Monitoring systems become a critical thing for having a robust system running in production. But moving to microservices can break the monitoring strategy as traditional methods alone cannot connect performance and dependencies across the distributed architecture. It also leads to an increase in your mean time to resolution (MTTR).
It is essential to monitor microservices as the increased complexity of software can make it difficult to understand the performance and troubleshooting problems. A robust monitoring system will allow you to gather metrics from infrastructure and services while using those metrics to gather insights from the operation of the system.
With system monitoring, you are not only obliged to react to unconventional issues but it also helps in predicting system behavior. The monitoring stack is developed out of three major components: metrics, logs, and traces where each of them feeds collected data from several services into its dashboard.
While collecting metrics from the systems you must focus on the latency, traffic, errors, and saturation of the services that will help in determining when there is a need for alerts in the system.
Moreover, to achieve observability data needs to be collected from multiple sources, i.e., from both infrastructure and running services. There must be a consistent data format to store and process the collected data which can also make logs searchable. And to understand the system behavior, logs must include information like timestamps, identifiers, source, and level/category. They should be human-readable as well as parseable by a machine.
Every service should have an ID field that enables you to follow the execution path of a system request. It will allow logs to be grouped in the same context. Besides, you can set up distributed tracing to better reconstruct the requested journey through visualization of execution flow within the system. It will also offer insights on the duration of operations and how services relate to each other while performing the given task.
Given the dramatic changes in software delivery, monitoring requires an overhaul to perform accurately in the microservice environment. But, what is the principle behind which microservice monitoring actually works?
Monitoring microservices have five principles, including:
Monitoring containers and what’s inside
Regular alert on the service performance rather than container performance
Monitoring multi-location and elastic services
Mapping monitoring to organizational structure.
These five principles of monitoring microservices will enable you to establish effective monitoring while addressing both organizational and technological changes.
So, let's dig a bit deep into each principle.
Containers gained popularity as the building blocks of microservices. The portability, speed and isolation of the container made it easy for developers to eagerly accept a microservice model. They are black boxes to almost every system which is incredibly useful for the developers. It enables a high level of portability throughout the system development.
However, when it comes to monitoring, operating and troubleshooting service, black boxes make most common activities harder. And from the DevOps perspective, one needs in-depth visibility of containers instead of just knowing their existence.
The typical instrumentation process in a non-containerized environment (an agent residing in the user space of VM or host) doesn’t work well for the containers because of its small, independent processes and low dependencies. Moreover, at scale, running multiple monitoring agents around a medium-sized deployment can be highly expensive.
To overcome this, developers can either instrument their code directly or leverage a universal kernel-level approach for instrumentation to monitor all containers and application activity on hosts.
Another challenge that comes with containerized environments is making sense of their operational data. The metrics of a single container have a lower margin value than accumulated information from all the containers that build a service or function.
This applies to both application-level information and infrastructure level monitoring to know the queries with slowest response times, URLs seeing errors and which services’ containers are utilizing most resources apart from the one allocated to them.
Software deployment requires an orchestration system like Kubernetes, Docker Swarm, and Mesosphere DC/OS to convert a logical blueprint of application into physical containers. Developers use these systems to define the microservices and understand the service state in the deployment.
DevOps teams need to redefine alerts to concentrate on characteristics getting close to the monitoring experience of the service. These alerts will be the first-line defense while accessing whether or not something is affecting the application. Container native solutions will leverage orchestration metadata to dynamically aggregate container & application data and account monitoring metrics on a per-service basis.
Elastic services are not a new concept, however, the velocity of change will be much faster in container environments rather than in virtualized environments. The continuously changing environments can wreak havoc on tenacious monitoring systems.
Often monitoring legacy systems requires manual tuning of metrics based on individual deployments. This manual tuning can range from configuring collection based on the application operating on a certain container or defining individual metrics to be stored. Although this might work on a small-scale application it wouldn’t work well in anything larger. That is, microservices monitoring should be able to expand and shrink in steps with elastic services without manual intervention. Moreover, you will need to implement a monitoring system that can span different locations like multiple data centers or clouds and operate dynamically in container-native environments.
APIs or Application Programming Interfaces are the only elements in the microservice model that comes in direct contact with other teams or service providers. It makes it essential to monitor APIs.
API monitoring can be done in several ways but it should go beyond binary up or down checks. For instance, you can consider the slowest endpoints of the service that can reveal certain issues or check the most frequently used endpoints to see if there are any changes in the service usage due to design or user change.
Not only that but you could also trace service calls throughout the system to understand the user experience while breaking the information into application and infrastructure based views of the system environment.
If a business wants to take advantage of the microservices architecture approach, its team should also mirror microservices. It means smaller and loosely coupled teams that can opt for their directions to meet the needs of the entire system. And each team takes more responsibility for language to be used, bugs handling and well-structured operations.
There is a wide range of tools available to assist in developing microservices architecture where each of them has its task to fulfil. So what are these top tools to monitor microservices?
Logstash is a free, open-source tool that runs on Java Virtual Machine (JVM). It provides a wide range of input mechanisms like Unix Syslogs, Microsoft Windows Event logs, TCP/UDP, STDIN and more. It helps in centralizing, transforming and stashing data.
Reimann is a monitoring tool that aggregates events from applications or hosts and feeds them into a stream processing language enabling them to be manipulated, summarized and actioned. It can also track events and allows build checks taking advantage of combinations and sequences of events. Additionally, it offers notifications the ability to send events to other storage and services.
Prometheus is also an open-source metrics-based system for monitoring. It has a simple, powerful data model and a query language to help developers in analyzing the performance of infrastructure and applications. Software like Docker and Kubernetes already have Prometheus client libraries while other third-party software that doesn’t have Prometheus format metrics can implement hundreds of available integrations.
Elastic Stack is an end-to-end software stack that helps in search and analysis solutions. It can help in getting data from any source in any format to enable searching, analyzing and visualizing real-time data.
Kibana is a dashboard and virtualization tools attached to ElasticSearch. It can help in creating graphs and dashboards. Having its web server, it can run on any host connecting ElasticSearch back end.
Glowroot is the fast, simple and clean application performance monitoring tool that traces errors and slow requests. It also offers support for visualizing response time percentiles and breakdown in charts.
AWS Cloudwatch is highly beneficial for system architects, developers and administrators. It helps in monitoring AWS applications in the cloud. It is designed to give metrics based on CPU usage, request counts and latency. Users can send the metrics and logs to be monitored by CloudWatch.
Datadog is a monitoring tool for cloud-based applications that offers monitoring of servers, tools, databases and services via a SaaS-based analytics platform. It can be used for monitoring Docker performance by collecting metrics of all the containers.
LightStep delivers unified visibility and observability across the multi-layered architecture. It allows teams to identify and resolve regressions, regardless of the complexity and scale of the system. It is helping numerous developers to improve the way they build and operate microservices.
You can use Graylog in conjunction with LogStash as a centralized server. With its usage, one can easily explore data within the system. Moreover, it is scalable and developed to expand with the needs of a business.
With the help of the above-mentioned five principles and tools for monitoring microservices, it will become easier and efficient for the development teams to control the errors and other slowdowns of the system. Not only the data tracing will help in identifying the issues and resolve them but also allows developers or DevOps teams to measure system health and performance to understand the effect of change while prioritizing areas of improvements.