[{"data":1,"prerenderedAt":795},["ShallowReactive",2],{"/en-us/blog/two-sizes-fit-most-postgresql-and-clickhouse":3,"navigation-en-us":33,"banner-en-us":433,"footer-en-us":443,"blog-post-authors-en-us-Sid Sijbrandij":685,"blog-related-posts-en-us-two-sizes-fit-most-postgresql-and-clickhouse":703,"assessment-promotions-en-us":745,"next-steps-en-us":785},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":22,"isFeatured":12,"meta":23,"navigation":24,"path":25,"publishedDate":20,"seo":26,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/two-sizes-fit-most-postgresql-and-clickhouse.yml","Two Sizes Fit Most Postgresql And Clickhouse",[7],"sid-sijbrandij",null,"insights",{"slug":11,"featured":12,"template":13},"two-sizes-fit-most-postgresql-and-clickhouse",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9},"Two sizes fit most: PostgreSQL and Clickhouse","Relational databases are not in decline. Here's why.",[18],"Sid Sijbrandij","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663397/Blog/Hero%20Images/logoforblogpost.jpg","2022-04-29","\nSince the introduction of [System R](https://en.wikipedia.org/wiki/IBM_System_R_) in 1974, relational databases in general, and SQL databases in particular, have risen to become the dominant approach to data persistence in the industry, and have maintained that dominance despite various significant challengers. Though some have rumored the death and decline of traditional relational databases, PostgreSQL has turned out to be an improvement on its predecessors, as well as its supposed successors. \n\nIn fact, the open-source MySQL database was so ubiquitous that it became part of the eponymous LAMP stack (Linux, Apache, MySQL, Perl) that dominated early web development.\n\nThe one big exception to this trend is OLAP, where specialized techniques that can drastically improve the performance of certain workloads have met with use-cases that actually require these techniques, with new contenders such as Clickhouse enabling qualitatively different approaches to analytics.\n\n## One size does not fit all\n\nAs often happens when a technology becomes dominant, it gets applied unthinkingly even when it may not actually be appropriate, and so all kinds of data was and is being pushed into general-purpose relational databases.\nExtreme examples could be found, such as developers creating remote Oracle databases for data sets with a total of 5 small elements (not columns, pieces of data) or Apple pushing their system logs into an SQLite database (a mistake they later corrected).\n\nBind10 development started under the premise to solve scaling issues with Bind9 as DNS nameserver, using SQLite as backend. The DNS development was discontinued by ISC in 2014, and the OSS project [Bundy](https://github.com/bundy-dns/bundy) remains inactive. PowerDNS focussed on [performance scaling with MySQL/PostgreSQL](https://doc.powerdns.com/authoritative/performance.html) early.\n\nIn 2005, Michael Stonebraker, database researcher behind Ingres and later PostgreSQL, together with Uğur Çetintemel, penned a paper [“One Size Fits All”: An Idea Whose Time Has Come and Gone](http://cs.brown.edu/%7Eugur/fits_all.pdf) arguing that this had gone too far too long and backing up that argument with [benchmark results](http://nms.csail.mit.edu/%7Estavros/pubs/osfa.pdf).\n\nIn short, there were many workloads outside of the core application of databases, Online Transaction Processing (OLTP), where the general database architectures were outclassed sufficiently that it did not make sense to use them.\n\nIt should be noted that Stonebraker and Çetintemel argued not against relational databases or SQL, but against a specific architecture descendent from the original System R and Ingres systems that were and still are being used by most general purpose database systems.\n\nThis architecture has the following features:\n\n- Disk and row-oriented storage and indexing structures\n- Multithreading to hide latency\n- Locking-based concurrency control mechanisms\n- Log-based recovery\n\nIn addition to special-purpose text indexing, the primary use-case for which the traditional architecture was proving inadequate was data warehouses, for which column stores were proving 10-100x more efficient than the traditional row stores.\n\n## Clickhouse\n\nThe prediction that OLAP database engines would split off from mainstream databases has largely come to pass in the industry, with OLAP databases now being a significant category in its own right, with vertica, the commercial offshot of the original cstore discussed in the paper, one of the major players.\n\nThe practical advantages of these databases for analytical work are, as predicted, substantial enough that having a separate database engine is warranted.\n\nOr even necessary, as was the case for Yandex's clickhouse OLAP database, recently spun out into a startup that just received a US $250m series B.\nThe clickhouse developers wanted to have realtime analytics, but do so not with customized data structures, as is customary in this application domain, but instead with a generalized database engine queryable with SQL.\nOf course, there is a reason this is usually done with customised data structures: doing so with a generalized database engine was considered impossible, partly because it was impossible with existing engines.\nAs is often the case, impossible turns out to \"just\" be a lot of work and some brilliant engineering, and after a few years the developers had what they had sought after: a database engine specialised for OLAP, but general enough, queryiable via SQL and still capable of real-time analytics.\n\nBoth the engineering and the benchmark results are impressive, including our own [tests](https://gitlab.com/gitlab-org/incubation-engineering/apm/apm/-/issues/4#results) [(video0)](https://www.youtube.com/watch?v=cMdQsxolcqc).\n\nIt is significantly faster than PostgreSQL extensions such as CitusDB or Timescale DB, and reportedly also faster than vertica.\n\n## The end of an era?\n\nThe 2005 paper left OLTP as the only area where the traditional (disk-based, row-oriented, multi-threaded) architecture was viable. Two years later, he published [The end of an Architectural Era (It’s Time for a Complete Rewrite)](http://nms.csail.mit.edu/~stavros/pubs/hstore.pdf), where he argues that even for OLTP, the existing engines can be surpassed by more than a factor of ten.\n\nThe key insight was that long held assumptions about the relative performance of different components were no longer accurate, and so it turns out that, according to Stonbraker et al, around 90% of the performance budget of the database engine is used not for actually processing data, but for overheads such as buffer management and locking.\nSo if we could remove those overheads, we could make a database engine that is 10x faster than existing ones. Achieving these gains would require building a database engine that is single-threaded and works in main memory, a radical departure from the existing architecture.\n\nBut not an unprecedented one. Main memory capacities have improved many more than a million times since those early databases were designed, so many workloads that used to require persistent storage due to size can now be handled in memory.\n\nFor example, even in the early 2000s Yahoo had a policy that any dataset less than 2GB should live in RAM, not on disk. A little later, [EventPoster](https://www.martinfowler.com/bliki/EventPoster.html) architectures, [In-Process REST](https://link.springer.com/chapter/10.1007/978-1-4614-9299-3_11) and the [LMAX](https://martinfowler.com/articles/lmax.html) exchange with the [Disruptor](https://lmax-exchange.github.io/disruptor/) pattern demonstrated that moving from complex multi-threaded, disk-based systems to single threaded RAM based architectures could yield tremendous benefits in terms of simplicity, reliability and performance.\n\nAnd that was with 32 bit computing. Nowadays, we can get single servers with tens of terabytes of memory configured for us at the click of a mouse (with a subsequent bill...), so the workloads we can keep in RAM are quite substantial.\n\nStonebraker and his team built [H-Store](https://hstore.cs.brown.edu), an academic prototype, and [voltdb](https://en.wikipedia.org/wiki/VoltDB), a commercial product with the associated startup.\n\n## It hasn't taken the database world by storm.\n\nSurprisingly, that isn't because it doesn't work as intended. From all reports it seems like it does, in fact, work pretty much as advertised and can fulfill its promises.\n\nHowever, those promises came with tradeoffs, and it seems like those tradeoffs aren't ideal for most domains. First, though keeping the entire database in RAM at all times is feasible nowadays, it's probably not a good price/performance tradeoff for most domainst, for which most data is cold.\nSecond, although much higher peak performance is possible, machines are now so fast that the the highest possible peak performance is only necessary for very special domains.\n\nThird, with machines now so fast and performance now usually adequate, performance focus has shifted from peak or even throughput to worst-case latencies. With only a single thread accessing the database, a single long query can stall the entire database and cause extremely bad tail-end latencies. So the fastest database using conventional measures of throughput and peak performance may actually require supreme care not to score worst in the performance metric people now care most about.\nLast not least, requiring a distributed setup, while fine for large installs, creates a high burden for entry-level setups, meaning there is no good on-ramp for this technology.\n\n## PostgreSQL\n\nSo it looks like the era of the conventionally architected OLTP database has not, in fact, ended. Of course, this shouldn't be of too much concern to Professor Stonebraker as the contender coming out on top is still his brainchild, just an earlier one. No, not [Ingres](https://en.wikipedia.org/wiki/Ingres_%28database%29) the early public peer to IBM's System R, but its successor: PostgreSQL.\n\nPostgreSQL is becoming, or has become, the dominant variant of these conventionally architected databases according to both industry sentiment and database [rankings](https://db-engines.com/en/ranking/relational+dbms). Certainly among the engines not beholden to a major database vendor in one way or another.\n\nAt GitLab, we also use PostgreSQL with replication, having [moved away](/blog/removing-mysql-support/) from MySQL in 2019, partly because PostgreSQL actually had features that are important for us, partly because most of our customers were using it anyhow.\n\n## What about NoSQL?\n\nWell, what about it? Although the NoSQL movement of the early 2000s did point to some shortcomings in the dominant databases, the technological prescriptions actually only made sense in very rare circumstances.\nNoSQL isn't really a category, but more a feature of rich database engines like PostgreSQL.\n\nWith increasing compute power and fast storage, high volumes and transaction rates can be handled with traditional database engines, and non-traditional engines can serve relational models using SQL as the interface, see voltdb and Google's Spanner, built on top of Bigtable.\nThere are use-cases where a relational database is not needed and a key-value store is sufficient, or a JSON document store is called for, but for example JSON types in PostgreSQL handle most of these use cases just fine. Even for very specialised use-cases such as text or GIS, modern relational database engines provide direct support.\n","yml",{},true,"/en-us/blog/two-sizes-fit-most-postgresql-and-clickhouse",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":27,"ogSiteName":28,"ogType":29,"canonicalUrls":27},"https://about.gitlab.com/blog/two-sizes-fit-most-postgresql-and-clickhouse","https://about.gitlab.com","article","en-us/blog/two-sizes-fit-most-postgresql-and-clickhouse",[],"0cEPggO9yjyg3urF5ViomRaeOyqaHMXKx0eyZqEOqok",{"data":34},{"logo":35,"freeTrial":40,"sales":45,"login":50,"items":55,"search":363,"minimal":394,"duo":413,"pricingDeployment":423},{"config":36},{"href":37,"dataGaName":38,"dataGaLocation":39},"/","gitlab logo","header",{"text":41,"config":42},"Get free trial",{"href":43,"dataGaName":44,"dataGaLocation":39},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free 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statement",{"items":675},[676,679,682],{"text":677,"config":678},"Terms",{"href":503,"dataGaName":504,"dataGaLocation":451},{"text":680,"config":681},"Cookies",{"dataGaName":513,"dataGaLocation":451,"id":514,"isOneTrustButton":24},{"text":683,"config":684},"Privacy",{"href":508,"dataGaName":509,"dataGaLocation":451},[686],{"id":687,"title":18,"body":8,"config":688,"content":690,"description":8,"extension":22,"meta":698,"navigation":24,"path":699,"seo":700,"stem":701,"__hash__":702},"blogAuthors/en-us/blog/authors/sid-sijbrandij.yml",{"template":689},"BlogAuthor",{"role":691,"name":18,"bio":692,"config":693},"Co-founder, Chief Executive Officer and Board Chair of GitLab Inc.","Sid Sijbrandij (pronounced see-brandy) is the Co-founder, Chief Executive Officer and Board Chair of GitLab Inc., the most comprehensive AI-powered DevSecOps platform. GitLab's single application helps organizations deliver software faster and more efficiently while strengthening their security and compliance.\n\nSid's career path has been anything but traditional. He spent four years building recreational submarines for U-Boat Worx and while at Ministerie van Justitie en Veiligheid he worked on the Legis project, which developed several innovative web applications to aid lawmaking. He first saw Ruby code in 2007 and loved it so much that he taught himself how to program. In 2012, as a Ruby programmer, he encountered GitLab and discovered his passion for open source. Soon after, Sid commercialized GitLab, and by 2015 he led the company through Y Combinator's Winter 2015 batch. Under his leadership, the company has grown with an estimated 30 million+ registered users from startups to global enterprises.\n\nSid studied at the University of Twente in the Netherlands where he received an M.S. in Management Science. Sid was named one of the greatest minds of the pandemic by Forbes for spreading the gospel of remote work.",{"headshot":694,"twitter":695,"linkedin":696,"ctfId":697},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665383/Blog/Author%20Headshots/sytses-headshot.png","https://twitter.com/sytses","https://www.linkedin.com/in/sijbrandij","sytses",{},"/en-us/blog/authors/sid-sijbrandij",{},"en-us/blog/authors/sid-sijbrandij","ZdVvFbtL6NKLtKZEjFCVOecdpvuPzX3wmEZBrC6pRWg",[704,719,732],{"content":705,"config":717},{"title":706,"description":707,"authors":708,"heroImage":710,"date":711,"body":712,"category":9,"tags":713},"How we overhauled GitLab navigation","Users weren't getting what they needed from our navigation. Here are the steps we took to turn that experience around.",[709],"Ashley Knobloch","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682884/Blog/Hero%20Images/navigation.jpg","2023-08-15","\nGitLab navigation was complex and confusing - that was the message we received from our users through issues and other feedback channels. Initially, to address these concerns, we conducted research around proposed solutions, but quickly found they wouldn't help users achieve their goals well enough to warrant implementing them. In the process of learning what wasn't working and what wouldn't work, we still didn't have clarity around *why* the navigation wasn't working. This article chronicles our journey to finding that clarity and developing navigation that is easier to use and better suited to our users' needs.\n\n## Our approach\nAs a first step, we reviewed past research and user feedback to ensure we had a solid understanding of what we had done and learned already. We found that we still needed more insight into why proposed changes weren’t receiving enough positive feedback to implement them.\n\nOur goals were straightforward:\n- understand what users are doing in GitLab\n- study how they navigate the platform\n- learn why they need certain navigation elements\n\nOur perspective shifted from validating proposed solutions to going back to revalidate the problems that exist with our navigation experience. Our hypothesis was that with a deeper understanding of our users’ behavior and mental models for how they navigate around GitLab, we could develop concepts to better match their needs and improve their overall experience.\n\nThe scope of features in GitLab and the number of user personas across GitLab made this challenging. We have [16 personas](https://handbook.gitlab.com/handbook/product/personas/#user-personas) to represent different types of users, all with unique goals and techniques to achieve those goals. We focused our efforts on a subset of those personas that best represented usage across GitLab to ensure a holistic understanding of different user needs. We wanted to learn how navigation among different personas was similar and where it differed, what worked well with the current navigation, and what challenges users faced.\n\n## Studying key persona cohorts\nWe conducted [diary studies](https://handbook.gitlab.com/handbook/product/ux/ux-research/diary-studies/) with cohorts of our key personas to learn what their primary tasks and workflows were at a deeper level. This provided us with many real-world examples of how they navigate to their tasks and why. We also learned what worked well with their current workflows, what pain points existed, and what workarounds were being used (such as creating browser bookmarks, typing in the URL to pull browser history, or keeping a bunch of browser tabs open) to streamline their tasks in GitLab.\n\nWe learned that for some users, many of their primary tasks don’t require much navigation within GitLab because they use outside tools that link into GitLab through notifications (e.g., Slack and email) or use direct links through other tools. We also learned that often users’ work is quite scoped in GitLab, and they would like easier access to some of their core features without having to wade through all of the other features they don’t use. This illuminated some unmet needs that would improve their workflows, such as having the ability to customize navigation to access things important to them more quickly and streamline their path to relevant projects.\n\nLearning more about our users from a foundational perspective ensured that we had a solid base to build upon when considering changes to the navigation.\n\n## Anchoring to a North Star\nTo anchor the redesign process in user problems more broadly, a review of past feedback was analyzed that revealed three overarching themes with navigation-related feedback. These themes helped to guide the process and to remind us of the key problems we were trying to solve:\n- minimize feeling overwhelmed (ability to customize left sidebar)\n- orient users across the platform (differentiating groups and projects)\n- pick up where you left off (switching contexts)\n\nThe team continually mapped back design concepts to these themes to ensure potential solutions were rooted in user problems.\n\n## Evaluating and iterating\nNext, several navigation design concepts were developed and shared with users for feedback. Multiple rounds of [solution validation testing](https://handbook.gitlab.com/handbook/product/ux/ux-research/solution-validation-and-methods/) were conducted with our key personas to determine which design concepts to move forward with. The testing revealed how users felt about each design and also how well each design supported users completing core tasks. We identified a final concept that supported mature and new GitLab users with common workflows.\n\n## Understanding mental models for sidebar organization\nWe wanted to revisit our groupings in the left sidebar because we’ve heard over time that the organization can be confusing and unintuitive, especially some categories such as Operations. We needed to understand our users’ mental models for how they would group these items, and why. Learning the thought processes behind their organization was critical for us to know what changes to make that would align with user expectations.\n\nWe ran facilitated [card sort](https://handbook.gitlab.com/handbook/product/ux/ux-research/mental-modeling/#card-sorting) studies with our key personas to understand how they would group items in the left sidebar, and why. This helped us learn some areas that could benefit from readjusting, such as the Manage and Operate categories. We learned that users most often preferred to have analytics items together, for example, which is reflected in the Analyze tab. This insight, combined with patterns in analytics data, informed changes to the groupings in the left sidebar to better support workflows.\n\n## Launching and learning\nPrior to launching to external users, the new navigation was released to internal team members and we collected [feedback](https://gitlab.com/gitlab-org/gitlab/-/issues/403059) to help iterate and improve the experience.\n\nNext, we launched the new navigation to external users as a toggle that could be turned on optionally. During this initial launch, a [longitudinal study](https://handbook.gitlab.com/handbook/product/ux/ux-research/longitudinal-studies/) was conducted with a sample of GitLab users to learn how they experienced the change in the context of their real work. Over time, the study would provide insight into adoption among the entire user base.\n\nWe interviewed users prior to the monthlong study to learn more about their experience with the existing navigation. Then, they began using the new navigation while completing surveys and participating in interviews at checkpoints in the beginning, middle, and end of the month. This enabled us to capture their initial impressions of the new navigation, what they liked/disliked, how the new experience compared to the previous one, and if their sentiment changed over the course of the month as they continued to use the new navigation.\n\nUsers in this study found the new navigation to be an improvement from the previous one, and most preferred its features, including:\n- the ability to pin items streamlined common workflows\n- the new task-based sidebar categories in the sidebar, which they said felt more approachable, especially for newer users\n- the new navigation changes, which they said weren’t too overwhelming and felt familiar\n\nWe also learned about some opportunities to iterate and improve the new experience. For instance, some users pointed out:\n- the inability to pin entire Projects, Groups, or specific pages makes it difficult to streamline other workflows\n- some users unpin items accidentally\n- the overall lack of color can cause some features to blend in or be missed\n- it's not always easy to know what’s new in GitLab\n\n## What’s next: Iterate, listen, and iterate again\nTo capture large-scale feedback on navigation over time, we launched a new navigation-focused quarterly survey in Q1 (February) of this year. This first quarter data established a baseline of our old navigation, and beginning in Q2 (May), we began collecting data on the new navigation experience. We will monitor this closely, and look for themes to help us learn what is working well and what may need further iteration.\n\nThis survey, along with our longitudinal study feedback and various other user feedback sources, will provide insights to help prioritize iterative improvements to the new navigation experience. Stay tuned for changes, and keep sharing [your navigation feedback](https://gitlab.com/gitlab-org/gitlab/-/issues/409005) with us!\n",[714,715,716],"inside GitLab","UX","research",{"slug":718,"featured":12,"template":13},"navigation-research-blog-post",{"content":720,"config":730},{"title":721,"description":722,"authors":723,"heroImage":725,"date":726,"body":727,"category":9,"tags":728},"Beautifying our UI: Giving GitLab build features a fresh look","Get an inside look at how we are improving the usability of GitLab build features with multiple visual design improvements.",[724],"Veethika Mishra","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682807/Blog/Hero%20Images/beautify.jpg","2023-07-05","\n\nThe current technical landscape is completely different from what it was this time last year. As the software development industry is busy evolving its understanding of _automating early and often_ in the presence of new AI capabilities, we have been focused on feature work. However, it's equally important to make sure we are adapting our UI to match up to the experience and addressing, where necessary, the misalignment between the two.\n\nIn a scaling product, where issues are competing to be prioritized, it might feel convenient to tackle the next feature issue as opposed to focusing on small visual design improvements. Advocating for the value that a small visual design change in isolation brings to the product is never easy for all the practical reasons, and this is where [the \"Beautifying our UI\" initiative](https://handbook.gitlab.com/handbook/product/ux/product-design/#beautifying-our-ui) becomes useful at GitLab. It allows a product designer and a frontend engineer to voluntarily pair up, like we did, and make self-directed improvements to the usability of GitLab.\n\nWe collaborated on many pipeline-related features in the past three years. As our responsibilities pulled us in different directions, we had to put many of our aspirational plans for improving the presentation of CI/CD features in GitLab on hold in favor of other more important things.\n\nHowever, once those were addressed, we decided to volunteer for a session of Beautifying our UI in the 16.1 milestone. To make the most of a single milestone, we began preparing a couple months in advance, soliciting ideas from team members and getting the design proposals ready in [an issue](https://gitlab.com/gitlab-org/gitlab/-/issues/394768/). After a quick prioritization exercise to understand which of the suggested improvements would be most meaningful to our users, we made a number of contributions to the product.\n\nHere are some of those contributions:\n\n### Improvement to pipeline detail page\nIn the process of troubleshooting a failing pipeline, users often have to visit their detail page for better insight into what's causing the failure. The top of the page previously had a table with all the metadata around that pipeline. Over the years, a lot of information was added to this table but the layout was never optimized to accommodate that information, which in return impacted the usability of the page. The page headers were also very different from other examples found in GitLab.\n\nBy critically looking at every piece of information displayed on the page, we made informed decisions using the qualitative insights and the usage data at hand to completely redesign the pipeline header.\n\n![image of pipeline detail page before](https://about.gitlab.com/images/blogimages/Beautifying-of-our-ui-16-1/pipeline-detail-before.png)\nBefore\n\n\n![image of pipeline detail page after making changes](https://about.gitlab.com/images/blogimages/Beautifying-of-our-ui-16-1/pipeline-detail-after.png)\nAfter\n\n\nThis work was substantial and while we did our best to avoid any negative impact to our users, we realize there might be a few issues. Please share your comments in this [feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/414756) about the redesign and we'll prioritize addressing them.\n\nRedesigning the pipeline header came with a few technical challenges because a lot of the code was a mix between HAML and Vue. We had to slowly refactor the pipeline header over to Vue/GraphQL to allow our code to be more performant and maintainable. It’s pretty much like building a completely new feature — we had to get creative with passing data to the Vue app from Rails.\n\n### Harmonizing badges and link styles on pipeline list view\nThe pipeline index page (list view) is one of the most visited pages in GitLab because users need to make sure any failing pipelines are identified quickly for troubleshooting. Since there's a lot going on on this page, it is critical that the UI leads users' attention to the right areas. Previously, almost every link presented in the pipeline column had a different visual treatment, which made the page visually noisy and harmed the usability and scannability of the information. Our goal was to remove anything that isn't required and harmonize the visual language so it is easy for CI/CD users to perform their jobs effectively.\n\n![image of pipeline detail page before](https://about.gitlab.com/images/blogimages/Beautifying-of-our-ui-16-1/pipeline-index-page-before.png)\nBefore\n\n\n\n![image of pipeline detail page after making changes](https://about.gitlab.com/images/blogimages/Beautifying-of-our-ui-16-1/pipeline-index-page-after.png)\nAfter\n\n\n### Linking runner number to runner admin page\nTo allow easy management of runners across an instance, we've now provided easy access to the runner admin page right from the job detail page. Previously a static test, now the runner number can directly take users with the runner admin page where they can make changes to the specific runner's configuration.\n\n![image of cancel pipeline label](https://about.gitlab.com/images/blogimages/Beautifying-of-our-ui-16-1/runner-link-from-job-logs.png)\nLinking runner admin page from job logs page\n\n\n### Improving tooltips and button text\nThe tooltips on the jobs list view were using native browser tooltips. We've changed those to use a design-system-compliant tooltip for consistency and better readability.\n\nWe gathered some useful feedback on the usability of the button labels and took this as an opportunity to improve a few of them. Here's one example where we changed the label text for the button for canceling a running pipeline from **Cancel running** to **Cancel pipeline** and added an appropriate tooltip to clearly communicate the action.\n\n![image of cancel pipeline label](https://about.gitlab.com/images/blogimages/Beautifying-of-our-ui-16-1/cancel-pipeline-label.png)\nButton with new label text\n\n\n## More to come\nWe are not stopping with this list! We will continue our partnership to bring in more visual and usability improvements to the continuous integration area in the coming months. If you are interested in taking a look at the complete list of changes we have made and the ones we still plan to make, [you can find the issue here](https://gitlab.com/gitlab-org/gitlab/-/issues/394768/).\n\n\n",[715,729],"design",{"slug":731,"featured":12,"template":13},"beautifying-of-our-ui",{"content":733,"config":743},{"title":734,"description":735,"authors":736,"heroImage":738,"date":739,"body":740,"category":9,"tags":741},"4 best practices leading orgs to release software faster","GitLab's 2023 Global DevSecOps Survey illuminates the strategies that organizations deploying more frequently have in common.",[737],"Kristina Weis","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663908/Blog/Hero%20Images/2023-devsecops-report-blog-banner2.png","2023-06-08","\nReleasing software faster is one of the biggest goals of many organizations — and for good reason. It helps them keep up with competitors, land and keep more customers, improve employee satisfaction, and much more. But maintaining that velocity requires investment in processes and technologies that help DevSecOps teams deliver, secure, and deploy software faster without compromising quality.\n\nIn our [2023 Global DevSecOps Survey](https://about.gitlab.com/developer-survey/) we asked more than 5,000 development, security, and operations professionals about everything from deployment frequency to the practices teams have adopted – all to learn what the most agile and efficient organizations have in common. One respondent, a director of IT security in the retail sector, summed up the challenge as follows: “Software customers are increasingly vocal and demanding, expecting faster releases and greater customizability. Developers will need to keep up with these demands while still maintaining stability and usability.”\n\nSo what’s helping organizations be more productive and efficient? Here are four of the best practices that, according to the survey, help organizations release software faster and deploy more frequently:\n\n## 1. Running applications in the cloud\nOne of the benefits people commonly attribute to deploying to the cloud is increased development speed. As it turns out, this year’s survey shows there’s some serious truth to that. Respondents with at least a quarter of their applications in the cloud were 2.2 times more likely to be releasing software faster than they were a year ago — and respondents with at least half of their applications in the cloud were 4.2 times more likely to deploy to production multiple times per day.\n\nSeveral respondents commented on the value of the cloud while also acknowledging the complexities cloud computing can bring to software development. An IT operations manager in the industrial manufacturing sector shared that “developing software that is designed for the cloud-native environment” is one of the top challenges facing software development this year. Likewise, an IT operations manager in the telecommunications sector said: “With the increase in the use of cloud computing and IoT devices, there is a greater need for secure coding practices to protect sensitive data from cyber attacks.” As organizations move to a cloud-first model for software development, they will need to adopt technologies that allow them to build natively in the cloud while keeping security top of mind throughout the development process.\n\n## 2. BizDevOps\nThough DevOps and DevSecOps mostly steal the show in terms of methodologies, some organizations go a step further and [practice BizDevOps](https://about.gitlab.com/blog/a-snapshot-of-modern-devops-practices-today/) — that is, incorporating business teams alongside development, security, and operations teams. An IT operations manager in the software sector emphasized the importance of collaboration with the business, sharing that “as software projects become larger and more complex, developers will need to work closely with other team members, including designers, testers, project managers, and business stakeholders.” This approach appears to be paying off for some: Respondents whose organizations practice BizDevOps were 1.4 times more likely to be releasing software faster than they were a year ago.\n\n## 3. CI/CD\nIt’s not surprising that automating the software development lifecycle with [CI/CD](https://docs.gitlab.com/ee/ci/) would help teams release software faster and more efficiently; however, it’s nice to see confirmation and put some numbers to the difference it can make. The survey shows that respondents [practicing CI/CD](https://about.gitlab.com/blog/how-to-keep-up-with-ci-cd-best-practices/) were twice as likely to deploy multiple times per day and 1.2 times more likely to release software faster than they did a year ago.\n\nDespite the value of CI/CD for driving efficiency, respondents also identified challenges. For instance, an IT operations associate in the aerospace/defense sector pointed to “management that doesn't understand CI/CD at all” as a blocker to more efficient software development. Meanwhile, a software development intern in the biotech sector shared that “tools to automate CI/CD, together with code editors, APM software, and defect trackers, can help with a faster and quality development cycle,” but “companies are hesitant to spend on tools that can help increase their developers’ productivity.” These responses underscore the value of investing in tools that unify CI/CD with other DevSecOps practices — such as incorporating security early in the development process and creating tighter feedback loops — to help organizations break down development silos.\n\n## 4. DORA and other metrics\nOrganizations that [make a conscious effort to track key development metrics](https://about.gitlab.com/blog/how-zoopla-uses-dora-metrics-and-your-team-can-too/) are more likely to improve them, according to the survey. This makes sense because by virtue of an organization choosing to track a metric, they’re signaling to their teams that it’s important, likely reminding them of whether the metric is improving (or not) periodically, and quite possibly prioritizing initiatives aimed at improving those metrics. We found that respondents whose organizations track their [DORA metrics](https://docs.gitlab.com/ee/user/analytics/dora_metrics.html) and other similar metrics were 1.4 times more likely to deploy multiple times per day.\n\n## A deeper dive on productivity and efficiency\n\nFor a deeper look into release velocity and deployment frequency, and all the practices that made respondents more likely to release software faster and deploy multiple times per day, check out our [2023 DevSecOps Report: Productivity & Efficiency Within Reach](https://about.gitlab.com/developer-survey/).\n\nThe report also digs into two other key factors that can have a big impact on productivity and efficiency: how long it takes to onboard new developers and how difficult or easy it is for organizations to attract, hire, and retain developers. We’ll show you where things stand and the practices that made respondents more likely to be successful.\n\n_[Read the highlights from “Security Without Sacrifices,” the first report in our 2023 Global DevSecOps Report series.](/blog/gitlab-survey-highlights-wins-challenges-as-orgs-adopt-devsecops/)_\n",[742,102,558,550],"developer survey",{"slug":744,"featured":12,"template":13},"best-practices-leading-orgs-to-release-software-faster",{"promotions":746},[747,761,773],{"id":748,"categories":749,"header":751,"text":752,"button":753,"image":758},"ai-modernization",[750],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":754,"config":755},"Get your AI maturity score",{"href":756,"dataGaName":757,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":759},{"src":760},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":762,"categories":763,"header":765,"text":752,"button":766,"image":770},"devops-modernization",[764,553],"product","Are you just managing tools or shipping innovation?",{"text":767,"config":768},"Get your DevOps maturity score",{"href":769,"dataGaName":757,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":774,"categories":775,"header":777,"text":752,"button":778,"image":782},"security-modernization",[776],"security","Are you trading speed for security?",{"text":779,"config":780},"Get your security maturity score",{"href":781,"dataGaName":757,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":783},{"src":784},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":786,"blurb":787,"button":788,"secondaryButton":793},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":789,"config":790},"Get your free trial",{"href":791,"dataGaName":44,"dataGaLocation":792},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":489,"config":794},{"href":48,"dataGaName":49,"dataGaLocation":792},1772652090806]