[{"data":1,"prerenderedAt":820},["ShallowReactive",2],{"/en-us/blog/kubernetes-the-container-orchestration-solution":3,"navigation-en-us":39,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-GitLab Team":702,"blog-related-posts-en-us-kubernetes-the-container-orchestration-solution":717,"blog-promotions-en-us":756,"next-steps-en-us":810},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":25,"externalUrl":26,"featured":14,"heroImage":19,"isFeatured":14,"meta":27,"navigation":28,"path":29,"publishedDate":20,"rawbody":30,"seo":31,"slug":13,"stem":35,"tagSlugs":36,"tags":37,"template":15,"updatedDate":24,"__hash__":38},"blogPosts/en-us/blog/kubernetes-the-container-orchestration-solution.yml","Kubernetes: Get to know the container orchestration solution",[7],"gitlab-team",[9],"GitLab Team","Kubernetes automates the tasks of deploying and managing containerized applications on a large scale. Over time, Kubernetes has become an essential tool for developing applications in many areas, such as [microservices](https://about.gitlab.com/topics/microservices/), web applications, and databases. Its performance and scalability make it a recognized standard in container management today.\n\nDiscover everything you need to know about Kubernetes in this article.\n\n## What is Kubernetes?\n\nKubernetes is an open-source system for efficiently orchestrating the containers of a software application. Containerization is a widely acclaimed approach to developing applications, especially in the areas of digital transformation and the cloud.\n\nIf you're not familiar with the concept of containers, note that it is an application development method that groups the components of an application into standardized units – or containers – that are independent of the devices and operating systems they are located on. By isolating applications from their environment, this technology facilitates their deployment and portability, as well as reduces interoperability conflicts.\n\nThis is where we use the Kubernetes software. Certainly, containers allow applications to be divided into smaller and autonomous modules, thus facilitating their deployment. However, for containers to interact within an application, a management system encompassing these modules is necessary. That's exactly what Kubernetes does. Kubernetes provides a platform to control where and how containers run, so you can orchestrate and schedule their execution to manage containerized applications on a large scale.\n\n> Browse [GitLab articles about Kubernetes](https://about.gitlab.com/blog/tags/kubernetes/).\n\n## How does a Kubernetes architecture work?\n\nTo understand how a Kubernetes architecture works, it is essential to become familiar with certain concepts, starting with that of the cluster, which is the most extensive within the architecture. A Kubernetes cluster is defined as the set of virtual or physical machines on which a containerized application is installed.\n\n![Components of Kubernetes](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749673941/Blog/Content%20Images/components-of-kubernetes.png)\n\nSource: [Kubernetes](https://kubernetes.io/docs/concepts/overview/components/).\n\nThis cluster comprises different elements:\n- Node: This is a work unit in a Kubernetes cluster. It is a virtual or physical machine that performs tasks on behalf of the application.\n- Pod: A pod is the smallest deployable unit in Kubernetes. It is a group of containers working together on the same node. Containers inside a pod share the same network and can communicate with each other via localhost.\n- Service: A Kubernetes service exposes a pod to the network or other pods. It offers a stable and well-defined access point to applications hosted by pods.\n- Volume: A folder abstraction that solves problems of sharing and retrieving files within a container.\n- Namespace: A namespace allows you to group and isolate resources to form a virtual cluster.\n\nThe Kubernetes architecture is based on two main types of nodes: the master node and the worker nodes. The master node is responsible for the overall management of the Kubernetes cluster and communication with the worker nodes. Among its key components, the API is the central point of contact for all communications between users and the cluster. The [etcd](https://kubernetes.io/docs/concepts/overview/components/#etcd) is the key-value database where the configurations, the system state and the object metadata, are stored. The controller manager coordinates background operations such as pod replication, and the scheduler places pods on nodes based on available resources.\n\nWorker nodes, on the other hand, are the machines that run and manage the applications contained in the pods. Within them, the [kubelet](https://kubernetes.io/docs/concepts/overview/components/#kubelet) is the agent that runs on each node and communicates with the master to receive the commands and transmit the status of the pods. The network proxy or [kube-proxy](https://kubernetes.io/docs/concepts/overview/components/) maintains network rules on nodes to allow access to services from outside the Kubernetes cluster. Finally, the container runtime is the software responsible for the execution and management of containers within the pods.\n\n### Docker's role\n\nAmong all the components of a K8s cluster, the choice of runtime within the worker nodes is important. Different software is available for this, such as rkt or CRI-O, but Docker is the most commonly used tool.\n\n### What is the difference between Docker and Kubernetes?\n\nDocker is an open-source solution that is specifically used at the container level. It allows containers to be packaged in a standardized and lightweight format, which increases their portability in different environments. It is therefore a complementary tool to K8s that facilitates the management of containers themselves, while Kubernetes simplifies their integration and communication within the application.\n\n## What are the benefits of Kubernetes?\n\nLaunched by Google in 2014, the first stable version of Kubernetes appeared in July 2015. Since then, the popularity of this software has not wavered, making K8s a benchmark in the field of container orchestration, especially for microservice-oriented architectures. So then, why use Kubernetes? This success is primarily due to the excellent performance of this software in container orchestration.\n\nThe benefits of Kubernetes are plenty, as follows:\n- Automation: Kubernetes facilitates the automation of tasks related to the deployment, scaling, and updating of containerized applications.\n- Flexibility: The software adapts to different container technologies, as well as various hardware architectures and operating systems.\n- Scalability: K8s facilitates the deployment and management of thousands of containers, regardless of their status: running, paused, or stopped.\n- Migration: It is possible to easily migrate applications to Kubernetes without having to change the source code.\n- Multi-cluster support: Kubernetes centrally manages multiple container clusters distributed across different infrastructures.\n- Update management: The software supports rolling update deployments to update applications without service disruption.\n\n## A robust and scalable ecosystem\n\nKubernetes stands out for its ability to manage containers efficiently and securely, while maintaining its independence from cloud infrastructure providers. Its modular architecture adapts to the specific needs of each company and supports a very wide range of applications and services (web services, data processing, mobile applications, etc.).\n\nIn the race for digital transformation, Kubernetes also wins over people, thanks to its rich and scalable ecosystem within the open-source community. Managed by the Cloud Native Computing Foundation ([CNCF](https://www.cncf.io/)), K8s is supported by thousands of developers around the world. They contribute to the development of the project and the continuous improvement of its features.\n\n## What are the limitations of Kubernetes?\n\nThe benefits of Kubernetes make it a safe choice for many development teams in the cloud-native application space. Nevertheless, it is worth pointing out some of its limitations. Kubernetes requires a solid technical background and training in new development concepts and methods. The software can be complex to configure at the beginning of a project. However, configuration is crucial, especially to secure the platform. Having an experienced development team for K8s projects is therefore a significant asset.\n\nAnother challenge is the implementation and maintenance of a K8s architecture, which also requires time and resources, especially to update its various components and software. This raises the question of possible oversizing. In the case of a small application, or a project with no particular challenge in terms of scalability, a more basic architecture may suffice while being more economical.\n\n## Using Kubernetes within your teams\n\nTens of thousands of companies have adopted a Kubernetes architecture to carry out their digital transition. K8s is used by companies of all sizes, from startups to multinationals.\n\nThere are many examples of successful integrations, such as for Haven Technologies. Haven Technologies has migrated its SaaS services to K8s and relies in particular on a Kubernetes strategy with the GitLab DevSecOps platform to help its teams improve efficiency, security, and speed of software development. Check out [our client story](https://about.gitlab.com/customers/haven-technologies/) to learn more!\n\n## Kubernetes, Git, and GitLab\n\nKubernetes, Git, and GitLab are essential elements of the DevOps landscape. Kubernetes offers great flexibility to deploy and manage the various components of an application, while GitLab, which is built around Git and its native version control system, allows rigorous and accurate tracking of source code and changes, while providing a comprehensive suite of tools to manage the entire software development lifecycle.\n\nThis combination, together with a [GitOps approach](https://about.gitlab.com/topics/gitops/), which aims to automate the provisioning of modern cloud infrastructures, creates an agile environment for application development and deployment, thus making it possible to provide powerful, flexible, and scalable software. For more details, discover all [GitLab solutions to launch an application with Kubernetes](https://about.gitlab.com/solutions/kubernetes/).\n\n## Kubernetes FAQ\n### What are the competing solutions to K8s?\n\nThere are several alternatives to Kubernetes, including Docker Swarm, and Marathon. However, Kubernetes is considered the most mature and popular solution on the market. Its broad user base, abundant documentation, and active community support make Kubernetes an excellent choice for those looking to adopt a container orchestration system.\n\n### What is a Kubernetes cluster?\n\nA Kubernetes cluster is composed of a master node and several worker nodes. The master node is responsible for coordinating the tasks in the cluster, while the worker nodes execute these orchestration tasks and host the containers. K8s clusters are highly scalable – nodes can be added or removed to adapt cluster resources to the needs of the application.\n\n### How to get started with Kubernetes?\n\nTo begin, it is necessary to install the Kubernetes software on a compatible environment (Linux, macOS, or Windows). Kubernetes can be installed in a traditional hosting environment, but also in a cloud environment (Google Kubernetes Engine or Amazon EKS, for example). Users can download and install Kubernetes directly from their official site, and then proceed with the initial configuration necessary to connect the master and worker nodes. Once this step is completed, users are ready to deploy a first application using Kubernetes.\n\n### Why choose Kubernetes?\n\nKubernetes offers great flexibility and total portability between different cloud platforms or on-site infrastructures. By automating orchestration tasks, K8s helps to optimize resources, reduce operating costs, and free up time for developers and system administrators. Finally, the Kubernetes ecosystem is vast and is continuously developed by a large open-source community, enabling rapid innovation.\n\n## Learn more\n\n- [How to stream logs through the GitLab Dashboard for Kubernetes](https://about.gitlab.com/blog/how-to-stream-logs-through-the-gitlab-dashboard-for-kubernetes/)\n- [Kubernetes overview: Operate cluster data on the frontend](https://about.gitlab.com/blog/kubernetes-overview-operate-cluster-data-on-the-frontend/)\n- [Simplify your cloud account management for Kubernetes access](https://about.gitlab.com/blog/simplify-your-cloud-account-management-for-kubernetes-access/)\n","open-source",{"slug":13,"featured":14,"template":15},"kubernetes-the-container-orchestration-solution",false,"BlogPost",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21,"updatedDate":24},"Kubernetes, also known as K8s, is a must-have solution for deploying and maintaining applications, especially in the cloud. Learn the basics of Kubernetes with this introductory guide.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749660215/Blog/Hero%20Images/kubernetes-container-orchestration-solution.jpg","2024-07-25",[22,23],"kubernetes","open source","2024-08-22","yml",null,{},true,"/en-us/blog/kubernetes-the-container-orchestration-solution","seo:\n  title: 'Kubernetes: Get to know the container orchestration solution'\n  description: >-\n    Kubernetes, also known as K8s, is a must-have solution for deploying and\n    maintaining applications, especially in the cloud. Learn the basics of\n    Kubernetes with this introductory guide.\n  ogTitle: 'Kubernetes: Get to know the container orchestration solution'\n  ogDescription: >-\n    Kubernetes, also known as K8s, is a must-have solution for deploying and\n    maintaining applications, especially in the cloud. Learn the basics of\n    Kubernetes with this introductory guide.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749660215/Blog/Hero%20Images/kubernetes-container-orchestration-solution.jpg\n  ogUrl: >-\n    https://about.gitlab.com/blog/kubernetes-the-container-orchestration-solution\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/kubernetes-the-container-orchestration-solution\ncontent:\n  title: 'Kubernetes: Get to know the container orchestration solution'\n  description: >-\n    Kubernetes, also known as K8s, is a must-have solution for deploying and\n    maintaining applications, especially in the cloud. Learn the basics of\n    Kubernetes with this introductory guide.\n  authors:\n    - GitLab Team\n  heroImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749660215/Blog/Hero%20Images/kubernetes-container-orchestration-solution.jpg\n  date: '2024-07-25'\n  body: >\n    Kubernetes automates the tasks of deploying and managing containerized\n    applications on a large scale. Over time, Kubernetes has become an essential\n    tool for developing applications in many areas, such as\n    [microservices](https://about.gitlab.com/topics/microservices/), web\n    applications, and databases. Its performance and scalability make it a\n    recognized standard in container management today.\n\n\n    Discover everything you need to know about Kubernetes in this article.\n\n\n    ## What is Kubernetes?\n\n\n    Kubernetes is an open-source system for efficiently orchestrating the\n    containers of a software application. Containerization is a widely acclaimed\n    approach to developing applications, especially in the areas of digital\n    transformation and the cloud.\n\n\n    If you're not familiar with the concept of containers, note that it is an\n    application development method that groups the components of an application\n    into standardized units – or containers – that are independent of the\n    devices and operating systems they are located on. By isolating applications\n    from their environment, this technology facilitates their deployment and\n    portability, as well as reduces interoperability conflicts.\n\n\n    This is where we use the Kubernetes software. Certainly, containers allow\n    applications to be divided into smaller and autonomous modules, thus\n    facilitating their deployment. However, for containers to interact within an\n    application, a management system encompassing these modules is necessary.\n    That's exactly what Kubernetes does. Kubernetes provides a platform to\n    control where and how containers run, so you can orchestrate and schedule\n    their execution to manage containerized applications on a large scale.\n\n\n    > Browse [GitLab articles about\n    Kubernetes](https://about.gitlab.com/blog/tags/kubernetes/).\n\n\n    ## How does a Kubernetes architecture work?\n\n\n    To understand how a Kubernetes architecture works, it is essential to become\n    familiar with certain concepts, starting with that of the cluster, which is\n    the most extensive within the architecture. A Kubernetes cluster is defined\n    as the set of virtual or physical machines on which a containerized\n    application is installed.\n\n\n    ![Components of\n    Kubernetes](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749673941/Blog/Content%20Images/components-of-kubernetes.png)\n\n\n    Source:\n    [Kubernetes](https://kubernetes.io/docs/concepts/overview/components/).\n\n\n    This cluster comprises different elements:\n\n    - Node: This is a work unit in a Kubernetes cluster. It is a virtual or\n    physical machine that performs tasks on behalf of the application.\n\n    - Pod: A pod is the smallest deployable unit in Kubernetes. It is a group of\n    containers working together on the same node. Containers inside a pod share\n    the same network and can communicate with each other via localhost.\n\n    - Service: A Kubernetes service exposes a pod to the network or other pods.\n    It offers a stable and well-defined access point to applications hosted by\n    pods.\n\n    - Volume: A folder abstraction that solves problems of sharing and\n    retrieving files within a container.\n\n    - Namespace: A namespace allows you to group and isolate resources to form a\n    virtual cluster.\n\n\n    The Kubernetes architecture is based on two main types of nodes: the master\n    node and the worker nodes. The master node is responsible for the overall\n    management of the Kubernetes cluster and communication with the worker\n    nodes. Among its key components, the API is the central point of contact for\n    all communications between users and the cluster. The\n    [etcd](https://kubernetes.io/docs/concepts/overview/components/#etcd) is the\n    key-value database where the configurations, the system state and the object\n    metadata, are stored. The controller manager coordinates background\n    operations such as pod replication, and the scheduler places pods on nodes\n    based on available resources.\n\n\n    Worker nodes, on the other hand, are the machines that run and manage the\n    applications contained in the pods. Within them, the\n    [kubelet](https://kubernetes.io/docs/concepts/overview/components/#kubelet)\n    is the agent that runs on each node and communicates with the master to\n    receive the commands and transmit the status of the pods. The network proxy\n    or [kube-proxy](https://kubernetes.io/docs/concepts/overview/components/)\n    maintains network rules on nodes to allow access to services from outside\n    the Kubernetes cluster. Finally, the container runtime is the software\n    responsible for the execution and management of containers within the pods.\n\n\n    ### Docker's role\n\n\n    Among all the components of a K8s cluster, the choice of runtime within the\n    worker nodes is important. Different software is available for this, such as\n    rkt or CRI-O, but Docker is the most commonly used tool.\n\n\n    ### What is the difference between Docker and Kubernetes?\n\n\n    Docker is an open-source solution that is specifically used at the container\n    level. It allows containers to be packaged in a standardized and lightweight\n    format, which increases their portability in different environments. It is\n    therefore a complementary tool to K8s that facilitates the management of\n    containers themselves, while Kubernetes simplifies their integration and\n    communication within the application.\n\n\n    ## What are the benefits of Kubernetes?\n\n\n    Launched by Google in 2014, the first stable version of Kubernetes appeared\n    in July 2015. Since then, the popularity of this software has not wavered,\n    making K8s a benchmark in the field of container orchestration, especially\n    for microservice-oriented architectures. So then, why use Kubernetes? This\n    success is primarily due to the excellent performance of this software in\n    container orchestration.\n\n\n    The benefits of Kubernetes are plenty, as follows:\n\n    - Automation: Kubernetes facilitates the automation of tasks related to the\n    deployment, scaling, and updating of containerized applications.\n\n    - Flexibility: The software adapts to different container technologies, as\n    well as various hardware architectures and operating systems.\n\n    - Scalability: K8s facilitates the deployment and management of thousands of\n    containers, regardless of their status: running, paused, or stopped.\n\n    - Migration: It is possible to easily migrate applications to Kubernetes\n    without having to change the source code.\n\n    - Multi-cluster support: Kubernetes centrally manages multiple container\n    clusters distributed across different infrastructures.\n\n    - Update management: The software supports rolling update deployments to\n    update applications without service disruption.\n\n\n    ## A robust and scalable ecosystem\n\n\n    Kubernetes stands out for its ability to manage containers efficiently and\n    securely, while maintaining its independence from cloud infrastructure\n    providers. Its modular architecture adapts to the specific needs of each\n    company and supports a very wide range of applications and services (web\n    services, data processing, mobile applications, etc.).\n\n\n    In the race for digital transformation, Kubernetes also wins over people,\n    thanks to its rich and scalable ecosystem within the open-source community.\n    Managed by the Cloud Native Computing Foundation\n    ([CNCF](https://www.cncf.io/)), K8s is supported by thousands of developers\n    around the world. They contribute to the development of the project and the\n    continuous improvement of its features.\n\n\n    ## What are the limitations of Kubernetes?\n\n\n    The benefits of Kubernetes make it a safe choice for many development teams\n    in the cloud-native application space. Nevertheless, it is worth pointing\n    out some of its limitations. Kubernetes requires a solid technical\n    background and training in new development concepts and methods. The\n    software can be complex to configure at the beginning of a project. However,\n    configuration is crucial, especially to secure the platform. Having an\n    experienced development team for K8s projects is therefore a significant\n    asset.\n\n\n    Another challenge is the implementation and maintenance of a K8s\n    architecture, which also requires time and resources, especially to update\n    its various components and software. This raises the question of possible\n    oversizing. In the case of a small application, or a project with no\n    particular challenge in terms of scalability, a more basic architecture may\n    suffice while being more economical.\n\n\n    ## Using Kubernetes within your teams\n\n\n    Tens of thousands of companies have adopted a Kubernetes architecture to\n    carry out their digital transition. K8s is used by companies of all sizes,\n    from startups to multinationals.\n\n\n    There are many examples of successful integrations, such as for Haven\n    Technologies. Haven Technologies has migrated its SaaS services to K8s and\n    relies in particular on a Kubernetes strategy with the GitLab DevSecOps\n    platform to help its teams improve efficiency, security, and speed of\n    software development. Check out [our client\n    story](https://about.gitlab.com/customers/haven-technologies/) to learn\n    more!\n\n\n    ## Kubernetes, Git, and GitLab\n\n\n    Kubernetes, Git, and GitLab are essential elements of the DevOps landscape.\n    Kubernetes offers great flexibility to deploy and manage the various\n    components of an application, while GitLab, which is built around Git and\n    its native version control system, allows rigorous and accurate tracking of\n    source code and changes, while providing a comprehensive suite of tools to\n    manage the entire software development lifecycle.\n\n\n    This combination, together with a [GitOps\n    approach](https://about.gitlab.com/topics/gitops/), which aims to automate\n    the provisioning of modern cloud infrastructures, creates an agile\n    environment for application development and deployment, thus making it\n    possible to provide powerful, flexible, and scalable software. For more\n    details, discover all [GitLab solutions to launch an application with\n    Kubernetes](https://about.gitlab.com/solutions/kubernetes/).\n\n\n    ## Kubernetes FAQ\n\n    ### What are the competing solutions to K8s?\n\n\n    There are several alternatives to Kubernetes, including Docker Swarm, and\n    Marathon. However, Kubernetes is considered the most mature and popular\n    solution on the market. Its broad user base, abundant documentation, and\n    active community support make Kubernetes an excellent choice for those\n    looking to adopt a container orchestration system.\n\n\n    ### What is a Kubernetes cluster?\n\n\n    A Kubernetes cluster is composed of a master node and several worker nodes.\n    The master node is responsible for coordinating the tasks in the cluster,\n    while the worker nodes execute these orchestration tasks and host the\n    containers. K8s clusters are highly scalable – nodes can be added or removed\n    to adapt cluster resources to the needs of the application.\n\n\n    ### How to get started with Kubernetes?\n\n\n    To begin, it is necessary to install the Kubernetes software on a compatible\n    environment (Linux, macOS, or Windows). Kubernetes can be installed in a\n    traditional hosting environment, but also in a cloud environment (Google\n    Kubernetes Engine or Amazon EKS, for example). Users can download and\n    install Kubernetes directly from their official site, and then proceed with\n    the initial configuration necessary to connect the master and worker nodes.\n    Once this step is completed, users are ready to deploy a first application\n    using Kubernetes.\n\n\n    ### Why choose Kubernetes?\n\n\n    Kubernetes offers great flexibility and total portability between different\n    cloud platforms or on-site infrastructures. By automating orchestration\n    tasks, K8s helps to optimize resources, reduce operating costs, and free up\n    time for developers and system administrators. Finally, the Kubernetes\n    ecosystem is vast and is continuously developed by a large open-source\n    community, enabling rapid innovation.\n\n\n    ## Learn more\n\n\n    - [How to stream logs through the GitLab Dashboard for\n    Kubernetes](https://about.gitlab.com/blog/how-to-stream-logs-through-the-gitlab-dashboard-for-kubernetes/)\n\n    - [Kubernetes overview: Operate cluster data on the\n    frontend](https://about.gitlab.com/blog/kubernetes-overview-operate-cluster-data-on-the-frontend/)\n\n    - [Simplify your cloud account management for Kubernetes\n    access](https://about.gitlab.com/blog/simplify-your-cloud-account-management-for-kubernetes-access/)\n  category: open-source\n  tags:\n    - kubernetes\n    - open source\n  updatedDate: '2024-08-22'\nconfig:\n  slug: kubernetes-the-container-orchestration-solution\n  featured: false\n  template: 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Find out who won and what they created.",[723],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776457632/llddiylsgwuze0u1rjks.png","2026-04-22","AI writes code. That is expected now. But planning, security, compliance, and deployments? Those gaps remain. I have run contributor programs for years. I have never seen a community respond to technology like this.\n\nThat is why we opened [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) and invited developers worldwide to build AI agents that help teams ship secure software faster. Not chatbots that answer questions, but agents that jump into workflows, respond to events, and act on your behalf. The GitLab AI Hackathon ran from February 9 to March 25, 2026, on Devpost, the hackathon platform. Google Cloud and Anthropic joined as co-sponsors.\n\nWhen my team planned this hackathon with Google Cloud and Anthropic, I asked the judges to score four things: technical work, design, potential impact, and idea quality. We hoped for strong turnout. What we got surprised all of us. Nineteen judges spent 18 days reviewing every entry. Google Cloud and Anthropic provided judges, prizes, and cloud access. The community built hundreds of agents and flows because they wanted to solve these problems.\n\nNearly 7,000 developers showed up. They built 600+ agents and flows in weeks. The prizes across all categories totaled $65,000 from GitLab, Google Cloud, and Anthropic.\n\n\nIf you have ever watched a senior engineer leave and take half the team's knowledge with them, you know why the winning project hit so hard.\n\nRead on to find out what the community built.\n\n## Grand Prize: LORE\n\n[LORE](https://devpost.com/software/lore-living-organizational-record-engine), the Living Organizational Record Engine, uses eight agents with a router that sends each question to the right agent, logic to prevent circular loops in the knowledge graph, a visual dashboard, and carbon tracking. The command-line tool ships with 43 tests (yes, 43 tests in a hackathon project).\n\nLORE solves a real problem: the knowledge that lives in engineers' heads and walks out the door when they leave. In my experience, a hackathon project with 43 tests is rare. That many tests in a hackathon project tells you something about the team behind it.\n\nJudge April Guo (Anthropic) wrote: \"This feels like a product, not a hackathon project.\"\n\n\n### Google Cloud winners\n\n[Gitdefender](https://devpost.com/software/gitdefender) won the Google Cloud Grand Prize. It works inside code review workflows, finding and fixing security issues. It spots the bug, writes the fix, and opens the code review. No developer needs to step in.\n\n[Aegis](https://devpost.com/software/aegis-2m1oq0) won the Google Cloud Runner Up. It gives AI-powered explanations for every decision it makes, deployed to Google Cloud and ready for production use.\n\n### Anthropic winners\n\n[GraphDev](https://devpost.com/software/graphdev) won the Anthropic Grand Prize. It maps code links and shows how systems change over time. Judge Aboobacker MK (GitLab) noted it was \"in sync with our work on GitLab knowledge graph.\" Judge Ayush Billore (GitLab) wrote: \"Loved the demo and UX, super useful for understanding how the system evolved and what gets impacted by changes.\" You can see the full impact of a change before you make it.\n\n[DocSync](https://devpost.com/software/pipeheal) won the Anthropic Runner Up. It uses three agents: Detector, Writer, and Reviewer. If DocSync is confident in the fix, it opens a code review. If not, it creates an issue for a human to check.\n\n## Category winners\n\n### Most Technically Impressive\n\nDatabase migrations break things. [Time-Traveler](https://devpost.com/software/time-traveler-w3cxp0) creates a safe copy of your production setup, runs the migration against that copy, and reports the result. It runs five agents connected by a bridge, with real Google Cloud deployment, real PostgreSQL migrations, and real data.\n\n### Most Impactful\n\n[RedAgent](https://devpost.com/software/redagent) checks AI-generated security reports, closing the trust gap between AI findings and developer action. If your team uses AI for security scanning, you know this problem. I have seen teams dismiss AI findings because they could not verify them. RedAgent gives teams a way to check AI output before it reaches developers.\n\n### Easiest to Use\n\n[Launch Control](https://devpost.com/software/launch-control-bgp8az) delivers polished UX and solid infrastructure, and scored well on sustainability too.\n\n## The sustainability signal\n\nFive projects won prizes or bonuses for environmental impact. Software delivery has a carbon cost as CI/CD pipelines, but now LLMs also run compute at scale. We created the Green Agent category to challenge developers to measure and reduce that footprint. Stacy Cline and Kim Buncle from GitLab's sustainability team helped judge the Green Agent category. \n\n### Green Agent prize\n\n[GreenPipe](https://devpost.com/software/greenpipe) scans CI/CD pipelines for environmental impact and produces carbon footprint reports. Judges Kim Buncle and Rajesh Agadi (Google) both backed the project.\n\n### Sustainable Design bonus\n\nSustainable Design bonuses were awarded to the projects with exceptional sustainability practices in their design, from model optimization techniques to energy-efficient architecture choices.\n\n* [BugFlow](https://devpost.com/software/bugflow-ai-regression-detective-ci-optimizer) turned one bug report into 10 fixes in 20 minutes. \n* [DELTA Cyber Reasoning](https://devpost.com/software/delta-cyber-reasoning-system) is automated fuzz testing for security. \n* [CarbonLint](https://devpost.com/software/carbonlint) applied code analysis to energy use.\n* [TFGuardian](https://devpost.com/software/tfguardian) features a carbon footprint analyzer, among other agents.\n\nCongratulations on all the Sustainable Design bonus winners! \n\nJudge Jens-Joris Decorte (TechWolf) cited the result: Costs dropped from $556 to $18 per month, a 96% carbon cut (that is a $538 monthly saving with a sustainability label on it).\n\n## Honorable mentions and the long tail\n\nSix projects received honorable mentions:\n\n\n- [SecurityMonkey](https://devpost.com/software/securitymonkey) injects known vulnerabilities into a test branch and scores how well your security scanners catch them.\n- [stregent](https://devpost.com/software/stregent) monitors CI/CD pipelines and lets developers investigate and merge fixes from WhatsApp without opening a laptop.\n- [Compliance Sentinel](https://devpost.com/software/compliance-sentinel-autonomous-devsecops-governance) scores every merge request for compliance risk and blocks the merge if critical violations are detected.\n- [Carbon Tracker](https://devpost.com/software/carbon-tracker-ij25kf) calculates the carbon footprint of each CI/CD pipeline job and posts optimization tips on the merge request.\n- [RepoWarden](https://devpost.com/software/docuguard) is the first Living Specification Engine, an AI system that captures why code was written, not just what it does.\n- [MR Compliance Auditor](https://devpost.com/software/mr-compliance-auditor) collects evidence across merge requests, maps it to SOC 2 controls, and streams compliance scores to a live dashboard.\n\nMy favorite quote from the judging came from Luca Chun Lun Lit (Anthropic), who described stregent's mobile-first approach: \"Being able to essentially code from your phone is a next level in the engineering experience.\"\n\n> Explore the 600+ entries in the [project gallery](https://gitlab.devpost.com/project-gallery).\n\n## What comes next\n\nEvery agent in this hackathon worked within a single project. They still delivered impressive results. Some participants ran a local knowledge graph alongside their agents to surface code relationships and dependencies within the repo. LORE captures project history. Gitdefender finds vulnerabilities. Pairing agents with richer local context is already helping contributors build sharper tools. The next hackathon will build on what contributors are already doing with richer context. Sign up on [contributors.gitlab.com](https://contributors.gitlab.com/) to be the first to know when details drop.\n\n\n## Get started\n\nA special thanks to Lee Tickett (GitLab) and Mattias Michaux (GitLab) for orchestrating the orchestrators and innovators behind this hackathon!\n\nThank you to every developer who submitted. Nearly 7,000 of you showed what GitLab Duo Agent Platform can do when a community decides to build. I am proud of what you built here, and I cannot wait to see what you build next.\n\nBuild your own agent on [GitLab Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/). Browse community-built agents in the [AI Catalog](https://docs.gitlab.com/user/duo_agent_platform/ai_catalog/). You orchestrate. AI accelerates.\n",[728,266],"AI/ML",{"featured":14,"template":15,"slug":730},"gitlab-ai-hackathon-2026-meet-the-winners",{"content":732,"config":742},{"title":733,"description":734,"authors":735,"heroImage":737,"date":738,"category":11,"tags":739,"body":741},"What’s new in Git 2.54.0?","Learn about release contributions, including new repository maintenance, a new command to edit commit history, a replacement for git-sizer(1), and more.",[736],"Patrick Steinhardt","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776711651/sj7xxyyuimlarswbyft5.png","2026-04-20",[23,740,266],"git","The Git project recently released [Git 2.54.0](https://lore.kernel.org/git/xmqqa4uxsjrs.fsf@gitster.g/T/#u). Let's look at a few notable highlights from this release, which includes contributions from the Git team at GitLab.\n\n## Pluggable Object Databases\n\nGit already has the ability to store references with either the \"files\" backend or with the [\"reftable\" backend](https://about.gitlab.com/blog/a-beginners-guide-to-the-git-reftable-format/). This is achieved by having proper abstractions in Git that allows us to have different backends.\n\nBut references are just one of the two important types of data that are stored in repositories, with the other being objects. Objects are stored in the object database, and each object database in turn consists of multiple object sources where objects can be read from or written to. Each object source either stores individual objects as so-called \"loose\" objects, or compresses multiple objects into a \"packfile\" in your `.git/objects` directory.\n\nUntil now, however, these sources did not have a proper abstraction boundary, so the storage format for objects is completely hardcoded into Git. But this is finally changing with pluggable object databases! The concept is straightforward and similar to how we did this for references in the past: Instead of having hardcoded code paths for how to store objects, we introduce an abstraction boundary that allows us to have different backends for storing objects.\n\nWhile the idea is simple, the implementation is not, as we have hardcoded assumptions about the storage formats used in Git all over the place. In fact, we have started working on this topic in Git 2.48, which was released in January 2025. Initially, we focused on making object-related subsystems self-contained and creating proper subsystems for the existing backends that we had in Git.\n\nWith Git 2.54, we have now reached a milestone: The object database backend is now pluggable. Not all of Git's functionality is covered yet, but introducing an alternate backend that handles a meaningful subset of operations is now a realistic undertaking.\n\nFor now, only local workflows like creating commits, showing commit graphs, or performing merges will work with such an alternative implementation. This notably excludes anything that interacts with a remote, such as when you want to fetch or push changes. Regardless, this is the culmination of almost two years of work spanning across almost 400 commits that have been merged upstream, and we will of course continue to iterate on this effort.\n\nSo why does this matter? The idea is that it becomes practical to introduce new storage formats into Git. Examples could be:\n- A storage format that is able to store large binary files more efficiently\n  than packfiles do today\n\n- A storage format that is custom-tailored for GitLab to ensure that we can\n  serve repositories to our users even more efficiently than we currently can\n\n\nThis is a large-scale effort that is likely to shape the future of Git and GitLab.\n\n*This project was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab).*\n\n## Easier editing of your commit history\n\nIn many software development projects it is common practice for developers to not only polish the code they want to contribute, but to also polish the commit history so that it becomes easy to review. The result is a set of small and atomic commits that each do one thing, with a good commit message that describes the intent of the commit as well as specific nuances.\n\nOf course, more often than not, these atomic commits are not something that just happens naturally during the development process. Instead, the author of the changes will gain a better understanding of what they are while iterating on them, and the way to split up the commits will become clearer over time. Furthermore, the subsequent review process may result in feedback that requires changes to the crafted commits.\n\nThe consequence of this process is that the developer will have to rewrite their commit history many times during the development process. Historically, Git has allowed for this use case via [interactive rebases](https://git-scm.com/docs/git-rebase#_interactive_mode). These interactive rebases are an extremely powerful tool: They let you reorder commits, rewrite commit messages, squash multiple commits together, or perform arbitrary edits of any commit.\n\nBut they are also somewhat arcane and hard to understand. The user needs to figure out the base commit for the rebase, they need to understand how to edit a somewhat obscure \"instruction sheet,\" and they need to be aware of how the stateful rebasing process works. For example, users are presented with an instruction sheet similar to the following when rebasing a topic branch:\n\n```shell\npick b60623f382 # t: detect errors outside of test cases # empty\npick b80cb55882 # t: prepare `test_match_signal ()` calls for `set -e`\npick 5ffe397f30 # t: prepare `test_must_fail ()` for `set -e`\npick 5e9b0cf5e1 # t: prepare `stop_git_daemon ()` for `set -e`\npick 299561e7a2 # t: prepare `git config --unset` calls for `set -e`\npick ed0e7ca2b5 # t: detect errors outside of test cases\n```\n\nSo while interactive rebases are powerful, they are also quite intimidating for the average user.\n\nIt doesn't have to be this way, though. Tools like [Jujutsu](https://www.jj-vcs.dev/latest/) provide interfaces that are much easier to use compared to Git, as you can for example simply execute `jj split` to split up a commit into two commits. With Git and interactive rebases, this use case requires a lot of different steps with confusing command line arguments.\n\nWe have thus taken inspiration from Jujutsu and have introduced a new git-history(1) command into Git that is the foundation for better history editing. For now, this command has two subcommands:\n\n- `git history reword` allows you to easily rewrite a commit message. You simply\n  give it the commit whose message you want to reword, Git asks you for the new\n  commit message, and that's it.\n\n- `git history split` allows you to split up a commit into two, which is\n  inspired by `jj split`. You give it a commit, Git asks you which changes to\n  stage into which commit and for the two commit messages, and then you're done.\n\n\nThis is of course only a start, and we want to add additional subcommands over time. For example:\n\n- `git history fixup` to take staged changes and automatically amend them to a\n  specific commit\n\n- `git history drop` to remove a commit\n- `git history reorder` to reorder the sequence of commits\n- `git history squash` to squash a range of commits\n\nBut that's not all! In addition to making history editing easy, this new command also knows to automatically rebase all of your local branches that previously included this commit. So that means that you can even edit a commit that is not on the current branch, and all branches that contain the commit will be rewritten.\n\nIt may seem puzzling at first that Git is automatically rebasing dependent branches, as that is a significant diversion from how git-rebase(1) works. But this is part of a bigger effort to bring better support for Stacked Diffs to Git, which are a way to create a series of multiple dependent branches that can be reviewed independently, but that together work towards a bigger goal.\n\n*This project was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab) with support from [Elijah Newren](https://github.com/newren).*\n\n## A native replacement for git-sizer(1)\n\nThe size of a Git repository is an important factor that determines how well Git and GitLab can handle it. But size alone is not the only factor, as the performance of a repository is ultimately a combination of multiple different dimensions:\n\n- The depth of the commit history\n- The shape of the directory structure\n- The size of files stored in the repository\n- The number of references\n\nThese are only some of the dimensions one needs to consider when trying to predict whether Git will be able to handle a repository well.\n\nBut while it is clear that the mere repository size is insufficient, Git itself does not provide any tooling that gives the user an easy overview of these metrics. Instead, users are forced to rely on third-party tools like [git-sizer(1)](https://github.com/github/git-sizer) to fill this gap. This tool does an excellent job at surfacing this information, but it is not part of Git itself and thus needs to be installed separately.\n\nObservability of repository internals is critical to us at GitLab, so we introduced a [new `git repo structure` command into Git 2.52](https://about.gitlab.com/blog/whats-new-in-git-2-52-0/#new-subcommand-for-git-repo1-to-display-repository-metrics) to display repository metrics, which we have extended in Git 2.53 to [show inflated and disk sizes for objects by type](https://about.gitlab.com/blog/whats-new-in-git-2-53-0/#more-data-collected-in-git-repo-structure).\n\nIn Git 2.54, we are now iterating some more on this command so that we don't only show the overall size, but also show the largest objects by type:\n\n```shell\n$ git clone https://gitlab.com/git-scm/git.git\n$ cd git\n$ git repo structure\nCounting objects: 410445, done.\n| Repository structure      | Value       |\n| ------------------------- | ----------- |\n| * References              |             |\n|   * Count                 |    1.01 k   |\n|     * Branches            |       1     |\n|     * Tags                |    1.00 k   |\n|     * Remotes             |       9     |\n|     * Others              |       0     |\n|                           |             |\n| * Reachable objects       |             |\n|   * Count                 |  410.45 k   |\n|     * Commits             |   83.99 k   |\n|     * Trees               |  164.46 k   |\n|     * Blobs               |  161.00 k   |\n|     * Tags                |    1.00 k   |\n|   * Inflated size         |    7.46 GiB |\n|     * Commits             |   57.53 MiB |\n|     * Trees               |    2.33 GiB |\n|     * Blobs               |    5.07 GiB |\n|     * Tags                |  737.48 KiB |\n|   * Disk size             |  181.37 MiB |\n|     * Commits             |   33.11 MiB |\n|     * Trees               |   40.58 MiB |\n|     * Blobs               |  107.11 MiB |\n|     * Tags                |  582.67 KiB |\n|                           |             |\n| * Largest objects         |             |\n|   * Commits               |             |\n|     * Maximum size    [1] |   17.23 KiB |\n|     * Maximum parents [2] |      10     |\n|   * Trees                 |             |\n|     * Maximum size    [3] |   58.85 KiB |\n|     * Maximum entries [4] |    1.18 k   |\n|   * Blobs                 |             |\n|     * Maximum size    [5] | 1019.51 KiB |\n|   * Tags                  |             |\n\n|     * Maximum size    [6] |    7.13 KiB |\n\n[1] f6ecb603ff8af608a417d7724727d6bc3a9dbfdf\n[2] 16d7601e176cd53f3c2f02367698d06b85e08879\n[3] 203ee97047731b9fd3ad220faa607b6677861a0d\n[4] 203ee97047731b9fd3ad220faa607b6677861a0d\n[5] aa96f8bc361fd84a1459440f1e7de02ab0dc3543\n[6] 07e38db6a5a03690034d27104401f6c8ea40f1fc\n```\n\nWith this information we're now almost feature-complete as compared to git-sizer(1). We're not done yet, though — we plan to eventually add additional features such as:\n\n- Severity levels as they exist in git-sizer(1)\n- Graphs that show you the distribution of object sizes\n- The ability to scan objects reachable via a subset of references\n\n*This project was led by [Justin Tobler](https://gitlab.com/justintobler).*\n\n## New infrastructure for repository maintenance\n\nWhenever you write data into a Git repository you will typically end up adding more loose objects. Left unmanaged, this leads to a large number of separate files in your `.git/objects/` directory, which slows down several operations that want to access many objects at once. Git thus regularly packs these objects into \"packfiles\" to ensure good performance.\n\nThis isn't the only data structure that may become inefficient over time: Updating references may create loose references, reflogs will need trimming, worktrees may become stale, and caches like commit-graphs need to be refreshed regularly.\n\nAll of these tasks have historically been managed by [git-gc(1)](https://git-scm.com/docs/git-gc). However, this tool has a monolithic architecture, where it basically executes all of the tasks required in sequential order. This foundation is hard to extend and doesn't give the end user much flexibility in case they want to slightly modify how housekeeping is performed.\n\nThe Git project introduced the new [git-maintenance(1)](https://git-scm.com/docs/git-maintenance) tool in Git 2.29. In contrast to git-gc(1), git-maintenance(1) is not monolithic but is instead structured around tasks. These tasks are freely configurable by the user so that the user can control which tasks are running, giving them much more fine-grained control over repository maintenance.\n\nEventually, Git has migrated to use git-maintenance(1) by default. But in the beginning, the only task that was default-enabled was the git-gc(1) task, which as you might have guessed, simply executes `git gc`. To manually run maintenance using this new command you can execute `git maintenance run`, but Git knows to execute this automatically after several other commands.\n\nOver the last couple releases we have implemented all the individual tasks that are supported by git-gc(1) in git-maintenance(1) to ensure that we have feature parity between these two tools.\n\nFurthermore, we have implemented a new task that uses Git's modern architecture for repacking objects with [geometric compaction](https://git-scm.com/docs/git-repack#Documentation/git-repack.txt---geometricfactor).\nGeometric compaction is a much better fit for large monorepos, and with our efforts to make them work well with partial clones [that landed in Git 2.53](https://about.gitlab.com/blog/whats-new-in-git-2-53-0/#geometric-repacking-support-with-promisor-remotes) they are now a full replacement for our previous repacking strategy in Git.\n\nIn Git 2.54, we have now reached another significant milestone: Instead of using the git-gc(1)-based strategy by default, we are now using geometric repacking with fine-grained individual maintenance tasks! Besides being more efficient for large monorepos, it also ensures that we have an easier foundation to iterate on going forward.\n\n*The git-maintenance(1) infrastructure was originally implemented by [Derrick Stolee](https://github.com/derrickstolee) and geometric maintenance was introduced by [Taylor Blau](https://github.com/ttaylorr). The effort to introduce the new fine-grained tasks and migrate to the new maintenance strategy was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab).*\n\n## Read more\n\nThis article highlighted just a few of the contributions made by GitLab and the wider Git community for this latest release. You can learn about these from the [official release announcement](https://lore.kernel.org/git/xmqqa4uxsjrs.fsf@gitster.g/T/#u) of the Git project. Also, check out our [previous Git release blog posts](https://about.gitlab.com/blog/tags/git/) to see other past highlights of contributions from GitLab team members.",{"slug":743,"featured":14,"template":15},"whats-new-in-git-2-54-0",{"content":745,"config":754},{"title":746,"description":747,"authors":748,"date":750,"body":751,"heroImage":752,"category":11,"tags":753},"What’s new in Git 2.53.0?","Learn about release contributions, including fixes for geometric repacking, updates to git-fast-import(1) commit signature handing options, and more.",[749],"Justin Tobler","2026-02-02","The Git project recently released [Git 2.53.0](https://lore.kernel.org/git/xmqq4inz13e3.fsf@gitster.g/T/#u). Let's look at a few notable highlights from this release, which includes\ncontributions from the Git team at GitLab.\n\n## Geometric repacking support with promisor remotes\n\nNewly written objects in a Git repository are often stored as individual loose files. To ensure good performance and optimal use of disk space, these loose objects are regularly compressed into so-called packfiles. The number of packfiles in a repository grows over time as a result of the user’s activities, like writing new commits or fetching from a remote. As the number of packfiles in a repository increases, Git has to do more work to look up individual objects. Therefore, to preserve optimal repository performance, packfiles are periodically repacked via git-repack(1) to consolidate the objects into fewer packfiles. When repacking there are two strategies: “all-into-one” and “geometric”.\n\nThe all-into-one strategy is fairly straightforward and the current default. As its name implies, all objects in the repository are packed into a single packfile. From a performance perspective this is great for the repository as Git only has to scan through a single packfile when looking up objects. The main downside of such a repacking strategy is that computing a single packfile for a repository can take a significant amount of time for large repositories.\n\nThe geometric strategy helps mitigate this concern by maintaining a geometric progression of packfiles based on their size instead of always repacking into a single packfile. To explain more plainly, when repacking Git maintains a set of packfiles ordered by size where each packfile in the sequence is expected to be at least twice the size of the preceding packfile. If a packfile in the sequence violates this property, packfiles are combined as needed until the progression is restored. This strategy has the advantage of still minimizing the number of packfiles in a repository while also minimizing the amount of work that must be done for most repacking operations.\n\nOne problem with the geometric repacking strategy was that it was not compatible with partial clones. Partial clones allow the user to clone only parts of a repository by, for example, skipping all blobs larger than 1 megabyte. This can significantly reduce the size of a repository, and Git knows how to backfill missing objects that it needs to access at a later point in time.\n\nThe result is a repository that is missing some objects, and any object that may not be fully connected is stored in a “promisor” packfile.  When repacking, this promisor property needs to be retained going forward for packfiles containing a promisor object so it is known whether a missing object is expected and can be backfilled from the promisor remote. With an all-into-one repack, Git knows how to handle promisor objects properly and stores them in a separate promisor packfile. Unfortunately, the geometric repacking strategy did not know to give special treatment to promisor packfiles and instead would merge them with normal packfiles without considering whether they reference promisor objects. Luckily, due to a bug the underlying git-pack-objects(1) dies when using geometric repacking in a partial clone repository. So this means repositories in this configuration were not able to be repacked anyways which isn’t great, but better than repository corruption.\n\nWith the release of Git 2.53, geometric repacking now works with partial clone repositories. When performing a geometric repack, promisor packfiles are handled separately in order to preserve the promisor marker and repacked following a separate geometric progression. With this fix, the geometric strategy moves closer towards becoming the default repacking strategy. For more information check out the corresponding [mailing list thread](https://lore.kernel.org/git/20260105-pks-geometric-repack-with-promisors-v1-0-c4660573437e@pks.im/).\n\nThis project was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab).\n\n## git-fast-import(1) learned to preserve only valid signatures\n\nIn our [Git 2.52 release article](https://about.gitlab.com/blog/whats-new-in-git-2-52-0/), we covered signature related improvements to git-fast-import(1) and git-fast-export(1). Be sure to check out that post for a more detailed explanation of these commands, how they are used, and the changes being made with regards to signatures.\n\nTo quickly recap, git-fast-import(1) provides a backend to efficiently import data into a repository and is used by tools such as [git-filter-repo(1)](https://github.com/newren/git-filter-repo) to help rewrite the history of a repository in bulk. In the Git 2.52 release, git-fast-import(1) learned the `--signed-commits=\u003Cmode>` option similar to the same option in git-fast-export(1). With this option, it became possible to unconditionally retain or strip signatures from commits/tags.\n\nIn situations where only part of the repository history has been rewritten, any signature for rewritten commits/tags becomes invalid. This means git-fast-import(1) is limited to either stripping all signatures or keeping all signatures even if they have become invalid. But retaining invalid signatures doesn’t make much sense, so rewriting history with git-repo-filter(1) results in all signatures being stripped, even if the underlying commit/tag is not rewritten. This is unfortunate because if the commit/tag is unchanged, its signature is still valid and thus there is no real reason to strip it. What is really needed is a means to preserve signatures for unchanged objects, but strip invalid ones.\n\nWith the release of Git 2.53, the git-fast-import(1) `--signed-commits=\u003Cmode>` option has learned a new `strip-if-invalid` mode which, when specified, only strips signatures from commits that become invalid due to being rewritten. Thus, with this option it becomes possible to preserve some commit signatures when using git-fast-import(1). This is a critical step towards providing the foundation for tools like git-repo-filter(1) to preserve valid signatures and eventually re-sign invalid signatures.\n\nThis project was led by [Christian Couder](https://gitlab.com/chriscool).\n\n## More data collected in git-repo-structure\n\nIn the Git 2.52 release, the “structure” subcommand was introduced to git-repo(1). The intent of this command was to collect information about the repository and eventually become a native replacement for tools such as [git-sizer(1)](https://github.com/github/git-sizer). At GitLab, we host some extremely large repositories, and having insight into the general structure of a repository is critical to understand its performance characteristics. In this release, the command now also collects total size information for reachable objects in a repository to help understand the overall size of the repository. In the output below, you can see the command now collects both the total inflated and disk sizes of reachable objects by object type.\n\n```shell\n$ git repo structure\n\n| Repository structure | Value      |\n| -------------------- | ---------- |\n| * References         |            |\n|   * Count            |   1.78 k   |\n|     * Branches       |      5     |\n|     * Tags           |   1.03 k   |\n|     * Remotes        |    749     |\n|     * Others         |      0     |\n|                      |            |\n| * Reachable objects  |            |\n|   * Count            | 421.37 k   |\n|     * Commits        |  88.03 k   |\n|     * Trees          | 169.95 k   |\n|     * Blobs          | 162.40 k   |\n|     * Tags           |    994     |\n|   * Inflated size    |   7.61 GiB |\n|     * Commits        |  60.95 MiB |\n|     * Trees          |   2.44 GiB |\n|     * Blobs          |   5.11 GiB |\n|     * Tags           | 731.73 KiB |\n|   * Disk size        | 301.50 MiB |\n|     * Commits        |  33.57 MiB |\n|     * Trees          |  77.92 MiB |\n|     * Blobs          | 189.44 MiB |\n|     * Tags           | 578.13 KiB |\n```\n\nThe keen-eyed among you may have also noticed that the size values in the table output are also now listed in a more human-friendly manner with units appended. In subsequent releases we hope to further expand this command's output to provide additional data points such as the largest individual objects in the repository.\n\nThis project was led by [Justin Tobler](https://gitlab.com/justintobler).\n\n## Read more\n\nThis article highlighted just a few of the contributions made by GitLab and\nthe wider Git community for this latest release. You can learn about these from\nthe [official release announcement](https://lore.kernel.org/git/xmqq4inz13e3.fsf@gitster.g/T/#u) of the Git project. Also, check\nout our [previous Git release blog posts](https://about.gitlab.com/blog/tags/git/)\nto see other past highlights of contributions from GitLab team members.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663087/Blog/Hero%20Images/git3-cover.png",[23,740,266],{"featured":28,"template":15,"slug":755},"whats-new-in-git-2-53-0",{"promotions":757},[758,772,784,796],{"id":759,"categories":760,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[761],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":776,"text":763,"button":777,"image":781},"devops-modernization",[775,570],"product","Are you just managing tools or shipping innovation?",{"text":778,"config":779},"Get your DevOps maturity score",{"href":780,"dataGaName":768,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":785,"categories":786,"header":788,"text":763,"button":789,"image":793},"security-modernization",[787],"security","Are you trading speed for security?",{"text":790,"config":791},"Get your security maturity score",{"href":792,"dataGaName":768,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":794},{"src":795},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":797,"paths":798,"header":801,"text":802,"button":803,"image":808},"github-azure-migration",[799,800],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":804,"config":805},"See how GitLab compares to GitHub",{"href":806,"dataGaName":807,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":809},{"src":783},{"header":811,"blurb":812,"button":813,"secondaryButton":818},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":814,"config":815},"Get your free trial",{"href":816,"dataGaName":50,"dataGaLocation":817},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":819},{"href":54,"dataGaName":55,"dataGaLocation":817},1777493599469]