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Discover the importance of DevOps monitoring — including what exactly you should look to monitor, as well as the tools you can use to be successful.
DevOps is a practice that aims for continuous improvement, rapid delivery, and cost optimization — combining several engineering best practices to execute successfully.
As a result, DevOps requires a diverse set of engineers to support the practice within an organization. Implementing DevOps at an enterprise level often requires a team of platform engineers, automation engineers, build and release engineers, data analysts, database engineers, and product managers.
Yet, DevOps best practices emphasize using the right tool for the right engineering task.
In this guide, we’ll cover the importance of DevOps monitoring — including what exactly you should look to monitor, as well as the tools you can use to be successful.
Table Of Contents
DevOps monitoring refers to the continuous, automated process of identifying, tracking, analyzing, and reporting on specific components of the entire pipeline. The pipeline comprises continuous planning, continuous development, continuous integration, continuous testing, continuous deployment, and operations.
Continuous Monitoring (CM) and Continuous Control Monitoring (CCM) are terms engineers also use to refer to DevOps monitoring.
Monitoring DevOps increases development efficiency by allowing teams to find potential issues before releasing code to production. Engineers in DevOps can accomplish this in Sprints, which involve planning, designing, developing, testing, deploying, and reviewing a set amount of work within a specified period.
The benefits justify the effort. With DevOps monitoring, you can:
Overall, DevOps monitoring helps ensure an organization follows best practices throughout the DevOps lifecycle to maintain optimal customer experiences at the lowest cost.
Continuous monitoring in DevOps comes in four forms:
Here are brief descriptions of each:
Infrastructure monitoring involves detecting, tracking, and compiling real-time data on the health and performance of the backend components of your DevOps tech stack. Those components include servers, databases, virtual machines, and containers, among other computing components in a system.
There are two types of infrastructure monitoring:
Each approach has its advantages and disadvantages. For example, agent-based monitoring collects more in-depth data because it is designed specifically for a particular monitoring platform. On the flip side, if you want to migrate to another platform, the agent may not be compatible with your new platform, resulting in data loss.
Several infrastructure components, including VMs (such as Hyper-V and VMware), servers, networking, storage, and flow devices, come with built-in agentless monitoring capabilities. You can also manage these components' monitoring centrally. You can combine the two approaches to build a comprehensive monitoring strategy.
This ongoing process involves monitoring an application's performance and availability, along with the effects the two have on the user's experience. A monitoring application tracks your app's hardware utilization, SLA status, platform performance, and user response times.
Among the metrics, DevOps engineers can monitor here are server diagnostics, error logs, network traffic reports, historical statistics, and failure diagnostics.
The software and hardware engineers use here enable them to monitor the health and performance of network components, such as switches, servers, and routers. A network monitoring system tracks bandwidth, uptime, and bottlenecks, such as failing switches or routers.
Monitoring tools perform periodic checks to enable engineers to detect failing or failed incidences before they can affect user experiences.
The DevOps pipeline involves a multitude of changes that can cause significant cost overruns, so tracking costs throughout is essential. Thus, any cloud cost anomalies will not surprise you with a hefty bill.
Monitoring costs involves identifying resource usage. Besides real-time metrics, some advanced cost intelligence tools can collect exact costs per unit and per customer or project and transmit that information to engineers and finance.
With this capability, you can forecast cost of goods sold (COGS), secure gross margins, and optimize resource utilization throughout different phases of DevOps.
So, what are the best tools for continuous monitoring in DevOps?
DevOps tools offer several potent benefits:
Here are some of the top monitoring tools you can use, organized into several DevOps categories.
If you are on a tight budget or want continuous monitoring that you can customize, open-source software may be helpful. Here are four examples:
A pioneering DevOps monitoring tool, Nagios offers server, application, and network monitoring capabilities. It can track any device with an IP address. It also monitors multiple server services, including POP, SMTP, IMAP, HTTP, and Proxy under Linux and Windows. It enables application monitoring as well, including CPU, swap, memory, and load analysis.
Nagios is a free download, has a simple web interface, and supports over 5,000 server monitoring integrations. The free, open-source version of Nagios is called Nagios Core. The paid version, Nagios XI, monitors infrastructure, applications, networking, services, log files, SNMP, and operating systems.
Prometheus is also downloadable. It comprises several monitoring tools useful in a DevOps culture, such as alerting, saving time series on local disks or memory, and displaying data graphically (with Grafana). It also supports many integrations, libraries, and metrics types.
Take a look at SysDig if you prefer a managed enterprise Prometheus monitoring service.
A top Nagios alternative, Zabbix also monitors real-time network traffic, services, applications, clouds, and servers. You can also run it on-premises or in the cloud. Zabbix 5.4 features improved distributed monitoring, high availability, and support for many types of monitoring metrics, allowing you to scale your monitoring capabilities in a continuously evolving culture like DevOps.
If you are looking for a small monitoring solution for Unix systems, Monit can help. In Monit, you can observe daemon processes, especially those that start at system boot from /etc / init /, such as apache, sshd, sendmail, and MySQL.
Monit also offers error detection and alerting as well as monitoring for filesystems, directories, and files on the localhost. Also, you can use it to monitor cloud, host, and systems, including various internet protocols (HTTP, SMTP, etc.) and CPU and memory usage, as well as load average.
The following tools offer a nearly “all-in-one” solution for continuous monitoring.
Sensu's monitoring as code solution provides health checks, incident management, self-healing, alerting, and OSS observability across multiple environments. You can codify monitoring workflows in declarative configuration files and share them with your engineers.
You can also treat them like code, which means you can review, edit, and version them. Sensu Go is not only scalable, but it also integrates with other DevOps monitoring solutions like Splunk, PageDuty, ServiceNow, and Elasticsearch.
Splunk's continuous monitoring features let enterprises monitor the entire application lifecycle. It provides real-time infrastructure monitoring, analytics, and troubleshooting capabilities for on-premises, multi-cloud, and hybrid environments. Also included are real-time alerts, full-stack visibility, Kubernetes monitoring, visualization, scaling, and monitoring automation in one place.
Splunk's online community of over 13,000 active users and over 200 integrations can be a great source of support and customization as well.
If you are comfortable using Amazon S3 or Google Cloud Storage buckets as your backend storage, ChaosSearch makes it easy to collect, aggregate, summarize, and analyze metrics and logs. You can also set up triggers and alerts to send engineers timely notifications about anomalies and monitor infrastructure components, including servers, load balancers, and services.
It also monitors Kubernetes or Docker containers. As well as allowing storage-based isolation on Amazon S3, it supports SSO and RBAC data protection.
Sematext is an all-in-one monitoring solution designed for DevOps teams who need to monitor both back-end and front-end logs, performance, APIs, and the health of all computing environments.
You can also monitor real users, devices, networks, containers, microservices, and databases. Still, you can set up log management, synthetic management, and triggers and alerts. Sematext's dashboards enable users to visualize all data and derive actionable insights from it.
Engineers can store, search, and analyze data from multiple sources with Elastic Stack, a more sophisticated version of the popular DevOps tool, ELK. Logs, SIEM, endpoints, metrics, uptime, and APM with security are among ELK's use cases.
ELK is an acronym that stands for ElasticSearch, Logstach, and Kibana, its three key components. Elasticsearch allows you to ingest data from any source, in any format, and simultaneously feed it to Logstash. Logstash processes the data on the server-side while Kibana visualizes and shares the transformed and stored data.
Honorary Mentions: LogicMonitor, New Relic, Dynatrace, DataDog, Sumo Logic, and BMC Helix Operations Management.
This category includes a new generation of AIOps tools that leverage artificial intelligence and machine learning techniques to enrich telemetry data. AIOps tools help identify issues in your enterprise system by automatically collecting massive amounts of data from multiple sources.
BigPanda's event correlation algorithms automate the process of aggregating, enriching, and correlating alerts from various infrastructure, clouds, and applications. It reduces alert noise by combining different alerts into one, high-level incident. It also sends alerts via pre-defined channels, such as ticketing, collaboration, and reports.
DevOps is characterized by multiple teams working on code simultaneously to foster rapid and frequent application updates. The continuous improvement results in many code changes. Teams must be able to ensure all engineers are using the same version of source code. Source code control tools help with that.
Many DevOps teams use Git as their source code management platform. Its local branching model, multiple workflows, and staging areas make it a popular alternative to Mercurial, CVS, Helix Core, and Subversion.
It is installed locally, however. GitHub allows for remote teamwork and distributed source code control in the cloud. Bitbucket and GitLab are both suitable for enterprise use cases.
Jenkins, RedHat Ansible, Bamboo, Chef, Puppet, and CircleCI are some of the best CI/CD tools out there. Monitoring the CI/CD pipelines of these tools can increase visibility into your pipeline in all environments, whether it is in development, test, or production.
There are several tools and methods for getting visibility at the code level. As an example, you can use Jenkins with Prometheus (ingest and store) and Grafana (visualization). Or you can use an end-to-end continuous monitoring solution for your CI/CD pipeline, such as AppDynamics or Splunk.
AppDynamics provides real-time customer and business telemetry, enabling you to monitor infrastructure, services, networks, and applications with multi-cloud support. It also offers visibility into Kubernetes, Docker, and Evolven. It also provides root-cause diagnostics, a pay-per-use pricing model, and hybrid monitoring.
A test monitor evaluates an ongoing test and provides feedback. In addition, test progress monitoring and control involve several techniques and components that ensure the test meets specific benchmarks at every stage. Selenium is an excellent example of a test progress monitoring tool.
Selenium is an open-source tool for automating web apps for testing. But you can do more with it. Using Selenium WebDriver, for instance, you can automate regression tests and suites using browser-based regression tests that are scalable and distributed across multiple environments.
Selenium Grid provides a central point from which you can distribute and run tests at scale (several machines, various OS/browsers, and many environments). Selenium IDE is the Firefox, Chrome, and Edge add-on that will let you do simple record and playback of interactions with the browsers.
There are several enterprise-grade tools available that can aggregate and cross-analyze data. Even though BigPanda can aggregate data from multiple sources, PageDuty is a suitable solution for DevOps teams who need on-call management, incident response, event management, and operational analytics.
PageDuty is a dispatching service that also aggregates alarms without creating alert noise. By offering an easy-to-use GUI and well-organized data, it helps show correlations between events.
It integrates monitoring systems, customer support, API management, and performance management. Since it supports over 550 integrations, you can connect nearly any monitoring tool or log management tool as long as they can start REST calls or send emails. The integrations include AppDynamics, Microsoft Teams, AWS, ServiceNow, and Slack, which you might be already using.
Alternatives to PageDuty include ServiceNow and Slack.
While some cost optimization tools offer traditional cost reporting, more advanced cloud cost intelligence platforms provide rich insights in the context of your business — like CloudZero.
Whether you are an engineer, manager, or part of a DevOps team, CloudZero has powerful features you will love.
With CloudZero, you will be able to: