Why Development Environments?

A development environment, like a test environment, is a critical component of any software development lifecycle, providing developers with the space and tools they need to implement and test application features.

This type of test environment may include a variety of tools and resources, such as testing frameworks, code repositories,test data, debuggers, and more. It is important for developers to carefully consider their needs when creating a development environment, as this will impact both the quality and efficiency of their work.

Some key considerations to keep in mind when designing a development environment include selecting appropriate tools, setting up robust processes and procedures, and optimizing communication among team members.

Additionally, it is important to ensure that your development environment not only supports your current project needs but can also grow with you over time. By taking these factors into account, you can create a development environment that will help to maximize the success of your software projects.

In this article, we will first explore what a development environment is, its purpose, and some examples. After that, we’ll go more in-depth and discover the best practices for creating a good dev environment.

Development Environment: The Basics

Why do we need a Development Environment?

Development environments let software developers create, run, and test their application code in a way that simulations real-world scenarios adequately. If that’s still too vague for you, here are some specific examples.

There are a number of key benefits to using a development environment, including increased user experience, reduced costs, and improved safety and privacy.

For example, by interacting with simulated dependencies instead of the real services, developers can avoid creating problems in the production app and incurring unnecessary costs. Additionally, working with real services may raise security or privacy concerns that can be avoided by using a development environment. Ultimately, utilizing a development environment helps software developers create better code more efficiently and safely.

How do we Implement a Development Environment?

There are a number of factors to consider when creating a development environment, including the size and complexity of your team, the maturity of your infrastructure, and the dependencies that your code relies on.

At its most basic level, a development environment is simply the developer’s machine itself. However, with advances in technologies like Docker, it has become easier to create self-contained and reproducible environments that can be activated by running a single command.

In some cases, however, this may not be sufficient for meeting all of your needs. In these situations, it might make sense to leverage existing infrastructures or resources in order to create development environments for engineers. For example, you may choose to create mock APIs or databases to avoid accessing real systems, or use sample data that has been anonymized to protect sensitive information.

Ultimately, the key to successfully implementing a development environment is to consider all of your requirements and find the right balance between flexibility and control. By taking these factors into account, you can create an environment that will enable your team to be more effective and efficient in their work.

Development Environment: A Few Best Practices

We’ve just covered the fundamentals of test environments. You’ve learned what they are, why they exist, and how to implement one. Now, let’s walk through some best practices to keep in mind when implementing a dev environment.

Your Development Environment Should Be Fast

One of the key best practices for a successful development environment is to ensure that it is fast and efficient. This involves using high-performance hardware, as well as optimizing your software and coding practices to maximize performance.

Your Development Environment Should Offer Isolation

Additionally, it is important to ensure that your development environment offers adequate isolation from other systems or processes in order to give developers the freedom they need to experiment and explore while minimizing the risk of errors or bugs in production.

Your Development Environment Should Be Realistic, But Not That Much

The phrase “It works on my machine!” is often used in software development, and for good reason. It can be frustating when the code you write doesn’t work when it’s transferred to the production server. This happens because the development environments and production environments are not identical copies of each other.

For example, my front-end code relies on an Apache version, or configuration, that has been activated in my developer test bed, however still needs to be deployed on to the target server. Consequently,the code doesn’t run correctly

By keeping the environments close to each other, for example same versions of the operating system and software stack, we are able to fix this problem. Containers, as referenced before, help us reach that goal.

However we should also appreciate it can’t, and shouldnt normally be an exact copy. For example, unlike production, you dont want customer sensitive data. In short, your dev environment should only be as realistic as it needs to be for developers to run their code safely and predictably.

Your Development Environment Should Be Compliant

Finally, it is important to keep your development environment as realistic as possible while still maintaining compliance with various regulatory requirements around user data protection and security.

Overall, creating an optimal dev environment requires careful planning and attention to detail in order to help developers work efficiently while also ensuring a reliable and secure end product.

When it comes to data there are ultimately two solutions: synthetic data generation or production data cloning (plus data masking methods). While both come

Conclusion

Like test environments, development (or dev) environments are an essential part of modern software development. All of these environments working together, and when done properly allow organizations to deliver high-quality software quickly.

Overall, creating an effective dev environment requires careful planning and attention to detail in order to help developers work efficiently while also ensuring a high quality end product.

Whether through containerization, synthetic data generation or production cloning, it is important to carefully consider the various requirements of your development and software testing process in order to create a productive and compliant environment that can support your team’s work. By taking these factors into account, you can ensure that your development efforts are as efficient and successful as possible.

What is Site Reliability Engineering?

An Introduction to SRE

SRE or site reliability engineering has become increasingly essential as businesses confront an ever-growing IT infrastructure that manages cloud-native services. One of the reasons is that the way software development and deployment teams operate has altered considerably.

DevOps principles, which include continuous integration and deployment, have fueled the adoption of DevOps concepts and a transition from departmental silos to a new engineering culture in an ever-changing world. This way of thinking endorses and lives the “you build it, you run it” mentality.

Site reliability engineers are hired by businesses to keep their new IT architecture stable and enhance their competitive advantage. SREs use a variety of engineering principles to assist product engineering teams in optimizing their processes. The team’s fundamental objective is to develop highly dependable software systems by analyzing the current infrastructure and finding methods to improve it with software solutions.

In this post, you’ll discover more about the function and advantages of site reliability engineering, the fundamental principles utilized in SRE, as well as the distinction between a site reliability engineer and a platform engineer.

What is Site Reliability Engineering?

Site reliability engineering, also known as SRE, is a software engineering method that aids in the management of large systems via code. It’s the job of a site reliability engineer to develop a stable infrastructure and efficient engineering processes by following SRE standards. This also includes the use of monitoring and improvement tools as well as metrics.

Even though SRE appears to be a relatively new position in the world of cloud-native application engineering and management, it has been around even longer than DevOps – the phenomenon that successfully connected software development and IT operations.

In fact, it was Google that entrusted its software engineers to make large-scale sites more dependable, efficient, and scalable through the use of automated solutions. The procedures that Google’s engineers began experimenting with in 2003 are now part of the full-fledged IT domain.

Site reliability engineering, in a sense, takes on the responsibilities that operations teams previously performed. However, operational difficulties are addressed with an engineering approach rather than a manual one.

With the use of sophisticated software and Site/Environment Management tools, SREs may establish a link between development and operations to create an IT infrastructure that is dependable and allows for easy deployment of new services and features.

Site reliability engineers are especially important when a firm switches from a traditional IT approach to a cloud-native one. Next, discover more about the responsibilities of a site reliability engineer and what sort of talents are required in this line of work.

What does a Site Reliability Engineer (SRE) do?

A site reliability engineer is someone who has a background in software creation as well as significant operations and business intelligence expertise. All are required to deal with technical issues using code. While DevOps focuses on automating IT operations, SRE teams focus more on planning and design.

They track operations in production, and ideally in non-production (shifting SRE Left), and study their performance to find areas for improvement. Their comments also assist them in predicting the cost of outages and preparing for contingencies.

SRE Engineers will divide their time between operations and the development of systems and software. On-call duties include updating run sheets, tools, and documentation to ensure that engineering teams are ready for the next emergency. They generally conduct deep post-incident interviews to figure out what’s working and what isn’t after an incident occurs.

This is how they acquire important “tribal wisdom.” Because they engage in software development, support, and IT development, this information is no longer compartmentalized and can be put to use to build more reliable systems.

A site reliability engineer’s work is also spent developing and enabling services that improve operations for IT and support personnel. This might imply creating a new tool to repair the flaws in current software delivery or incident management.

And last but not least, SREs are in charge of determining whether new features can be added and when, utilizing the aid of service-level agreements (SLAs), service-level indicators (SLIs), and service-level objectives (SLOs).

Learn more about SRE key performance indicators SLA, SLI, and SLO in the following section, as well as how they are used in site reliability engineering.

The difference between SLOs, SLIs, and SLAs

Site reliability engineers employ three metrics to monitor and improve the performance of IT systems: They write service-level agreements (SLAs), service-level indicators (SLIs), and service-level objectives (SLOs). These related service level measurements not only assist firms in building a more dependable system but also increase consumer confidence.

Let us define each of these key SRE metrics in more detail.

SLI Metric

SLI stands for the service-level indicator. An SLI measures the qualities of service, to provide input for a service provider’s objective.

Referencing the SRE Handbook, Google defines it as “a carefully defined quantitative measure of some aspect of the level of service that is provided.”

The analysis of behavioral data is a critical part of any successful customer experience optimization program. Four golden signals are the most frequent SLIs: latency, traffic, error rate, and saturation.

When SRE teams build SLIs to assess a service, they usually use two stages.

They determine the SLIs that will directly impact the customers.
They determine which SLIs influence the availability or latency or performance of the service.

SRE SLI Formula

The formula used to calculate ones SLIs is:

SLI = (Good Events * 100) / Valid Events

Note: An SLI value of 100 is optimal, whereas a drop to 0 means that a system is broken.

It’s critical to build SLIs that are appropriate for the user’s experience. This implies that a single SLI is not capable of capturing the whole customer experience as a typical user may be concerned with more than one thing while using the service. Simultaneously, creating SLIs for every imaginable statistic is not desirable since you would lose focus on what matters most.

Site reliability engineers generally focus on the most pressing problems as users go through a system. Once the SLIs have been established, an SRE connects them to SLOs, which are important threshold values defining the availability and quality of service.

SLO Metric

The SLO or service level objective is used to assess the quality of a service’s reliability or performance criteria.

Referencing the SRE Handbook, Google says that they “specify a target level for the reliability of your service” and “because SLOs are key to making data-driven decisions about reliability, they’re at the core of SRE practices.”

Unlike SLIs which are product-centric, SLOs are customer-centric.

Their relationship can be defined as follows:

SLO (Lower bound) <= SLI <= SLO (Upper bound SLO)

However, establishing appropriate SLOs is a difficult process. Targets should generally be established based on historical system performance rather than current conditions. And targets should be realistic, as opposed to too ambitious.

Tip! Absolute values are not required. It is often better to identify a “realistic” range based on historical data.

SLOs should be viewed as a unifying mechanism that fosters a cohesive language and shared objectives across various departments. And you’re considerably more likely to succeed if all key stakeholders are on board.

However, many firms are preoccupied with product innovation and fail to recognize the link between business success and dependability. Siloed data and the mistaken belief that once standards have been established, they don’t need to be re-examined or adjusted are two frequent stumbling blocks.

SLA Metric

A service-level agreement, or SLLA, is a contract that specifies the level of service provided by a platform. Similar to SLOs, service levels are a client-focused measure.

Referencing the SRE Handbook, Google says, an SLA is defined as “an explicit or implicit contract with your users that includes consequences of meeting (or missing) the SLOs they contain.”

An SLA is triggered as soon as an SLO is “breached”. In most cases, you should anticipate fines and financial repercussions if you do not fulfill the requirements of the SLA. If your firm breaches a term established in the SLA, it generally has to repay its clients.

Service-level agreements (SLA) provide transparency and trust between the company and its consumers. They’re similar to SLOs, but instead of being used by a business, they apply to external rather than internal activities. SLAs are less conservative than SLOs, implying that the value of reliability is always somewhat lower than the historical average of an availability SLO. This can be seen as a safety precaution if the average is set too high because there were just a few incidents in the past.

In Conclusion

Site reliability engineering is a critical process for ensuring that websites and online services are available and functioning properly. By establishing key metrics and thresholds, SREs can prevent outages and disruptions in service. SLAs, SLOs, and SLIs are three important tools that SREs use to measure and manage system performance. To be successful, SREs must have a deep understanding of these metrics and how they relate to one another.

The Top Deployment Strategies Explained

What is a Release

The process of “shipping” new features or bug fixes, usually more than one, to users is known as a software release. A software release can be a patched version, a major new version, or a hotfix for an issue found in a previous version. Software releases go through several development stages before they are ready to be made available to users (in what is called production).

A typical software development life cycle includes the following stages:

Development
System, Integration, and User Acceptance Testing
Staging
Production

Your deployment process, or deployment plan, defines the rules and steps of how software code should be moved (or deployed) from one stage to the next. It is important to have a well-defined deployment strategy because it will help ensure that code changes do not break the software in production and that users always have access to the latest version of the software.

To complete this important job, the DevOps team incorporates deployment procedures into their day-to-day operations. Various approaches have been developed throughout time to help software companies with application deployments.

What Is a Deployment Strategy?

A deployment strategy is a technique used by DevOps teams to launch a new version of their software. These strategies cover how traffic is transitioned from the old version to the new version and can influence downtime and operational cost. Depending on the company’s specialty, the right deployment strategy can make all the difference.

Various Types of Deployment Strategies

There are several types of deployment strategies, each with its advantages and disadvantages. The right strategy for your company will depend on your needs and goals.

1. Blue/Green Deployment

This type of deployment process involves maintaining two identical production environments—one is the “live” environment that serves customers, while the other is the “staging” environment. When it’s time to release a new version of the software, the staging environment is switched to live, and vice versa.

Benefit:

This strategy minimizes downtime because there is always a production environment available.

Disadvantage:

However, it can be costly to maintain two identical production environments.

2. Canary Release

In this strategy, the new version of the software is first released to a small subset of users. If there are no major issues, the new version is then gradually rolled out to a larger subset of users until it is finally made available to the entire user base.

For example, the older version may retain 90% of all traffic for the software at a certain point in time during the deployment process, while the newer version hosts 10% of all traffic. This method helps DevOps engineers to test the new version’s stability. It utilizes real traffic from a fraction of end-users at different phases throughout production.

Benefit:

Better performance monitoring is possible with Canary deployment. It also aids in the quicker and more successful rollback of software if a new version fails.

Disadvantage:

However, it does require more effort and typically, a long deployment cycle.

3. A/B Testing

May, also be called Incremental Rollout

In the A/B testing deployment process, developers deploy the new version alongside the older version. This type of testing is used to compare two versions of a software feature to see which performs better. Version A is the control and is made available to the entire user base, while version B is the test and is only made available to a subset of users.

A/B testing has several deployment process benefits:

It allows software developers to compare two versions of a software feature to see which performs better.
It is easier and less risky to test a new version of the software on a small subset of users before rolling it out to the entire user base.
Developers can easily accept/reject either version.

Disadvantage:

Increased user/customer coordination.

4. Feature Toggles (Feature Flags)

Feature flags are a type of deployment strategy that allows developers to turn on or off certain features of the software for different users. This allows developers to test new features without making them available to the entire user base. Feature flags can be used in conjunction with other deployment strategies, such as A/B testing, to help developers test new features before

5. Recreate Deployment

In this deployment approach, the development team completely shuts down the old software, then deploys and reboots the new version. This method causes a system outage between shutting down the old program and booting up the new one.

Benefits:

It is less expensive and is primarily utilized when the software company wishes to rewrite the application from the ground up. There’s no need for a load balancer since there are no changes in traffic flow in the live production environment.

Disadvantages:

This method has a significant impact on end-users since it is unavailable/suspended. Users must wait until the software is reactivated before using it. As a result, few developers employ this technique unless they have no other option.

6. Trunk-Based Deployment

In this strategy, all code changes are first merged into a main trunk or branch. Developers then create a new branch for each new feature. Once the feature is complete, it is merged back into the main trunk. This strategy eliminates the need for long-running feature branches and makes it easier to deploy new changes.

Note: This is more a pre-deployment method of Software Version Control.

7. Ramped Deployment

The ramped deployment method moves from one version to the next in a gradual process. Unlike canary deployment, which replaces instances of the old application version with those from the new application version one at a time, the ramped deployment approach makes its change by replacing instances of the old application version with new applications. The rolling upgrade deployment strategy is another name for this method.

The second method, as the name implies, is to delete the old version from production. When all of its instances are deleted, the older edition is manually shut down. The new edition then controls all production traffic.

Advantages:

No need to take the entire application offline for an upgrade.
The process is gradual, so it’s less risky.

Disadvantages:

Takes longer to complete than other methods.
Requires more instances to be available during the process.
Rollback is more complicated & long.

8. Rolling deployment

For those using containers.

Rolling deployment is a gradual process of replacing pods running the old version of the application with the new version, one by one, without downtime to the cluster. It is less risky and takes longer to complete than other types of deployment, but it doesn’t require taking the entire application offline.

Advantage:

Lower Risk
High Availability

Disadvantage:

Only really applicable for container-based architectures.

9. Shadow Deployment

Developers deploy the new version alongside the existing one in this deployment method. Users, on the other hand, won’t be able to access it right away. The newest version hides in the shadows, just as its name implies. Developers send a fork or copy of the previous version’s requests to the shadow version so they can examine how the new variant will work and if it can process the same amount of requests.

When the shadow version can handle the same load as the original, the traffic is finally routed to the new version, and it becomes live. The cutover from the original to the new version happens without any significant downtime since there’s no need to take down or restart either version.

Advantages:

valuable feedback can be gathered about how the new version will work in production
there’s no need to take down or restart either version during the cutover process

Disadvantages:

more complicated to set up and maintain than other deployment strategies
if not done correctly, it can cause issues with the live version

When to use:

when you want to gather feedback about how the new version will work in production
when you want to avoid any significant downtime during the cutover process

Deploy Better with a Software Deployment Tool

Managing your deployments without tools can be fraught with danger.

As seen above, the different deployment processes can be quite fragile/awkward, and if done incorrectly could lead to production issues, outages, and the need to roll back.

Using tools to control your “implementation day events” can uplift visibility, improve collaboration, support rehearsal, standardize your operations and also streamline the tasks*.

*Tasks that may be manual or preferably automated.

Fortunately, there are various Release Management tools that can help your organization with the various aspects of Environments, Release Management & Application Deployment.

The best software deployment tools included features like:

Release Management Governance for Scale Delivery*

*for managing the End to End Release / Release Train.

Implementation Plans (for Deployment Planning)
Operational Runsheets / Standardized Operating Procedures
DevOps Automation e.g. Software Deployments
Orchestrations / Integration with other tools*

*deployment tools, ticketing tools, CI/CD i.e. continuous integration, and continuous delivery tools

Deployment Version Tracking

*tracking code deployments across Environment Instances, Components & Microservices.

Environment Drift Reports

*supporting holistic, cross-environment, version control

Conclusion

You may use any of these methods to upgrade your applications. Each of these approaches has advantages and disadvantages, and each is appropriate in certain circumstances. The only question now is which one makes the most sense for your DevOps team to utilize.

Consider the demands of your team, project, and company as well as corporate objectives. Also, keep track of how much downtime your business can tolerate and any other cost limitations.

Make your go-live events into non-events!

Uplift your Implementation Planning, and Deployment Management capability today. Find the best software deployment tool (or tools) to help with your automatic deployments.