DevOps is the practice of methodically monitoring numerous development-related areas, beginning with formulating an operation plan, carrying out development work involving integrating applications and testing, and finishing with deployment and operations.
DevOps can incorporate several engineering best practices for successful operation. It constantly attempts to accomplish continuous process improvement, better resource management, cost optimization, and speedy delivery of final goods.
So, to handle the activities that are required of them inside an organization, many engineers are needed to support and aid DevOps. A group of knowledgeable automation engineers, platform engineers, data analysts, build and release specialists, product managers, and database engineers are required to manage DevOps.
It is feasible to increase development productivity and provide the teams more time to identify potential problems before releasing the code to production by monitoring the DevOps. By carefully planning, creating, designing, evaluating, testing, deploying, and reviewing some work within a given time frame, the engineers working with DevOps can accomplish this.
What is DevOps Monitoring?
DevOps monitoring is the process of observing and evaluating the functionality and state of systems and applications to spot and address problems as they arise. This involves gathering information on a variety of topics, such as CPU usage, disk space, and application response times. DevOps monitoring best practices can assist teams in preventing outages or service degradation by seeing issues early.
This may resemble the type of monitoring used in any well-planned IT operation in many aspects. DevOps monitoring and alerting, though, goes further; Planning, development, deployment, and review/evaluation are all short cycles that are guided by the DevOps methodology for teams. DevOps monitoring must consequently be continuous for it to be completely integrated.
What exactly is continuous monitoring in DevOps, then? The process of routinely and vigilantly looking for indications of performance degradation in systems, networks, and data is known as continuous monitoring. Continuous monitoring, which can be carried out manually or automatically, often entails utilizing software to check for vulnerabilities and follow changes in security settings. The DevOps operations tools for continuous monitoring try to spot hazards before they become problems and handle them.
Types of DevOps Monitoring
Following are the types of monitoring in DevOps:
1. System performance monitoring
Undoubtedly, we will start with system performance monitoring while discussing different forms of monitoring in DevOps. DevOps application monitoring professionals closely monitor every factor that could affect your system's performance, including CPU usage, RAM usage, disk space usage, network usage, and more.
2. Hardware health monitoring
Hardware health monitoring is the next sort of monitoring. The health of your hardware is essential for the operation of your system, much as your body depends on your brain. As a result, variables like CPU temperature, HDD health (RAID state, SMART values), and others are frequently monitored.
3. Service monitoring
The availability and DevOps performance monitoring of services is a crucial task for DevOps. They also examine web server statuses, processes, and other things.
4. Control and audit over system security
Malware or viruses are frequently to blame for subpar system performance or outright system failure. DevOps, therefore, frequently scans files for malware and potential network intrusions. They employ firewalls and application-level detectors in their work.
5. Monitoring of third-party integration
In the era of third-party integration, an explanation of API monitoring is required. DevOps professionals monitor API performance and availability to ensure seamless integration with other systems.
Aims of DevOps Monitoring
One objective of monitoring is to minimize important metrics that are measured in terms of time to achieve high availability:
- Time to detect (TTD): When performance or other difficulties occur, development teams are provided with detailed diagnostic data about the problems via automated monitoring.
- Time to mitigate (TTM): DevOps teams act based on the knowledge to resolve problems as soon as feasible so that users are no longer impacted.
- Time to remediate (TTR): Teams measure resolution times and strive to become better over time. Teams then focus on finding the fundamental causes of issues to fix to prevent a recurrence.
Monitoring also aims to track usage to enable validated learning.
Every deployment is an opportunity to monitor experimental outcomes that strengthen or weaken the hypotheses that motivated the deployment, according to the central idea of validated learning. Teams can assess the impact of change and inform business decisions by tracking use and version differences. The team can pivot or fail quickly if a hypothesis is weakened. The group can either continue or double down if the hypothesis is confirmed. New hypotheses and backlog prioritization are the results of these data-driven judgments.
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Importance of DevOps Monitoring
Continuous monitoring is expanded by a DevOps strategy to include development, testing, and staging environments. There are several explanations for this.
1. Code changes will be visible frequently
Continuous integration and deployment have accelerated change and made production environments more difficult due to the rapid code changes they have enabled. Numerous workloads with various operational needs for scale, latency, redundancy, and security are operating in production as a result of the introduction of microservices and micro front ends into contemporary cloud native systems.
The need for more visibility has been pushed by this. Teams must not just identify and address a deteriorating customer experience but do it quickly.
2. Automated collaboration
Greater team communication between the development, operations, and business functions is implicitly necessary for DevOps. A notable finding from Atlassian's DevOps study was that cooperation could be hampered by a lack of tool integration, which makes it difficult to coordinate with various teams.
Collaboration may be automated by using techniques like viewing the entire development pipeline inside the editor. Additionally, create automation rules to change the status of associated Jira issues and post messages to the team's Slack channel in response to commits or pull requests. Additionally, benefit from insights that include reports on testing, scanning, and analysis.
Personalization and improved conversion funnels create the need to adapt products to client needs, which necessitates ongoing testing. Monitoring systems find it difficult to express the root cause of a poor user experience in production situations because they can run hundreds of trials and have flags.
Apps may become more vulnerable when the demand for always-on services and applications rises, along with the strict SLA promises. Service-level objectives (SLOs) and service-level indicators (SLI) must be specified by development teams and must be tracked and used as a basis for decision-making.
4. Change administration
Change management is crucial, especially for mission-critical applications like those in the financial and healthcare sectors, as changes are the primary cause of production outages. It is necessary to identify the risks connected to modifications before automating approval processes based on such risks.
A thorough understanding and monitoring plan are needed to handle this complexity. This calls for the definition and adoption of monitoring techniques as well as the possession of a suite of sophisticated monitoring instruments that are essential to the development processes.
DevOps monitoring vs Observability
By using outdated techniques that are built around dealing with predictable failures, modern distributed applications cannot be successfully monitored. Effective debugging and diagnostics demand that the system be observable, which means that its internal state can be inferred by looking at its output. Microservices design has become the de facto standard for web applications.
For development teams, the distinction between observability and monitoring is frequently hazy.
Businesses that have adopted the application monitoring tools in DevOps philosophy typically start by breaking the application down into smaller units called microservices, which increases operability and speeds up incident response. But as their systems become more intricate, they must make sure that they can still identify system faults and respond to them quickly.
Building dashboards, assessing long-term trends, and setting alerts all require monitoring. It provides you with information about the operation, development, and usage of your apps. However, the difficulty in predicting production failures due to their nonlinear nature makes monitoring sophisticated distributed applications problematic.
For creating and maintaining microservice-based systems, monitoring is still a crucial tool. A clear understanding of your system's health can be obtained from monitoring rules and metrics that are uncomplicated and centred on actionable data. Although monitoring will not guarantee complete system immunity from failure, it will give you a comprehensive picture of how the system behaves and performs in the real world, enabling you to understand the effects of any failure and the fixes that must be made.
The control theory concept of observability gauges how well one can deduce a system's internal states from its outputs. Instrumentation is used by observability to produce insights that support monitoring. Monitoring, then, is what you carry out once a system is observable. Monitoring is not possible without observability on some level.
Even with a complicated microservice design, an observable system enables you to comprehend and measure a system's internals to more quickly go from the effects to the cause.
Three major pillars can be used to categorize observability:
- Logs: Timestamped, unchangeable records of discrete events that can reveal systemic unpredictability and shed light on how the system behaved when things went wrong. For log visualization systems to automatically index and make logs easily queryable, it is strongly advised to ingest logs in a structured manner, such as in JSON format.
- Metrics: They are the cornerstones of monitoring and are counts or measures that are averaged across time. Metrics provide information such as how much of the total memory is consumed by a method or how many requests are processed by a service each second.
- Traces: In a distributed system, a single trace for a single transaction or request shows the operation as it goes from one node to another. Using traces, you may dig into the specifics of a given request to identify the modules that lead to system problems, track data flow through the modules, and identify performance bottlenecks. s in JSON format, which enables auto-indexing and simple querying of logs by log visualization tools.
DevOps Monitoring Use Cases
Your DevOps production needs to be transparent at every level. A broad perspective of the condition and activity on your infrastructure platform is also included. The smallest units of value, such as a single line of code, however, require your attention. Let us go over the primary tasks at hand.
1. linting the code
The style, syntax, and potential problems in your code are examined by code linting tools. They frequently also look for compliance with coding standards and best practices. Linting can assist you in identifying and resolving bugs in your code before they result in runtime errors or other problems. Linting also helps to guarantee that your code is clear and logical.
2. Git workflow procedures
When two or more developers try to work on the same section of a project at the same time, codebase conflicts may result. Commits and rollbacks are only two of the capabilities in Git that can assist you in managing and resolving conflicts. You may make sure that your project is cohesive and consistent by keeping an eye out for conflicts in the git workflow processes.
3. Logs for continuous integration (CI)
CI logs can be used to check whether your code builds are succeeding or whether issues or warnings have occurred. Resources will be needed to investigate, troubleshoot, and correct any faults. Additionally, keeping an eye on your logs might help you spot any potential problems with your build process or codebase that require attention.
4. pipeline logs for continuous deployment (CD)
Monitoring your CD records can give you important information about the pipeline's condition and health. You can debug unsuccessful deployments and find any potential issues by keeping an eye on the logs.
5. Changelogs for configuration management
Your configuration management changelogs might offer insightful information about the key changes you have made to your system. You may track human and automated changes made to your systems, spot unlawful modifications, and solve problems by keeping an eye on these logs.
6. Logs for infrastructure deployment
Your deployment logs keep track of when new stacks deploy and whether they succeed or fail. These logs can be utilized to track down problems with stack deployments as well as find unauthorized changes to the infrastructure that might have contributed to a failure.
7. Instrumenting using codes
The technique of adding code to your program to gather information about its performance and course of operation is known as code instrumentation. Instrumentation allows you to track stack calls and view context information. Monitoring the output of code instrumentation enables you to evaluate the success of your DevOps procedures and spot any areas in need of development. Additionally, it can assist with testing and bug identification.
8. Spreading tracing
Monitoring and debugging microservices applications require distributed tracing. It is simpler to find and fix problems when you are aware of how your programs interact with one another (typically through APIs). By locating bottlenecks, distributed tracing can also assist you in improving the performance of your application.
9. Infrastructure monitoring
Network and computer system performance and availability are monitored by infrastructure. Tools for monitoring infrastructure can offer real-time data on variables like CPU usage, disk space, memory, and network traffic. These instruments can assist in locating resource problems before they result in failure or other issues.
10. Network monitoring
A computer network's availability and performance are monitored through network monitoring. Network monitoring tools are used by network administrators to find problems with the network and fix them. Network monitoring analyzes network flow records as well to spot any odd activity.
11. Synthetic monitoring
Software testing called "synthetic monitoring" makes use of digital replicas of real-world systems and parts. Individual system components can have their performance, usability, and dependability tested through synthetic monitoring. DevOps Foundation Certification Online is a great option for individuals involved in areas of service and product lifecycle and want to upskill on the go. It is also useful for managers and employees working in IT firms and bolsters the design and development processes. DevOps Foundation certification can also help developmental consultants and external and internal suppliers.
DataDog is a SaaS platform that provides monitoring services for servers, apps, tools, databases, and services. It is a paid monitoring service. Some of its salient characteristics are:
- Provides turn-key visibility in app and infrastructure components as well as 500 vendor-supported integrations.
- Helps put Auto discovery into practice so that monitoring check configuration may be automated.
- Provides a unified view of the logs, traces, and metrics produced by the infrastructure and application components.
It is a tool that developers can pay for to access and use machine data. Some of its salient characteristics are:
- It looks up, examines, and presents machine data generated by various IT infrastructure components.
- Enables easy navigation between infrastructure monitoring, log monitoring, and APM, as well as a unified picture of various IT services.
- Provides contextual logs for quicker deployments, automatic continuous monitoring, and real-time monitoring of all phases of a development lifecycle.
It is an open-source DevOps monitoring tool for locating and fixing network and infrastructure issues. Some of its salient characteristics are:
- Provides server monitoring services with 5000 add-ons and agent-based and agentless monitoring options.
- Provides network monitoring services to easily find network outages and protocol errors and to keep audit and legal compliance.
It is a free monitoring tool that can be used to find and set up services in any infrastructure. Some of its salient characteristics are:
- Allows for the development of a central register for the tracking of services, health conditions, and updates in real time.
- Facilitates communication between services that have been approved and validated.
- Enables networking task automation to lighten the load on your developers.
Monit, another free and open-source DevOps monitoring software, was developed for UNIX OS monitoring. Some of its salient characteristics are:
- Contains an HTTP(S) interface for navigating the Monit server to verify the status of the services you are monitoring.
- Updated Monit manual provided for user convenience.
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Monitoring systems must advance in step with DevOps deployments and keep up with the complexity and depth of software development. Making the optimal monitoring selection that considers an application's numerous moving pieces and how they work together can be crucial for your upcoming development efforts. To ensure you take the proper action at the right time and receive the highest ROI, it is advised that you hire skilled service providers.
Infrastructure monitoring that keeps an eye on the servers, storage, and other hardware and software components, monitoring of the application that keeps track of API and system responses, transaction time and volume, and general application health. Network monitoring that locates, distinguishes, finds, notices, and reports network inconsistencies.
The need for continuous monitoring in DevOps enhances security, finds and fixes performance issues, minimizes system outages, and helps businesses develop stronger growth and revenue-generating business plans. KnowledgeHut DevOps Tools Full Course will make your career dream a reality. The course is designed to prepare individuals to achieve their dream careers by expanding their horizons and instilling them with DevOps job-ready skills they need. Join us to make your DevOps journey today!
1. What is monitoring in DevOps?
Monitoring the entire development process, including planning, development, integration and testing, deployment, and operations, is a component of DevOps. It entails a thorough and current picture of the state of the infrastructure, services, and applications in the production environment.
2. Which DevOps platform is used for monitoring?
There are many different platforms for monitoring:
- Datical Deployment Monitoring Console.
- Tasktop Integration Hub.
3. How do you monitor a DevOps pipeline?
DevOps pipeline monitoring of each step is essential to spot any compliance or security issues that can prevent the creation of high-quality output. In DevOps, continuous monitoring is responsible for keeping an eye on every step of the pipeline and continually keeping an eye out for any unforeseen dangers.