Modern applications are increasingly distributed in nature – that is, they follow the microservices model where a single application is decomposed into numerous independent, and interdependent services. This is great for reducing technical debt, and advancing innovation. However, with the complexity of distributed applications, diagnosing errors and performance issues that impact customer experience becomes a challenge. Observability, and in particular distributed tracing, helps companies cut through software complexity by enabling teams to solve problems faster, offering end-to-end visibility, working smarter, and creating better digital experiences for their customers.
Tracing or distributed tracing is essential for teams transitioning to the cloud and adopting microservices. It’s because distributed tracing is the most suitable way to understand how requests that make up your distributed applications transit through the microservices. It offers actionable insight by piecing together four essential types of observability data: logs, metrics, events, and traces. Whether you’re a DevOps engineer, site reliability engineer, software team leader, or product owner, you can benefit from distributed tracing.
What is distributed tracing?
Distributed tracing is the process of tracking and observing service requests as they flow through distributed systems. It collects data as the requests go from one service to another, which helps businesses understand the flow of requests through their microservices environment and pinpoint failures or performance issues in the system. With distributed tracing, you can easily observe microservices or functions in serverless environments, virtual machines, multiple containers, on-premises, different cloud providers, or any combination of these.
Why does distributed tracing matter in cloud-native monitoring
Organizations have understood that they need visibility into individual microservices, but more importantly, across the entire request flow. New solutions have risen that quickly and easily instrument applications for collecting, analyzing, and visualizing tracing data with minimal effort. Various open standards for sharing data and instrumenting applications are also in place – giving rise to the perfect storm for innovation in the distributed tracing space.
Benefits of distributed tracing
Reduce MTTD and MTTR
When a customer face an issue or latency in an application, the support team can look at distributed traces to check if it’s a frontend or backend issue. Once the trace shows the cause of the issue, engineers can swiftly troubleshoot and resolve that particular service.
Understand service relationships
Distributed tracing helps developers to discover cause-and-effect connections of the request between services. It also helps to optimize performance by studying distributed traces.
Measure specific user actions
Distributed tracing can measure the time it takes to accomplish various user actions, such as adding an item to the cart and placing an order. It can also identify bottlenecks that degrade the user experience.
Maintain Service-Level Agreements & Objectives (SLAs & SLOs)
SLAs are contracts to satisfy performance standards for a customer, and SLOs are their internal counterpart that set internal performance benchmarks. It helps teams determine if they’re meeting SLAs and SLOs using distributed tracing systems since it collects performance data from precise services.
Improve collaboration and productivity
In a microservice architecture, different teams are involved in completing a request. Distributed tracing can identify the source of an error and the team responsible for resolving it.
Recent trends around distributed tracing
OpenTelemetry is a collection of APIs, tools, and SDKs used to generate, collect, instrument, and export telemetry data. It tracks data points like metrics, logs, and traces to help businesses analyze software’s performance and behavior. OpenTelemetry integrates with popular frameworks and libraries such as ASP.NET Core, Spring, Express, Quarkus, and more. In fact, installation, and integration is quick and you only need a few lines of code.
OpenMetrics is an initiative that utilizes text representation and protocol buffers to transmit metrics at scale. The Prometheus exposition influenced this project and has been described as a project that enables all the systems to ingest and emit data in a certain wire format. The wire format is agreed upon beforehand. It also aims to introduce the n-dimensional space concept via labels.
Momentum behind eBPF
eBPF or Extended Berkeley Packet Filter allows programs to run in the operating system’s kernel space without adding additional modules or changing the kernel source code. It will enable organizations to embrace no-code instrumentation from the OS kernel level. It also provides easier observability into Kubernetes environments and offers benefits around networking. Businesses can help organizations collect full-body trace requests, HTTP requests, database queries, and gRPC streams. They can also collect resource utilization metrics, CPU usage, and bytes sent, allowing businesses to calculate relevant statistics.
Unification of siloed tools
Organizations needs a holistic observability platform that offers integrated solutions rather than siloed tools they have used in the past. These unified tools can better position developers and DevOps to address visualization, querying, and correlation. This unification can be seen with major vendors such as Datadog, Grafana Labs, AppDynamics, and Logz.io that have started offering more comprehensive observability.
Organizations increasingly rely on modern cloud-native applications to identify the root causes of performance issues to ensure a high-quality user experience. Distributed tracing offers crucial insight into application performance by locating the entire journey of a request as it travels throughout the application stack. It is indispensable in a cloud-native world.
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