We have often discussed how the monolithic structures are different from the microservices in terms of architectural functionalities and security integration. As major organizations are now adopting microservices, it is crucial that they are familiar with the know-how of the systems.
The primary step of installing microservices is monitoring. From predicting failure to reporting glitches, monitoring the containers is an important measure to implement the microservices for enterprises that are still establishing or are looking for expansion.
In this blog, we will be addressing concepts like, why monitor, what principles to follow, measures to take and tools that monitor the microservices.
The failure of systems is an inevitable fact. Even the most high-tech application cannot escape the risks of failure. Thus monitoring is one of the most important steps in developing and maintaining your microservices. A major part of monitoring is the real-time report it generates on the functioning of the services. A collective data from monitoring can indicate the organizations to focus on the critical areas. For instance, in case the data reveals that a pattern of failure is often noticed during the initial phase of development, the developers can put in extra effort into decreasing the failure.
Open source software applications can gather data, monitor your microservices, reports with filtering out critical events that need to be addressed. They are gaining momentum among many upcoming organizations.
Microservices breed on fewer dependencies and divert from DevOps practices of visibility.
Monitoring with Principles
The principles of microservices provision the designing and successful integration of the containers for the enterprises. However, for more effective monitoring, establish these principles and allow a shift in organizational and technological improvements:
What’s Inside the Containers
The primary characteristic of microservices is that they are built-in containers that separate each activity. These black boxes(containers) do not necessarily contact with the environment around, which makes the development easier for the organizations. However, it makes the task of monitoring and troubleshooting difficult too. The isolation of microservices hinders these common activities and make the developers wonder if the application or the code is performing or not. They breed on fewer dependencies and divert from DevOps practices of visibility.
Therefore, either the developers can integrate their code directly into the environment or you can leverage the kernel-level instrumentation approach.
The Orchestration Systems
Sometimes, the infrastructure of the microservices also needs monitoring for reporting on issues like which container is utilizing CPU shares more than the allocated amount. They prove to be more important than the containers which are ephemeral.
Thus, an orchestration system is required to alert the monitoring tool about such shifts in the infrastructure. This helps the enterprises to define the microservices and comprehend the state of deployment for each service.
APIs in microservices are the only loose ends and are exposed to other teams. The fact that they are prone to hacking, monitoring becomes a mandate for APIs. More than the binary up/down checks, it becomes crucial to understand any noticeable changes in the usage of services.
Map Monitoring to Your Organizational Structure
Monitoring doesn’t end with technological systems only. It extends to the organizational structures and resources too. The developers and other teams working on the microservices should also adapt to the faster and agile software networks that restructure the development process. This can include, small teams, control over each team in terms of operations and their course of functionalities.
Tools facilitate the delivery of capabilities to customers for the organisations.
Monitoring Microservices: What to Measure
Since monitoring is performed to gather the data based on a bunch of metrics, the metadata generated by the tools ends up in gigabytes in no time. There has to be a parameter that dictates what to measure first and how. The most important metrics you would want to include are:
As the name goes, the application and its performance is the first taker of monitoring. You’d want to know if the application is successful in registration and acceptance of users. Therefore, the functional behavior of the system in making a task successfully complete requires end-to-end application monitoring.
Often there is nitty-gritty to the infrastructure that needs to be kept in check too. For instance, the execution time, a tab on the number of requests and average response time. These parameters of the platform collectively make a dashboard where the performance and behavior of the system are mapped. Any degradation in the performance is reported via the monitoring tool along with predicting failures that might occur. Thus, these metrics help in maintaining the steady progress of your microservices.
While most things can be anticipated in the microservices, new developments still remain outside the circle of forces. They are a threat in terms of interfering with the system and leading to failure. Therefore, monitoring those new code deployments and recording the time-series data along with configuration updates becomes important in curbing risks and future failures.
Monitoring Tools For You
Tools facilitate the delivery of capabilities to customers for the organizations. They provide and perform the necessary actions that shell out results and thus, success. Tools for monitoring behave in two ways, as libraries to instrument code and as a platform for a collection of codes.
These are a few microservices monitoring platforms you can choose from:
As a platform for all, Grafana allows you to create, explore, and share dashboards where you can query, visualize, alert on metrics. The key here is to interpret and analyze the gathered data in an optimized manner. An open source platform backed by a vibrant community, it helps foster a data-driven culture for your teams.
It is another leading open source solution that powers your metrics and alerting. It offers features like efficient storage, instrumentation of series in libraries and bridging of third-party data. Prometheus was originally developed by SoundCloud.
A smart application performance monitoring(APM), Raygun diagnoses and fixes performance issues for your microservices with easy identification. It supports .NET while Java and Ruby support is in the development phase.
It allows building real-time data pipelines and streaming apps. It facilitates integration with other platforms like Zipkin for delivering robust solutions.
Future-Proof, multi-cloud monitoring at scale, the platform promises developers of monitoring servers, services, applications, and business KPIs. Sensu allows usage with containers like Docker, RKT, and LXC.
Featuring collection and lookup of data, Zipkin is a distributed tracing system. The open source platform analyzes latency problems and includes instrumentation libraries along with the collector processes.
It collects, aggregates, processes, and exports information about running containers. It natively supports Docker containers while it keeps resource isolation parameters and histograms of historical resource usage for each container.
Originally called Simulate Protocol Interactions in Go simulates protocol interactions in the Go language. Spigo pushes new visualizations and metrics for your microservices architecture simulations.
While there are many other solutions out there in the market, these were few of the most used ones by the organizations.
The architectural up-gradation of infrastructures in technology gave rise to microservices in forms of containers. It is now being utilized for accelerating speed and performance of the system. This resulted in the need for proactive monitoring of this precipitation for the ease of management that lowers the rate of failures. Organizations need to adapt to these operational and technological changes for a smooth shift into the world of microservices.
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