DevOps

Most formatting languages display data in a non-human readable format. Even JSON, the most popular data format in use, has poor code readability.

YAML is an alternative to JSON that formats data in a natural, easy-to-read, and concise manner.

This article will introduce you to the YAML markup language. We cover the basic concepts behind this markup language, explain its key features, and show what YAML offers to DevOps teams.

What is YAML?

YAML is a data serialization language. Back when it came out in 2001, YAML stood for “Yet Another Markup Language.” The acronym was later changed to “YAML Ain’t Markup Language” to emphasize that the language is intended for data and not documents.

It is not a programming language in the true sense of the word. YAML files store information, so they do not include actions and decisions.

Unlike XML or JSON, YAML presents data in a way that makes it easy for a human to read. The simple syntax does not impact the language’s capabilities. Any data or structure added to an XML or JSON file can also be stored in YAML.

Besides human-readable code, YAML also features:

• Cross-language data portability
• A consistent data model
• One-pass processing
• Ease of implementation and use

Users can write code for reading and generating YAML in any programming language. The extensions in YAML are .yaml and .yml. Both extensions stand for the same file type.

Yaml Features

YAML has several features that make it an excellent option for data formatting.

Multi-Document Support

Users can add multiple documents to a single YAML file. Separate different documents with three dashes (---), like this:

---

time: 19:04:12

player: playerOne

action: strike (miss)

---

time: 20:03:47

player: playerTwo

action: strike (hit)

...

Three dots (“…“) mark the end of a document without starting a new one.

Built-In Comments

YAML allows users to add comments to their code. YAML comments start with the # symbol and do not have to be on a separate line:

key: #This is a single line comment 

   - value line 10

   #This is a 

   #multi-line comment

   - value line 20

Clean Syntax

Like Python, YAML relies on indentations to show the levels and structure in the data. There are no usual format symbols, such as braces, square brackets, closing tags, or quote marks. The syntax is clean and easy to scan through.

The clean syntax is why several popular tools rely on YAML, such as Ansible, Kubernetes, and OpenStack.

No Tabs

YAML does not allow tabs. Spaces are the only way to achieve indentation.

It is good practice to display whitespace characters in your text editor to prevent accidental uses of tabs.

Precise Feedback

YAML feedback refers to specific lines in the file. You can quickly find and fix errors when you know where to look.

Support for Complex Structures

YAML provides the ability to reference other data objects. With referencing, you can write recursive data in the YAML file and build advanced data structures.

Explicit Data Types with Tags

YAML auto-detects the type of data, but users are free to specify the type they need. To specify the type of data, you include a “!!” symbol:

# The value should be an int:

is-an-int: !!int 5.6

# Turn any value to a string:

is-a-str: !!str 90.88

# The next value should be a boolean:

is-a-bool: !!bool yes

No Executable Commands

YAML is a data-representation format. There are no executable commands, which makes the language highly secure when exchanging files with third parties.

If a user wishes to add an executable command, YAML must be integrated with other languages. Add Perl parsers, for example, to enable Perl code execution.

How YAML Works

YAML matches native data structures of agile methodology and its languages, such as Perl, Python, PHP, Ruby, and JavaScript. It also derives features from other languages:

• Scalars, lists, and arrays come from Perl.
• The three-dash separator comes from MIME.
• Whitespace wrapping comes from HTML.
• Escape sequences come from C.

YAML supports all essential data types, including nulls, numbers, strings, arrays, and maps. It recognizes some language-specific data types, such as dates, timestamps, and special numerical values.

Colon and a single space define a scalar (or a variable):

string: "17"

integer: 17

float: 17.0

boolean: No

A | character denotes a string that preserves newlines and a > character denotes a string that folds newlines:

data: |

   Every one 

   Of these

   Newlines

   Will be 

   Broken up.

data: >

   This text 

   is wrapped 

   and will

   be formed into

   a single paragraph.

Basics aside, there are two vital types of structures you need to know about in YAML:

• YAML lists
• YAML maps

Use these two structures for formatting in YAML.

YAML Maps (With Examples)

Maps associate name-value pairs, a vital aspect of setting up data. A YAML configuration file can start like this:

---

apiVersion: v3

kind: Pod

Here is the JSON equivalent of the same file opening:

{

   "apiVersion": "v3",

   "kind": "Pod"

}

Both codes have two values, v3 and Pod, mapped to two keys, apiVersion and kind. In YAML, the quotation marks are optional, and there are no brackets.

This markup language allows you to specify more complex structures by creating a key that maps to another map rather than a string. See the YAML example below:

---

apiVersion: v3

kind: Pod

metadata:

  name: rss-site

  labels:

    app: web

We have a key (metadata) with two other keys as its value name and labels. The labels key has another map as its value. YAML allows you to nest maps as far as you need to.

The number of spaces does not matter, but it must be consistent throughout the file. In our example, we used two spaces for readability. Name and labels have the same indentation level, so the processor knows both are part of the same map.

The same mapping would look like this in JSON:

{

  "apiVersion": "v3",

  "kind": "Pod",

  "metadata": {

               "name": "rss-site",

               "labels": {

                          "app": "web"

                         }
              }
}

YAML Lists (With Examples)

A YAML list is a sequence of items. For example:

args:

  - shutdown

  - "1000"

  - msg

  - "Restart the system"

A list may contain any number of items. An item starts with a dash, while indentation separates it from the parent. You can also store maps within a list:

---

apiVersion: v3

kind: Pod

metadata:

  name: rss-site

  labels:

    app: web

spec:

  containers:

    - name: front-end

      image: nginx

      ports:

        - containerPort: 80

    - name: rss-reader

      image: nickchase/rss-php-nginx:v1

      ports:

        - containerPort: 88

We have a list of containers (objects). Each consists of a name, an image, and a list of ports. Each item under ports is a map that lists the containerPort and its value.

Our example would look like this in JSON:

{ “apiVersion”: “v3”, “kind”: “Pod”, “metadata”: { “name”: “rss-site”, “labels”: { “app”: “web” } }, “spec”: { “containers”: [{ “name”: “front-end”, “image”: “nginx”, “ports”: [{ “containerPort”: “80” }] }, { “name”: “rss-reader”, “image”: “nickchase/rss-php-nginx:v1”, “ports”: [{ “containerPort”: “88” }] }] } }

What is the difference between YAML and JSON?

JSON and YAML are used interchangeably, and they serve the same purpose. However, there are significant differences between the two:

The main difference between YAML and JSON is code readability. The best example of this is the official YAML homepage. That website is itself valid YAML, yet it is easy for a human to read.

YAML is a superset of JSON. If you paste JSON directly into a YAML file, it resolves the same through YAML parsers. Users can also convert most documents between the two formats. It is possible to convert JSON files into YAML either online or use a tool like Syck or XS.

YAML in IaC

YAML is a common option when writing configuration files for Infrastructure as Code. These files store parameters and settings for the desired cloud environment.

Red Hat’s Ansible, one of the most popular IaC tools, uses YAML for file management. Ansible users create so-called playbooks written in YAML code that automate manual tasks of provisioning and deploying a cloud environment.

In the example below, we define an Ansible playbook verify-apache.yml:

---

- hosts: webservers

  vars:
    http_port: 90
    max_clients: 250

  remote_user: root

  tasks:
  - name: ensure apache is at the latest version
    yum:
      name: httpd
      state: latest
  - name: write the apache config file
    template:
      src: /srv/httpd.j2
      dest: /etc/httpd.conf
    notify:
    - restart apache
  - name: ensure apache is running

    service:
      name: httpd
      state: started

  handlers:
    - name: restart apache
      service:
        name: httpd
        state: restarted

There are three tasks in this YAML playbook:

• We update Apache to the latest version using the yum command.
• We use a template to copy the Apache configuration file. The playbook then restarts the Apache service.
• We start the Apache service.

Once set, a playbook is run from the command line. While the path varies based on the setup, the following command runs the playbook:

ansible-playbook -i hosts/groups verify_apache.yml

The Use of YAML in DevOps

Many DevOps teams define their development pipelines using YAML. YAML allows users to approach pipeline features like a markup file and manage them as any source file. Pipelines are versioned with the code, so teams can identify issues and roll back changes quickly.

To add a YAML build definition, a developer adds a source file to the root of the repository.

Thanks to YAML, DevOps separate logic from the configuration. That way, the configuration code follows best practices, such as:

• No hard-coded strings.
• Methods that perform one function and one function only.
• Testable code.

Several tools with a prominent role in DevOps rely on YAML:

• Azure DevOps provides a YAML designer to simplify defining build and release tasks.
• Kubernetes uses YAML to create storage and lightweight Linux virtual machines.
• Docker features YAML files called Dockerfiles. Dockerfiles are blueprints for everything you need to run software, including codes, runtime, tools, settings, and libraries.

An Efficient, User-Friendly Data Formatting Language

YAML offers levels of code readability other data-formatting languages cannot deliver. YAML also allows users to perform more operations with less code, making it an ideal option for DevOps teams that wish to speed up their delivery cycles.

The evolution of software development has three significant milestones. First was introducing the waterfall method that focused on the time required to release a product. Then came the agile methodology which optimized the development life-cycle.

Now, DevOps seeks to unite development and operations to work together as a single team. It increases productivity, improves collaboration, and delivers superior products.

Adopting agile and DevOps practices in software development presents a challenge to many. The first step to overcoming this obstacle is understanding the difference between agile and DevOps and the role these development methodologies play.

What is Agile?

Agile is a methodology that focuses on continuously delivering small manageable increments of a project through iterative development and testing. It was introduced as an alternative to the traditional waterfall methodology, known for its structured, linear, sequential life-cycle.

Dynamic processes such as project management and software development require the ability to adapt to changes and new conditions. The inflexible waterfall approach couldn’t meet the expectations of the fast-paced world of continuous technological innovation. Thus, agile was born.

Agile provides effective, day-to-day management of complex projects, improving communication and collaboration among team members and customers.

Agile Values

The methodology is defined by the Agile Manifesto, 12 principles that lay the foundation and values of “working agile.”

There are four core values at the heart of agile software development:

Individuals and interactions over processes and tools. The manifesto emphasizes the importance of valuing each team member and fostering a healthy and stimulating work environment. To maximize efficiency, it encourages constant communication between teammates, so everyone is involved in the development process.

Working software over comprehensive documentation. Documentation cannot stand in the way of delivering software. Previously, every project had to start with detailed documentation of the requirements and expectations of the developing software. Agile is focused on embarrassing changes and avoids spending too much time on documentation that will probably get altered later.

Customer collaboration over contract negotiation. Continuous development involves collaborating with the customer regularly. Immediate feedback guides the project in the direction which will eventually give the best results. Negotiating a contract with the customer before development and referring back to it after production leads to potential miscommunication. It should be avoided.

Responding to change over following a plan. Changes made mid-project need to be readily accepted as they can help with the product’s overall success. Adapting to new circumstances and embracing new features is one of the prominent differences between agile and waterfall.

Agile Software Development

Agile software development involves implementing agile frameworks, such as Scrum and Kanban. Each software development life-cycle starts with breaking up the project into manageable stories and requirements. The tasks are organized into sprints. A sprint takes place over two weeks, during which the team works on getting a specific feature up and running.

During the sprint, the team focuses on building, testing, and deploying software, making adjustments along the way. Once they complete a sprint, they move on to the next, until the project is complete. Such a practice allows continuous delivery of software. At the same time, customers, stakeholders, and project managers can follow and give feedback to ensure satisfactory results.

Some development stages can also include automated processes to speed up integration (such as automation testing and code management) and ensure everything is working correctly.

During development, the team collaborates, gives each other feedback, and reviews their work after each sprint, during regular retrospective sessions.

What is DevOps?

DevOps is a software development culture in which the development team and operations team work together to improve collaboration and productivity. The practice also involves implementing DevOps principles and practices and using a set of DevOps tools for testing.

DevOps principles foster communication, end-to-end responsibility, and information sharing. They define DevOps and set their goals.

Unlike traditional software development, DevOps consists of a continuous cycle of building, testing, deploying, and monitoring software. DevOps’ main objective is to deliver quality software efficiently.

DevOps Principles

More and more companies are transitioning to DevOps. Implementing DevOps has many advantages, such as fast and easily integrated software deployments.

The transition to this new culture is impossible without understanding the fundamental values that drive it. It requires a change of mindset within the development and the operations team, which inspires them to work as a united front.

The following principles are the foundation that steers the engineering process in a DevOps environment:

Version Control. Developers submit code changes to a central repository several times a day. Prior to submitting code to the master repository (master branch), all code must be verified. To facilitate collaboration, other developers can track changes.

Continuous Integration. Members of the development team integrate their code in a shared repository, several times a day. Each developer segments the work into small, manageable chunks of code and detects potential merge conflicts and bugs quicker.

Continuous Delivery. As the code is continuously integrated, it is also consistently delivered to the end-user. Smaller contributions allow faster update releases, which is a crucial factor for customer satisfaction.

Continuous Deployment. A big part of DevOps is automating processes to speed up production. Continuous deployment involves automating releases of minor updates that do not pose a substantial threat to the existing architecture.

Continuous Testing. Such a strategy involves testing as much as possible in every step of development. Automated tests give valuable feedback and a risk assessment of the process at hand.

Continuous Operations. The DevOps team is always working on upgrading software with small but frequent releases. That is why DevOps requires constant monitoring of performance. Its main goal is to prevent downtime and availability issues during code release.

Collaboration. One of the main goals of DevOps is to foster collaboration and feedback sharing. Development and Operations need to proactively communicate and share feedback to maintain an efficient DevOps pipeline.

DevOps Software Development

DevOps software development focuses on an established pipeline the project has to pass through. The number of stages depends on the complexity and type of software the team is developing. The key stages include developing, building, testing, and deploying.

A planning stage often precedes all the previously mentioned, and a monitoring stage is also added after deployment.

Agile vs. DevOps

Merging Agile and DevOps

There are many advantages to merging agile and DevOps. Including, speeding up delivery,  higher user satisfaction, and effective collaboration within a team.

Combining the practices of DevOps with the culture of agile requires changing existing strategies and attitudes.

Understanding. The agile methodology requires team members to understand each other’s tasks. Mutual understanding is especially important for Scrum Masters, Project Managers, and Product Owners. To successfully manage a project, they need to know every step required to deliver the product.

Collaboration. DevOps involves the development team working together with the operations team. The previously mentioned roles now need to understand all aspects of the development process and operations.

DevOps practices in sprints. Next, the team needs to adopt integrating DevOps while handling sprints. That involves including the entire DevOps team (along with QA) in planning, daily standups, and retrospections.

Automate workflows. As automation is an essential part of DevOps development, it should be included in the agile workflow and project planning. Emerging fields, such as AIOps, use artificial intelligence to automate manual workflows in the IT environment.

Measure success. Merging agile and DevOps also involves assessing and measuring key DevOps metrics and KPIs within end-to-end development.

DevOps and Agile Can Work Together

Agile and DevOps both aim towards delivering quality software in a timely manner. The difference between agile and DevOps is that agile focuses on optimizing the development life-cycle, while DevOps unites development and operations in a CI/CD environment.

DevOps and agile are not mutually exclusive. Any organization transitioning to DevOps should not abandon existing agile workflows. DevOps is an extension of agile built around the practices that are not in agile’s focus. When used together, both practices improve software development and lead to better products.

DevOps is a set of principles that Development and Operations teams implement to deliver high-quality software in record time.

In Agile, Dev and Ops teams worked separately in silos, which negatively impacted the organization’s overall productivity. DevOps solves that problem by uniting Development and Operations into one group tasked with working together to achieve common objectives.

In this article, learn nine crucial Devops principles and practices and how implementing them can help your organization get the most out of DevOps.

What is DevOps?

DevOps is a software development culture that unites development, operations, and quality assurance processes into a continuous set of actions. It facilitates cross-functional communication, end-to-end responsibility, and collaboration, and is a natural extension of the Agile methodology.

Transitioning to DevOps does not require any technical innovation. It depends on adopting the right DevOps principles, values, and adapting them to your organization’s needs.

DevOps Principles

DevOps is a mindset or a philosophy that encompasses collaboration, communication, sharing, openness, and a holistic approach to software development.

DevOps relies on a comprehensive set of strategies and methodologies. They ensure the timely delivery of quality software. DevOps principles guide how to organize a DevOps environment.

1. Incremental Releases

Before DevOps, teams had to wait their turn to deploy code. A delay in code release often caused bottlenecks or what is known as “merge hell.” When developers have to wait for a prolonged period of time to contribute code, errors, and incompatibility issues are inevitable.

DevOps encourages developers to contribute new code as often as possible, usually many times during the day. In a DevOps environment, a single project is divided into small, manageable chunks, and teams submit their code in increments. That makes it easier to troubleshoot problematic code before it gets released to production. Depending on the workflow, DevOps teams release code updates and bug fixes on a daily, weekly, or monthly basis.

Incremental releases make the development and deployment cycle more flexible. As a result, teams can quickly respond to sudden changes and fix errors and bugs immediately. The primary goal is to prevent bad code from being deployed to the end-user.

2. Automation

One of the critical practices of DevOps is automating as much of the software development process as possible. Automating workflows allows developers to focus solely on writing code and developing new features.

Anything that can be automated should be automated in a DevOps environment. For example, instead of wasting time on manually checking code for errors, DevOps teams use different software solutions to build and test applications automatically. It is as simple as running a single command to compile the source code to determine if it will work in production.

If the application is written in a language that doesn’t need to be compiled, DevOps teams can run automated tests to check if the new code is production-ready. If there are any errors or bugs in the code, automation will trigger an alert letting developers know which lines of code are causing issues.

Automation also plays a vital role in dealing with infrastructure management procedures.

In a DevOps environment, teams utilize Infrastructure-as-Code (IaC). Infrastructure-automation software, such as Pulumi, helps manage the provisioning and decommissioning of resources. It involves utilizing scripts, APIs, and CLIs to manage infrastructure as code rather than doing everything manually.

The goal is to enable developers to quickly provision anything from containers and virtual machines to bare metal cloud servers, storage, databases, and other infrastructure.

3. DevOps Pipeline

DevOps aims to establish a repeatable system, a loop that facilitates continuity in development. To achieve that, DevOps teams create pipelines.

A pipeline denotes a repeatable system made up of stages through which code has to pass before being deployed to production. A typical DevOps pipeline consists of four primary phases:

• Develop. First, the developers have to write the code.
• Build. Then, the team compiles the code into a build to check for errors.
• Test. After the build phase, operations teams run tests to ensure that the new code will behave as intended in the production environment.
• Deploy. Once the new code has passed the testing phase, it gets deployed to the end-user.

DevOps teams implement critical strategies to achieve a steady flow of code through the pipeline. The most important among them is Continuous Integration (CI) and Continuous Delivery (CD), also known as CI/CD.

Continuous Integration has to do with enabling multiple developers to submit and merge their code regularly, while Continuous Delivery is all about releasing code updates to production as often as possible.

In that regard, “continuous everything” is a DevOps principle that has to do with creating a never-ending development and deployment pipeline.

Along with CI/CD, in professional DevOps environments, teams strive to ensure continuity in every aspect of the pipeline – monitoring, feedback gathering, and deployments.

4. Continuous Integration

Continuous Integration (CI) plays a pivotal role in a DevOps pipeline. It encourages developers to submit their code to a central code repository multiple times a day.

By integrating smaller chunks of code regularly, the likelihood of bad code moving down the pipeline and causing service interruptions is significantly reduced. Some of the largest organizations that implement DevOps practices commit new code hundreds of times a day.

Another critical aspect of Continuous Integration is automated testing. Before developers commit their code to the master branch, they create builds of the project to determine if the new code is compatible with the existing code. If the build is successful, developers will submit their code to the shared repository. CI relies on a version control system, a central code repository that helps teams track code changes and manage merge requests.

5. Continuous Delivery

Continuous Delivery (CD) is all about releasing code updates frequently and fast. CD relies on developers to manually deploy code to production as opposed to Continuous Deployment, which takes an automated approach to code release. For CD to yield positive results, it’s paramount to create a repeatable system that will push the code through the DevOps pipeline.

Developers working in a CD environment need to keep in mind that their code contributions may get deployed to production at any moment. Having passed all tests and reviews, code updates can be released to production with a click of a button.

Numerous benefits come with continuous delivery. First, it eliminates the risk of downtime and other performance issues because code changes are usually minor. Second, CD enables organizations to release high-quality features much easier, ensuring faster time-to-market, and ultimately eliminating fixed costs associated with the deployment process.

Quality Assurance teams set a committed code test using automation testing tools such as UFT, Ranorex, or Selenium. If QA finds vulnerabilities or bugs, they go back to the engineers. This stage features version control to look for any integration problem in advance. The Version Control System allows developers to record the changes in the files for sharing with other team members, regardless of location.

Codes that pass automated tests get integrated into one shared repository. Several code submissions serve to prevent future drastic differences in code branch and mainline code, lengthening integration. Popular continuous integration tools include GitLab CI, Jenkins, TeamCity, and Bamboo.

6. Continuous Monitoring

Continuous Monitoring builds on the concepts of CI/CD, and it ensures the application performs without issues. DevOps teams implement monitoring technologies and techniques to keep track of how the application is behaving.

DevOps teams monitor logs, apps, systems, and infrastructure. Once a problem is detected, DevOps teams can quickly revert the app to a previous state. During that time, the team works on resolving known issues without making the user aware that the code is updated continuously.

Continuous monitoring also helps DevOps teams detect issues that are hindering productivity in the pipeline. After each release cycle, teams should optimize the CI/CD pipeline to eliminate any bottlenecks and ensure a smooth transition of code from one stage to the next.

7. Feedback Sharing

DevOps thrives on feedback. Developers need actionable information from different sources to help them improve the overall quality of the application. Without feedback, DevOps teams can fall victim to spending time on building products that don’t bring value to stakeholders or customers.

DevOps teams usually gather feedback from stakeholders, end-users, and software-based monitoring technologies before, during, and after the release cycle. They collect feedback through various channels, such as social media and surveys, or by discussing with colleagues.

Teams need to have procedures that will help them sift through the feedback they’ve gathered to extract the most valuable information. Misleading or inaccurate feedback could prove to be detrimental to the entire development process.

8. Version Control

Version control, also known as source control, lies at the heart of every successful DevOps workflow. It helps DevOps teams stay organized, focused, and up to date with what members of the team are doing. Version control is also crucial for ensuring teams collaborate faster and easier to support frequent software releases.

In a nutshell, version control is a central code repository — a place where developers contribute their code and track changes during the development process. Most version control systems allow team members to create branches of the main project. Branches are copies of the project’s source code that individual developers can work on without modifying the original code.

In a typical scenario, each developer works on a separate branch of the same project, submitting code updates and running automated tests. Before the newly written code gets merged with the master branch, automation will create a build of the application that will ensure the new code is compatible with the existing code.

If the build is successful, developers merge the new code with the master branch and deploy it to production or run other tests, depending on the workflow.

With a robust version control system in place, DevOps teams can be confident that only error-free code is moving down the pipeline and getting deployed to production.

9. Collaboration

The main idea behind DevOps is to establish trust among developers and operations. Dev and Ops teams need to communicate, share feedback, and collaborate throughout the entire development and deployment cycle.

In such a setting, both groups are responsible for ensuring the application delivers on its promises. That requires continuously optimizing and improving the performance, costs, and delivery of services while keeping users satisfied.

Creating this type of inclusive, collaborative environment also involves accepting a cultural shift within the company. To execute DevOps successfully, it’s crucial to have the stakeholders and DevOps teams be on the same page, working together to deliver software solutions that bring real value to the company and its customers. DevOps requires the entire company to behave like a startup, continuously adjusting to market changes, and investing time and resources in features that attract more customers.

Remember, a Devops Culture Change Requires a Unified Team

DevOps is just another buzzword without the proper implementation of certain principles that sit at the heart of DevOps.

DevOps revolves around specific technologies that help teams get the job done. However, DevOps is, first and foremost, a culture. Building a devops culture requires an organization to work as a unified team, from Development and Operations to stakeholders and management. That is what sets DevOps apart from other development models.

When transitioning to DevOps, remember that these principles are not set in stone. Organizations should implement DevOps methodologies based on their goals, workflows, resources, and the team’s skill set.

From Business Analysis to DevOps and Data Analysis, Artificial Intelligence (AI) has been branching out to all IT segments. The latest application of AI, AIOps, helps IT teams automate tedious tasks and minimizes the chances for human error.

Learn what AIOps is, how organizations use it to enhance their IT workflows, and how to start using AIOps to improve your IT environment’s efficiency.

What is AIOps?

AIOps stands for Artificial Intelligence for IT Operations. AIOps is using AI and machine learning to monitor and analyze data from every corner of an IT environment. It uses algorithmic analysis of data to provide DevOps and ITOps teams with the means to make informed decisions and automate tasks. It’s vital to note that AIOps does not take people out of the equation. The use of AI fills in the operational gaps that commonly cause difficulties for humans.

Here’s a quick summary of what AIOps can do for an organization:

• Filter low-priority alerts from attention-worthy problems
• Help identify and quickly resolve system issues
• Automate repetitive tasks
• Detect system anomalies and deviations
• Put a stop to traditional team silos

It is challenging to assess how AIOps fits into the current IT landscape. It does not replace any existing monitoring, log management, or orchestration tools. AIOps exists at the junction of all the tools and domains, processing and integrating information across the entire IT infrastructure. By doing so, AIOps turns partial views into a synchronized, 360-degree picture of operations that is easy to keep track of.

AIOps environments are made out of sets of specialized algorithms narrowly focused on specific tasks.

These algorithms can pick out alerts from noisy event streams, identify correlations between issues, use historical data to auto-resolve reoccurring problems, etc. The cumulative effect of all these processes can do wonders for a business. It boosts system’s stability and performance while preventing issues from impairing critical operations.

AIOps Architecture

AIOps has two central components: Big Data and Machine Learning.

It aggregates observational data from monitoring systems and engagement data from tickets, incidents, and event recordings. AIOps then performs comprehensive analytics of the gathered data and uses machine learning to figure out improvements and fixes. Think of it as an automation-driven continuous integration and deployment (CI/CD) for IT functions.

The entire process starts with monitoring. An essential aspect of an AIOps architecture, these tools can work with multiple sources and handle the immense volume and wide disparity of data in modern IT environments. Once it has access to all the info, an AIOps platform typically uses a data lake or a similar repository to collect and disperse the data.

After processing data, AIOps systems derive insights through various AI-fueled activities, such as analytics, pattern matching, natural language processing, correlation, and anomaly detection. Finally, AIOps makes extensive use of automation to act upon its findings.

AIOps Enhances DevOps

AIOps is not a separate entity from DevOps, but instead a set of technologies that complement the goals of DevOps engineers and help them embrace the scale and speed needed for modern development.

The world of DevOps revolves around agility and flexibility. AIOps platforms can help automate steps from development to production, projecting the effects of deployment and auto-responding to alterations in a dynamic IT environment.

AIOps can also help handle the velocity, volume, and variety of data generated by DevOps pipelines, sorting and making sense of them in real-time to keep app delivery stable and fast.

Here are the benefits AIOps can offer to DevOps engineers:

• Help understand the ins and outs of DEV, QA, and production environments.
• Identify optimal fixes
• Test ideas in a quick and safe fashion
• Automate repetitive tasks
• Minimize human error
• Determine improvements and optimizations

AIOps ensures the use of DevOps engineers goes towards complex tasks that cannot be automated. It allows humans to focus on areas of development that drive maximum profitability for a business.

Benefits of AIOps

Depending on individual operations and workflows, certain benefits of AIOps can be more impactful than others. Nevertheless, here’s a breakdown of key advantages that come with deploying AIOps:

Noise reduction

By identifying low-priority alerts through machine learning and pattern recognition, AIOps helps IT specialists comb through high volumes of event alarms without getting caught up in irrelevant or false alerts.

Noise reduction saves a lot of time, but it also allows business-affecting incidents to be spotted and resolved before they cause damage.

Unified view of the IT environment

AIOps correlates data across various data sources and analyzes them as one. AIOps eliminates information silos and gives a contextualized vision across the entire IT estate. That allows all teams to be on the same holistic page, turning the entire system into a well-oiled machine.

Meaningful data analysis

AIOps brings all the data across the system into one place, enabling more meaningful analysis that is quick due to AI and thorough as it leaves no digital stone unturned.

By bringing all the data together and accurately analyzing it, AIOps makes a substantial impact on the decision-making front.

Time-saving process automation

Thanks to knowledge recycling and root cause analysis, AIOps automates simple recurring operations.

Upon discovering an issue, AIOps react to it in real-time. Depending on the nature of the problem, it initiates an action or moves to the next step without the need for human intervention.

A proactive approach to problem management

AIOps analyze historical data in search of patterns in system behavior. AiOps is a great way to stay ahead of future incidents, enabling teams to fix root causes and run a more seamless system.

Between automation, problem-solving, and in-depth analytics, AIOps tools make workflows quicker and more consistent. Consequently, it reduces the chance for human errors to occur. Meanwhile, IT teams get to focus on their areas of expertise instead of having to deal with low-value tasks that distract and slow them down.

AIOps Use Cases

AIOps continually evolves and offers new functionalities, and currently, it is used in the following use cases:

Intelligent alert monitoring and escalation

By ingesting data from all sections of an IT environment, AIOps tools stop alert storms from causing domino effects through connected systems. It reduces alert fatigue and helps accurately prioritize issues.

Cross-domain situational understanding

AIOps create causality/relationships while aggregating data, granting a continuous clear line of sight across the entire IT estate.

Automatic root cause analysis

Once an alert happens, an AIOps platform presents top suspected causes, as well as the evidence that led it to such a conclusion.

Automatic remediation of IT environment issues

AIOps automate remediation for problems that already transpired on several occasions. They use historical data from past issues to identify them and either offer the best solution or remedy the issues outright.

Monitor application uptime

By proactively monitoring raw utilization, bandwidth, CPU, memory, and more, AI-based analytics can be used to increase overall application uptime.

Cohort analysis

While humans struggle with it, analyzing vast amounts of data is a forte of AIOps that can handle large systems with ease.

Organizations Adopting AIOps

Different reasons push organizations towards AIOps. Organizations at the forefront of adopting artificial intelligence in IT operations are enterprises with large environments, cloud-native SMEs, organizations with overburdened DevOps teams, and companies with hybrid cloud and on-prem environments.

Most organizations hail the same positive effects following deployment, as evidenced by a survey performed by OpsRamp:

• Around 87% of organizations said that the AIOps-powered solution delivered good results
• The most common use case was Intelligent alerting, mentioned by 67% of organizations, followed by root cause analysis which was highlighted by 61% of the respondents
• Over 50% of organizations mentioned anomaly/threat detection, capacity optimization, and incident auto-remediation as crucial upgrades to their system(s)
• Over 85% of polled organizations said they were able to automate tedious tasks thanks to AIOps
• 77% of organizations claim the number of open incident tickets went down upon implementing AIOps

It is unsurprising that there’s been an 83% increase in the number of organizations currently deploying or thinking about deploying AIOps since 2018. Forward-thinking companies see AIOps as an opportunity to move past brittle rules-based processes, information silos, and the excess of repetitive manual activities.

While evaluating the benefits of AIOps, it’s essential to look beyond its ability to reduce costs directly. By preventing disruptions of critical digital services, as well as accelerating detection and resolution, AIOps paves the way towards better user experience and bumps up customer retention. It also leaves ample room for an IT team to innovate and focus on value-packed activities, which keeps top talent happy and away from competitors.

Implement & Get Started With Artificial Intelligence for IT Operations

The shift towards AIOps starts at the drawing board. Here are pointers in case you’re weighing whether AIOps is the right choice for your team:

Learn about AI

You should know as much about AI-powered operations as humanly possible. This article is a great start, but you should consider hiring an IT consultant to better gauge your system’s fit with AIOps. You also want to familiarize yourself with the capabilities of AI and ML to get a better sense of what these technologies can offer.

Identify Time-Consuming IT Tasks

Identify where the bulk of the team’s time and effort is being poured. If you conclude that most of the energy is going towards solving mundane and repetitive tasks, you’re likely a prime candidate for implementing AIOps.

Consider other applications

Data management is a massive component of AIOps that’s not reserved exclusively for your IT department. Business analytics and statistical analysis are key for any modern organization, so check if you have needs for AIOps on those fronts as well.

Automate one process at a time

There’s no need to go all-in on AIOps immediately. Identify your highest-priority problem and assess how this technology could solve it. Deploy the use of AI there first and later consider expanding it across other systems if it yields results.

Measure speed and efficiency

To know whether hefty investments in AIOps are paying off, you need to know which metrics you’ll keep an eye on. Ways of measuring ROI and success vary on a business-to-business basis, but most metrics involve measuring improvements in the speed and efficiency of processes.

Takeaway

The global AIOps market is projected to grow to $11.02 billion by 2023, enjoying a 34% combined annual growth rate (CAGR) in the meantime.

Interest in benefits of AIOps picked up as organizations began to seek ways to manage the velocity, volume, and variety of digital data that goes beyond human scale. The field demonstrates a clear ability to improve customer and talent retention rates. It is expected that the number of companies looking to implement artificial intelligence in IT operations will skyrocket.

AIOps is here to stay. Its ability to allow ITOps to focus on solving critical and high-value issues instead of “keeping the lights on” is game-changing. It’s only a matter of time before AIOps becomes the industry norm.

Today, people expect to see their favorite apps with the latest bells and whistles faster than ever. Long gone are the days when developers had years to develop and release new software products.

Hence, every software-development company needs an effective DevOps pipeline to keep up with customers’ demands and requirements.

This article covers the basic concepts of a DevOps pipeline, how pipelines work in DevOps environments, and explains the stages code has to pass through before being deployed to production.

What is a DevOps Pipeline?

A DevOps pipeline is a set of practices that the development (Dev) and operations (Ops) teams implement to build, test, and deploy software faster and easier. One of the primary purposes of a pipeline is to keep the software development process organized and focused.

The term “pipeline” might be a bit misleading, though. An assembly line in a car factory might be a more appropriate analogy since software development is a continuous cycle.

Before the manufacturer releases the car to the public, it must pass through numerous assembly stages, tests, and quality checks. Workers have to build the chassis, add the motor, wheels, doors, electronics, and a finishing paint job to make it appealing to customers.

DevOps pipelines work similarly.

Before releasing an app or a new feature to users, you first have to write the code. Then, make sure that it does not lead to any fatal errors that might cause the app to crash. Avoiding such a scenario involves running various tests to fish out any bugs, typos, or mistakes. Finally, once everything is working as intended, you can release the code to users.

From this simplified explanation, you can conclude that a DevOps pipeline consists of the build, test, and deploy stages.

Components of a DevOps Pipeline

To ensure the code moves from one stage to the next seamlessly requires implementing several DevOps strategies and practices. The most important among them are continuous integration and continuous delivery (CI/CD).

Continuous Integration

Continuous integration (CI) is a method of integrating small chunks of code from multiple developers into a shared code repository as often as possible. With a CI strategy, you can automatically test the code for errors without having to wait on other team members to contribute their code.

One of the key benefits of CI is that it helps large teams prevent what is known as integration hell.

In the early days of software development, developers had to wait for a long time to submit their code. That delay significantly increased the risk of code-integration conflicts and the deployment of bad code. As opposed to the old way of doing things, CI encourages developers to submit their code daily. As a result, they can catch errors faster and, ultimately, spend less time fixing them.

At the heart of CI is a central source control system. Its primary purpose is to help teams organize their code, track changes, and enable automated testing.

In a typical CI set-up, whenever a developer pushes new code to the shared code repository, automation kicks in to compile the new and existing code into a build. If the build process fails, developers get an alert which informs them which lines of code need to be reworked.

Making sure only quality code passes through the pipeline is of paramount importance. Therefore, the entire process is repeated every time someone submits new code to the shared repository.

Continuous Delivery

Continuous delivery (CD) is an extension of CI. It involves speeding up the release process by encouraging developers to release code to production in incremental chunks.

Having passed the CI stage, the code build moves to a holding area. At this point in the pipeline, it’s up to you to decide whether to push the build to production or hold it for further evaluation.

In a typical DevOps scenario, developers first push their code into a production-like environment to assess how it behaves. However, the new build can also go live right away, and developers can deploy it at any time with a push of a button.

To take full advantage of continuous delivery, deploy code updates as often as possible. The release frequency depends on the workflow, but it’s usually daily, weekly, or monthly. Releasing code in smaller chunks is much easier to troubleshoot compared to releasing all changes at once. As a result, you avoid bottlenecks and merge conflicts, thus maintaining a steady, continuous integration pipeline flow.

Continuous Deployment

Continuous delivery and continuous deployment are similar in many ways, but there are critical differences between the two.

While continuous delivery enables development teams to deploy software, features, and code updates manually, continuous deployment is all about automating the entire release cycle.

At the continuous deployment stage, code updates are released automatically to the end-user without any manual interventions. However, implementing an automated release strategy can be dangerous. If it fails to mitigate all errors detected along the way, bad code will get deployed to production. In the worst-case scenario, this may cause the application to break or users to experience downtime.

Automated deployments should only be used when releasing minor code updates. In case something goes wrong, you can roll back the changes without causing the app to malfunction.

To leverage the full potential of continuous deployment involves having robust testing frameworks that ensure the new code is truly error-free and ready to be immediately deployed to production.

Continuous Testing

Continuous testing is a practice of running tests as often as possible at every stage of the development process to detect issues before reaching the production environment. Implementing a continuous testing strategy allows quick evaluation of the business risks of specific release candidates in the delivery pipeline.

The scope of testing should cover both functional and non-functional tests. This includes running unit, system, integration, and tests that deal with security and performance aspects of an app and server infrastructure.

Continuous testing encompasses a broader sense of quality control that includes risk assessment and compliance with internal policies.

Continuous Operations

Having a comprehensive continuous operations strategy helps maintain maximum availability of apps and environments. The goal is for users to be unaware that of constantly releasing code updates, bug fixes, and patches. A continuous operations strategy can help prevent downtime and availability issues during code release.

To reap the benefits of continuous operations, you need to have a robust automation and orchestration architecture that can handle continuous performance monitoring of servers, databases, containers, networks, services, and applications.

Phases of DevOps Pipeline

There are no fixed rules as to how you should structure the pipeline. DevOps teams add and remove certain stages depending on their specific workflows. Still, four core stages make up almost every pipeline: develop, build, test, and deploy.

That set-up can be extended by adding two more stages — plan and monitor — since they are also quite common in professional DevOps environments.

Plan

The planning stage involves planning out the entire workflow before developers start coding. In this stage, product managers and project managers play an essential role. It’s their job to create a development roadmap that will guide the whole team along the process.

After gathering feedback and relevant information from users and stakeholders, the work is broken down into a list of tasks. By segmenting the project into smaller, manageable chunks, teams can deliver results faster, resolve issues on the spot, and adapt to sudden changes easier.

In a DevOps environment, teams work in sprints — a shorter period of time (usually two weeks long) during which individual team members work on their assigned tasks.

Develop

In the Develop stage, developers start coding. Depending on the programming language, developers install on their local machines appropriate IDEs, code editors, and other technologies for achieving maximum productivity.

In most cases, developers have to follow certain coding styles and standards to ensure a uniform coding pattern. This makes it easier for any team member to read and understand the code.

When developers are ready to submit their code, they make a pull request to the shared source code repository. Team members can then manually review the newly submitted code and merge it with the master branch by approving the initial pull request.

Build

The build phase of a DevOps pipeline is crucial because it allows developers to detect errors in the code before they make their way down the pipeline and cause a major disaster.

After the newly written code has been merged with the shared repository, developers run a series of automated tests. In a typical scenario, the pull request initiates an automated process that compiles the code into a build — a deployable package or an executable.

Keep in mind that some programming languages don’t need to be compiled. For example, applications written in Java and C need to be compiled to run, while those written in PHP and Python do not.

If there is a problem with the code, the build fails, and the developer is notified of the issues. If that happens, the initial pull request also fails.

Developers repeat this process every time they submit to the shared repository to ensure only error-free code continues down the pipeline.

Test

If the build is successful, it moves to the testing phase. There, developers run manual and automated tests to validate the integrity of the code further.

In most cases, a User Acceptance Test is performed. People interact with the app as the end-user to determine if the code requires additional changes before sending it to production. At this stage, it’s also common to perform security, performance, and load testing.

Deploy

When the build reaches the Deploy stage, the software is ready to be pushed to production. An automated deployment method is used if the code only needs minor changes. However, if the application has gone through a major overhaul, the build is first deployed to a production-like environment to monitor how the newly added code will behave.

Implementing a blue-green deployment strategy is also common when releasing significant updates.

A blue-green deployment means having two identical production environments where one environment hosts the current application while the other hosts the updated version. To release the changes to the end-user, developers can simply forward all requests to the appropriate servers. If there are problems, developers can simply revert to the previous production environment without causing service disruptions.

Monitor

At this final stage in the DevOps pipeline, operations teams are hard at work continuously monitoring the infrastructure, systems, and applications to make sure everything is running smoothly. They collect valuable data from logs, analytics, and monitoring systems as well as feedback from users to uncover any performance issues.

Feedback gathered at the Monitor stage is used to improve the overall efficiency of the DevOps pipeline. It’s good practice to tweak the pipeline after each release cycle to eliminate potential bottlenecks or other issues that might hinder productivity.

How to Create a DevOps Pipeline

Now that you have a better understanding of what a DevOps pipeline is and how it works let’s explore the steps required when creating a CI/CD pipeline.

Set Up a Source Control Environment

Before you and the team start building and deploying code, decide where to store the source code. GitHub is by far the most popular code-hosting website. GitLab and BitBucket are powerful alternatives.

To start using GitHub, open a free account, and create a shared repository. To push code to GitHub, first install Git on the local machine. Once you finish writing the code, push it to the shared source code repository. If multiple developers are working on the same project, other team members usually manually review the new code before merging it with the master branch.

Set Up a Build Server

Once the code is on GitHub, the next step is to test it. Running tests against the code helps prevent errors, bugs, or typos from being deployed to users.

Numerous tests can determine if the code is production-ready. Deciding which analyses to run depends on the scope of the project and the programming languages used to run the app.

Two of the most popular solutions for creating builds are Jenkins and Travis-CI. Jenkins is completely free and open-source, while Travis-CI is a hosted solution that is also free but only for open-source projects.

To start running tests, install Jenkins on a server and connect it to the GitHub repository. You can then configure Jenkins to run every time changes are made to the code in the shared repository. It compiles the code and creates a build. During the build process, Jenkins automatically alerts if it encounters any issues.

Run Automated Tests

There are numerous tests, but the most common are unit tests, integration tests, and functional tests.

Depending on the development environment, it’s best to arrange automated tests to run one after the other. Usually, you want to run the shortest tests at the beginning of the testing process.

For example, you would run unit tests before functional tests since they usually take more time to complete. If the build passes the testing phase with flying colors, you can deploy the code to production or a production-like environment for further evaluation.

Deploy to Production

Before deploying the code to production, first set up the server infrastructure. For instance, for deploying a web app, you need to install a web server like Apache. Assuming the app will be running in the cloud, you’ll most likely deploy it to a virtual machine.

For apps that require the full processing potential of the physical hardware, you can deploy to dedicated servers or bare metal cloud servers.

There are two ways to deploy an app — manually or automatically. At first, it is best to deploy code manually to get a feel for the deployment process. Later, automation can speed up the process, but only if you are confident, there are barriers that will stop bad code from ending up in production.

Releasing code to production is a straightforward process. The easiest way to deploy is by configuring the build server to execute a script that automatically releases the code to production.

In Closing

Now you understand what a devops pipeline is and how it can help speed up your software development life cycle.

However, this is just the tip of the iceberg.

The DevOps pipeline is a broad subject, and every organization will have its way of integrating one into their workflows. The ultimate goal is to create a repeatable system that takes advantage of automation and enables continuous improvements to help deliver high-quality products faster and easier.

A year ago, the executives at phoenixNAP decided to embark on a new project – to create a flagship product that would be part of the solutions portfolio. The executives challenged the way products are developed and wanted to explore new ways of launching products swiftly.

Following internal discussions, the decision was to try out a new method of developing products. The decision was to go with a DevOps team.

In this article, Peter Borg, SCRUM Master at phoenixNAP, explains the necessary steps and challenges to be overcome to transition to a DevOps culture.

Steps to Transition to DevOps

Historically the company has faced some issues with a traditional Agile setup, which has led to delays in delivering projects. If a SCRUM team is operating at full speed to achieve a goal, but the infrastructure is not ready, delivery is delayed. Department segregation might create a situation where a supporting team might not have the resources to support a SCRUM team. One department alone does not deliver software products, and that is what we wanted to change by transitioning to DevOps.

1. Create Self-Sufficient Teams

To kick start the new DevOps culture change, we formed a new team whose job descriptions were unique to the company. We moved away from full stack developers to DevOps software engineers, and from SysAdmins to Site Reliability Engineers (SREs).

By doing so, we were able to formulate a self-sufficient team, and its goal was to deliver the project at hand. Hiring the right people was vital, in addition to having expertise in specific aspects of the new technology stack where they can deliver the product without relying on people outside the team.

SREs were tasked to code the infrastructure, Software Engineers to develop the applications, QA Engineers to set up the automation testing frameworks, and Software and System Architects to design the solution.

Even though people were earmarked for specific tasks based on their area of knowledge, we promoted the cross-pollination of knowledge. SREs contributed to the software code, software engineers pitched into our Infrastructure-as-Code (IaC), QA was involved with our Test Driven Development (TDD) strategy and Continuous Integration (CI) pipelines, and Architects helped out with development and troubleshooting.

2. Embrace Test-Driven Development

Everyone hates big-bang releases, which causes problems with discovering late integration issues, refactoring complex solutions, and possible product vision divergence.

To not fall into these pitfalls, we opted for test-driven development (TDD). TDD allowed us to break down, implement, test, review, and expand complex solutions while always getting feedback from the Product Owner (PO) as we went along building the product. This iterative approach and feedback loop gave us the ability to continue improving a feature after establishing a strong foundation, which is the first principle in the Agile Manifesto (Kent Beck et al., 2001). The most important consideration to keep in mind when adopting a DevOps approach is to make sure that the team does not tie themselves emotionally to their implementation since it might be there one day but gone the next.

By opting for a TDD approach, we implemented complex problems with non-over-engineered solutions. TDD is an iterative approach to developing features making refactoring easier to achieve while also adding quality to the solution. “Start somewhere and learn from experience” (John Shook. 2008. Managing to Learn: Using the A3 Management Process to Solve Problems, Gain Agreement, Mentor, and Lead).

3. Push the DevOps Culture Change

Introducing culture change is not just hard. It’s exhausting, frustrating, and demoralizing. Pushing a different mindset in a multi-structural organization is a lot harder than building a company culture within a startup.

That is why I believe change can be achieved by treating them in the same manner. If you are trying to build a different implementation methodology within an already established company, it is achievable only if you create a mini organization ecosystem. Form team structures that the company will perceive as a startup when implementing a new project.

You will be faced with restraints, such as “That’s not how we do things” or “We already have different tools for that.” If you hear those statements, you are on the right track. Questioning the company’s processes and tools is the first thing you need to do when proposing a culture change. Consider whether the existing tools and methods are relevant at all.

In our case, by questioning, we were able to leverage Cloud Native technologies as opposed to systems that the company had considered as ‘standard.’ You will not have buy-in from everyone in pushing the DevOps change. However, having support from top executives is critical. It will help motivate people within the company who are less open to change since the vision is supported by higher up management.

4. Test Your Progress

Test the progress of your efforts at various DevOps transition stages.

Start by testing the velocity. You want to be agile and implement solutions quickly. By comparing the ‘startup’ project to other ongoing or historical solutions, you should be able to know whether the DevOps team is delivering faster. It is also essential to gauge the team’s ability to adapt to change. If changes from the feedback loop turn out to be an overhaul of the system, then something went wrong during the design stage.

Secondly, measure success by analyzing the team’s morale. People should be excited and motivated when a new adaptation of implementation is introduced. You will realize that you have achieved this by overhearing someone from the team talking to somebody outside the team, explaining how we managed to solve a problem using the new mentality or technology, which is new to the company. This will spark interest from other teams and, it’s at that point that you’ll realize that the culture change is spreading throughout the organization. Consider that the real measure of success – the new DevOps methodology gets noticed. Other team members want to try out things differently and come to you for advice on how to tackle problems using DevOps solutions.

Learn more about DevOps Metrics and KPIs you should be tracking.

5. Be Uncompromising

When I took on the role of SCRUM Master, I knew I had to deliver a new product and a DevOps culture change as fast as possible. It was imperative to keep both goals aligned throughout the product development phase. When faced with such challenges, buy-in and trust from executives can make or break a culture change. At times, management might not concern themselves about how a project is achieved as long as it’s delivered – even if we have to fall back onto an outdated technology.

That brings me to my next point, start with a blank canvas. Do not try to fit the current company standards into a DevOps way of developing software. Start from scratch and maintain a lean solution. Let the team research the best tools for solving a problem. If they identify a solution that the company is already using, all well and good. If they don’t, then present the new tool to whoever champions the current solution to get their buy-in to changing it. Ensure the team keeps in mind that nothing is set in stone, tools can change, someone might have a better idea, and requirements can change; it’s iterative.

6. Transition Other Teams to DevOps

Once the DevOps culture change within this micro organization starts to bear fruit, you need to consider the next step – growth.

How to transition more teams to DevOps?

The Toyota Production System (TPS) philosophy (Toshiko Narusawa & John Shook. 2009. Kaizen Express) seems to fall in line with Spotify’s Agile Scaling model. Maintaining an organization structure that is as flat and self-contained as possible is key. That empowers teams, making them more accountable for their successes and failures. Instead of forming departments of same-skilled people, set up ‘Chapters’ where these members form part of SCRUM teams and encourage them to liaise with each other to help solve problems. Promoting constant communication retains the project priority within the team..

Set up knowledge-sharing events to inform other departments about any technical decisions the team has taken. Knowledge sharing will help drive a company-wide transition to DevOps and rally up support across the whole organization. We’ve learned a lot over the past year, and we are continually looking for ways to improve our processes and structure through different iterations. Retaining the mentality of being lean and agile and ensuring the product is a success story will be the most important factors to maintain.

Final Advice on Switching to Devops

The last and most crucial point for anyone transitioning to DevOps is never to give up. There will be times when you will think that embracing a DevOps culture is hard to achieve, but if you have the right support system, from the team, management, and executives, you will get there. A day will come when someone from another team will request a meeting wanting to know how you implemented something.

That’s when you realize that the company is on the right track to accepting a DevOps culture. Spread the philosophy from one team to another, in phases, and take your time to move the entire organization to DevOps.

Microservices — now the de-facto choice for how we build our infrastructure — naturally paved the way for containers. With container orchestration tools like Docker and Kubernetes, organizations can now ship applications more quickly, at greater scale. But, with all that power and automation come challenges, especially around maintaining visibility into this ephemeral infrastructure.

Monitoring Kubernetes Workloads

Kubernetes is complex, (to find out exactly what Kubernetes is and how it works, read our complete guide on Kubernetes). To use it successfully, it requires that several components be monitored simultaneously. To make your strategy for monitoring easier, separate monitoring operations into several areas, with each section referring to an individual layer of the Kubernetes environment. Then break down the monitoring of workload from top-down: clusters, pods, applications, and finally, the end-user experience.

Monitoring Kubernetes Clusters

The cluster is the highest-level constituent of Kubernetes. Most Kubernetes installations have just one cluster. This is why when you monitor the cluster, you get a full view across all areas. And can easily ascertain the health of pods, nodes, and apps that make up the cluster.

When deploying multiple clusters using federation, each cluster must be monitored individually. The areas you will monitor at the cluster level would be:

• Unsuccessful pods: Pods which fail and abort are a normal part of Kubernetes processes. When a pod that should be working at a more efficient level or is inactive, it’s critical to look into the reason behind the anomalies in pod failures.
• Node load: Tracking the load on each node is integral to monitoring efficiency. Some nodes may have a lot more usage than others. Rebalancing the load distribution is key to keeping workloads fluid and effectual. This can be done via DaemsonSets.
• Cluster usage: Monitoring cluster infrastructure allows you to adjust the number of nodes in use and dedicate resources to power workloads efficiently. You can see how resources are being distributed so you can scale up or down and avoid the costs of additional infrastructure. To that end, we recommend learning how to set a container’s memory and CPU usage limit.

Monitoring Kubernetes Pods

Cluster monitoring gives a macro view of the Kubernetes environment, but collecting data from individual pods is also essential. It reveals the health of individual pods and the workloads they are hosting. You get a clearer picture of pod performance at a granular level, beyond the cluster. Here you would monitor:

• Total pod instances: There need to be enough instances of a pod to ensure high availability. This way hosting bandwidth is not wasted, and you do not run more pod instances than needed.
• Pod deployment: Monitoring pods deployment allows you to see if any misconfigurations might be diminishing the availability of pods. It’s critical to keep an eye on how resources distribute to nodes.
• Actual pod instances: Monitoring the number of instances for each pod is running versus what you expected to be running will reveal how to can redistribute resources to achieve the desired state in terms of pods instances. ReplicaSets could be misconfigured if you see varying metrics, so it’s important to analyze these regularly.

Monitoring Applications Running in Kubernetes

Applications are not a part of Kubernetes, but wanting to host an application is the whole point of using Kubernetes. That’s why monitoring the application that’s hosted on the cluster is integral for success. Issues that application monitoring reveals could be a problem with the Kubernetes environment, or in the application’s code.

By monitorings apps, you can identify the glitches and resolve them without delay. Start by monitoring:

• Errors: If an error happens, you can get to it quickly when monitoring, and resolve it before it affects end-users.
• Transaction traces: Transaction traces assist you in troubleshooting if apps experience availability or performance problems.
• Application responsiveness: You can monitor how long it takes for an app to respond to a request. You can see if they can handle current workloads or if they are struggling to maintain performance.
• Application availability: Monitor if apps are active and up, and efficiently responding.

Monitoring End-user Experience when Running Kubernetes

End-user experience, like applications, technically is not a part of the Kubernetes platform. The overall goal for an application is to give end-users a positive experience and should be a part of your Kubernetes monitoring strategy.

Collecting data will let you know how the app is performing, its responsiveness, and its usability. Doing real-user and synthetic monitoring is essential to understand how users interact with Kubernetes workloads. It lets you know if you need to make any adaptations or adjustments which will enhance usability and improve the frontend.

Monitoring Kubernetes in a Cloud Environment

When Kubernetes is running in the cloud, there are specific factors to consider when planning your monitoring strategy. In the cloud, you will also have to monitor:

• IAM events: You will have to monitor for IAM activity. That includes permissions changes and logins, which is a best practice for security in a cloud-based installation or environment.
• Cloud APIs: A cloud provider has its own APIs, and your Kubernetes installation uses it to request resources, so it needs to be monitored.
• Costs: Costs on the cloud can quickly run-up. Cost monitoring assists you with budgeting and ensures that you do not overspend on cloud-based Kubernetes services.
• Network performance: In a cloud-based installation, the network can become the largest hindrance to the performance of your applications. If you monitor the cloud network regularly, you can be sure that data is moving as rapidly as needed so that you can avoid network-related problems.

Monitoring Metrics in Kubernetes

To gain higher visibility into a Kubernetes installation outside of performing different types of monitoring for Kubernetes, there are also several metrics that will give you valuable insight into how your apps are running.

Common Metrics

These are metrics collected from Kubernetes’ code (written in Golang). It allows you to understand what’s going on at a cellular level in the platform.

Node Metrics

Metrics from operating systems enabling Kubernetes’ nodes can give you insight into the overall health of individual nodes. You can monitor memory consumption, filesystem activity, CPU load, usage, and network activity.

Kubelet Metrics

To make sure the Control Plane is communicating efficiently with each individual node that a Kubelet runs on, you should monitor the Kubelet agent regularly.

Kube-State-Metrics

You can get an elective Kubernetes add-on which generates metrics from the Kubernetes API called Kube-State-Metrics.

Controller Manager Metrics

To ensure that workloads are orchestrated effectively you can monitor the requests that the Controller is making to external APIs. This is critical in cloud-based Kubernetes deployments.

Scheduler Metrics

If you want to identify and prevent delays, you should monitor latency in the Scheduler. This way you can ensure Kubernetes is deploying pods smoothly and on time.

Etcd Metrics

Etcd stores all the configuration data for Kubernetes. Etcd metrics will give you essential visibility into the condition of your cluster.

Container Metrics

Looking specifically into individual containers will allow you to monitor exact resource consumption rather than more general Kubernetes metrics. CAdvisor allows you to analyzes resource usage happening inside containers.

API Server Metrics

APIs keeps the Kubernetes frontend together and so these metrics are vital for gaining visibility into the API Server, and thereby into the whole frontend.

Log Data

Logs are useful to examine when you find a problem revealed by metrics. They give you exact and invaluable information which provides more details than metrics. There are many options for logging in most of Kubernetes’ components. Applications also generate log data.

Kubernetes Monitoring Challenges, Solutions and Tips

Migrating applications from monolithic infrastructures to microservices managed by Kubernetes is a long and intensive process. It can be full of pitfalls and can prove to be error-prone. But to achieve higher availability, innovation, cost benefits, scalability, and agility, it’s the only way to grow your business, especially in the cloud. Visibility is the main issue when it comes to Kubernetes environments as seeing real-time interactions of each microservice is challenging, due to the complexity of the platform. Monitoring is a specialized skill each enterprise will need to practice and improve upon to be successful.

A Kubernetes cluster can be considered complex due to its multiple servers and integrated private and public cloud services. When an issue arises, there are many logs, data, and other factors to examine. Legacy monolithic environments only need a few log searches to ascertain the problem. Kubernetes environments, on the other hand, have one or several logs for the multiple microservices implicated in the issue you’re experiencing.

To address these challenges, we’ve put together the following recommendations for effectively monitoring containerized infrastructure.

Effective Use of the Sidecar Pattern for Improved Application Monitoring in Kubernetes

One key best practice is leveraging role-based access within Kubernetes to provide end-to-end control by a single team with their monitoring solution, and without having full control of the cluster. Leveraging a monitoring solution under a team namespace helps operators easily control monitoring for their microservice-based container application inside the scope of their team.

However, they can add additional monitoring support without having to rebuild their application container. A dynamic approach to monitoring improves observability and drives context — without having to pull containers down if they start to exhibit issues.

Namespace Observability

By leveraging an open source monitoring event pipeline, such as Sensu Go, operations teams can get a dedicated team view of containers to improve visibility into their applications and increase insight into possible anomalies. These types of solutions offer dynamic monitoring changes for ephemeral infrastructure. As a result, operators can help drive collaboration securely by using Kubernetes’ built-in concept for role-based access control.

Kubernetes provides namespace scoping for resources, making it possible to give individual teams full control of applications under their namespace. Operators can also create containers and pods in a Kubernetes namespace and map it directly to code-driven monitoring tools, leveraging the same namespace as well.

For example, you can have an ‘Associated’ namespace in open source monitoring event pipeline – similar to Kubernetes — so that one team can control containers and monitoring around it using a repository of declarative YAML config files. With RBAC (role-based access control), you can mitigate risk by providing only necessary access to a user so they don’t have more than is needed.

Codifying monitoring workflows into declarative configuration files allows you to monitor at the speed of automation. It can be shared, treated as code, reviewed, edited, and versioned, allowing for efficient multi-cloud operation. Read more on how to monitor Kubernetes with Prometheus

Best Practices for Logs in Kubernetes

Application log aggregation for containerized workloads is an essential best practice that can improve software development. Because of the ephemeral nature of containerized workloads, the number of log entries being generated in a cluster can be quite large.

Logging agents like Fluentd and FluentBit — cross-platform, open-source data collection software projects originally developed at Treasure Data — are typically used as DaemonSets to collect the logs for all pods running on a node, using a privileged volume mount of the log files stored by the container runtime. These are cluster-level tools used to aggregate logs into a data lake such as Elasticsearch or send them into a stream processor solution like Kafka — and you might want to use functional role-based monitoring to track these additional pieces of log aggregation infrastructure running outside of your Kubernetes cluster.

Use a Kubernetes Monitoring Solution

Visibility is essential for enterprises to identify container issues that impede application performance. You can monitor containerized applications running inside Kubernetes pods more efficiently and scale up or down depending on the need. This is why it is critical to have a comprehensive Kubernetes monitoring solution that will give you end-to-end visibility into each and every component of your applications. From pods, nodes, containers, infrastructure, Kubernetes platform, to each microservice and end-user device.

Monitor Kubernetes with APM

Implementing an application performance monitoring solution (APM) gives enterprises visibility into their applications and allows them to asses overall performance. It organizes and offers insights into Kubernetes clusters, Docker containers, and containerized applications. You can examine the infrastructure’s fundamental metrics, learn about potential impediments, and make adjustments.

Get instant visibility into, memory, CPU and network utilization, and resource usage statistics when deploying APM-monitored container applications. APM metrics quickly identify common issues such as bandwidth- monopolizing applications or recognize far-reaching container-level network errors.

With these tips and monitoring strategies, operators can take huge leaps forward to gain greater visibility into their container-based infrastructure. And embrace multi-cloud operation with confidence. Want help getting started? Contact one of our experts today.