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vector

The container platform tailored for Kubernetes multi-cloud, datacenter, and edge management

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项目 README

来源文件:README.md

抓取于 2026年8月1日

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The container platform tailored for Kubernetes multi-cloud, datacenter, and edge management

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What is KubeSphere

English | 中文

KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy, see Feature List for details.

The following screenshots give a close insight into KubeSphere. Please check What is KubeSphere for further information.

WorkbenchProject Resources
CI/CD PipelineApp Store

Demo environment

🎮 KubeSphere Lite provides you with free, stable, and out-of-the-box managed cluster service. After registration and login, you can easily create a K8s cluster with KubeSphere installed in only 5 seconds and experience feature-rich KubeSphere.

🖥 You can view the Demo Video to get started with KubeSphere.

Features

🧩 Extensible Architecture Designed for flexibility, supporting plugin-based extensions and seamless integrations. Easily customize and expand functionalities to meet evolving needs. Learn more.
🕸 Provisioning Kubernetes Cluster Support deploy Kubernetes on any infrastructure, support online and air-gapped installation. Learn more.
🔗 Kubernetes Multi-cluster Management Provide a centralized control plane to manage multiple Kubernetes clusters, and support the ability to propagate an app to multiple K8s clusters across different cloud providers.
🤖 Kubernetes DevOps Provide GitOps-based CD solutions and use Argo CD to provide the underlying support, collecting CD status information in real time. With the mainstream CI engine Jenkins integrated, DevOps has never been easier. Learn more.
🔎 Cloud Native Observability Multi-dimensional monitoring, events and auditing logs are supported; multi-tenant log query and collection, alerting and notification are built-in. Learn more.
🌐 Service Mesh (Istio-based) Provide fine-grained traffic management, observability and tracing for distributed microservice applications, provides visualization for traffic topology. Learn more.
💻 App Store Provide an App Store for Helm-based applications, and offer application lifecycle management on Kubernetes platform. Learn more.
💡 Edge Computing Platform KubeSphere integrates KubeEdge to enable users to deploy applications on the edge devices and view logs and monitoring metrics of them on the console. Learn more.
🗃 Support Multiple Storage and Networking Solutions
  • Support GlusterFS, CephRBD, NFS, LocalPV solutions, and provide CSI plugins to consume storage from multiple cloud providers.
  • Provide Load Balancer Implementation OpenELB for Kubernetes in bare-metal, edge, and virtualization.
  • Provides network policy and Pod IP pools management, support Calico, Flannel, Kube-OVN
  • ..
    🏢 Multi-Tenancy Isolated workspaces with role-based access control ensure secure resource sharing across multiple tenants. Supports fine-grained permissions and quota management. Learn more.
    🧠 GPU Workloads Scheduling and Monitoring Create GPU workloads on the GUI, schedule GPU resources, and manage GPU resource quotas by tenant.

    Architecture

    KubeSphere 4.x adopts a microkernel + extension components architecture (codename LuBan). The core part (KubeSphere Core) only includes the essential basic functions required for system operation, with independent functional modules split and provided in the form of extension components. Users can dynamically manage the extension components during system operation. With the extension capabilities, KubeSphere can support more application scenarios and meet the needs of different users.

    Architecture


    Latest release

    🎉 KubeSphere v4.1.2 was released! It brings enhancements and better user experience, see the Release Notes For 4.1.2 for the updates.

    Installation

    KubeSphere can run anywhere from on-premise datacenter to any cloud to edge. In addition, it can be deployed on any version-compatible Kubernetes cluster. KubeSphere consumes very few resources, and you can optionally install additional extensions after installation.

    Quick start

    Installing on K8s

    Run the following commands to install KubeSphere on an existing Kubernetes cluster:

    helm upgrade --install -n kubesphere-system --create-namespace ks-core https://charts.kubesphere.io/main/ks-core-1.1.3.tgz --debug --wait
    

    KubeSphere for hosted Kubernetes services

    KubeSphere is hosted on the following cloud providers, and you can try KubeSphere by one-click installation on their hosted Kubernetes services.

    You can also install KubeSphere on other hosted Kubernetes services within minutes, see the step-by-step guides to get started.

    👨‍💻 No internet access? Refer to the Air-gapped Installation.

    Guidance, discussion, contribution, and support

    You can reach the KubeSphere community and developers via the following channels:

    :hugs: Please submit any KubeSphere bugs, issues, and feature requests to KubeSphere GitHub Issue.

    :heart_decoration: The KubeSphere team also provides efficient official ticket support to respond in hours. For more information, click KubeSphere Online Support.

    Contribution

    Code of conduct

    Participation in the KubeSphere community is governed by the Code of Conduct.

    Security

    The security process for reporting vulnerabilities is described in SECURITY.md.

    Who are using KubeSphere

    The user case studies page includes the user list of the project. You can leave a comment to let us know your use case.




        

    KubeSphere is a member of CNCF and a Kubernetes Conformance Certified platform , which enriches the CNCF CLOUD NATIVE Landscape.

    数据与 AI

    中风险

    • 来源需自行核对维护者身份。
    • 包含脚本或命令调用,安装前请复核。
    • 可能需要外部 token、网络权限或第三方服务。
    • 未检测到高风险命令。
    • 扫描发现:2 条。

    Codex — Git Clone 安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 克隆仓库:git clone https://github.com/kubesphere/kubesphere.git
    3. 将 "skills/vector" 文件夹复制到 Codex 的 skills 目录中。
    4. 重启 Codex 让新的 skill 生效。

    Codex — 手动复制安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 从源仓库下载 SKILL.md 及相关文件。
    3. 在 Codex 的 skills 目录中创建新文件夹。
    4. 将所有 skill 文件复制到新文件夹中。
    5. 重启 Codex 让新的 skill 生效。

    Claude Code — Git Clone 安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 克隆仓库:git clone https://github.com/kubesphere/kubesphere.git
    3. 将 "skills/vector" 文件夹复制到 Claude Code 的 skills 目录中。
    4. 重启 Claude Code 让新的 skill 生效。

    Claude Code — 手动复制安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 从源仓库下载 SKILL.md 及相关文件。
    3. 在 Claude Code 的 skills 目录中创建新文件夹。
    4. 将所有 skill 文件复制到新文件夹中。
    5. 重启 Claude Code 让新的 skill 生效。

    Cursor — Git Clone 安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 克隆仓库:git clone https://github.com/kubesphere/kubesphere.git
    3. 将 "skills/vector" 文件夹复制到 Cursor 的 skills 目录中。
    4. 重启 Cursor 让新的 skill 生效。

    Cursor — 手动复制安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 从源仓库下载 SKILL.md 及相关文件。
    3. 在 Cursor 的 skills 目录中创建新文件夹。
    4. 将所有 skill 文件复制到新文件夹中。
    5. 重启 Cursor 让新的 skill 生效。

    GitHub Copilot — Git Clone 安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 克隆仓库:git clone https://github.com/kubesphere/kubesphere.git
    3. 将 "skills/vector" 文件夹复制到 GitHub Copilot 的 skills 目录中。
    4. 重启 GitHub Copilot 让新的 skill 生效。

    GitHub Copilot — 手动复制安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 从源仓库下载 SKILL.md 及相关文件。
    3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
    4. 将所有 skill 文件复制到新文件夹中。
    5. 重启 GitHub Copilot 让新的 skill 生效。

    Windsurf — Git Clone 安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 克隆仓库:git clone https://github.com/kubesphere/kubesphere.git
    3. 将 "skills/vector" 文件夹复制到 Windsurf 的 skills 目录中。
    4. 重启 Windsurf 让新的 skill 生效。

    Windsurf — 手动复制安装

    1. 安装前请先查看来源仓库和风险报告。
    2. 从源仓库下载 SKILL.md 及相关文件。
    3. 在 Windsurf 的 skills 目录中创建新文件夹。
    4. 将所有 skill 文件复制到新文件夹中。
    5. 重启 Windsurf 让新的 skill 生效。
    查看 SKILL.md 原文
    name: vector
    description: Use when installing or configuring the WizTelemetry Data Pipeline (vector) extension for KubeSphere, which provides data collection, transformation, and routing for observability data including logs, auditing, events, and notifications

    WizTelemetry Data Pipeline (Vector)

    Overview

    WizTelemetry Data Pipeline is an extension based on vector (https://vector.dev/) that provides the ability to collect, transform, and route observability data. It is a core dependency for other WizTelemetry extensions like Logging, Auditing, Events, and Notification.

    When to Use

    • Installing or configuring the WizTelemetry Data Pipeline extension
    • Setting up data collection for logs, auditing, events, and notifications
    • Configuring Vector sinks (OpenSearch)
    • Managing Vector agent components

    Installation

    Prerequisites

    REQUIRED: Complete all steps in order before generating InstallPlan.

    Step 1: Get Available Clusters and Confirm Target

    ⚠️ CRITICAL: DO NOT proceed until target clusters are determined.

    Step 1.1: Get available clusters

    kubectl get clusters -o jsonpath='{.items[*].metadata.name}'
    

    Step 1.2: Determine target clusters

    • If user explicitly specified target clusters in the request → Use those clusters directly, proceed to Step 2
    • If user did NOT specify target clusters → Ask user to confirm which clusters to deploy to, then proceed to Step 2

    Ask user (if not specified):

    Available clusters: host, dev
    Which clusters do you want to deploy Vector to?
    

    Step 2: Get OpenSearch Endpoint and Credentials (MUST DO)

    • If user already provided OpenSearch endpoint and credentials in the request → Use those directly, proceed to Step 3
    • If user did NOT provide → You MUST ask user for OpenSearch endpoint and credentials

    Ask user for (if not provided):

    1. OpenSearch endpoint URL (required)

      • Example: http://<node-ip>:30920 or https://opensearch.example.com:9200
    2. OpenSearch credentials (required)

      • Username (default: admin)
      • Password

    DO NOT proceed to Step 3 until user provides both endpoint and credentials.

    Step 3: Get Latest Vector Version (if not provided by user)

    MUST do this to get the latest version:

    kubectl get extensionversions -l kubesphere.io/extension-ref=vector -o jsonpath='{range .items[*]}{.spec.version}{"\n"}{end}' | sort -V | tail -1
    

    This outputs the latest version (e.g., 1.1.4). Note this down - you'll use it in the InstallPlan.

    Install Vector Extension

    ⚠️ IMPORTANT: Complete prerequisite steps (1-3) BEFORE this step.

    ⚠️ CRITICAL: InstallPlan metadata.name MUST be vector. DO NOT use any other name.

    Based on your selections:

    • Target clusters: Use the user-confirmed cluster names
    • OpenSearch endpoint: User-provided endpoint
    • OpenSearch credentials: User-provided username and password

    ⚠️ CRITICAL: config field is YAML format. You MUST:

    • Use the config structure exactly as shown in the template
    • DO NOT add configuration fields that are not shown in the template
    • DO NOT modify the structure or hierarchy

    ⚠️ CRITICAL: All placeholders MUST be replaced with actual values. DO NOT leave them as placeholders.

    Template

    apiVersion: kubesphere.io/v1alpha1
    kind: InstallPlan
    metadata:
      name: vector
    spec:
      extension:
        name: vector
        version: <VECTOR_VERSION>  # From Step 3
      enabled: true
      upgradeStrategy: Manual
      config: |
        agent:
          sinks:
            opensearch:
              auth:
                strategy: basic
                user: <OPENSEARCH_USER>
                password: <OPENSEARCH_PASSWORD>
              endpoints:
                - <OPENSEARCH_ENDPOINT>
      clusterScheduling:
        placement:
          clusters:
            - <TARGET_CLUSTERS>
    

    Replace placeholders:

    • <VECTOR_VERSION>: From Step 2 (e.g., 1.1.4)
    • <OPENSEARCH_ENDPOINT>: User-provided endpoint (e.g., http://<node-ip>:30920)
    • <OPENSEARCH_USER>: User-provided username (default: admin)
    • <OPENSEARCH_PASSWORD>: User-provided password
    • <TARGET_CLUSTERS>: User-confirmed cluster names

    ⚠️ DO NOT generate InstallPlan until all placeholders have real values.

    Wait for Deployment

    After applying InstallPlan, you MUST wait for deployment to complete:

    # Wait for Vector pods to be ready (on each cluster)
    kubectl wait --for=condition=Ready pods -n kubesphere-logging-system -l app.kubernetes.io/instance=vector --timeout=300s
    
    # Verify deployment status
    kubectl get pods -n kubesphere-logging-system -l app.kubernetes.io/instance=vector
    

    Show deployment summary to user:

    • Which clusters Vector was deployed to
    • OpenSearch endpoint used
    • Pod status (Ready/Total)

    Enable Metrics Export

    apiVersion: kubesphere.io/v1alpha1
    kind: InstallPlan
    metadata:
      name: vector
    spec:
      extension:
        name: vector
        version: <VECTOR_VERSION>  # From Step 2
      enabled: true
      upgradeStrategy: Manual
      config: |
        agent:
          sinks:
            opensearch:
              auth:
                strategy: basic
                user: <OPENSEARCH_USER>
                password: <OPENSEARCH_PASSWORD>
              endpoints:
                - <OPENSEARCH_ENDPOINT>
          exportMetrics:
            enabled: true
      clusterScheduling:
        placement:
          clusters:
            - <TARGET_CLUSTERS>
    

    Configuration Parameters

    Agent Parameters

    ParameterTypeDefaultDescription
    agent.rolestring"Agent"Role identifier
    agent.image.tagstring"0.53.0-debian"Vector image tag
    agent.resources.requests.cpustring"100m"CPU request
    agent.resources.requests.memorystring"100Mi"Memory request
    agent.resources.limits.cpustring"2000m"CPU limit
    agent.resources.limits.memorystring"2000Mi"Memory limit
    agent.service.portslistsee values.yamlService ports
    agent.exportMetrics.enabledboolfalseEnable metrics export

    Agent Sinks OpenSearch Parameters

    ParameterTypeRequiredDescription
    agent.sinks.opensearch.endpointslistYesOpenSearch endpoint URLs
    agent.sinks.opensearch.auth.strategystringYesAuthentication strategy (set to basic)
    agent.sinks.opensearch.auth.userstringYesUsername for authentication
    agent.sinks.opensearch.auth.passwordstringYesPassword for authentication
    agent.sinks.opensearch.tls.verifyboolNoEnable TLS verification (default: false)

    Example:

    agent:
      sinks:
        opensearch:
          endpoints:
            - http://<node-ip>:30920
          auth:
            strategy: basic
            user: admin
            password: admin
          tls:
            verify: false
    

    Docker Root Directory Configuration

    If Docker root directory is not /var/lib:

    agent:
      extraVolumes:
        - name: docker-root
          hostPath:
            path: /path/to/docker
            type: ''
      extraVolumeMounts:
        - name: docker-root
          mountPath: /path/to/docker
    

    Extension Operations

    Check Extension Status

    # View extension installation status
    kubectl get installplan vector
    
    # View extension version
    kubectl get extensionversions -l kubesphere.io/extension-ref=vector
    

    Check Pod Status

    # View all Vector pods
    kubectl get pods -n kubesphere-logging-system -l app.kubernetes.io/name=vector
    
    # View agent pods
    kubectl get pods -n kubesphere-logging-system -l app.kubernetes.io/name=vector,app.kubernetes.io/component=agent
    

    View Logs

    # View agent logs
    kubectl logs -n kubesphere-logging-system -l app.kubernetes.io/name=vector,app.kubernetes.io/component=agent --tail=100
    

    Update Configuration

    apiVersion: kubesphere.io/v1alpha1
    kind: InstallPlan
    metadata:
      name: vector
    spec:
      extension:
        name: vector
        version: <VECTOR_VERSION>
      enabled: true
      upgradeStrategy: Manual
      config: |
        agent:
          sinks:
            opensearch:
              auth:
                strategy: basic
                user: <OPENSEARCH_USER>
                password: <OPENSEARCH_PASSWORD>
              endpoints:
                - <OPENSEARCH_ENDPOINT>
      clusterScheduling:
        placement:
          clusters:
            - <TARGET_CLUSTERS>
    

    Uninstall Extension

    Uninstall from all clusters:

    kubectl delete installplan vector
    

    Uninstall from specific cluster:

    To remove Vector from a specific cluster, update the InstallPlan by removing that cluster from clusterScheduling.placement.clusters:

    apiVersion: kubesphere.io/v1alpha1
    kind: InstallPlan
    metadata:
      name: vector
    spec:
      extension:
        name: vector
        version: <VECTOR_VERSION>
      enabled: true
      upgradeStrategy: Manual
      config: |
        agent:
          sinks:
            opensearch:
              auth:
                strategy: basic
                user: <OPENSEARCH_USER>
                password: <OPENSEARCH_PASSWORD>
              endpoints:
                - <OPENSEARCH_ENDPOINT>
      clusterScheduling:
        placement:
          clusters:
            - <REMAINING_CLUSTERS>  # Remove the cluster you want to uninstall from
    

    Important Notes

    1. Dependency: Vector is a core dependency for WizTelemetry extensions. Install it first before installing Logging, Auditing, Events, or Notification.
    2. OpenSearch Required: User must provide OpenSearch endpoint and credentials.
    3. Multicluster: The extension uses installationMode: Multicluster:
      • agent (tag: agent) is deployed to all selected member clusters
    4. Agent Scheduling: Agent pods have affinity to avoid edge nodes and tolerate all taints.
    5. Cross-cluster Access: Ensure OpenSearch endpoint is accessible from all Vector clusters.

    Troubleshooting

    Check Vector Configuration

    # View Vector configmap
    kubectl get configmap -n kubesphere-logging-system -l app.kubernetes.io/name=vector
    
    # View specific config
    kubectl get configmap -n kubesphere-logging-system vector-config -o yaml
    

    Verify Sinks

    # Check if sinks are configured correctly
    kubectl get secret -n kubesphere-logging-system vector-sinks -o yaml
    

    Common Issues

    IssueSolution
    Pods not startingCheck if OpenSearch is accessible
    Data not flowingVerify sink configuration and network connectivity
    Agent not on member clusterCheck multicluster installation settings
    Out of memoryIncrease resource limits in configuration

    发现问题?提交给管理员复核

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