[{"data":1,"prerenderedAt":786},["ShallowReactive",2],{"/en-us/blog/monitor-application-performance-with-distributed-tracing":3,"navigation-en-us":40,"banner-en-us":440,"footer-en-us":450,"blog-post-authors-en-us-Sacha Guyon":691,"blog-related-posts-en-us-monitor-application-performance-with-distributed-tracing":705,"assessment-promotions-en-us":737,"next-steps-en-us":776},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":28,"isFeatured":12,"meta":29,"navigation":12,"path":30,"publishedDate":20,"seo":31,"stem":36,"tagSlugs":37,"__hash__":39},"blogPosts/en-us/blog/monitor-application-performance-with-distributed-tracing.yml","Monitor Application Performance With Distributed Tracing",[7],"sacha-guyon",null,"product",{"slug":11,"featured":12,"template":13},"monitor-application-performance-with-distributed-tracing",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Monitor application performance with Distributed Tracing","Learn how Distributed Tracing helps troubleshoot application performance issues by providing end-to-end visibility and seamless collaboration across your organization.",[18],"Sacha Guyon","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098000/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%288%29_5x6kH5vwjz8cwKgSBh1w11_1750098000511.png","2024-06-13","Downtime due to application defects or performance issues can have devastating financial consequences for businesses. An hour of downtime is estimated to cost firms $301,000 or more, according to [Information Technology Intelligence Consulting's 2022 Global Server Hardware and Server OS Reliability Survey](https://itic-corp.com/server-and-application-by-the-numbers-understanding-the-nines/). These issues often originate from human-introduced changes, such as code or configuration changes.\n\nResolving such incidents requires development and operations teams to collaborate closely, investigating the various components of the system to find the root cause change, and promptly restore the system back to normal operation. However, these teams commonly use separate tools to build, manage, and monitor their application services and infrastructure. This approach leads to siloed data, fragmented communication, and inefficient context switching, increasing the time spent to detect and resolve incidents.\n\nGitLab aims to address this challenge by combining software delivery and monitoring functionalities within the same platform. Last year, we released [Error Tracking](https://docs.gitlab.com/ee/operations/error_tracking.html) as a general availability feature in [GitLab 16.0](https://about.gitlab.com/releases/2023/05/22/gitlab-16-0-released/#error-tracking-is-now-generally-available). Now, we're excited to announce the [Beta release of Distributed Tracing](https://docs.gitlab.com/ee/operations/tracing), the next step toward a comprehensive observability offering seamlessly integrated into the GitLab DevSecOps platform.\n\n## A new era of efficiency: GitLab Observability\n\nGitLab Observability empowers development and operations teams to visualize and analyze errors, traces, logs, and metrics from their applications and infrastructure. By integrating application performance monitoring into existing software delivery workflows, context switching is minimized and productivity is increased, keeping teams focused and collaborative on a unified platform.\n\nAdditionally, GitLab Observability bridges the gap between development and operations by providing insights into application performance in production. This enhances transparency, information sharing, and communication between teams. Consequently, they can detect and resolve bugs and performance issues arising from new code or configuration changes sooner and more effectively, preventing those issues from escalating into major incidents that could negatively impact the business.\n\n## What is Distributed Tracing?\n\nWith Distributed Tracing, engineers can identify the source of application performance issues. A trace represents a single user request that moves through different services and systems. Engineers are able to analyze the timing of each operation and any errors as they occur.\n\nEach trace is composed of one or more spans, which represent individual operations or units of work. Spans contain metadata like the name, timestamps, status, and relevant tags or logs. By examining the relationships between spans, developers can understand the request flow, identify performance bottlenecks, and pinpoint issues.\n\nDistributed Tracing is especially valuable for [microservices architecture](https://about.gitlab.com/topics/microservices/), where a single request may involve numerous service calls across a complex system. Tracing provides visibility into this interaction, empowering teams to quickly diagnose and resolve problems.\n\n![tracing example](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750098009139.png)\n\nFor example, this trace illustrates a how a user request flows through difference services to fetch product recommendations on a e-commerce website:\n\n- `User Action`: This indicates the user's initial action, such as clicking a button to request product recommendations on a product page.\n-  `Web front-end`: The web front-end sends a request to the recommendation service to retrieve product recommendations.\n- `Recommendation service`: The request from the web front-end is handled by the recommendation service, which processes the request to generate a list of recommended products.\n- `Catalog service`: The recommendation service calls the catalog service to fetch details of the recommended products. An alert icon suggests an issue or delay at this stage, such as a slow response or error in fetching product details.\n- `Database`: The catalog service queries the database to retrieve the actual product details. This span shows the SQL query in the database.\n\nBy visualizing this end-to-end trace, developers can identify performance issues – here, an error in the Catalog service – and quickly diagnose and resolve issues across the distributed system.\n\n![End-to-end trace](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750098009140.png)\n\n## How Distributed Tracing works\n\nHere is a breakdown of how Distributed Tracing works.\n\n### Collect data from any application with OpenTelemetry\n\nTraces and spans can be collected using [OpenTelemetry](https://opentelemetry.io/docs/what-is-opentelemetry/), an open-source observability framework that supports a wide array of SDKs and libraries across [major programming languages and frameworks](https://opentelemetry.io/docs/languages/). This framework offers a vendor-neutral approach for collecting and exporting telemetry data, enabling developers to avoid vendor lock-in and choose the tools that best fit their needs.\n\nThis means that if you are already using OpenTelemetry with another vendor, you can send data to us simply by adding our endpoint to your configuration file, making it very easy to try out our features!\n\n![Distributed tracing workflow diagram](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750098009141.png)\n\n### Ingest and retain data at scale with fast, real-time queries\n\nObservability requires the storage and querying of vast amounts of data while maintaining low latency for real-time analytics. To meet these needs, we developed a horizontally scalable, long-term storage solution using ClickHouse and Kubernetes, based on our [acquisition of Opstrace](https://about.gitlab.com/press/releases/2021-12-14-gitlab-acquires-opstrace-to-expand-its-devops-platform-with-open-source-observability-solution/). This [open-source platform](https://gitlab.com/gitlab-org/opstrace/opstrace) ensures rapid query performance and enterprise-grade scalability, all while minimizing costs.\n\n### Explore and analyze traces effortlessly\nAn advanced, native-level user interface is crucial for effective data exploration. We built such an interface from the ground up, starting with our Trace Explorer, which allows users to examine traces and understand their application's performance:\n- __Advanced filtering:__ Filter by services, operation names, status, and time range. Autocomplete helps simplify querying.\n- __Error highlighting:__ Easily identify error spans in search results.\n- __RED metrics:__ Visualize the Requests rate, Errors rate, and average Duration as a time-series chart for any search in real-time.\n- __Timeline view:__ Individual traces are displayed as a waterfall diagram, providing a complete view of a request distributed across different services and operations.\n- __Historical data:__ Users can query traces up to 30 days in the past.\n\n![Distributed Tracing - image 5](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750098009141.png)\n\n## How we use Distributed Tracing at GitLab\n[Dogfooding](https://handbook.gitlab.com/handbook/values/#dogfooding) is a core value and practice at GitLab. We've been already using early versions of Distributed Tracing for our engineering and operations needs. Here are a couple example use cases from our teams:\n\n### 1. Debug errors and performance Issues in GitLab Agent for Kubernetes\n\nThe [Environments group](https://handbook.gitlab.com/handbook/engineering/development/ops/deploy/environments/) has been using Distributed Tracing to troubleshoot and resolve issues with the [GitLab Agent for Kubernetes](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent), such as timeouts or high latency issues. The Trace List and Trace Timeline views offer valuable insights for the team to address these concerns efficiently. These traces are shared and discussed in the [related GitLab issues](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/issues/386#note_1576431796), where the team collaborates on resolution.\n\n\u003Ccenter>\u003Ci>\"The Distributed Tracing feature has been invaluable in pinpointing where latency issues are occurring, allowing us to focus on the root cause and resolve it faster.\" - Mikhail, GitLab Engineer\u003C/i>\u003C/center>\u003Cp>\n\n### 2. Optimize GitLab’s build pipeline duration by identifying performance bottlenecks\n\nSlow deployments of GitLab source code can significantly impact the productivity of the whole company, as well as our compute spending. Our main repository runs [over 100,000 pipelines every month](https://gitlab.com/gitlab-org/gitlab/-/pipelines/charts). If the time it takes for these pipelines to run changes by just one minute, it can add or remove more than 2,000 hours of work time. That's 87 extra days!\n\nTo optimize pipeline execution time, GitLab's [platform engineering teams](https://handbook.gitlab.com/handbook/engineering/infrastructure/) utilize a [custom-built tool](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace) that converts GitLab deployment pipelines into traces.\n\nThe Trace Timeline view allows them to visualize the detailed execution timeline of complex pipelines and pinpoint which jobs are part of the critical path and slowing down the entire process. By identifying these bottlenecks, they can optimize job execution – for example, making the job fail faster, or running more jobs in parallel – to improve overall pipeline efficiency.\n\n![Distributed Tracing - image 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750098009143.gif)\n\n[The script is freely available](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace), so you can adapt it for your own pipelines.\n\n\u003Ccenter>\u003Ci>\"Using Distributed Tracing for our deployment pipelines has been a game-changer. It's helped us quickly identify and eliminate bottlenecks, significantly reducing our deployment times.\"- Reuben, GitLab Engineer\u003C/i>\u003C/center>\u003Cp>\n\n## What's coming next?\n\nThis release is just the start: In the next few months, we'll continue to expand our observability and monitoring features with the upcoming Metrics and Logging releases. Check out [our Observability direction page](https://about.gitlab.com/direction/analytics/platform-insights/) for more info, and keep an eye out for updates!\n\n## Join the private Beta\n\nInterested in being part of this exciting journey? [Sign up to enroll in the private Beta](https://docs.gitlab.com/operations/observability/) and try out our features. Your contribution can help shape the future of observability within GitLab, ensuring our tools are perfectly aligned with your needs and challenges.\n\n> Help shape the future of GitLab Observability. 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GitLab metrics and registry features help reduce CI/CD bottlenecks","See how CI/CD Job Performance Metrics and Container Virtual Registry, currently in beta, help platform teams quickly spot slow jobs and simplify multi-registry container pulls.",[711],"Talia Armato-Helle","https://res.cloudinary.com/about-gitlab-com/image/upload/v1771438388/t6sts5qw4z8561gtlxiq.png","Platform and DevOps engineers spend too much time piecing together visibility across fragmented tools and managing infrastructure that should just work.\n\nTwo new GitLab features currently in beta tackle this from different angles but share the same goal: giving practitioners direct control over the CI/CD infrastructure they depend on, without adding another third-party tool. One surfaces job-level performance data right where you monitor pipelines. The other simplifies how you pull container images from multiple registries with built-in caching.\n\nBoth features are open for feedback now. Your input will help shape what ships next.\n\n## CI/CD Job Performance Metrics\n\n* **Available tiers:** GitLab Premium, GitLab Ultimate\n* **Status:** Limited-availability beta on GitLab.com; available on GitLab Self-Managed and GitLab Dedicated when ClickHouse is configured\n\nToday, there’s no simple way to see when a particular job’s duration starts increasing or which jobs are quietly dragging down your pipeline runtimes. Most teams either build custom dashboards or manually dig through logs to answer basic questions like:\n\n* Which jobs are slowest?  \n* Where are failure rates climbing?  \n* Which stage is the real bottleneck?\n\nCI/CD Job Performance Metrics changes that by adding a new job-focused panel to the CI/CD analytics page at the project level.\n\nFor each job in your pipelines, you can see:\n\n* Typical (P50, median) and worst‑case (P95) job duration, so you can quickly view normal versus slowest runs  \n* Failure rate, so you can spot fragile or flaky jobs  \n* Job name and stage, covering the last 30 days by default\n\nThe table is sortable, searchable by job name, and paginated, so platform teams get a single view to answer questions that previously required separate tools or custom reporting.\n\n**Try it now**\n\n* Navigate to your project and select **Analyze \\> CI/CD analytics**.  \n* Look for the CI/CD job performance metrics panel and sort by duration or failure rate to find your slowest or least reliable jobs.\n\n**Documentation**\n\n* [CI/CD analytics – CI/CD job performance metrics](https://docs.gitlab.com/user/analytics/ci_cd_analytics/#cicd-job-performance-metrics)\n\n**What’s coming next**\n\nWe’re working on stage-level grouping, so you can view aggregated metrics across your build, test, and deploy stages, and quickly understand where to focus optimization work.\n\n**Share your feedback:**\n\n* [CI/CD job performance metrics epic](https://gitlab.com/groups/gitlab-org/-/work_items/18548)\n\n## Container Virtual Registry\n\n**Tier:** GitLab Premium, GitLab Ultimate\n**Status:** Beta, API-ready in 18.9\n\nMost organizations pulling container images into CI/CD pipelines rely on multiple registries: Docker Hub, Harbor, Quay, and internal registries, to name a few. Managing authentication, availability, and caching across all of them is operational overhead that slows pipelines down and introduces fragility.\n\nThe Container Virtual Registry lets you create a single GitLab endpoint that pulls from multiple upstream container sources with built-in caching.\n\nInstead of configuring credentials and availability for each registry individually in your pipeline configuration, you can:\n\n* Point your pipelines at one GitLab virtual registry endpoint  \n* Configure multiple upstream registries (Docker Hub, Harbor, Quay, and others using long-lived token authentication)  \n* Let GitLab resolve image pulls automatically, with pull-through caching to reduce bandwidth costs and improve reliability\n\nFor teams evaluating GitLab as a container registry replacement, this closes a critical capability gap. For teams already managing multi-registry container workflows, it centralizes image management into GitLab and cuts down on repeated pulls.\n\n**What the beta supports today**\n\n* Upstream registries using long-lived token authentication: Docker Hub, Harbor, Quay, and other compatible registries  \n* Pull-through caching so commonly used images are served from GitLab after the first pull  \n* API-first configuration, with UI management in progress++\n\nCloud provider registries requiring IAM authentication (such as Amazon Elastic Container Registry, Google Artifact Registry, and Azure Container Registry) are being considered for future iterations.\n\n**Test it today**\n\n* The Container Virtual Registry is API-ready in 18.9.  \n* SaaS (GitLab.com): Request access through your CSM or by commenting on the feedback issue below to have the feature flag enabled for your group.  \n* Self-managed: Enable the feature flag and configure the virtual registry using the API.\n\n**Documentation**\n\n* [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)  \n* [Pull container images from the virtual registry](https://docs.gitlab.com/user/packages/virtual_registry/container/#pull-container-images-from-the-virtual-registry)\n\n\n Watch this walkthrough of the Container Virtual Registry Beta:\n   \n\n  \u003Ciframe src=\"https://player.vimeo.com/video/1167512082?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"20260223_Container Virtual Registry Beta_V1\">\u003C/iframe>\u003C\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n  \u003Cbr>\u003C/br>\n\n\n\n**Share your feedback:**\n\n* [Container virtual registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/589630)\n\n## Help us build what matters\n\nEveryone in the GitLab community is a contributor. We built these betas based on community requests.\n\n* **CI/CD Job Performance Metrics** came from teams who had no easy way to see when build times started trending in the wrong direction, or which jobs were hurting pipeline reliability.  \n* **Container Virtual Registry** came from enterprise customers managing multiple registries and looking to reduce tool sprawl and bandwidth costs while evaluating GitLab as a central registry.\n\nYour feedback shapes what we create next. Try one or both of these betas, and share your experience in the linked feedback issues.\n\nThis is the first in a series of Core DevOps betas we plan to highlight. 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