4 Continuous Delivery Software Process Essentials to Consider

Continuous Delivery Software Process

This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Edge Delta Founder and CEO Ozan Unlu offers an example continuous delivery software process through four essential keys.

SR Premium ContentContinuously delivering value has become a mandatory requirement for many organizations. As such, continuous delivery – a software development practice using automation to speed the release of new code – has increasingly been adopted, enabling organizations to roll out changes to their software quickly (daily, or even hourly) and safely. In our software-driven economy, some are realizing they can’t afford to not do continuous delivery, which is proven to drive substantial increases in new products and services delivered; application quality; and even revenues.

Continuous delivery is powerful, but it can also make for a challenging journey, as many aren’t prepared for the strain it can place on supporting systems and disciplines like observability. A system is considered “observable” if the current state can be estimated by using information from outputs, namely logs, metrics and traces. With observability insights, teams gain comprehensive visibility across the entire IT environment (applications and infrastructure) and can ensure its ongoing health, reliability and performance.

In a continuous delivery model, the volume and pace of software roll-outs increases exponentially, but there can be huge risks when traditional observability approaches fail to keep up, remaining stuck as overly time-consuming and expensive processes. How must observability change?

Continuous Delivery Software Process

Automatic Discovery

Most system crashes occur right at the starting gate, within hours or even minutes of going live. Throughout the years, several highly publicized examples have borne witness to this, the most recent being leading UK-based retailer Primark’s highly anticipated “Click and Collect” online service which went down for several hours on its very first day, during what was supposed to be a milestone and very important moment for the company. In 2013, the Obama Administration’s Healthcare.gov crashed within two hours of going live, leaving such an indelible memory that the Biden administration opted for a beta before formally launching their student loan forgiveness website just a few weeks ago. Certainly, a sudden onslaught of traffic played a role in these outages, but the point is the first few minutes and hours of any system going live are critical, as they’re likely to be the most fraught with unanticipated problems.

In this context, development teams cannot afford any time lapses between the moment a production deployment goes live to the time it is incorporated into an observability initiative. Such blind spots leave them extremely vulnerable. In continuous delivery, huge numbers of production environments are being spun up almost constantly. Observability tools must be able to automatically detect and monitor new deployments and begin surfacing anomalies immediately – even issues developers may not yet be looking for, and for which alerts and dashboards are not yet built. This makes it possible to detect “unknown unknowns,” or unanticipated issues which often occur at launch, and are the cause of the vast majority of outages. Immediate, real-time visibility into mission-critical production systems is always imperative, no matter the volume or speed at which they are being created.

Decentralized Data Management

Many organizations adhere to a “centralize and analyze”’ observability approach, whereby data is collected and integrated into a central repository for analysis. Not only is this centralization process slow in and of itself (taking hours instead of seconds), performance of these platforms often slows to a crawl as more data is ingested, requiring much longer wait times for data queries. It’s also an extremely costly process, with data being uniformly relegated to high-cost storage tiers. These costs drive many teams to indiscriminately “filter out” data sets, but what if a problem occurs and this “filtered out” data is precisely that needed for troubleshooting?

A New Method

Which entails applying distributed stream processing and machine learning at the source so all datasets can be viewed and analyzed as they’re being created – updates this approach. When observability data is decentralized, developers are empowered in several ways. First, they always have full access to all the data they need to verify performance and health as well as make necessary fixes whenever a problem is detected. The concept of data limits becomes irrelevant, enabling all data to be pre-processed inexpensively. Painful trade-offs between cost and having the entirety of data at one’s disposal no longer have to be made. Second, when you analyze data as it’s being created, at the source, developers can pinpoint exactly where the problem is, which is critical in highly transitory cloud environments where continuous delivery drives constant workload proliferation and shifts.

Keeping the Developer Experience Top of Mind

As noted, a benefit of decentralizing data is so developers have access to all their data, whenever and wherever they may need it. But what can inhibit the developer experience is the fact that many observability platforms are complex and hard to master. Frequently, this expertise lives in the operations side of the house, making developers dependent on ops teams to verify the health and performance of production applications and provide data access when developers need to troubleshoot.

All of the data generated can be so useful, and development teams should be looking to tap it. But its utility is compromised if developers cannot easily access it. Observability approaches must evolve to enable developers to access all their data easily, not having to do the ask and instead fixing their own problems more quickly, which will be important as continuous delivery naturally increases the number of production environments they’re overseeing.

Final Thoughts

To a large extent, the rapidity of continuous delivery – a delivery tempo that’s typically less than a week and not more than a couple of days, and in some cases a few hours – is what makes it so advantageous when it comes to delivering customer and user value. But it’s important to remember, continuous delivery also emphasizes the ability to deliver more quickly while maintaining extremely high levels of application stability and performance quality. Eliminating time lags between the deployment and discovery of new production environments; adopting decentralization and moving away from inefficient and costly “centralize and analyze” approaches; and empowering developers through easier, faster work experiences will be key to observability keeping up with the cadence of continuous delivery.

Ozan Unlu
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