With the rise of microservices based cloud applications & its corresponding complexities, the need for observability is greater than ever. This blog looks into the what-why of distributed tracing along with few best practices to adopt for the same in microservices architecture.
Distributed tracing for Microservices architecture is an emerging concept that is gaining momentum across internet-based business organizations.
We know that microservices architecture introduced an all-new way to scale an application (cloud) with several independent services. It does facilitate high resiliency, scalability, productivity, and efficiency when compared to monolithic architectures.
However, this comes with its own complexities like difficulty in tracing out the bugs or monitoring the traffic flow across the entire infrastructure.
So to eliminate these complexities, distributed tracing was introduced. This way of tracing helps in solving high-level debugging issues and improving visibility within the network. It also supports developers by narrowing down the end-to-end latency and errors that a specific service or function is experiencing at the moment.
This article aims at giving you an overall picture of the distributed tracing world, and its implications over microservices architecture.
Observability is monitoring the behavior of infrastructure at a granular level. This facilitates maximum visibility within the infrastructure and supports the incident management team to maintain the reliability of the architecture.
Observability is done by recording the system data in various forms (tools) such as metrics, alerts (events), logs, and traces. These functions help in deriving insights about the internal health of the infrastructure. Here, we are going to discuss the importance of tracing and how it evolved to a technique called distributed tracing.
A 2018 research shows that 63% of traditional enterprises are changing their facilities to microservices architecture. Since there was a major shift from monolithic to microservices architecture, the need for data tracing within a heavily distributed system became more evident. This distributed tracing drastically reduces the common challenges in monitoring systems with granular observability features.
Let’s imagine an interactive social gaming platform that has millions of users across the globe in all age groups. When a user has checked in some preferences in the platform, the system has to process the data with tight latency and deliver the appropriate outcomes. Here, distributed tracing plays a vital role in capturing each users' requests, processing them across various microservices, and delivers the expected results within a fraction of time.
Let’s see how distributed tracing helps the gaming infrastructure to handle the same.
Some of the use cases are,
Before we look into how distributed tracing is performed during a user request, let’s take a look at the basic terminologies.
Request: This denotes how various cloud applications, microservices, and other functions communicate with each other
Span: This informs about the work done by a single service with respect to time intervals and corresponding meta-data. These are the basic building blocks of trace.
Trace: This implies the end-to-end user requests which consist of single or multiple spans.
Tag: These are the pieces of information (meta-data) associated with each span (recorded along the path) that provide a detailed overview of the actions performed during a span.
A single trace contains a series of spans with associated tags.
Let's now discuss how Distributed Tracing handles a single request.
We have separate tools for performing distributed tracing across the architecture and these fall into three categories.
Additional Reading: Top Observability tools for DevOps Engineers and SREs
Listed below are few links that can be helpful in getting started with distributed tracing within microservices architecture.
So, by executing or practicing the above strategies, a distributed tracing system can be implemented across any microservices architecture.
Now, with the increased adoption of distributed tracing, along comes practical challenges. To stay reliable, we should maintain best practices while implementing this functionality.
Additional Reading: Kubernetes Operators for Automated SRE
Distributed tracing is an efficient technique for monitoring microservices architecture. It gives more precise data and information about the network path. By adopting standardized distributed tracing tools along with end-to-end instrumentation of SRE golden signals metrics, we can wade through the challenges in implementing the same.
Squadcast is an incident management tool that’s purpose-built for SRE. Your team can get rid of unwanted alerts, receive relevant notifications, work in collaboration using the virtual incident war rooms, and use automated tools like runbooks to eliminate toil.