These scripts captured critical and frequently used workflows and were enhanced to simulate varying workloads and configurations. This repository was a centralized location for storing and managing JMeter scripts and their dependencies, such as test data files.

Additionally, a Docker Compose file was maintained in the repository to define and manage the desired number of JMeter secondary instances, ensuring the scalability of the testing infrastructure. JMeter scripts were executed automatically on code commits or trigger events using GitLab Runners.

The test execution instructions were embedded within the pipeline, and Docker images were maintained. Create Docker Images: The entire tool setup and provisioning were maintained as Docker images in the GitLab Repository. These images included configurations for the JMeter master driver and JMeter slave machines load generators.

Bundled alongside were the necessary plugins for distributed load testing using JMeter. Storing the images in the repository ensured version control and easy accessibility for the team. Configure Docker Compose: Docker Compose defined the configuration required to dynamically create the load generation environment during the test run.

It included specifications for networking, storage, and other requirements to provide the desired number of slave containers accurately. Spin-Up Containers: Docker CLI and Docker Compose facilitated the dynamic spin-up of JMeter master and slave containers as part of the build step.

The Docker Compose file, with the specified number of slave containers, ensured the proper provisioning of the load testing environment. This dynamic approach optimized infrastructure usage and resulted in cost savings by only utilizing resources when needed.

Executing Load Tests and Saving Results: Once the containers were up and running, the test run was triggered. The JMeter master container coordinated the test execution across the slave containers, simulating the desired load profiles based on the defined number of slave containers.

The master container automatically captured and collated results, including relevant metrics and data. The results were then stored in the centralized GitLab repository for analysis and continuous improvement.

As you can see, a well-thought-out approach and model can successfully integrate performance testing into the DevOps pipeline.

This results in cost savings and efficient management of the load-testing infrastructure, which in turn delivers a greater ROI and ensure greater speed and scale. Innominds is an AI-first, platform-led Digital Transformation and full-cycle Product Engineering Services company headquartered in San Jose, CA.

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Revolutionizing Performance Testing: A DevOps Integration Approach By Hari Kumar Mutyala, June 27, Can performance testing be conducted early in the lifecycle?

Can it be seamlessly integrated with other tests as part of the build process, providing instant feedback? Can the entire performance test suite be maintained as code and easily port across different environments? In implementing the performance testing solution, we prioritized the following additional key themes: Leveraging modern DevOps tools: To streamline and optimize the performance testing processes, we utilized various modern DevOps tools.

The solution implemented for performance testing in the Retail organization's Supply Chain transformation project involved the following steps: Create JMeter Scripts : JMeter, an open-source load testing tool, created test scripts that replicated real-world user actions.

Hari Kumar Mutyala. Introduction to Custom Fonts as Resource. Revolutionizing Performance Testing: A DevOps Integration Approach.

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Stress tests also look for eventual denials of service, slowdowns, security issues, and data corruption. Stress testing can be conducted through load testing tools by defining a test case with a very high number of concurrent virtual users.

Just as a stress test is a type of performance test, there are types of load tests as well. If your stress test includes a sudden, high ramp-up in the number of virtual users, it is called a Spike Test.

We presume the system will be under traffic three minutes into the test. Run stress tests against your website or app before major events, like Black Friday , ticket selling for a popular concert with high demand, or elections.

Another possible positive outcome of stress testing is reducing operating costs. When it comes to cloud providers, they tend to charge for CPU and RAM usage or more powerful instances that cost more.

For on-premise deployments, resource-intensive applications consume more electricity and produce more heat. So, identifying bottlenecks not only improves perceived user experience but also saves money and trees.

While load testing and stress testing are two of the most popular performance testing types, they are far from the only performance testing options available. Let us explore three other types of performance tests: soak tests, spike tests , and scalability tests. Also known as endurance testing, capacity testing, or longevity testing, soak testing tracks how an application performs under a growing number of users or draining tasks happening over an extended period.

Soak tests are especially known for their extended duration. Once you go through a ramp-up process and reach the target load that you want to test, soak tests maintain this load for a longer timeframe, ranging from a few hours to a few days. The main goal of soak testing is to detect memory leaks.

Spike testing assesses performance by quickly increasing the number of requests up to stress levels and decreasing it again soon after. A spike test will then continue to run with additional ramp-up and ramp-down sequences in either random or constant intervals to ensure continued performance.

Spike tests are great to use for scenarios like auto-scaling, failure recovery, and peak events like Black Friday. Scalability tests measure how an application can scale certain performance test attributes up or down. When running a scalability test based on a factor like the number of user requests, testers can determine the performance of an application when the user requests scale up or down.

The main metric is whether the scaling out is proportional to the applied load. If not, this is an indication of a performance problem, since the scalability factor should be as close to the load multiplier as possible.

Running your performance tests is an important part of the development process. Here are the different steps you should take for performance testing your application:. Decide on the metrics you want to test.

For example, determine your acceptable response time or non-acceptable error rate. These KPIs should be derived based on product requirements and business needs. If you're running these tests continuously, you can use baseline tests to enforce these SLAs.

Detail which scenarios you will be testing. For example, if you have an e-commerce site, you might test the checkout flow. There are many excellent open source solutions out there, like JMeter, Taurus, and Gatling.

You can also use BlazeMeter to get additional capabilities like more geolocations, test data, and advanced reporting. Build the script in the performance testing tool.

Simulate the expected load, the capabilities you are testing, test frequency, ramp-up, and any other part of the scenario. To simplify the process, you can record the scenarios and then edit them for accuracy.

If you need test data, add it to the script. Analyze the test results to identify any bottlenecks, performance issues, or other problems. You can use the dashboards provided by the performance testing tool or you can look at solutions like APMs for more information.

Fix the performance issues and retest the application until it meets the performance requirements. Performance testing and performance engineering are related concepts but they mean different things.

Performance testing evaluates the stability, responsiveness, reliability, speed, and scalability of a system or application under varying workloads. The performance of the system or application is tested and analyzed to ensure that it meets the performance requirements.

Performance engineering, on the other hand, is a proactive approach to software development that identifies and mitigates performance issues early in the development cycle, from the design.

By addressing issues earlier, engineering organizations prevent issues and accelerate time-to-market. Performance testing tools are platforms that evaluate and analyze the speed, scalability, robustness and stability of the system under tests.

These solutions help ensure that applications and websites can handle the expected level of user traffic and function reliably under different loads. As a result, they are an important component of the software development lifecycle.

One such leading performance testing tool is BlazeMeter. BlazeMeter is a continuous testing platform that enables developers and testers to test the performance of their web and mobile applications under different user loads.

It provides a comprehensive range of testing capabilities, including load testing, stress testing, and endurance testing that is open-source compatible. BlazeMeter also supports functional testing and API testing, and provides capabilities like mocking and test data.

Utilize each of the performance testing types detailed in this blog to ensure you are always aware of any issues and can have a plan for dealing with them. With BlazeMeter, teams can run their performance testing at a massive scale against all your apps, including web and mobile apps, microservices, and APIs.

With advanced analytics, teams using BlazeMeter can validate their app performance at every software delivery stage. BlazeMeter lets you simulate over two million virtual users from 56 locations across the globe Asia Pacific, Europe, North, and South America to execute performance tests continuously from development to production.

Snort can be used in the DevOps methodology to ensure the security of the application and the infrastructure. Docker is a DevOps technology suite that allows DevOps teams to build, ship, and run distributed applications.

This tool allows users to assemble apps from components and work collaboratively. Docker is an open-source platform for managing containers of an app as a single group.

It helps to increase the efficiency and consistency of the application deployment process. Stackify Retrace is a lightweight DevOps testing tool. It is one of the best continuous testing tools in DevOps that shows real-time logs, error queries, and more directly into the workstation.

It is an ideal solution for intelligent orchestration for the software-defined data center. Stackify Retrace allows teams to quickly identify and resolve issues, ensuring that the application is always available and performing as expected. Ansible is an open-source automation tool for IT configuration management, application deployment, and task automation.

It can be used in the DevOps methodology to automate repetitive tasks and ensure consistency in the application deployment process. Ansible allows teams to easily manage and scale infrastructure, making it a popular tool among DevOps teams.

Nagios is an open-source monitoring tool for IT infrastructure and applications. It can be used in the DevOps methodology to proactively monitor the application and infrastructure, ensuring that issues are identified and resolved quickly.

Nagios allows teams to easily monitor system metrics and receive alerts when issues arise, making it a popular tool among DevOps teams. Puppet is an open-source automation tool for IT configuration management.

Puppet allows teams to easily manage and scale infrastructure, making it a popular tool among DevOps teams. GitLab is an open-source platform for Git repository management, issue tracking, and continuous integration.

It can be used in the DevOps methodology to manage code repositories and automate the build, test, and deployment process. GitLab allows teams to easily collaborate on code, making it a popular tool among DevOps teams. Terraform is an open-source infrastructure as code tool.

It can be used in the DevOps methodology to provision and manage infrastructure resources in a consistent and automated way. Terraform allows teams to easily define and manage infrastructure as code, making it a popular tool among DevOps teams.

SaltStack is an open-source remote execution and configuration management tool. It can be used in the DevOps methodology to automate the configuration and management of servers.

SaltStack allows teams to easily manage and scale infrastructure, making it a popular tool among DevOps teams. It can be overwhelming to determine which DevOps tool is the best fit for you, as each of them has its unique features and capabilities..

That's why we've compiled a list of frequently asked questions about DevOps testing tools, to help you make an informed decision. From understanding the basics of these tools, to identifying the features that are important to you.

So, let's dive in and unlock the full potential of your DevOps strategy together! DevOps testing tools are a set of software applications that are designed to automate and streamline the testing process in DevOps methodology. These tools help teams to quickly identify and resolve issues, and continuously improve the performance of their applications.

They are essential for ensuring the quality and reliability of software deliverables in today's fast-paced, agile development environments. DevOps testing tools can be used for various types of testing, including functional, performance, security, and compliance testing, and can integrate seamlessly with other DevOps practices and tools.

Yes, Selenium is widely used in DevOps as it is one of the most popular automated testing tools. Selenium is specifically designed to support automation testing of a wide range of browsers and is often used in the DevOps process to automate functional and performance testing.

Selenium can be integrated with other DevOps tools and practices, such as Continuous Integration and Continuous Deployment, to improve the speed and quality of software delivery.

Azure DevOps is a cloud-based platform that provides a set of services for software development, including testing. While Azure DevOps does include testing tools, it is not solely a testing tool.

It offers a wide range of services such as project management, continuous integration and delivery, and collaboration features that help in software development. Azure DevOps provides a comprehensive platform for teams to manage their entire software development process, including testing.

It does include testing tools such as Azure Test Plans, Azure Test Cases, and Azure DevTest Labs, but it also provides other services that are important for software development. Tamas Cser is the founder, CTO, and Chief Evangelist at Functionize, the leading provider of AI-powered test automation.

With over 15 years in the software industry, he launched Functionize after experiencing the painstaking bottlenecks with software testing at his previous consulting company.

Tamas is a former child violin prodigy turned AI-powered software testing guru. He grew up under a communist regime in Hungary, and after studying the violin at the University for Music and Performing Arts in Vienna, toured the world playing violin. He was bitten by the tech bug and decided to shift his talents to coding, eventually starting a consulting company before Functionize.

Tamas and his family live in the San Francisco Bay Area. Since functional quality tends to take precedence over non-functional aspects, many software teams leave performance testing until a build is almost ready for release, or even skip performance testing altogether.

This often happens because teams may not have time to create or update performance test scripts when there are code changes, or they lack the tools or expertise to conduct performance tests and analyze the results themselves.

The problem is that it can be more difficult to identify the root cause of performance issues later on. By the time developers have received feedback from performance testing, they may have moved on to other tasks. It can also be harder to reproduce the issue because environment conditions may have changed or additional code changes can obscure the root cause.

Delayed feedback can also increase the cost and time required to fix performance issues. The longer it takes to identify a bug or defect, the more resources are needed to resolve them because developers no longer have the proper context. They may need to spend additional time to understand issues discovered later than they would have if they knew about the issue during the build process.

In addition, unresolved performance issues can compound over time. Delayed feedback not only makes issues harder and more expensive to troubleshoot, but also increases the risk that underlying inefficiencies and technical debt can progressively degrade the performance of an application.

Integrating performance testing in DevOps allows organizations to adopt a shift-left testing approach. Developers can get feedback earlier in the development process, and resolve issues significantly faster than a delayed performance testing approach.

This helps developers resolve potential performance issues before they ship code to avoid project delays. These smaller load tests require far fewer computing resources and can uncover many high-impact issues before simulating real-world workloads with more expensive types of performance tests.

Many organizations lack adequate tools for running performance tests in development pipelines without slowing down software teams. Automated performance testing also makes it easier to run performance tests whenever new code changes are deployed to a production-like environment such as staging or pre-production.

This ensures software teams have immediate feedback on the performance impact of every new build. An effective performance testing solution should also include low code features for creating, running, and managing test suites.

By reducing the setup time and eliminating script maintenance, low-code testing tools streamline the testing process, increase productivity, and lower the barrier to entry for team members with limited coding expertise.

This can improve DevOps metrics and Practices , leading to higher software quality and increased development velocity at the same time, and increase collaboration between quality and development teams.

Mabl is a low-code automated testing solution with scalable and continuous performance testing capabilities. By enabling teams to run API load tests without scripts or specialized frameworks, the platform makes it easy for anyone to evaluate application performance.

LoadView is a cloud-based load and stress testing solution that offers an easy way to create test cases and run them on real browsers and devices across geo-locations. This makes the most realistic test environment that actual users face.

Jenkins is one of the most preferred tools for automation in the CD pipeline. LoadView has a plugin for Jenkins, which can be set up in minutes to automate Load testing in the CD pipeline. Read more on how you can set up Jenkins with the LoadView plugin to perform load testing for your web pages and applications.

DevOps practices are a next generation agile process for rapid IT service delivery. One of the most important aspects of deployment is to do performance testing to avoid availability and accessibility problems for websites or applications.

Integrating load testing with DevOps practices in the CD pipeline have huge benefits for delivering better performance and user experience. This can be achieved by automating load testing in the CD pipeline using Jenkins with the LoadView plugin. Start load testing your websites, web-apps, and APIs with the LoadView free trial.

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Load Testing within DevOps Practices Last Updated: April 19, Performance Testing , User Experience. Most companies still follow the traditional way of doing performance testing in the QA phase or when the bottleneck problems occur in production. They completely ignore testing in the development phase.

This causes low-quality code and inefficient utilization of resources. With DevOps gaining popularity for development efficiency, companies need to integrate performance testing with DevOps practices. Load testing within DevOps practices enables developers and testers to work together and bring out the best in your website and applications.

This also benefits the complete development life cycle by detecting and resolving performance problems at the early stages for efficient resource utilization.

What is DevOps? DevOps Practices Organizations adopt different DevOps practices according to their goals and resources. Following are some fundamental capabilities that are common to all DevOps practices: Collaboration DevOps incorporates collaboration between all stakeholders for any website, application, or software delivery.

Automation DevOps focuses on the toolchain to automate most of the software development and deployment. Continuous Integration CI It is a development process that allows multiple developers to integrate their code into a shared repository multiple times a day.

Continuous Testing In DevOps, testing is not just the responsibility of QA but the developers too. Continuous Delivery CD It is a practice that allows deployment of small and frequent changes such as updates, enhancements, patch, hotfix, etc.

Continuous Monitoring As DevOps is centered around rapid delivery, it avoids rigorous pre-release testing at the cost of delivery speed. Deployment Speed DevOps practices allow high performing developers to deploy features, changes, fixes, or updates multiple times a day.

Faster Delivery From a business point of view, DevOps allows faster shipping of features, fixes, and updates to support business growth and reduced time to market. Innovation DevOps significantly reduce the time investment in fixes and maintenance as opposed to waterfall development practices.

Integrating Load Testing within CD Pipeline CD acts as an extension of CI.

It ensures that the software performs well under unexpected situations like fluctuating networks, bandwidths, user load, etc. It is known that end-users prefer apps with seamless performance. Better app performance will generate more revenue for businesses as users prefer to download such seamless apps, especially for eCommerce, telecom, and healthcare sector apps.

Various types of Performance Testing. Testers perform this testing by simulating the number of virtual users that might use the application. The principal aim of this testing method is to ensure that the application performs well under normal and peak user loads.

This testing helps identify any resource leakage in the system, while it is subjected to normal user load for an extended duration like 8 hours, 12 hours, 24 hours, or 48 hours, etc.

of users. This testing checks if the system can handle the variations in user load. In this testing method, multiple data-intensive transactions are performed to validate how the system performs under such data volumes. This testing method determines the capability of the system to scale up in terms of user load, data volume, number of transactions, etc.

The main aim of this testing method is to determine the peak point beyond which the system prevents more scaling. An application comprises minor components that are the smallest parts of an application.

In component-level performance testing, the individual components of the application are tested to ensure the effective performance of components in isolation. Later, all the app components are tested as a group to ensure a high-performing and fully integrated application.

Further, these components are integrated, and finally, performance testing is executed after integration. An overview of performance testing in DevOps. Businesses continue to embrace DevOps to get faster releases and high-quality in less time.

This DevOps methodology promotes collaboration between teams to deliver faster and quality releases to the customer. The DevOps lifecycle includes various stages such as Continuous Integration CI , Continuous Testing CT , and Continuous Delivery CD.

Typically , to ensure the release of high-quality software in less time, performance testing in DevOps plays a critical role. Moreover, performance testing in DevOps is done by integrating continuous and automated performance testing in the continuous delivery pipeline.

However, to take up performance and load testing in DevOps, a series of steps should be followed at each stage of the DevOps lifecycle. DevOps performance testing starts with continuous performance testing at the build stage, which involves unit performance testing.

In this stage, the smallest unit of the software is checked to ensure they perform well in isolation. Once the unit performance testing is done, performance testing is done at the integration stage, where the smallest units of the software are integrated.

During this stage, system-level performance testing is done to ensure the software performs well as expected. Once the system-level performance tests are passed, the software moves to the release and deploy stage. During this stage, load testing and real user monitoring are performed to ensure that the software handles the user load effectively in the production environment.

After the software reaches the monitoring stage, continuous performance monitoring is done, where various performance metrics are evaluated to determine areas that need improvement. Performance testing process overview.

Testers should prepare a checklist before starting the test. Prepare a test plan or test strategy which covers the aim and scope of testing, application architecture, environment details, testing tools, roles, responsibilities, etc. Testers need to set up the test environment. There are two types of test environments, on-premise, and on-cloud.

The test environment should be chosen wisely, as the effectiveness of the testing process largely depends on the environment in which it is executed. Load generation environment should be configured to generate virtual load for load testing of the software.

To set up the test data, testers need to first extract test data, modify the data for testing the software, and generate enough test data to perform the tests.

This is an important step that involves preparation of test scripts, execution of test cases, and analysis of test results to know whether it is pass or fail.

The Dev team resolves all the bugs found during the testing process. Once all the bugs are fixed, the testing process is repeated to ensure defects are fixed.

Document all the test findings in one place and share the test report with all the stakeholders and project team. Performance testing metrics to measure mobile app performance. This metric gives the user a first impression of the app. This metric measures the app installation time and how it can be improved.

App launch time or app start time is another important metric that must be checked in an application. Ideally, it should not be more than 1 to 2 seconds. It is essential to ensure that app performance remains unaffected when multiple apps run parallelly. It is essential to ensure that no data loss should happen when the app runs in the background and is retrieved.

An app should not consume excess memory and must not heat the device, especially when it runs in the background. This metric measures the time is taken by an app to respond to a given input.

Faster response time ensures less wait time and high performance of the app. Faster loading time or speed ensures better performance of an app. Varying bandwidth and fluctuating networks affect the app loading time. However, to ensure effective app performance, it is essential to perform load testing of an app with minimal bandwidth and different network types and connections such as 3G, 4G, 5G, Wi-FI, etc.

This metric measures how many virtual users are active or accessing the app at a given point in time. It is the measure of how many requests by second the server can handle without degrading performance or resulting in error. Data migration is a complex but essential process for every business.

Effective data migration helps businesses ensure better data availability, reduced cost, improved performance, and more. This is where the need for performance testing during data migration comes into the picture.

The data is extracted from various sources during the ETL process, transformed into a consistent data type, and then loaded into the data warehouse or target system.

Performance testing in ETL is done to ensure that the ETL system can handle a high volume of transactions. Performance tests in ETL also verify the efficiency of the ETL system by determining the actual time taken by the system to process data.

The lesser the data processing time, the higher is the efficiency. Significance of think time in performance testing. Think time is the time difference between each action performed by the user. There are a number of specific requirements that need to be considered in a mocking tool for performance use, due to the load it will be placed under and the need to replicate time as well as data.

This may be done using built in system utilities or 3rd party toolsets. Whether the technology used is physical infrastructure, cloud based or completely serverless, monitoring the key service metrics is critical.

Monitoring should include servers, networks and storage as well as any other components. This needs to be in place both for test and production.

Application monitoring — Application Performance Monitoring APM tools are valuable sources of data about the performance of the application under test. Using an APM or log-parsing solution means quicker turnaround times when issues are detected. Being able to create dashboards for production monitoring is also important.

There may be other specific types of tool in this category depending on your sector — for instance in ecommerce you could use RUM real user monitoring and conversion tracking to monitor real user experiences.

Application Monitoring also needs to be in place both for test and production. After the test is complete comes the difficult part: what are the results and what needs to be done about it? Again, to achieve full Continuous Delivery this needs to be automated into a build pipeline.

Test results analysis — This is perhaps the hardest part of the process to automate but getting it right is crucial. False positives cause delays and slow down delivery. And false negatives — not spotting performance defects — are perhaps even worse! The analysis should include checking against known thresholds, assessing against past baselines and identifying known performance antipatterns.

Getting this right is critical to successfully achieving continuous delivery — without a reliable means of signing off performance automatically, changes cannot be automatically deployed to production.

Defect tracking — Ideally this should link into your test status reporting as defined above. In a cloud first DevOps organisation, environments become as much a part of the solution being developed as the software code. You will need to plan how environments will be set up and automated into the development pipeline.

First, work out what environments will be required and what confidence level each environment is designed to give you. The diagram below shows a fairly standard approach where there are 3 levels of test environments for performance:.

The CI performance stack will be spun up as required on build to enable a performance component test of a single service in isolation. The team performance test environment PTE contains all the services owned by a single DevOps team.

The PTE will be used for running performance component test on build for the entire product delivered by the associated team. An environment will be required to conduct representative performance tests across the full solution, including all products built by the various teams, and any external dependencies.

This will be a single Integration Performance Test Environment IPT. An IPT should be managed in the same way as the production environment so that releases do not hit the environment until they are ready for production. Depending on the type of Continuous Delivery CD being used and the level of risk acceptable, it may be deployed to in parallel with production, to allow integrated tests to proceed without delaying functionality release.

For each environment, it is important to ensure that it is designed efficiently to deliver performance while keeping costs under control. Non-production environments account for a significant proportion of cloud cost in most organisations. If performance environments are not sized and maintained appropriately, they can bring test costs higher than actual production hosting!

The reason data deserves special attention in a DevOps world is that it is crucial for getting meaningful results and needs to be automated in such a way that tests are representative and repeatable. Some tools exist which may help with managing this, but the hardest part is ensuring that the data being produced is correct and meets all the criteria.

Bulk data is relevant where there is a data repository of some kind, this could be a traditional or NoSQL database. Reference data is created in data stores or elsewhere for the purposes of referencing in a test script. This is the most complex data type to manage and requires special consideration when automating data creation, as the data needs to be available both to the script and in the target system.

This may involve automating extracts of data from the target system prior to script execution. Parameter data is referenced in the script without any specific requirement to create it anywhere else as part of the test scenario.

Again, ensuring that the range of data is understood and replicated is key to achieving meaningful test results. Incorrect parameter data may lead to unrepresentative use of cache or buffering or may ignore situations which are particularly heavy on performance.

There are several types of information which need to be stored and reported on during the DevOps change lifecycle:. This means that tracking dashboards can be produced which combine this data into a single view for the product, for example as below:.

Tracking performance in production is at the heart of maintaining a short feedback loop and delivering software quickly. There will always be some remaining risks which need to be identified in production and without the right data and reports, production performance assessment becomes impossible.

There are 2 high-level types of dashboards which should be created to track performance in the live system:. The integrated dashboard should focus on elements which are within the remit of the performance integration function, including:.

Without the necessary performance skills in place a DevOps team will fail to take full ownership of performance.

A Performance Champion is a member of a DevOps team who has a performance focussed skillset, understands performance anti-patterns, and is able to take accountability for coordinating the team to deliver a solution with the required performance.

The task of a Performance Champion should be to mentor and share aims, attitudes and knowledge regarding performance for assimilation by the rest of the team. The role of Performance Champion is typically important in a new DevOps team and becomes less important as time progresses if they're carrying out their role correctly!

In an integrated system where there are multiple product teams contributing towards a combined whole, there will be a need for a defined performance integration function if any of the following are true:. These introduce risks which are not owned by any one product team.

Performance integration is accountable for owning these risks and mitigating them to agreed levels, by coordinating between product teams.

This role may sit with a specific engineering team, a platform team, or a dedicated performance function. Performance Integration must also be responsible for managing NFRs at a business level and how these break down between different product teams, where there are calls between product services or multiple services rendered in a single page.

Now you know what is needed to provide confidence in DevOps performance from your team. How do you implement this strategy, to enable fast delivery of fast, efficient solutions?

Maintaining a performance process and artefacts in a DevOps organisation needs to be low effort and highly automated. There will be activities that need to be carried out constantly by the DevOps team, but these should become their normal way of working.

Performance will be embedded in their delivery. The most difficult parts of the process are firstly to build a strategy which will work for you and your teams and, secondly, to undertake the transformation to embed it.

This requires technical skills around scripting and automation, as well as business skills in creating and embedding processes, plus interpersonal skills for creating a culture of performance in the organisation. It also requires performance skills to design the right tests, environments, and data, to build automated signoff analysis and to create dashboards and reports that tell you what you need to know immediately.

A transformation process can take 6-months to a year in large organisations with many teams but needs to be prioritised to start delivering benefits immediately.

Once in place, the business benefits of embedding performance in DevOps should be quickly apparent. Code will be delivered faster, while still satisfying non-functional requirements. There will be fewer incidents in production, enabling the team to focus more on development and further increase velocity.

The costs of environments, both in production and development, will reduce as efficiency is built into the system. Capacitas brings clients a structured approach to performance in DevOps, with skilled consultants focussed on performance, and the technical tooling and automation to enable fast performance signoff in continuous delivery.

Our focus in DevOps is on delivering the processes, skills and tools needed to enable teams to become autonomous units, owning the performance of the solutions they deliver, and ensuring that not only do they deliver quickly but also meet the performance needs of the business and users.

Thomas Barns is Risk Modelling and Performance Engineering Service Lead at Capacitas, responsible for service definition and ensuring consistent best practice across projects.

During this time, he has seen a big shift in how software engineering is undertaken and viewed by the business and has built on this to introduce more effective and efficient performance risk management processes.

This has meant shifting focus away from large scale system testing to a full lifecycle approach, alongside research and development in automated data analysis. Thomas has recently been defining and governing Performance Engineering processes and standards for a multi-million pound multi-vendor programme of work at a FTSE company, and helping clients define performance approaches for DevOps.

If you want to see big boosts to performance, with risk managed and costs controlled, then talk to us now to see how our expertise gets you the most from your IT. Book a Consultation. Contact Us. Performance Testing and DevOps Strategies to Ensure Performance in the Cloud by Thomas Barns.

Home Resources Performance Testing and DevOps. Introduction Why is DevOps Important? Why Does Performance Matter? How Can You Ensure Performance in DevOps?

Performance Testing Process The first thing to get right when implementing DevOps performance is to ensure that the right process is in place. A good DevOps process consists of 6 activities : A combination of these activities will be carried out for any change delivered by the DevOps team.

Risk Assessment Objective A good process starts with understanding the performance risk of any change. Taking ownership of this risk as a DevOps team. Planning steps to mitigate it. Performance risk assessment takes place at the same time as maturing, planning, estimating.

As soon as the team starts thinking about the items in the backlog, they start thinking about risks to performance. Having this performance mindset is critical to making sure that the end product performs — while all the team should have the same performance focus, it can help to appoint a Performance Champion to lead on it; more on that later.

Performance NFRs need to be reviewed and updated where needed — for help on setting NFRs see our NFR Template. Use team mindset to eliminate risks early.

On one hand using knowledge of performance behaviours and antipatterns to improve implementation. On the other using understanding of change and implementation to improve smart test design, scripting and data. The first step in a performance design is to review the implementation for performance anti-patterns — behaviours which will impact performance.

At the same time the team will start updating scripts, data, mocks etc. to prepare for performance testing.