visualize trace data, allowing developers to analyze request flows, identify bottlenecks, and troubleshoot issues effectively.
The Apica Ascent platform implements protocol endpoints to ingest directly from both the Jaeger agent and the OTel Collector while streaming and indexing the data to any object store. This makes the Apica Ascent implementation infinitely scalable for large volumes of trace data.
Let’s understand it better with the help of a real-world example. Consider an e-commerce application where a user’s purchase request involves multiple microservices:
- Service A: Handles user authentication.
- Service B: Processes payment.
- Service C: Manages inventory.
- Service D: Generates order confirmation.
As the request flows through these services:
- A unique Trace ID is assigned at the entry point.
- Each service creates a Span detailing its operation.
- Context is propagated to maintain the trace’s continuity.
- All spans are collected and sent to a backend like Jaeger.
- Developers can then visualize the complete request path and analyze performance metrics.
This systematic approach provides end-to-end visibility into complex, distributed systems, facilitating efficient monitoring and debugging.
Difference between Distributed Tracing, Traditional Tracing and Logging
Distributed tracing differs from the traditional form of tracing and logging in many ways. Here’s a visual comparison:
| Aspect | Distributed Tracing | Traditional Tracing | Logging |
|---|---|---|---|
| Scope | Tracks requests across multiple services in distributed systems. | Monitors execution within a single application or process. | Captures discrete events within individual components or applications. |
| Purpose | Provides end-to-end visibility of requests to identify performance bottlenecks and errors. | Helps in understanding the flow and performance within a single application. | Captures events, errors, and informational messages for debugging and monitoring. |
| Data Structure | Encapsulates spans and traces representing the flow of requests across services. | Involves adding tracing code within the application, typically manually. | Implemented by inserting log statements in code, manually or through logging frameworks. |
| Instrumentation | Uses standardized protocols (like OpenTelemetry) to instrument across services. | Primarily manual instrumentation within monolithic applications. | Low-code or no-code insertion; supports search and filtering capabilities. |
| Use Cases | Microservices latency analysis, root cause identification, SLAs tracking. | Profiling and debugging in traditional or legacy environments. | Error tracking, usage monitoring, audit logging, and event capture. |
| Performance Impact | Can introduce overhead due to real-time tracing across services. | Lightweight since it targets single process execution. | Varies with volume; excessive logging may degrade performance. |
| Correlation | Correlates events across services via trace IDs, enabling full-stack insights. | Limited correlation confined within single app context. | Requires consistent log formatting and external tools for service-wide correlation. |
What are the Distributed Tracing Standards?
Distributed tracing standards establish consistent methods for instrumenting, collecting, and exporting trace data across diverse systems and programming languages. These standards facilitate interoperability, enabling developers to monitor and analyze distributed applications without being confined to specific vendors.
These are the key distributed tracing standards:
OpenTracing
OpenTracing, an initiative by the Cloud Native Computing Foundation (CNCF), provided a vendor-neutral API for distributed tracing. It defined standard interfaces for span creation, context propagation, and trace management, allowing developers to instrument applications consistently across various platforms. OpenTracing supported multiple languages, including Go, Java, Python, and C++.
OpenCensus
Developed by Google, OpenCensus offered a set of libraries for collecting application metrics and distributed traces. It enabled automatic instrumentation and provided exporters to various backends like Prometheus, Stackdriver, and Zipkin. OpenCensus emphasized a single, cohesive library per language, simplifying the instrumentation process.
OpenTelemetry
OpenTelemetry is a unified project that merges OpenTracing and OpenCensus, aiming to provide a comprehensive observability framework. It is an open-source project that provides a set of APIs and SDKs for instrumenting applications.
A few key points about OpenTelemetry:
- OpenTelemetry is designed to be vendor-neutral
- It offers APIs and SDKs for multiple languages
- It has an active community of contributors
OpenTelemetry supports multiple languages and is designed to integrate seamlessly with various backends, promoting vendor-agnostic observability.
The Benefits of Distributed Tracing
Distributed tracing is an imperative observability technique. It provides detailed insights into how requests flow through complex, distributed systems. Thus, by gathering and analyzing traces, organizations can achieve several key benefits:
- Accelerated Troubleshooting
Distributed tracing enables teams to pinpoint the exact location and cause of performance issues or errors within a system. By visualizing the path of a request across services, engineers can quickly identify bottlenecks or failures, reducing the time to detect (MTTD) and resolve (MTTR) incidents.
- Enhanced Observability
Distributed tracing provides a comprehensive view of service interactions, allowing teams to understand dependencies and monitor system health effectively.
- Improved Collaboration and Accountability
Distributed tracing clarifies which service is responsible for a particular issue in environments where multiple teams own different services. This clarity fosters better team collaboration and accountability, streamlining the debugging and resolution process.
- SLA Compliance Monitoring
Distributed tracing tools aggregate performance data, enabling organizations to monitor Service Level Agreements (SLAs) adherence. By tracking metrics like response times and error rates, teams can ensure they meet contractual obligations and maintain customer satisfaction.
- Support for Deployment Strategies
During canary or blue-green deployments, distributed tracing helps monitor the performance of new code changes in real time. This immediate feedback allows teams to detect issues early and make informed decisions about rolling out or rolling back changes.
- Reduced Reliance on Logs
While logs provide valuable information, they can be overwhelming and lack context. Distributed tracing offers a structured, visual representation of request flows, making it easier to understand system behavior without sifting through extensive log files.
What are the Challenges?
As with any technical practice, distributed tracing comes with its limitations. Below are some significant difficulties associated with distributed tracing:
1. Manual Instrumentation
Integrating tracing requires adding specific code to each service to emit trace data. This process can be time-consuming and may introduce inconsistencies, especially when different teams handle various services.
2. High Data Volume
Each request generates trace data, leading to substantial storage and processing demands. Managing this volume efficiently is crucial to prevent system strain.
3. Performance Overhead
Collecting and exporting trace data can impact application performance. If not optimized, tracking can slow down services, particularly in resource-constrained environments.
4. Sampling Complexity
Due to resource limitations, it isn’t feasible to capture every request. Implementing effective sampling strategies is essential to maintaining visibility without overwhelming storage.
Addressing these challenges involves careful planning, selecting appropriate tools, and ongoing optimization to ensure that distributed tracing provides meaningful insights without adversely affecting system performance.
How to Choose the Best Platform for Your Distributed Tracing Needs
Choosing a distributed tracing platform can feel overwhelming, especially with so many tools—open-source and commercial—available.
Here’s how to cut through the noise and find what works for your setup:
Step 1. Know Your Environment
Is your system small and service-light? Tools like Jaeger or Zipkin could be enough. Running at scale? You’ll likely need something more advanced, like Tempo or a commercial solution.
Step 2. Check Compatibility
Make sure the tool works well with your stack—languages, frameworks, and cloud setup. OpenTelemetry is a good bet for flexibility here.
Step 3. Storage Matters
Not all tools handle storage the same way. Dig into those details early if you need to keep trace data for a while or route it to different backends.
Step 4. UI and UX Count
A clean interface saves time during incidents. Some open-source tools are catching up, but commercial platforms still win on polish and ease of use. (Apica has a stellar UX with insightful dashboards and straightforward navigation; explore here.)
Step 5. Support and Docs
If you’re going open source, look for an active community and solid docs. If you’re buying, make sure the support justifies the price.
Step 6. Think Budget + Maintenance
Open-source may look free, but setup and upkeep take time. Commercial tools cost more upfront but may reduce ongoing engineering efforts.
Step 7. Run a Trial
Test it in your environment. Let your team play with it. See if it helps or adds more work.
Each step helps you move from “just exploring” to choosing a tool that works—for your architecture, team, and workflow.
How Apica helps with your Distributed Tracing Requirements
Apica provides an extensive distributed tracing platform engineered to monitor, analyze, and troubleshoot distributed systems at scale. Built to support both OpenTelemetry and Jaeger protocols, Apica enables direct instrumentation of applications and infrastructure components using industry-standard agents and collectors.
How Apica Supports Distributed Tracing:
Collects Traces Using OpenTelemetry and Jaeger
You can ingest traces through Jaeger agents or OpenTelemetry collectors. This flexibility supports a broad range of language SDKs and instrumentation options, making it easier to trace across services without vendor lock-in.
Connects Logs, Metrics, and Traces
Apica allows you to move from a metric to a related log and then to the trace that caused it—all within the same interface. Cross-linking reduces the time required for root cause analysis and eliminates the need to juggle multiple tools.
Stores Traces with High Efficiency
The SpanStore component, powered by Apica’s InstaStore™ object storage, handles trace data ingestion and indexing. It’s built to retain high-cardinality data while supporting fast retrieval, which is critical for analyzing high-throughput environments.
Pinpoints Bottlenecks Across Distributed Systems
You can follow a request as it moves through different services and quickly identify latency spikes or error-generating components. This helps isolate performance issues and reduce mean time to resolution (MTTR).
Visualizes End-to-End Service Flows Apica provides visual trace diagrams that map the full request path, giving you an accurate picture of service interactions and timing.
Apica simplifies observability across microservices, APIs, and cloud-native applications by combining distributed tracing with log and metric correlation. It equips engineering teams with the context to act quickly and maintain reliability without switching between disjointed tools.
Want to see it in action? Sign up and try it with your telemetry pipelines.
