In today’s complex software environments, ensuring that applications run smoothly and efficiently is more critical than ever. One of the key practices that developers and IT operations and DevOps professionals use to maintain the health and performance of their systems is application tracing. This blog post delves into application tracing, how it works, different types, and effective implementation. We’ll also cover essential tools and address common questions like the differences between tracing, logging, and monitoring.
Application tracing tracks executions of an application by recording information about its behavior and performance. Developers and IT operations use tracing to understand how their applications are performing, identify bottlenecks, and debug issues. By providing a detailed, step-by-step record of the application’s operations, tracing helps IT and DevOps teams ensure business-critical applications are optimized and safe for production.
Imagine you’re running a complex web application that handles numerous user requests. Suddenly, users start reporting that the application is slow. Using application tracing, you can track the flow of each request through various components of the application. Tracing helps pinpoint delays in database queries, API calls, or the application’s internal logic.
Software tracing works by inserting trace statements into the application code. These statements record information about the execution flow, such as function calls, variable values, and timing information. Trace data is then collected and stored in trace logs, which can be analyzed to understand the application’s behavior.
Tracing can be enabled for specific parts of the application or for the entire application, depending on the level of detail needed. Modern tracing tools can also automatically capture traces without requiring manual insertion of trace statements, making these tools easier to implement and maintain.
There are several types of application tracing, each serving different purposes and use cases. Understanding these types can help you choose the right approach for your needs.
Tracing events within a single thread or process, synchronous tracing provides a detailed view of the application’s behavior within a specific context, helping developers understand how different components interact within a single thread. A good example is a web application handling a user request. By tracing the request processing, DevOps can identify performance bottlenecks, optimize database queries, and troubleshoot errors.
Tracing events across multiple threads or processes, asynchronous tracing helps developers understand how different components of the application interact with each other, even when running concurrently. Asynchronous tracing records trace information in a non-blocking manner. Trace data is collected and stored in a buffer, which is then processed and written to the trace logs separately from the application’s main execution. A good example is a chat application with multiple users. By tracing the message processing, developers can identify performance bottlenecks, optimize message delivery, and troubleshoot errors.
Used in microservices architectures and other distributed systems, distributed tracing captures trace requests as they travel across multiple services. Providing a holistic view of the application’s behavior, distributed tracing correlates trace data from different services, making it easier to understand complex interactions and diagnose issues that span multiple cloud-based and containerized components.
Consider an e-commerce application built with a microservices architecture. A user request to place an order might involve several services, including user authentication, product catalog, inventory management, payment processing, and order fulfillment. Distributed tracing allows you to trace the entire request flow across these services, helping you identify where delays or errors occur and ensuring each service is performing as expected.
Implementing application tracing involves several steps:
Tool selection and proper configuration are important. If you configure tracing for every execution in every business application, for example, you risk data overload and alert fatigue. Stackify Retrace collects traces around certain types of requests that warrant review: faster or slower than normal requests, new web requests, requests introducing new SQL calls, requests introducing new exceptions, abnormal satisfaction scores, and others. In this way, Stackify delivers actionable insights on the areas of application code most prone to cause issues. Of course, Stackify supports customized tracing configurations to ensure DevOps teams capture necessary data for every application being monitored.
There are several tools available for application tracing, each with their own features and capabilities. Below are some of the most popular application tracing tools.
A powerful SaaS solution offering full lifecycle application performance management (APM), Stackify Retrace provides code-level tracing, metrics monitoring, centralized logging, and real user monitoring. Retrace helps developer and DevOps teams optimize performance, eliminate errors, and improve the user experience through advanced metrics, alerts, customizable dashboards, and extensive drill-down capabilities from virtually every screen. Paired with the Netreo OTel Appliance, Retrace provides end-to-end observability across modern, containerized applications, helping developers and DevOps teams optimize the performance of all your mission-critical, cloud-native applications.
A collection of tools, APIs, and SDKs, OpenTelemetry (an open source CNCF project) is used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis. OpenTelemetry is a popular choice for organizations looking to implement observability in their cloud-native applications, because the open-source tool captures telemetry data across modern, containerized environments. However, DevOps teams may also discover OpenTelemetry is the secret to ongoing success in other ways. Instrumenting OpenTelemetry in all applications during development ensures excellent observability, faster issue remediation when applications go into production, and interoperability across vendors tools that support the open-source solution.
Developed by Meta, Canopy is a distributed tracing tool designed to be highly scalable and efficient. Canopy focuses on providing detailed tracing information with minimal performance overhead and is known for handling high-throughput environments and integrating seamlessly with existing monitoring and logging systems.
An open-source tool for end-to-end distributed tracing, Jaeger is designed to help monitor and troubleshoot microservices-based systems. Jaeger supports multiple programming languages and integrates seamlessly with other monitoring tools.
Another open-source project based on Google’s Dapper, Zipkin captures timing data to troubleshoot latency problems in distributed systems by sending, receiving, storing, and visualizing traces both within and between services. Zipikin includes a web UI for viewing traces, supports integration with HTTP, Apache Kafka, Apache ActiveMQ, or gRPC for reporting trace data, and uses Cassandra and Elasticsearch for scalable storage.
Developed by Google, Dapper is a large-scale distributed systems tracing infrastructure. Dapper provides insights into the performance of complex, distributed applications by collecting and analyzing trace data across different services. The distributed tracing solution has influenced many other tracing tools and frameworks, contributing significantly to the development of tracing methodologies. While Dapper was at the forefront of distributed tracing, many more popular implementations of distributed tracing are in use today.
Application tracing and logging record information about an application’s behavior but serve different purposes and contexts.
Tracing and metrics are important elements of observability.
In summary, an observability solution would typically use metrics, traces, and logs for holistic insight into the performance of distributed applications and infrastructures.
Application tracing is a powerful technique for understanding and improving the performance and reliability of software applications. By recording detailed information about the application’s execution flow, tracing helps developers diagnose issues, identify bottlenecks, and ensure that complex systems run smoothly.
Whether you’re working with monolithic applications or modern microservices architectures, implementing effective tracing significantly enhances system maintenance and optimization. By using the right tools and techniques, you’ll gain valuable insights,, improve application performance, and deliver a better user experience.
Start your free Stackify Retrace trial today and experience the power of application tracing and full lifecycle APM.
Theophilus Onyejiaku has over five years of experience as data scientist and a machine learning engineer. His expertise includes data science, machine learning, computer vision, deep learning, object detection, model application development and deployment. He has written over 650 articles in python programming, data analytics, the aforementioned fields, and so much more.
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