The Ops Community ⚙️: Priyanshi Sharma

DevOps Testing Strategies

Priyanshi Sharma — Thu, 06 Jul 2023 06:55:50 +0000

Software development companies have been adopting DevOps since it helps automate and streamline their application development life cycle. Not just that, but, DevOps also improves the quality and speed of the project deliveries by making the coordination between development and operations teams better through planning, communication, processes and tools.

Since the evolution of DevOps, businesses are either using Agile+DevOps or just opting for DevOps methodologies.

(Agile is an iterative process focused on collabs, feedback and rapid releases.)

But what’s the best strategy for testing DevOps? To help you out, we will discuss the basic concept of DevOps, its lifecycle, best practices and tools that you should go for.

What is DevOps?

DevOps is the set of tools, cultural philosophies, and practices that improves the ability of an organization to deliver projects at high velocity by automating and integrating processes between development and operation teams. DevOps accentuates cross-team collaboration & communication, team empowerment, and automation.

Under a DevOps methodology, the development team and operations team are not isolated from each other. These two teams are often merged as one team where developers work on the app’s lifecycle from development to deployment and operations. Besides, the DevOps team has a wide range of skills that isn’t limited to one function or feature of the application.

Sometimes, the security and quality assurance teams get integrated with DevOps throughout the application development. In this case, where the focus of the DevOps team is on the security of the application, it is also referred to as DevSecOps.

Unlike traditional manual practices, the DevOps team uses a technology stack and tooling that automates the process to build applications reliably and quickly. In addition, DevOps tools allow developers to carry out tasks such as provisioning infrastructure or deploying code independently, which would have otherwise required assistance from other teams.

DevOps Lifecycle

DevOps lifecycle is an automated processes series within a continuous development lifecycle. DevOps lifecycle works on an iterative approach that’s the reason practitioners have symbolized it as an infinity loop. This infinity loop represents the continuous and collaborative strategy that includes technology stacks and tools for every stage in the lifecycle of an application.

The left part of the lifecycle deals with the application development and testing while the right side depicts the cycle of deployment and operations.

DevOps LifecycleLet’s get an overview of the working DevOps lifecycle.

Plans: The DevOps team identifies business needs and gathers feedback from users during the planning phase. To maximize the value to the business and provide the desired result, developers also create a roadmap of the project at this stage.
Code: In this stage, the application code is created and the process is streamlined by using tools and plugins like Git or GitHub to minimize lousy coding practices and shortcomings in application security.
Build: In the build stage, developers commit the code to the shared repository with tools like Gradle or Maven.
Test: In the test phase, the build is deployed to the test environment so that the application quality can be ensured by running different tests such as security, user acceptance, integration, performance, and more using tools like Selenium, JUnit, etc.
Release: When the build is ready to be deployed in the production environment after passing tests, releases are scheduled by the operations team.
Deploy: In the deployment stage, Infrastructure-as-Code uses different tools to build and deploy the production environment.
Operate: Once the release is accessible to the end-users, the operations team works on server configuring and provisioning using tools like Ansible, Saltstack, CFEngine, or Chef.
Monitor: As the name suggests, in the monitoring stage, the DevOps pipeline is monitored according to the collected information on application performance, user behavior, and so forth. With environment monitoring, teams can easily identify bottlenecks that affect productivity.

From Agile to DevOps

Even with subtle differences between DevOps and Agile testing, individuals working with Agile might find DevOps more familiar and eventually adopt it. Although the principles of Agile are successfully applied in the iterations of development and QA, it is not as successful on the operations side. This is where DevOps comes in.

DevOps has now replaced Continuous Integration with Continuous Development, in which teams work to develop applications in short cycles so the software can be automatically and reliably released anytime. Using CD, the software application can be developed, tested, and released at a great frequency.

As processes and environments are standardized in DevOps, the continuous development process benefits everyone in the entire chain. As all the processes in DevOps are automated, it allows developers to focus on designing and coding a high-quality application instead of stressing about builds, quality assurance, and operations processes.

Using continuous development dramatically reduces the duration between the time code is written and committed to deployment on production for users up to 4 hours.

In short, DevOps is an Agile extension or can be called "Agile on Steroids."

Best Practices For DevOps Testing

DevOps test engineers need to re-think software QA test strategies to align with pipeline stages from development to operations. Thankfully, several DevOps testing best practices can be understood and used for any application development. Although it is beyond the scope of this article to explain each of the test best practices of DevOps. That’s why we have summarized every best practice of DevOps testing and explained them below.

DevOps Test Culture

The testing culture of DevOps differs as the responsibility of providing high-quality applications is shared between cross-functional team members. The quality check is a crucial aspect of the pipeline phases and involves all the team members. Besides, quality testing cannot be left for the end of the pipeline by a completely different team. So, teams need to determine the test strategies to control the extent and volume of testing activities throughout the application development lifecycle.

To achieve the required results, each member of the cross-functional team has to take the responsibility for testing and its results.

DevOps Test Culture should include the following characteristics:
Encouragement of collaboration around testing and test results analysis instead of confrontations between testers and developers for code repairs.

Test coverage and creation terms have been consented to by the DevOps team.

Leaders should consider testing as a strategic part of the project development instead of seeing it as a cost that can be reduced. They need to budget money and time to provide test training resources, frameworks, tools, management and creating assessment policies for developers they want in the DevOps team.

Dev teams should embrace the test creation and result analysis, while the Ops teams should plan and execute cross-functional tests.

Continuous Test Strategies

The traditional waterfall approach for testing where an extensive volume of changes in the application is tested near the end of the development cycle by an independent QA team cannot work with DevOps.

As the DevOps team tests small changes over all the stages of the continuous delivery pipeline, agile methodologies are more compatible with DevOps testing.

While Agile emphasizes the importance of continuous testing and the need to integrate continuous testing into software tools, it does not define the methods for extending tests to deployment. The continuous testing strategies required for DevOps are more defined than those for waterfall or Agile. Continuous testing strategies must include the integration of testing in all the phases of the pipeline and deployment.

End-to-End Test Integration

DevOps requires horizontal integration of tests across end-to-end pipeline stages as well as vertical integration across different levels of continuous delivery infrastructure.

The best practices to achieve end-to-end test integration are enlisted below:

Conducting tests on the changes in the application before integration using a private instance to ensure the code changes do not break the branch. Some of the testing methods to do so are static code analysis, unit testing, performance testing, regression, scanning and functional testing.

In the pre-integration testing stage, automated tests should be created for use in later testing stages in the pipeline.

To verify test results from the pre-integration test, the DevOps team should conduct assessments on the code at the time of committing code.

In the build stage, tests should be performed to identify whether the integrated build meets the acceptance criteria.

To make sure that the performance and functionality of the build images meet the assessment criteria, performance and functional tests must be committed during the code testing process.

Similarly, at other stages such as regression, system tests, and delivery, a set of tests needs to be conducted to ensure that the code and application meet the expected assessment criteria.

DevOps Test Infrastructures

The application that is being tested can have either monolithic, 3-tier, service-oriented or microservices architecture. DevOps testing practices emphasize the importance of conducting tests in a production-like environment which assures that the test can cover all the configurations of the application once deployed to production.

For that, a best practice is to find and include infrastructure-as-code, dynamic infrastructure configuration tools, cloud services, and test-as-a-service that are more cost-efficient and feasible than dedicated infrastructures and easily stand up and release infrastructure configurations as required to run tests.

DevOps-Ready Test Tools

The continuous delivery test tools must offer capabilities to operate tests on the applications and provide data from the results required to set the test verdict. Some of the tools you can use are functional test tools, protocol test tools, API test tools, unit test tools, database simulators, performance/load test tools, and user interface test tools.

Test tools may be white box tools, gray box tools and black box tools. Test tools should be able to blend the test toolchain and framework to be DevOps-ready.

This will increase the elasticity of scale on-demand vertically or horizontally, and match the workload demands as well as the capacity of testing changes in application going through the continuous delivery pipeline. DevOps ready tools can be orchestrated, scaled, invoked, controlled, and monitored from an API. Resources, employing fail-fast test design techniques, test framework configurations that accelerate test results monitoring, and test tool configurations.

Test Analytics

If the analysis of the results from continuous testing does not get kept up with the speed of testing, it can not only increase the number of results to be analyzed but also lead to no time savings, mass confusion and overlooked valuable results that will slow the CI/CT cycles. Some techniques that can be used to match the speed of the test and its analysis include test result analysers, dashboards or adding analysis tools to the frameworks.

Microservices and Containers

From the testing perspective, with microservices architecture comes the need to verify each service contract with other services using it. Both inter-service dependencies and the independence of microservices should be well tested.

It is also necessary to verify the reliability and performance considerations when operating services over a network. Microservices need to be regression tested if they were affected by application changes or a dependent microservices group.

Containers provide the possibility of packaging test resources in special containers for convenience and immutability, as well as scalability on demand for testing changes.

Database DevOps Testing

It is crucial to have a strategy to test and verify any changes to the database or application using a database are performing as required throughout the pipeline of continuous delivery. Additionally, there must be tools that can be used to replicate data volumes from the production to ensure that the tests are conducted on the production like datasets before deployment.

DevOps Security Testing

By strategizing DevOps Security testing, it becomes easier to make the application free from vulnerabilities, threats and risks. DevOps team can apply automated tools and tests in the development cycle to minimize downtime, vulnerabilities and security threats.

Automate Tests

To eliminate the risks that come with continuous integration, it is essential to add test automation that will provide quick application quality feedback. Indulging automation testing with CI allows teams to test iterations of new code and minimize the possibility of errors.

DevOps Testing Tools

What are the best tools for DevOps Testing Strategy? Here is the list of different testing tools that can be used for the DevOps testing strategy.

Using DevOps testing tools in the software development life cycle offers several advantages to the development and operations team. Some of the benefits it offers are code quality improvement, fast and continuous feedback, and increased application time-to-market that helps in increasing development, operations and testing teams.

Unit Testing Tools

With unit testing, the DevOps team can examine the source code of the application individually to verify its functionality. Unit testing can be done even at the initial development stage of the application. It relies on test cases that imitate the functionality of the application. These test cases either pass or fail and provide results to the user so that they can debug the code.

Some unit testing tools are specifically designed for a given programming language. A few tools you can use are Mocha (for JavaScript), EMMA (for Java), Typemock (for .Net and C++), Parasoft (for C and C++), and SimpleTest (for PHP).

Performance Testing Tools

Performance testing is done at the later stages of DevOps, i.e when the code is written and integrated. According to the requirements of the project, performance testing tools will subject the application to stress, load, capacity, volume and recovery tests to check the performance of the application and how it recovers from an outrage.

The aim of using tools for performance testing is to detect the crash source and modify the system for peak efficiency before releasing it to end-users. A few tools that can be used for performance testing are Apache JMeter, k6, Watir, Predator, and TestComplete.

Automated Testing Tools

Automated testing tools help to run tests automatically, manage test data and use the result to improve the quality of the software. Apart from reducing human errors, automated testing tools also enable scaled evaluations. Automated tools in the CI/CD model trigger tests based on events.

Some of the tools that the DevOps team can use for automated testing are TestProject, Leapwork, Opkey, Selenium, Tosca, and Testsigma.

Continuous Testing Tools

Continuous testing is the process of testing applications that helps in code, feature and application validation at every phase of the DevOps pipeline to detect bugs and minimize the turnaround time.

A few examples of continuous testing tools that are used by the DevOps team include AppVerify, Appium, Docker, Bamboo, and Jenkins.

Conclusion

DevOps is the ideal solution for the development of applications for several businesses because of all the right reasons. However, the success and quality of the application completely depend on the strategy the DevOps team made for testing the application.

Top 10 AI Trends To Watch in 2023

Priyanshi Sharma — Thu, 02 Mar 2023 06:39:37 +0000

Artificial Intelligence is no longer fiction. With voice assistants like Alexa and Siri or personalized recommendations on social media platforms, AI has become an integral part of daily lives.

Moreover, AI continuously evolves with advancements in Machine Learning and Deep Learning algorithms, making it hard to predict the future. However, based on recent developments and industry trends, we have listed the top 10 AI trends to watch in 2023.

While many are aware of Artificial Intelligence technology, some might still be unaware of the basics. So, before we move on to the top 10 AI trends, let’s define AI.

Artificial Intelligence: An Overview

Artificial Intelligence, aka AI, is the human intelligence stimulation so machines can perceive, synthesize, and conclude information. AI systems work by ingesting labeled data in a large amount, analyzing the data for patterns and correlations, and using analysis results to make informed predictions.

AI programming is focused on three intellectual skills:

Learning
Reasoning
Self-Correction

AI is popular because it reduces the time to complete data-heavy, detail-oriented tasks while delivering consistent results. Some industries where AI has made its way include healthcare, education, banking, finance, corporate, law, manufacturing, transportation, and security.

Google Search, YouTube, Netflix, Amazon, Siri, Cortana, Alexa, Self-driving Cars, and ChatGPT are real-life examples of artificial intelligence performing specific tasks.

Top AI Trends You Must Know About
With a better understanding of AI, it’s time to glance at the top AI trends you should be aware of:

Generative Models: The Future of AI-Generated Content
Explainable AI: Bringing Transparency and Trust to AI Systems
Human-Centric AI: Designing AI for Human Interaction and Collaboration
Edge AI: Moving Intelligence Closer to Devices
AutoML: Automating the Machine Learning Process
Quantum AI: Bridging the Gap Between Quantum Computing and AI
Federated Learning: Collaborative Machine Learning Across Devices
AI-powered CyberSecurity: Addressing the cyber risks with AI
Sustainable AI: Reducing carbon footprints AI
Autonomous AI: Self-Learning and Self-Optimizing AI Systems

Now, let’s look at each of these AI trends in detail.

Generative Models:

Generative AI is machine learning’s sub-field that is used to generate new content using existing data such as photos, videos, code, text, or sound. ChatGPT, DALL-E, ArtBreeder, and Pikazo. Generative AI aims to create original results by processing large data sets using unsupervised or semi-supervised learning.

Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the commonly used generative models.

Generative Adversarial Networks (GANs) comprise generative and discriminative neural networks. While the generative network creates outputs on request, the discriminative network tries to differentiate between real-world and fake data to improve the content quality.

On the other hand, VAE encodes data into a low-dimensional representation that apprehends essential features, structure, and relationships of data by training a single machine learning model.

Explainable AI (XAI):

Explainable AI refers to the process and methods that allow humans to comprehend and trust machine learning algorithm-generated outputs. It is used to describe potential biases and impacts of AI models.

For AI decision-making to be successful, businesses must know how the AI decision-making process works, with model monitoring and accountability of AI.

In simple terms, XAI helps humans to understand the predictions and decisions made by the AI model. It differs from the “black box” machine learning, where even its designers can’t explain why the AI reached a decision. Instead, Explainable AI helps signify an AI-based decision's accuracy, transparency, fairness, and outcomes.

Human-Centric AI:

Human-Centric AI is an emerging practice aimed at creating AI systems that amplify and augment instead of replacing human abilities. HCAI will preserve human control while ensuring AI meets our needs by working transparently, respecting privacy, and producing unbiased outcomes.

Simply put, HCAI combines artificial intelligence, machine learning, and human-centered design to transform how businesses use, operate, and take advantage of data acquired without creating new algorithms for new jobs.

Edge AI:

The blend of artificial intelligence and edge computing is what we call Edge AI. Edge AI is an AI technology that brings the computational power of AI algorithms to the edge of the network rather than relying on a centralized cloud computing infrastructure.

The AI models used in edge AI are typically lightweight and optimized for a specific task, such as object detection, speech recognition, or natural language processing. As a result, edge AI improves the response time speed, increases privacy and security, reduces network latency and bandwidth usage, and makes edge computing cost-efficient.

AutoML:

Automated machine learning refers to the processes and methods that improve Machine Learning efficiency and make it available to non-ML experts. AutoML is used to apply the ML model to solve real-world problems through automation. It makes ML processes user-friendly and provides more accurate output than manually written algorithms.

Data preparation, Feature engineering, Ensembling, Model Selection, Hyperparameter Optimization, Pipeline Selection, Problem Checking, and Result Analysis are some ML processes that can be automated through AutoML.

Quantum AI:

As the name suggests, Quantum AI combines quantum computing and artificial intelligence. By using quantum AI, scientists can achieve results that conventional computers would not be able to achieve. The aim of quantum AI is to create algorithms for decision problems, learning, searching, game theory, etc., that work better than classical ones.

Federated Learning:

Federated Learning focuses on training machine learning algorithms across multiple decentralized data servers or edge devices without changing them. Multi-actor machine learning models can be built using Federated Learning without sharing data, which allows it to address critical issues such as data privacy, security, and access rights.

AI-powered CyberSecurity:

With the growth of cyber threats in complexity and volume, AI can help security operations analysts stay ahead by curating threat intelligence from millions of news stories, research papers, and blogs on cyberattacks. In addition, AI can provide insights to improve response times drastically. What makes AI-powered cybersecurity a trend is its ability to continuously learn, identify threats faster, and remove time-consuming tasks.

Sustainable AI:

Sustainable AI is devising energy-efficient, highly accurate, and explainable machine learning algorithms that support data processing from distributed sources.

In simple terms, it refers to the use of AI technologies that are socially and environmentally responsible. Sustainable AI aims to create AI systems that can reduce carbon emissions on the environment while empowering long-term viability and ethical use.

Moreover, to achieve sustainable AI, developers must consider various factors such as energy efficiency, data privacy, bias and fairness, and transparency.

Autonomous AI:

Autonomous AI refers to creating AI systems that can operate autonomously and make independent decisions without human intervention. These AI systems can learn and adapt using data analysis and machine learning algorithms to improve performance. Some popular examples of Autonomous AI systems are self-driving cars, robots, and drones that don’t require human intervention.

Takeaway

We live in an era of artificial intelligence. Several industries are about to be revolutionized by artificial intelligence, which is advancing exponentially. Needless to say, AI will continue to evolve, integrate more seamlessly into everyday life, and transform how we work, live, and interact.

Source: Dev.to

DevOps Career Roadmap

Priyanshi Sharma — Thu, 12 Jan 2023 06:48:14 +0000

DevOps has gained popularity in recent years. According to stats, around 47% of respondents use DevOps as the software development methodology. Moreover, according to Allied Market Research Insights, the value of the DevOps market which was valued at $6.78 billion in 2020 is expected to reach a CAGR of 24.2% by 2030.

And if you too have an interest in becoming a DevOps engineer, it is a must to know what DevOps is, what responsibilities you will have to handle, and what tools and technologies you will need to understand.

So, without further ado, let’s get started!

DevOps: An Introduction

DevOps doesn’t have any universal definition. It is the term that is derived from two words Development (Dev) and Operations (Ops). DevOps is a collection of cultural aspects, methods, and tools that helps in automating software development processes.

DevOps is used to manage the complete application development lifecycle consisting of planning, development, deployment, testing, monitoring, delivering, and operating. In simple terms, DevOps can be defined as the union of processes, technologies, and people to give continuous value to customers. DevOps allows developers, quality engineers, security, and IT operators to coordinate and collaborate for creating reliable and better-performing products.

Some of the benefits of DevOps include

A better time to market
Easy market and competition adoption
Improved recovery time
A shorter cycle for releases
Maintained reliability and stability of the software

What are the Responsibilities of DevOps Engineers?

A DevOps engineer has to work with different departments and teams to build and implement software solutions. Considered an all-rounder, a DevOps engineer is responsible for almost everything.

To be more precise, here is a list of tasks that a DevOps engineer is responsible for.

Planning: DevOps engineers need to plan projects by communicating operational requirements and development forecasts with other participants throughout the team and gain their knowledge of risks, system options, costs, impacts, etc. They also need to split the project into smaller independent tasks, create integrated plans that take errors and defects into account, and use feedback to re-plan.
Documentation: Taking notes and documenting specifications for the backend of the software is yet another responsibility that DevOps engineers need to take care of.
Analysis: If a DevOps engineer is working on a project that’s already in the market, it is important to analyze the technology being used to develop processes and plans to improve the software.
App Development: They are also responsible for developing codes, builds, configurations, installations, and maintenance of software solutions.
Testing: As the software gets developed and starts to receive continuous deployment, it becomes essential for DevOps engineers to conduct continuous testing to improve code quality, reliability, and security.
Automation: A DevOps engineer has to use automation to make the software development lifecycle reliable and consistent. Automation allows the DevOps team to easily scale environments, accelerate pipeline processes, alter CI/CD workflows, run reliable tests, set up infrastructure, and monitor pipelines.
Monitoring: To analyze the stability and performance of application infrastructure, the DevOps team needs to monitor the logging, alerting, and tracing of the web app.
Deployment: It refers to the act of setting up and installing a software version into the production environment. The software version can be an external, internal, or development release version. The responsibility of continuous deployment for automated releases is also of DevOps engineers.
Maintenance: DevOps engineers also have to ensure that all environments are running smoothly by identifying and removing vulnerabilities, improving pipelines, ensuring the availability of services, and keeping software updated as well as secured.

A Simple Roadmap to Become a DevOps Engineer

Now that we are much aware of what DevOps is and what responsibilities it entails, let’s dive into the roadmap that will help you set your path to becoming a successful DevOps Engineer.

DOWNLOAD PDF: DevOps Roadmap PDF

Using the Roadmap PDF or PNG is subject to sharing the orginal link.

Learn Programming Language for Automation

A programming language is used by developers to communicate with computers. It is a set of instructions written to perform certain tasks, especially to develop web apps, desktop apps, mobile apps, or websites.

To build a career as a DevOps engineer you need to learn programming languages that allow you to automate tasks like integration, testing, and deployment. Some of the programming languages for automation you can go for include Java, JavaScript, Python, C# (pronounced C Sharp), Rust, Go, and Ruby.

Learn Different Operating System Concepts

An operating system is a program that manages software resources and hardware of the computer system. It helps in the allocation of memory, input and output, file storage, and network connections.

A few concepts of Operating Systems that you as a DevOps Engineer will need to know are Input/Output Management, Computer Networking, POSIX (Portable Operating System Interface), Virtualization, Memory/Storage, File Systems, Sockets, Processes, Service Management (systemd), Startup Management (initd), Threads and Concurrency, etc.

Apart from the basic concepts of OS, you also need to understand operating systems like Linux (eg. SUSE Linux, Ubuntu/Debian, or RHEL/Derivatives), Unix (eg. OpenBSD, NetBSD, FreeBSD), and Windows.

Understand How to Manage Servers

Servers can be a piece of computer software or hardware that offers functionality for programs, called clients. A server is designed to process requests and return requested data to the user’s computer over a network.

And as a DevOps engineer, you need to get administrative knowledge of different types of operating systems to manage these servers. Server management can consist of monitoring and maintaining servers to ensure that they are performing as expected and are reliable.

Server management also helps in reducing slowdowns in servers, building secure servers, and scaling servers as per the requirements.

Learn to Work in the Terminal

A terminal is a text-based computer interface used to interact with the computer system through a command line interface (CLI).

And to be a DevOps engineer, you will have to learn about bash scripting, PowerShell/Emacs/Vim/Nano, Compiling apps from source, terminal multiplexers (screen or tmux), process monitoring, system performance, text manipulation tools, and network tools.

Understand Networking Security Protocols

Network security protocols are used to ensure the integrity and security of data transfer over a network connection. It defines the methodology and processes used to secure network data from unauthorized access.

Some of these network security protocols are HTTP, HTTPS, FTP/SFTP, SSL/TLS, SSH, Port Forwarding, SMTP, IMAPS, POP3S, Domain Keys, SPF, and DMARC.

Learn How To Setup Proxy, Load Balancer, Firewall, and Server

A DevOps Engineer needs to know the way to set up proxies like Reverse Proxy or Forward Proxy, caching servers, load balancers, firewalls, and web servers like Tomcat, IIS, Apache, and Nginx.

Here proxies act as an intermediary between clients and servers to help block client identity, access to certain content, web acceleration, and security and offer restricted internet to organizations.
Caching Servers acts as servers that store web pages locally in the form of a temporary cache to speed up data access.

Load Balancer helps in routing client requests across capable servers to process those requests to maximize the speed of the web app and maintain traffic on the servers.

Firewalls monitor and filter requests to ensure there is no threatening traffic on the server.

Learning to work with the above-mentioned technologies will ensure that there are no threats in the server and that all tasks run smoothly.

Understand Infrastructure as Code

Infrastructure as Code (IaC) refers to the management and provisioning of infrastructure such as virtual machines, networks, connection topologies, and load balancers through code instead of doing it manually.

IaC allows the DevOps team to create versions, rollback, and manage changes using the same workflow as used to develop software by programmers.

Some of the common concepts of Infrastructure as Code are Containers like Docker or Nomad, Secret Management through Sealed Secrets, Vault, SOPS, or Cloud Specific Tools, Container Orchestration through Docker Swarm, Kubernetes, or Nomad, Configuration Management through Ansible, Puppet, or Chef, and Infrastructure Provisioning through AWS CDK, Terraform, CloudFormation, or Pulumi.

Learn About CI/CD Tools

Continuous Integration and Continuous Deployment (CI/CD) is the process of automating the continuous integration of code changes into a single codebase and running integration and regression tests to automate the release process.

Continuous integration, continuous deployment, and continuous delivery are the core concepts of CI/CD. Learning the basic concepts of CI/CD as well as their tools like Gitlab CI, Jenkins, Azure DevOps Services, Drone, and Travis CI will help you make the process of integration, delivery, and deployment easier.

Learn to Monitor Infrastructure and Software

Monitoring the software and infrastructure of the project as a DevOps engineer includes overseeing every process from planning, development, integration, testing, deployment, and operations through historic replays, real-time streaming, and visualizations.

You can do infrastructure monitoring using tools such as Nagios, Grafana, Zabbix, Monit, DataDog, and Prometheus. For application monitoring, you can use Instana, OpenTelemetry, Jaeger, New Relic, and AppDynamics.

Also, you can manage logs using Elastic Stack, Splunk, Graylog, Loki, and Papertrail.

Learn About Different Cloud Providers

Last but not least, as a future DevOps Engineer, you also need to understand different cloud providers and the services they are providing along with their use cases in the market. A cloud provider is an IT company that offers computing resources over the internet to consumers and delivers them on demand.

Some of the popular cloud providers are Amazon Web Services (AWS), Google Cloud, Microsoft Azure, Heroku, Alibaba Cloud, Digital Ocean, Vultr, etc.

Apart from knowing cloud providers and their services, a DevOps engineer should also be aware of the cloud design patterns to build reliable, secure, and scalable cloud applications. Other basic concepts you need to understand about cloud services are availability, design and implementation, data management, and monitoring and management.

Conclusion

Remember that this roadmap will only help in choosing the right path for your DevOps career. Undoubtedly, we are going to see much more languages, tools, frameworks, and other technologies in the future, making it crucial to always keep learning to ensure a secure and successful career.

Source:Decipher Zone

How To Improve Code Quality

Priyanshi Sharma — Fri, 10 Jun 2022 07:15:00 +0000

Writing code and developing high-quality software is a work of art. As soon as a developer starts writing code, it becomes crucial to keep it bug-free, sustainable, easily understandable, and traceable.

For one developer, code quality might not be the issue because he/she will perceive the code from one angle. However, when it comes to the team where different developers come together from different perspectives and experiences - code quality becomes the arising problem.

Simply put, ensuring the quality of code is not only limited to the developer but also involves the tester and managers as even one line of poorly written code can cause the entire system to crash (maybe we exaggerate, but you know what we mean).

In this blog, we will cover what code quality is, why it matters, and ways to improve the quality of code with ease. So let’s get started!

What is Code Quality?

Code quality is the attributes and characteristics of code that may differ according to the goals of an organization and the needs of its team. As there are numerous criticalities and purposes a code may serve, code quality can be subjective and open to arguments.

However one thing is for sure, quality code does what is expected of it and follows a consistent style. According to our experience, when we hear the word high-quality code, we expect it to:

Be easily understandable
Have minimum bugs
Does what’s expected
Follow used programming language conventions

In simple terms, code quality is a term that describes readability, orderliness, understandability, and maintainability in the source code. Code understandability is important for both current and future programmers as it will help them in comprehending the purpose of code that was written by previous developers.

“It is important to maintain the quality of the code, but why?” You might ask. So let’s take a look at the reasons in the next section.

Why Code Quality Matters?

As the first law of programming states “lowering quality lengthens development time.”

In simpler terms, you can develop quality software in the least amount of time. Having code that is as simple as possible, a design that fits accurately, complete tests, and frequent changes help you develop and deploy an application within the given time with higher quality. On the other hand, having to look out for errors and bugs now and then due to lower code quality can lead to slower development and increased cost of development and maintenance.

Good code quality ensures highly readable codes with proper indentation, use of comments, simplicity, and clear notation in the development flow, making code editing more efficient.

Besides, with good quality code, software applications become more robust, and sustainable while enhancing data transferability and reducing development costs.

Practices to Improve Code Quality

Now that we know what code quality is and why it is important to maintain, it is time to move ahead and understand the ways to improve code quality.

Favor High Cohesion & Loose Coupling

The key to improving code quality is holding to high cohesion and loose coupling.

An element's cohesion describes how they are related.

Having low cohesion leads to a spread-out codebase making it hard to discover and keep track of code.

The closer related codes are, the more cohesive they will be. With high cohesion, we can achieve the Don’t Repeat Yourself (DRY) code and minimize module information duplication. This allows developers to easily design, write, deploy, test, and maintain code.

On the other hand, modules should be independent of each other so that changing one of them doesn’t impact the others. If your modules are coupled tightly, they will be privy to unnecessary details and the working of other modules, which will make coordination hard. While loose coupling makes changes to modules easier as it does not affect the working of other modules in the application system.

Comment Why, not What

Another thing to keep in mind while you want to improve the code quality is explaining why in the comments not what.

Simply, comments should never state what the code does as its purpose should be clear by itself. Comments need to explain WHY you were using that code.

Moreover, working in a team, you might notice that code is moved around by the developers as they edit your code lines. It makes it essential to leave your name next to a comment.

Another thing to do while writing code is explained without the assumption that others know what’s going on or how your system works. You will have to explain quirks of internal and external systems, legacies, when these legacies can be removed, hacks you used, and internal dependencies.

Use Code Linter

An analysis tool known as Lint is used to discover bugs, suspicious constructs, stylistic errors, and programming errors in a program. Using code linters like JSLint, ESLint, Sublime linter, etc. a developer can avoid several problems. It helps in identifying and debugging technical errors as code smells in the source code while web application development.

Linted makes the code readable, consistent, and maintainable by ensuring that minimal defects make it to the production environment.

Bring Coding Conventions into Practice

Coding conventions are guidelines for a certain language that is being used in the software development process and recommend programming practices, styles, and methods for every aspect of the language.

Sometimes a development team establishes code conventions for the entire business module and other times for certain projects. By using coding conventions, businesses can encourage developers to write code in the same pattern to make code easier to understand by others and find files and classes in a large project with ease.

Coding conventions cover indentations, white space, file organization, comments, naming conventions, programming principles, architectural best practices, statements, etc.

Write Clear Code Instead of Clever Code

Although the name “clever code” sounds like code should be easily understandable, it is not the case. In most cases, clever code is the code that does not explain its purpose in the source code. What this means is that if a beginner or intermediate programmer will see the code it will require a few minutes to figure the code out.

Sometimes, what is hard for me to understand in the code lines might be easier for you. So our main focus should be writing code that is understandable by others rather than just ourselves.

Comparatively, writing clear code that is simple, logical, and understandable will lead to fewer problems and always works as expected.

Give Meaningful Names to Variables

Most people go ahead with single or double variable names when they code, others use generic names like value, arr, etc. which makes debugging and understanding the code time-consuming and confusing. Instead of using variable names like a,b,c, or x,y,z, giving expressive or meaningful variable names makes it easier to understand the code.

To give meaningful names you can use intention-revealing, pronounceable, searchable names while avoiding encodings that will improve the code readability and understanding, leading to better code quality.

Code Testing

The biggest quality of good code is its proper functioning. Testing is the best way to ensure it.

Although writing tests can be hard, it will save a lot of errors and bugs in the future. These tests will be triggered locally before pushing or committing code to the repository to give you an evaluation of the app performance, hence, giving you confidence in its working.

Some of the tools that you can use for code testing are Selenium, QUnit, Chai, Jasmine, Mocha, and Karma.

Tactical Code Reviews

Code review is the process of systematically and consciously meeting with fellow developers to check code mistakes from each other leading to streamlined and accelerated development of the software application.

More often than not, software developers, as well as managers, ignore the incredible benefits code reviews can offer. When done tactfully, code reviews save a lot of time and money spent on identifying bugs that slip through automated testing systems.

Some of the common approaches to doing tactical code reviews include pair programming, over-the-shoulder, tool-assisted, eMail thread, etc.

However, it is extremely important to not be overly critical of someone’s code as you want the conversation about the code rather than the person who wrote it. Although there shouldn’t be arguments over differences in opinions, discussions are always welcome.

Takeaway

We hope now you understand what the code quality is, why it matters, and the ways to improve it. To give you a brief, you can improve the code quality by favoring high cohesion, loose coupling, commenting whys, using code linters, adding code conventions, writing clear code, giving meaningful variable names, code testing, and reviewing.

If you are a business owner who wants to develop their app with high code quality, then hire developers from authenticated and trustworthy companies with years of experience like Decipher Zone.

Microservices States, Scalability, and Streams

Priyanshi Sharma — Thu, 09 Jun 2022 10:26:02 +0000

For years now, most of us have heard the word “Microservices” and how it makes an application-independent. While some may also have worked using this architecture. But today’s article is majorly focused on the shifting from monolithic to microservices, how, in some cases, microservices are not acting as expected, and what is the solution for improving its state, scalability, and streams while actually making it an independent architecture.

Let’s get to the onset, where the hype of microservices architecture really started and the reasons it emerged in the first place. When we had big monolithic applications where making changes required negotiations between different teams and coming to a shared agreement to take even one step ahead. Modifications in monolithic apps had made the process slower and more frustrating, which led to the idea, “What if we put these components into an isolated context where different teams own different development contexts from beginning to end?” That’s where the concept of microservices started to emerge.

But what are monoliths, what are they slow, what’s microservice, and how can it help?

Without further ado, let’s get started with the basics.

Shifting from Monolithic to Microservices

A monolithic architecture is a unified model for designing a software application. “Monolithic” refers to all composed in one piece. Monolithic-based software is self-contained with tightly coupled components or functions and a large codebase that can be burdensome to manage over time.

When the complexity of the monolithic app increases, the codebase can be challenging to maintain, making it problematic for new developers to change or modify the code according to the changing technical or business requirements. Besides, with the continuously evolving necessities that can be more complex than before, implementing changes without compromising the code quality and affecting application performance becomes impossible.

Moreover, to update a feature in a monolithic application, developers have to compile the complete codebase and redeploy the entire application rather than that part, making deploying regularly difficult while reducing the agility and increasing the time to market.

Sometimes the resource requirements in the monolithic application can be conflicting for different application components, making it hard to find the required resources and scale the application. Another problem that had us thinking about shifting to microservices was the application’s reliability. A bug or error in any part of the application can bring down the application instantly and become unavailable to the users.

As discussed before, microservices evolved to overcome the drawbacks of monolithic applications.

Software development using microservices is a modular approach to creating large applications from small components (services). Microservices applications are distributed into multiple isolated services where each service runs a unique process. Developers can change segments independently without worrying about the other parts of the application and making unintentional changes within other aspects.

Typically, microservices speed up the development, deployment, and maintenance of an application independently. Usually, microservices are built using programming languages like Java and Spring Boot and communicate with each other using APIs. Microservices applications provide fault isolation and tolerance, i.e., in the case of a bug or error in one service that doesn’t take down the entire application. After debugging the component, it is deployed independently to its respective service instead of the complete application redeployment.

Microservices architecture offers a cleaner, independent, evolvable design that makes the application easily adaptable and scalable.

Challenges in Microservices

As we know, all the services need to work together and communicate with each other. What we did was, start using REST APIs with JSON as a data-interchange format. It sounded straightforward because who doesn’t know how to work with REST and send JSON. Additionally, JSON is supported by almost everything, and if it doesn’t, one might say, “I will write it down within a weekend” (Surely you will, but it can take anywhere from months to years.)

And this is the idea that all the developers around the world have adopted. As discovered after a bit, this pattern in a lot of ways, makes us all fall back into the tight coupling that we were trying to overcome. All this has ended up getting microservices architecture a humorous nickname: “distributed monolith” where you only have the problems of microservices but somehow none of its benefits, making debugging even more complex and you still have to negotiate every change.

Some of the other major challenges that developers had faced while using microservices are:

When you don't take precautions, your business logic can leak all over the place, and clients will know a great deal about the internal workings.
With the increasing complexity in architecture, making changes can become riskier, requiring you to run continuous tests on these services together, if you aren’t careful.
The biggest challenge a developer can face that also slows down the development in many companies is the need to convince others to connect with them if they have something to add to the application.
Another most annoying challenge one can face is the need to serialize and deserialize JSON on every hop, and open and close HTTP connections on almost anything, increasing latency.

What Can We Improve in Microservices?

Now that we know the challenges developers might face when using microservices architecture for application development, it is important to know the patterns, tools, and technologies that can help us overcome these challenges.

We have mentioned the ways that can make developers more efficient.

API Gateway

Most of you must be familiar with the idea of an API gateway which is an API management tool residing between a client and a backend services collection. It acts as a reverse proxy that accepts all the API calls, aggregates the required services, and returns the expected response.

Rate-limiting, authentication, and statistics are all managed by API Gateways. In the case of microservices applications where a single call can require calling dozens of services, API Gateway acts as one entry point for every client. It also provides request routing, protocol translation, security, and microservices composition in the app. API Gateway stops internal workings exposure to external clients, merges communication protocols, decreases microservices complexity, separates external APIs from microservices APIs to visualize design testing, and improves the efficiency of the application.

Service Mesh

A service mesh is a way to control the way different services share data. It is a dedicated platform layer above the infrastructure that can make the communication between services managed, secure, and observable. Service meshes use compatible tools to determine all the problems of executing services, including networking, monitoring, and security. It allows developers to stop worrying about taking measures for addressing challenges in microservices and focus on developing and managing applications for the client.

With a service mesh, a microservice won’t communicate with other microservices, instead, all the communication will happen on top of the service mesh (or sidecar proxy). Some of the built-in support that service mesh offers are service discovery, routing, security, observability, container deployment, access control, resilience, and interservice communication protocols like gRPC, HTTP 1.x, or HTTP2.

Moreover, a service mesh is language-agnostic, i.e, it is independent of any programming language. So you can write your microservice using any technology and it will still work with the service mesh. Two of the popular open-source platforms for service mesh implementation are Istio and Linkerd.

Sidecar Proxy

An application design pattern that separates features like monitoring and security, and inter-service communication from the main application architecture to ease the maintenance and tracking is “sidecar proxy”. It is attached to a parent application to help add or extend its functionality. It is typically attached to the service mesh control panel, containers, or microservices.

A sidecar proxy manages the flow of traffic between microservices, collects telemetry data (logs, metrics, and traces), and sets policies. In short, sidecar proxy minimizes code redundancy, reduced code complexity, and loose coupling between services of the microservice application.

Kafka for Data Streams

Kafka Streams, a client library, is used for building microservices, where the inputs and outputs are stored in clusters of Kafka. Apache Kafka is a distributed event streaming that is highly scalable and fault-tolerant. Kafka Streams blends the simplicity of writing and deploying Java applications on the client-side with the advantages of the server-side cluster of Kafka.

It is equally viable for different use cases, be it small, medium, or large. Kafka is integrated with Kafka security and does not require any processing cluster. Microservices applications can effectively manage large data volumes using Kafka.

Kafka accepts data streams from different sources and allows real-time data analysis. Additionally, it can quickly scale up and down with minimum downtime.

Event-Driven System

We had the service mesh to solve some of our internal communication. Now another issue with microservices is the risk of making changes in point-to-point requests and responses. Moreover, two services are more aware of each other than they should be while adding new things are much more difficult than anticipated.

In a request-response pattern, a service communicates with other services to maybe get validations or in other cases information about a client.

However, with an event-driven approach, microservices architecture becomes scalable, adaptable, dependable, and easy to maintain over time.

Decoupled services communicate with each other and trigger events with an event-driven architecture. These events can be an update, a state change, or items being placed in a shopping cart (in the case of the eCommerce web app).

Simply put, an event-driven system switches the request-response pattern and makes services more autonomous - which is why we opt for microservices in the first place.

Stateful Streaming in Microservices

Each microservice can either be stateful or stateless. Although when it comes to microservices, many of us think about the stateless services that communicate with each other through HTTP (REST APIs), it can sometimes create problems as mentioned above. So to avoid that, here we are about to discuss the stateful microservices achieved through an event-driven (streaming) system.

In the event-driven microservices, in addition to the request-response pattern, services publish messages that represent facts (events) and register to the topics/queues to receive responses/messages/events.

However, some of the patterns that you should acknowledge before implementing event-driven architectures are Saga, Command and Query Responsibility Segregation (CQRS), Event Sourcing, and Publish-Subscribe.

One thing to remember when the term “event-driven microservices” is used it means stateful services that have their own databases they maintain. A stateful microservice maintains a state in some form so that it can function. Moreover, instead of storing these states internally, event-driven microservices should store these states externally in data stores like NoSQL or RDBMS databases.
Usually, a stateful microservice is used in systems that require real-time updates on data and event changes. You can either use Apache Kafka or Amazon Kinesis which are distributed systems designed for streams and are fault-tolerant, horizontally scalable and often described as event streaming architectures.

Some of the advantages that stateful streaming in microservices can offer are event flow tracking, performance control, and reliable event sourcing.

Schema in Microservices

Even with stateful streaming, we still have services communicating with one another, whether through request-response or event-driven and it still has some issues involved. Until now, we have just discussed events. But we haven’t discussed events and what’s inside them.

The messages that are sent from one service to another are in JSON and have a bunch of fields like Social or Property IDs, and lots of metadata about everything going on. But the problem that we had learned at the very beginning of the blog is that HTTP and JSON can be ridiculously slow. Although it cannot be solved instantly, the popular choice here that can be used to solve the problem are gRPC and HTTP/2 which can make things remarkably faster.

But another problem that one might encounter is that no matter if you are using JSON or gRPC, some changes are still hazardous. For instance, messages that are used to communicate, have a schema with fields and fields of data. And to do any sort of testing or validation, there are a bunch of things that depend on the exact data type with the same field name that you might not even know about. However, if you make any changes, it will likely break the system.

The key here is that you have a great way to test the compatibility of schema, otherwise the system might have incredible damage.

However, the way to look at it is whether you are using REST or gRPC or even writing an event, you need contracts (APIs) of what communication and messages look like. So, testing and validating the APIs. If you have Kafka in the event-driven system as a large message queue - schema registries are used.

The idea here is that developers create events, register them on the schema registry and it gets validated with all the existing schemas automatically. In case of incompatibility, it will send a data synchronization error. But waiting till something hits production is frustrating. So, if a developer is using the schema registry, they can use the Maven plugin - to validate the schema, give it a definition, and check schema registry compatibility.

Serverless Microservices

Deploying, monitoring the services, and making sure that they can be easily scaled, takes a lot of effort. That’s where the concept of serverless arises. In serverless development, developers don't need to manage servers to create and run applications. Although the servers are still there they are abstracted from the development environment. All the provisioning, maintenance, and scaling are managed by the cloud provider. All that developers need to do is package the code into the containers to be deployed and the website or web app is scaled up and down automatically responding to the demands. Some of the serverless providers in the market are AWS Lambda, IBM Cloud Functions, Microsoft Azure Functions, Parse, and KNative.

Using serverless microservices will allow developers to write their function and send it to the cloud provider. And the cloud provider makes sure that the microservices web app is scaled immediately to handle every event that comes around.

Conclusion

So that was all about microservices’ state, scalability, and streams. And with that, we will like to wrap up the blog. We hope that you find the information shared with you interesting and useful for your future projects.

Keep in mind that microservices aren’t best in all the scenarios, and sometimes it is better to work with a monolithic architecture. But you can always enquire what is the best suitable architecture for your idea, and they will help with the right set of tools and technologies along with the budget to develop the overall project.

Source: Decipher Zone

Kubernetes Development Workflow

Priyanshi Sharma — Fri, 03 Jun 2022 06:38:10 +0000

A containerized application can be deployed, scaled, and managed with Kubernetes (k8s), an excellent open-source automating system. It is a portable and extensible platform that facilitates both declarative and automated containerized workflows.

Although containers are similar to virtual machines they are considered lightweight due to their isolation, and properties to share OS between applications. Containers also have their file system, the share of memory, CPU, process space, etc., making them a great way to package the code and run the application. Moreover, these containers are easily portable across the cloud because of their underlying infrastructure.

With the expanding adoption of containers, Kubernetes have become the standard to deploy, maintain, and operate containerized applications. But, sometimes achieving a frictionless Kubernetes development workflow can be painful.

So in this blog, we will look at the problems in creating a successful Kubernetes Workflow and the ways to solve them.

Ways to Create Powerful Kubernetes Development Workflow

Starting a project and deploying it on Kubernetes can be time-consuming. You can easily get caught up in infrastructure configurations instead of writing the business logic. Tools and practices that can help a developer focus on the code while streamlining the Kubernetes workflow are the keys to enhancing productivity.

Choose the right Development Environment for Kubernetes

There are a vast number of Integrated Development Environments (IDE) to choose from. But as a developer, you might have spent most of your time developing in your favorite IDE.

But when it comes to Kubernetes, you must make sure that the IDE should have a containerized build/runtime environment that allows services to always be built and run which will simplify the onboarding process for new developers and ensure environmental parity across different production and development environments.

However, if you still want to use the IDE you are used to then try adding extensions that might make the developing, deploying, managing, and scaling infrastructure of Kubernetes easier. Some of the IDEs that you can use are Cloud Code, CodeStream, Azure DevOps, Spring Tools 4, etc. Which will work like a charm with IntelliJ and Visual Studio Code.

In the case of local development workflow, you will also have to install git (for source control), Docker (to build & run containers), kubectl (for managing deployment), Telepresence (for locally developing service), and Forge (to deploy services into Kubernetes).

In case, you don’t want to work using local resources, you can go for a browser-based development environment like Cloud Shell editor from Azure or Google, Hubot, Drools, Teradata ViewPoint, iBwave Design, etc. Especially with tools such as kubectl, scaffold, and docker, these tools reduce setup time and also make it easier for developers to manage Kubernetes infrastructure directly from within the IDE.

Reduce Context Switching

Context switching involves storing the state or context of a process to restore and resume its execution when required. This allows multiple processors to share a single CPU and is a crucial feature of multitasking systems. However, context switching can be time-consuming and create friction in the workflow.

At the time of Kubernetes development, you might have to switch between documentation, Cloud Console, IDE, and logs. Hence, choosing the web-based development environment or adding an extension (with built-in features) to your local one can help minimize context switching now and then.

Local Development Simplification

Usually, using extensions in IDE makes the development and feedback process on local machines streamlined and easier.

And like all the developers out there, you must want to focus on business logic instead of running it in a container and Buildpacks can help you in achieving that. Open-source, cloud-native Buildpacks make it easier and faster to create production-ready and secured images of the container from source code with no need for writing and maintaining a Dockerfile.

Moreover, Buildpacks can work with Code Stream, Cloud Code, and other browser-based development environments which also allows remote clusters to offload memory resources and CPU from your machine. It allows developers to easily build containers and deploy them to MiniKube or Docker Desktop.

Besides, Minikube gives a platform to run and experiment with the Kubernetes application, by creating a cluster on your local machine.

With an online development environment, the repetitive tasks of building container images, updating manifests of Kubernetes, and redeploying applications are simplified. These web-based IDEs use Skaffold (a command-line tool for continuous development of apps on K8s) to automatically run all the iterative processes every time the code is changed.

Another thing to notice here is that Skaffold’s watch mode overviews the changes in local source code and then rebuilds/redeploys the app to the K8s cluster in real-time. The latest version of Skaffold offers the File Sync feature skips rebuilding and redeploying and allows developers to see code changes in seconds.

Opt for the Suitable Way to Deploy Kubernetes Services

Imperative and declarative are two ways a developer can deploy Kubernetes services. Here imperative refers to using a command-line interface for deployment creation and declarative means describing the desired deployment state in a YAML file.

Although the imperative method can be initially faster, it becomes hard to see the changes while managing deployments.

On the other hand, declarative deployments are self-documenting. This means that every configuration file can be managed in Git/GitHub, allowing many developers to work on the same deployments with clear details of what was changed by whom.

Moreover, declarative deployments allow one to use principles of GitOps, where every Git configuration is used as an authenticated truth source.

Undoubtedly, opting for the declarative deployment of Kubernetes services will be more beneficial than going for the imperative one.

Do Live Coding

Who doesn’t want a quick feedback cycle while developing a service? Everyone wants to immediately build and test the code after making any changes. However, the deployment process that I just talked about can add latency to the process, because developing and deploying containers with the latest changes can be time-consuming.

Technologies like Telepresence, DevSpace, Gefyra, and Docker Compose UI enable you to locally develop a service with a bi-directional proxy to a remote cluster of Kubernetes.

Add Monitoring Tools

Monitoring is one of the biggest challenges when it comes to Kubernetes adoption. After all, monitoring a distributed application environment has never been easy and Kubernetes has added additional complexities as well.

But to help you overcome the issue, several monitoring tools can be added to the Kubernetes ecosystem. Some of the most used open-source tools are Prometheus, The ELK Stack, Grafana, Fluent Bit, kubewatch, cAdvisor, kube-state-metrics, Jaeger, kube-ops-view, etc.

Debug Code in Real-Time

Debugging applications built and running on clusters of Kubernetes isn’t easy. However, to solve the problem, there are numerous methods in the market to help in debugging the running pods. However, replicating the local debugging experience in the IDE is difficult due to the need for port forwarding and exposing the debugging ports.

But tools like Skaffold, Helm, Sonarlint, etc, can make the experience of debugging smooth as the web-based IDEs can easily leverage them and place breakpoints in the code.

If you debug the Kubernetes cluster locally, discovering runtime errors before they make it to the integration, staging, or production becomes easier. Moreover, the faster you will identify the errors and bugs, the earlier they will be resolved, speeding up the time of development.

Go for Ideal Development & Deployment Pattern

Another important thing to note if you want to create a powerful and successful Kubernetes development workflow is choosing the right development and deployment pattern that is highly suitable for the project you are working on.

The patterns for development and deployment of applications in K8s are:
Foundational patterns enfold the root Kubernetes concepts. In building container-based cloud-native applications, these patterns act as the basis.

Behavioral patterns cover the foundational patterns and indulge granularity in the concepts of managing different container and platform interaction types.

Structural patterns include details on managing containers in a Kubernetes pod.

One can use Configuration patterns to understand the way to handle application configuration in Kubernetes. It includes the detailed steps for connecting applications to configurations.

Advanced patterns refer to the advanced concepts of Kubernetes. Some of the concepts entailed in this pattern are ways to extend the Kubernetes platform or build container images within the cluster.

Conclusion

So that was all about making your Kubernetes development workflow powerful and frictionless. I hope that this will help you in working with Kubernetes and microservices in your future projects.

Source: Decipher Zone

Observability, Monitoring, Alerting & Tracing Lineage in Microservices

Priyanshi Sharma — Fri, 27 May 2022 11:43:31 +0000

With the maturing of DevOps culture and the prevalence of cloud services, the microservice architecture system has become the de facto standard for developing modern-day large-scale applications. While scaling and managing distributed systems are easy, increasing service interactions create new problems.

At the dawn of these changes, teams responsible for delivering microservices-based applications are monitoring their performance in extremely different ways in comparison to the traditional monitoring strategies that have resulted in unnecessary data silos.

Traditional monitoring solutions won’t work anymore. DevOps teams, therefore, need a centralized solution that offers a complete view of their systems. And this is what an observability-based monitoring system can do.

But before moving ahead to the observability for monitoring, let’s briefly take a glance at what leads to the need for such changes in application monitoring.

The Unpredictability of Microservices

A decoupled system consists of multiple components that are located on different networked computers that coordinate and communicate with each other by passing messages. The integration and distributed nature of the system often lead to distinct ownership layers that are often challenging.

Microservices can also have issues like data inconsistency, network failure, operation overhead, complex testing, tracing failure, and much more. However, the implementation of observability across the microservices’ development environment can help a developer to understand the failure in the system, and trace errors to their root cause.

The Need To Speed Up & Maintain Software Code Development and Deployment

DevOps is all about faster development and delivery of the application with regular updates and continuous development, leading to a shorter development lifecycle with high-quality results. And if the development team cannot identify and address errors and problems before they occur, or act swiftly to make changes, it can be difficult to increase the time to market.

Besides, after designing, developing, and releasing the microservice-based application, it becomes essential to maintain it. So the developers need to continuously adjust the application as per the demands of the customer and make sure that the application works at its best.

That’s where observability comes in. By leveraging a strong observability software development teams will not only be able to increase the speed and efficiency of deployment, updates, and tracking changes but it also allows them to easily examine errors, debug code that might create bugs, customize apps according to the users’ requirements and eventually improve the performance of the application.

What is Observability?

Observability is a concept from dependability engineering directed to the concept that you don’t know about so you build a system that will enable you as a developer to debug the errors and bugs the first time it has been encountered.

Put simply, it is a technical solution that allows developers to continuously debug their application system. Observability is based on examining patterns and properties that aren’t defined in advance.

Observability uses tooling to give developers insights that can help in monitoring. In other words, monitoring can only be done after the system has some level of observability.

An observability system allows you to understand and easily trace the root cause of any errors - even in a complex application architecture like microservices. It helps in getting answers to the questions like:

Which services handled the request?
Where were the bottlenecks in application performance?
What were the differences between the execution of the request and the expected behavior of the application?
What caused the failure of the request?
How was the request processed by each microservice involved?

Why Observability is Important?

It doesn’t make sense to push changes in the production environment without understanding whether it is making the application process better or worse. Hence, to run the Continuous Integration and Continuous Delivery (CI/CD) process as expected, there must be some kind of feedback. The “Monitor” part in the DevOps lifecycle provides the required feedback that leads to reiterating in the future.

With an observability system, you can get better control over the complex application system. As it:

Provides insight into how the product works internally so that improvements can be made to ensure seamless performance for end-users.
Monitors the applications’ performance.
Easily recognize root causes of problems and aids troubleshooting
Provide an intuitive dashboard displaying real-time occurrences.
Has an integrated self-healing infrastructure.
Provides freely available information.

Four Pillars of Observability

Observability can be divided into four core pillars, including logging, tracing, alerting, and monitoring

Log aggregation/analytics

Logs are immutable, time-stamped records of different events that identify and provide insights on unpredictable behavior in the application system - including what happened in the system when things went wrong.

Logs are available in three formats, i.e., plain text, binary and structured. It uses one of the six generic protocols namely HTTP, Syslog, Kafka, SFTP, OpenStack, and Log Shuffle that instruct services to send logs to the specified destination, if you want to operate your log receiver. However, ingesting logs in a structured way (like JSON format) is much recommended as it offers additional data and metadata that make logs easy to query.

Alerting/visualization

Metrics, numerical representations of data, can be used to determine the overall behavior of a component or service over time. There are named attributes, labels, values, and timestamps to express information about Service-Level Agreements (SLAs), Service-Level Objectives (SLOs), and Service-Level Indicators (SLIs). Unlike logs, metrics are by default structured to make it easier to optimize and query storage so that they can be retained for the long term.

Metrics are measured using values derived from the performance of the system rather than from record-specific events. Metrics can be correlated across the component infrastructure and get an aggregated view of system performance and health, making it a real-time saver for developers.

Metrics can also be used to gather information like system uptime, the number of requests received per second, response time, and the processing power or memory being used by the application. Usually, Site Reliability Engineering (SRE) and DevOps engineers use metrics to trigger alerts when the system value exceeds the specified threshold.

Distributed systems tracing infrastructure

For any given transaction, a trace shows the operation as it moves from one node to another in a distributed system infrastructure. The host system encodes every operation (i.e. span) performed by the microservice that operates as it moves through its system. Traces can track the course of one or more spans in a distributed system and detect the cause of the breakdown or bottleneck.

In tracing, each event has global ID metadata incorporated into each step in the request flow; a distributed tracing system like Zipkin is used to inspect and visualize traces.

Monitoring

Monitoring is a crucial component that refers to the application and infrastructure controls and supports analyzing long-term trends to build the dashboard and alerting. It allows development teams to watch and understand the system’s state based on predefined sets of metrics and logs. In case of bottlenecks or errors, the development team gets notified before the user even knows about the existence of the problem.

Monitoring for Symptom-Based Alerting

The monitoring system needs to address the problem and its cause. Together, the observability and monitoring solutions are designed to:

Give major indicators of service degradation or outrage.

Identify service degradations, unauthorized activities, bugs, and outrages.
Troubleshooting.
Plan capacity and business purposes by detecting long-term trends.
Uncover unwanted side effects an added functionality and change can create.

But first, we will learn what monitoring systems can do, and then we will learn how implementing observation will help in overcoming the cons of monitoring.

Blackbox Monitoring: Here, the microservice system is examined from the outside. This technique is great to get answers like what is broken and alert about the issues that have already occurred and impact the end-users.

Whitebox Monitoring: On the other hand, Whitebox monitoring is known for the system’s hard failure modes. It gives information about applications’ internal states so that we can anticipate in advance and know what can cause the problem.

To develop monitoring systems you should have an understanding of failure in the crucial components of the system beforehand. And that’s something difficult to do if your system is a complex one like microservices.

The sources of potential problems and complexity are endless if we try to collect everything from a ton of metrics. So, designing a monitoring system that is simple, reliable, and predictable is important. Moreover, the data for monitoring should be actionable to send an alert in case of failure indicating its effect and impact on any fix that has been deployed.

However, combining observability with monitoring can create a remarkable solution as it will make the solution more accurate by providing details such as single-process debugging, detailed system profiling, log collection, load testing, analysis, and inspection of traffic.

Implementation of Observability

To achieve observability, proper system or app tooling to collect accurate telemetry data is important. You can create an observable system by developing tools using open source or commercial observability software. When it comes to observability implementation, four components usually play a role:

Data Correlation: Data is processed and correlated from across your system, enabling automated or custom data curation for time series visualization.

Instrumentation: These tools collect and analyze telemetry data from the containers, applications, hosts, and other components of your system, providing visibility into your whole infrastructure.

Incident Response: Automation technologies enable information about outages to be sent to the best people and teams in accordance with their on-call schedules and technical expertise.

AIOps: By aggregating, correlating, and prioritizing incident data using machine learning algorithms, you can remove alert noise, detect issues that could impact the system, and increase response time when they do.

The Bottom Line

Observability must become part of the culture of engineers and managers as the adoption of microservices and containers increases. By doing so, your teams will be able to maximize their cloud investment. Your continuous innovation culture will drive your ability to deliver high-end software to your customers.

You don't have to be an expert in DevOps to benefit from observability. Developing a culture that is observability-centric requires an understanding of the pillars of observability. We hope this blog was helpful to you.

SOURCE: Decipher Zone