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Viresh Garg

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originally published on Nov 21, 2024
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Executive Summary

With the emergence of cloud, digital, mobile at scale, organizations are striving to release their products and features digitally faster than ever before to achieve their time to market goals. And with the ever-increasing expectation from customers, there is also a strong push to provide the most stringent availability and performance SLAs by cutting downtimes and improving MTTD and MTTR for issues, bugs and gaps. This brought the concepts of agile releases and DevOps automation in the software delivery processes as a foundational paradigm shift from water falls and manual SDLC practices. 

But as organizations scale their DevOps practices, there are new sets of problems to deal with the sustainability and scalability of volume and disparity of complexity across departments and applications. This complexity often makes it challenging to track and optimize key CI/CD performance metrics, particularly the DORA metrics. Monitoring and constantly improving these metrics consistently enables meeting SLA commitments, reducing operational risk, and maximizing SLA compliance. 

Traditional approaches to capturing DORA metrics often fall short, requiring extensive custom integrations across multiple DevOps tools like SCM, CI/CD platforms, Testing Automation, and security scanning tools. The tools come from different vendors, have different architecture and APIs used to expose metrics and are at times harder to integrate with to capture everything, let alone the daunting task of stitching everything together in real time and historically for analytics. To address these challenges, a centralized, standards-based platform is necessary to leverage modern event standards like CloudEvents and CDEvents to provide seamless interoperability across the entire CI/CD and security toolchain in SDLC DevOps. 

This blog outlines how OpsMx’s DORA observability platform provides organizations with real-time, end-to-end visibility, predictive analytics, and automated compliance tracking across single and multi-cluster CI/CD environments. By integrating these capabilities, OpsMx empowers teams to streamline deployments, fast track time to market,  reduce failure rates, and deliver better availability and performance SLAs. 

The Need for Comprehensive DORA Metrics

The Advent of DevOps

In today’s fast-paced software environment with heavy penalties and the threat of customer drift on SLA breaches, and losing market opportunity with lag in time to market for new features, DevOps is not just a methodology; it’s a cultural shift that unifies development and operations teams, fostering collaboration, efficiency, agility and resilience in deployments through automation. Created initially to streamline deployments, DevOps has evolved into a comprehensive approach to breaking down traditional silos and encouraging a collaborative, transparent work environment with everyone equally accountable for quality, availability, performance SLA and other aspects of the applications. 

DevOps also enables critical advancements like zero-downtime deployments, automated rollbacks, and the segregation of duties, making it possible to meet ambitious goals, such as achieving “three nines” (99.9%) or higher availability and ensuring proper due diligence and oversight by separation of duties between developers, testers, reviewers, deployers and release approvers. Leading tech companies exemplify this progress: Netflix, for instance, performs thousands of production deployments per day, while Amazon deploys code every few seconds, continuously refining their products and responding quickly to market needs. 

Furthermore, DevOps has catalyzed the success of cloud models like IaaS, PaaS, and SaaS by supporting faster, more reliable updates and new releases that underpin the provider’s capabilities to be able to deliver the most stringent SLAs, something that was previously not achievable in an on-prem environment even with full control and autonomy over the systems. Delivering new features faster means better time to market and business enablement to target new prospects and delivering patches and bug fixes faster means better customer satisfaction, loyalty, reference-ability and SLA compliance. 

Why DORA Metrics?

With the advent of DevOps, the rise of microservices architectures, and the widespread shift from waterfall to agile development practices, deployment speeds have increased dramatically. However, this rapid pace has introduced new complexities, especially around dependencies and failure points that often stem from oversight, manual and undefined policies and procedures, training issues and/or a need for more disciplined/structured processes. In this landscape, organizations must meet SLA targets, avoid penalties or contract breaches, and continuously improve their time-to-market with frequent, successful deployments. The lack of visibility to make continuous improvements means inability to find issues coming in the way of achieving continuously increasing success rate, agility and number of deployments. That means not being able to achieve the anticipated ROI from the DevOps initiatives ultimately making the program weaker, less embraced or compliant, lacking proper executive support, investment and buying in and constant retooling and re-architecting. 

If these metrics are harvested and stored centrally across all teams, applications and releases, it provides an opportunity to baseline the acceptable and desired performance levels. Additionally organizations can benefit from understanding the outliers in terms of underperformers and outstanding performers using that visibility as an opportunity to understand, analyze and adopt the best practices employed by the outperforming teams across all organizations. 

DORA Metrics Overview

Here are the DORA metrics and how they can be enhanced with a comprehensive tracking platform:

Deployment Frequency

  • Definition: Rate at which new code is deployed to production and the code could be bug fixes, minor enhancements or new features – patches, patchsets, minor and major releases. 
  • Importance: A larger number for these metrics could indicate that the organization is meeting its DevOps goals and is consistently improving the agility in its SDLC processes. The agility enables better time to market for new capabilities and better customer satisfaction by fixing customer reported incidents.
  • Usage Examples: Organizations analyze deployment frequency in conjunction with Latency to understand the opportunities to further achieve better deployment velocity and frequency. It can also be used along with failure rate to find the opportunities for quality related improvements Some examples of findings from the analysis of these metrics include detecting bottlenecks such as time spent in manual testing, testing coverage gaps, bug-fixing delays, and gaps in automation to build and deploy. Using these insights to streamline processes for faster, more reliable deployments.

Latency Metrics (Lead Time for Changes)

  • Definition: Measure the time from code submission to various stages in the CI/CD pipeline leading to its deployment in production. The specific coverage includes:
      • CheckIn-to-Prod: The total time it takes to deploy from the code check-in time
      • Build-to-Prod: The total time it takes to build from the code check-in time
      • Integrate-to-Prod: The total time it takes to deploy to production after deploying to lower environments
      • Dev-to-QA, QA-to-Stage, Stage-to-Prod: The time to move from lower environments to production. 
  • Importance: Low latency ensures that developers’ work quickly translates into productive deployments, supporting better time-to-market for new features and enhancements.
  • Usage Examples: Latency inherently adds delays in the system. Some delays are to be expected like the time it takes to do various functional, performance, security and other tests and file bugs, the time it takes to fix those bugs etc, but the visibility into latency provides insights into opportunities for further automation and process improvements. For example some latency may point to lack of automation testing where manual testing is introducing the delays and latency , or a higher number of bugs filed may indicate process improvements in the architecture, design and code phases to reduce the quality issues, for example by introducing more peer reviews and standards. 

Change Failure Rate

  • Definition: The number and percentage of deployments that fail or require a rollback.
  • Importance: A lower Rate represents a stable, mature and reliable environment where deployment related disruptions have been reduced. High failure rates could indicate SLA breach or customer loyalty decline risks resulting in customer drift.
  • Usage Examples: Organizations use these metrics to find opportunities to bring better quality into production , for example by  improved testing coverage, and better architecture and design for forward and backward compatibility. This will also enable organizations to not only plan and choose but also optimize their deployment models for zero downtime, whether be rolling upgrades, blue-green deployments, canary deployments, or a hybrid model. 

Mean Time to Recovery (MTTR)

  • Definition: The average time required to recover from failed deployment. This metrics includes the MTTD also which is Mean Time to Detect the deployment failures after a deployment is done. 
  • Importance: A shorter MTTD and MTTR reduces downtime to enable SLA compliance and customer satisfaction. It also boosts organization’s confidence to then focus on faster vel;ocity and number of deployments to hit the time to market goals.
  • Usage Examples: Organizations monitor MTTR in real-time and historically to understand how to optimize the MTTD, for example by changing the deployment scheme to canary to detect the problem with a limited set of requests before the entire user traffic is redirected to the new deployment. Organizations can also use these metrics to introduce scripts and architectural changes required to restore data during rollback (most delays in MTTR are associated with the manual data changes that may be required due to a faulty deployment) and to ensure forward/backward compatibility in the releases to be able to have multiple versions co-existing in production at the same time. 

Challenges of Tracking DORA Metrics as a Custom Solution

Building a custom DORA metrics solution that tracks comprehensive set of metrics from all DevSecOps tools, stores it historically and real time and provides business intelligence and insights can be challenging for the following reasons: 

  • Complex Integrations: A typical DevSecOps environment consists of multiple commercial and open source tools, including SCM, CI/CD platforms, backlog tracking, ticketing, SAST, DAST, and more. This means not only a significant amount of integration build work to capture metrics from all of these tools but also  significant effort in  ongoing maintenance, especially as vendors frequently update standards and change how and what metrics are exposed.
  • Repeat Work Due to Retooling: As DevSecOps is a continuously emerging and evolving domain, organizations typically retool their DevOps architecture frequently embrace the capabilities and efficiencies enabled by new standards, tools and technologies. WIth the advent of AI and GenAI, this trend is going to continue for several coming years. Custom solutions for DORA metrics collection must be re-engineered each time, creating repetitive throw-away work that diverts focus and resources away from primary business goals, such as faster business application delivery and improved customer outcomes. These are the same resources that are competing for building the applications that actually deliver business enablement for the organization’s primary product and services. 
  • High Administrative and Storage Costs for Historical Tracking: If the historical system and analytical engine is not designed appropriately, this could mean significant resource and cost over a period of time as the data continues to grow with more applications and more releases.
  • Deployment and Operational Overhead: A custom metrics solution effectively becomes a standalone application requiring its architecture, design, and implementation expertise. This includes maintaining deployment pipelines, monitoring metrics collectors, scaling storage, and overseeing day-to-day operations. Managing these elements entails a significant end to end overhead that eats into bandwidth from all departments whether it be product management or architecture or fullstack developers or security engineers or DevOps and SRE engineers. 
  • Specialized Skillset Requirements: Building a comprehensive solution that harvests, stores, aggregates and rolls up the metrics to provide business insights is a complex distributed application problem from software engineering perspective and if done internally, will take the best resources away from building the business critical applications for the organization. 

In summary, creating a custom solution for DORA metrics involves a significant investment in time, cost, and resources and it still comes with the risk of not being able to get it right in terms of quality, usability, availability, cost and performance. It transforms what is supposed to be a supportive function into a full-scale enterprise application initiative with considerable complexity without contributing directly to core business outcomes. 

Solution: Unified DORA Metrics Tracking with Event-Driven Architecture

OpsMx is a centralized platform that connects with all of your CI, CD, Artifact Repository and SCM tools and tracks DORA metrics across clusters, and the entire DevOps toolchain is essential to overcoming these challenges. It co-exists with your CI/CD deployments in a frictionless way by not being disruptive to the existing CI/CD processes and deployments and does not introduce the risk of regressing in the architectural and deployment hardening achieved in DevOps tooling and processes.  By leveraging industry standards like CloudEvents and CDEvents, this platform not only seamlessly integrates with tools like Argo and Spinnaker, but also provides an extensible architecture enabling customers to integrate with any of the other tools in their CI/CD toolchain, enabling better business insights and analytical capabilities. 

Key Solution Features:

1. Centralized Real-Time and Historical Dashboards

  • Real-time dashboards aggregate DORA metrics across clusters and applications, offering comprehensive views into deployment frequency, multiple facets of latency metrics, deployment outcomes and success/failure rates, MTTD and MTTR. This visibility enables teams to track KPI, KRI and KOI for the deployments essential to troubleshoot issues, and analyze trends. The dashboards can be organized by application, clusters, teams or any other grouping method relevant to developers and executives using them for their area of focus. 

2. Anomaly Detection and Predictive Analytics

  • The analytical capabilities are able to detect trends and anomalies in deployment success rates, failure patterns, and recovery times, enabling teams to understand the root cause of the changes that led to these anomalies in a specific release or after a specific release providing an invaluable opportunity to address issues before they escalate. By analyzing historical data with trends, correlations and anomalies clearly called out, the platform can suggest improvements in deployment practices.

3. Cross-Cluster and Cross-Environment Baselining 

  • Aggregated metrics facilitate benchmarking and baselining  across clusters, environments, and applications, enabling teams to identify high-performing processes and adopt best practices. For single-cluster setups, benchmarking helps analyze application-specific performance within that cluster. Even for a single-application cluster, the benchmark and baseline can help quantify a baseline for current state and then define quantified goals to be achieved through improvements, and tracking against those goals over a period of time. 

4. Automated SLA Compliance 

  • Continuous monitoring of DORA metrics boosts the probability of meeting SLA and SLI in view of SLO and avoids customer satisfaction issues resulting in customer drift or heavy penalties. 

5. Extensibility

  • Thanks to the open, standards-based architecture supporting Cloud Events and CD Events, the platform is extensible to integrate with any of the toolchains in the CI/CD supply chain that support these standards. Customers can use the extensible SDK to integrate any of the tools in the toolchain exposing their metrics using any of these standards. 

Customer Case Studies

  • Customer-1 was able to get visibility into their Deployment Failure Rate and MTTR as the culprits for their SLA breach, which resulted in penalties and customer satisfaction issues. They then rolled out architectural enhancements to achieve and test rollbacks in lower environments, getting much closer to their SLA commitment within one year.
  • Customer-2 was able to retire a custom-built, home-grown solution that was not capturing the metrics from everywhere and required 30% cost in cloud and operations. By using a canned solution, they were able to release a four-person development and DevOps team to more strategic business imperatives. 
  • Customer-3 was able to track latency metrics and identify the lack of unit test cases as the culprit introducing high latency from code to production deployment and change their processes and standards mandating higher code coverage and success rates for unit tests. The visibility enabled them to reduce the latency by 52% to boost the time to market for their new product releases required to stay competitive in their product domain. 

Conclusion

Tracking DORA metrics across single and multi-cluster environments is crucial to continuously monitor and optimize the DevOps and SDLC performance critical to achieving business goals like SLA compliance, customer satisfaction and time to market. By leveraging CloudEvents and CDEvents standards, OpsMx provides a comprehensive solution that ensures interoperability across diverse DevOps toolchains, enabling monitoring, tracking and intelligent insights into deployment performance enabling better SLA compliance.

With a unified OpsMx DORA observability platform, organizations can drive continuous improvement and operational excellence across even the most complex DevOps environments. The platform’s intelligent, data-driven approach supports faster frictionless and non-disruptive  deployments, reduced failure rates, and enhanced stability, ultimately aligning DevOps practices with business imperatives. As DevOps practices continue to mature, OpsMx stands as a robust solution for enterprises seeking to maximize their CI/CD pipeline value and impact in today’s dynamic market.

About OpsMx

OpsMx is a leading innovator and thought leader in the Secure Continuous Delivery space. Leading technology companies such as Google, Cisco, Western Union, among others rely on OpsMx to ship better software faster.

OpsMx Secure CD is the industry’s first CI/CD solution designed for software supply chain security. With built-in compliance controls, automated security assessment, and policy enforcement, OpsMx Secure CD can help you deliver software quickly without sacrificing security.

OpsMx Delivery Shield adds DevSecOps capabilities to enterprise deployments by providing Application Security Posture Management (ASPM), unified visibility, compliance automation, and security policy enforcement to your existing application lifecycle.

Frequently Asked Questions

1. What are DORA metrics?

A: Metrics such as Deployment Frequency, Change Failure Rate (CFR), MTTR, Reliability, Lead Time for Changes (LTTC), and Cycle Time are referred to as DORA metrics. These metrics are used by organizations to measure the performance of their DevOps team and the efficiency of their CD processes. 

2. Why are DORA metrics important?

A: DORA (DevOps Research and Assessment) metrics are important because they measure the performance of DevOps and the overall software delivery processes on various parameters. These parameters help DevOps teams analyze their performance and optimize their output. 

3. How can OpsMx help with DORA metrics tracking?

A: OpsMx integrates with your existing CI/CD pipeline to track deployment lead time, change failure rate, fastest and slowest pipelines, and time to restore service.This gives DevOps teams insight and visibility into key performance indicators to enhance the efficiency of software delivery processes. 

4. How can organizations integrate OpsMx with their existing DevOps toolchain?

A: OpsMx natively integrates with over 90 DevOps and Security tools. For those without native integrations, OpsMx supports built-in connectors and APIs to integrate with other tools in the CI/CD pipeline. 

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