Application failures can rarely be predicted and cannot be entirely avoided. The growing complexity of modern applications makes it extremely challenging for even experienced developers to determine the root cause for failures. And they can hardly prepare for all scenarios that could potentially bring down their applications.
The need of the hour is a real-time application monitoring system. This is where microservices play a crucial role. Microservices observability is primarily the ability to detect any application problem in real-time and address them at the earliest. With this approach, developers now have easier access to real-time data that can help them locate issues when applications fail.
Microservices architecture is emerging as the industry standard for web applications. Here is everything you need to know about microservices observability.
What is Microservices Observability?
Largely aimed at development teams, microservices observability facilitates data access to identify application-related problems and failures. For example, observability systems can determine why a particular service call failed or the bottlenecks in an application workflow.
Microservice observability can span across multiple systems and run their operations independently. Observability systems perform various activities, including collecting and analyzing information in the form of logs, metrics, and traces. Effectively, they can facilitate raw and granular data that provides insights into the working of complex distributed systems.
It's noteworthy that observability is more effective than monitoring. While both activities can complement each other, observability is more beneficial as it helps developers understand "why" a problem occurred. On the other hand, monitoring uses predefined metrics that inform developers "when" a problem has occurred.
Typically, application monitoring comes after observability. Once observability provides the performance insights, monitoring determines the following action to be performed.
Besides the above, microservices observability is based on three foundational pillars. Let's discuss that in the following section.
Microservices Observability – 3 Pillars
Microservices observability comprises three foundational pillars, namely:
Essentially, the metrics component is a numerical measurement of the web application's speed, performance, and resource consumption. For instance, metrics can measure the number of service requests that an application can handle every second – and the total number of resources (memory and processing power) consumed in servicing this request.
The Traces component enables software architects to monitor applications that span multiple systems. This component keeps track of the application request's ID, name, and time value across its entire lifecycle.
For instance, traces can profile serverless or containerized applications.
The logs component of Microservices observability provides software architects and developers with detailed information on application resources. This time-stamped component provides insights into application behavior as it executes its functions.
Logs are crucial for troubleshooting problems in the microservices architecture. Architects can use log information to identify application defects and debug the code. Thus, logs provide vital insights into what went wrong at any time.
Regarded as a superset of application monitoring, observability provides a high-level view of the application's health. Application developers must build multiple design patterns to simplify troubleshooting to leverage its benefits.
Next, let us discuss six effective design patterns used in microservices.
Microservices Observability – Design Patterns
Design patterns can improve both service management and application troubleshooting. The following are six successful design patterns for service developers:
Health Check API
Occasionally, service developers come across running services that cannot handle requests. This service instance could still be initializing or performing sanity checks. During the initialization process, it's best not to route HTTP requests to this service instance before they can be processed.
A Health Check API provides the endpoint to measure the health of any service. Using this design pattern, the service instance informs the deployment infrastructure if it can (or not) handle requests.
Logs help determine what went wrong with an application instance. As previously mentioned, they aid in the troubleshooting process. Within a microservices architecture, log entries are typically scattered across log files and various services. The solution to that is log aggregation.
The log aggregation design pattern logs every service activity and stores the log files in a centralized location. This enables easier searches and alert functionality. For instance, logs can trigger alerts for selected messages.
When troubleshooting a slow API response, architects often deal with multiple services in the API response. Distributed tracking provides insights into the application instance.
This is similar to the role of a performance profiler in any monolithic application. A distributed tracer records the service call information made during the request.
With the distributed tracer pattern, every external request is assigned a unique ID and can be tracked as it flows between services.
Monitoring systems provide real-time information about application health by collecting relevant metrics from its technology stack. Standard metrics include application-related metrics (like the number of processed requests) and infrastructure-related metrics (like CPU and memory consumption).
Using the Application metrics design pattern, service developers can use their services to collect service metrics. Subsequently, they can expose these service metrics to the metric server.
Service developers need to identify the cause of exceptions when logged by any service. Exceptions can indicate a programming error that developers need to resolve.
Traditionally, developers use logs to view the exceptions. However, logs have multiple limitations, like single-line entries and duplicate exceptions, which cannot work for exceptions.
The Exception tracking pattern is a better approach, as it reports service-level exceptions to a centralized server.
Audit logging can efficiently track user actions. Each audit log record stores the user's identity, the business object, and the performed user action. The audit logging pattern can add the logging code to the business logic. Additionally, event sourcing also provides an audit log for operations.
All in all, audit logs typically prove helpful for customer support, detecting suspicious actions, and maintaining compliance.
Companies moving to the microservices architecture must be able to monitor their applications. When integrated, microservices monitoring and observability can reduce downtime and resolve customer issues.
At Wissen, we enable development companies to overcome the many challenges in microservices and build scale. We also offer a range of technology-based services, including application development, AI and machine learning, and Robotic Process Automation.
Looking for a technology partner that can help you achieve efficiency with microservices? Wissen can help. Connect with us today.