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Geoserver Cloud

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znlgis
geoserver-cloud

Use when deploying GeoServer Cloud 3.0.2 on Docker Compose or Kubernetes — gateway serving WMS/WFS/WCS/WPS/GWC microservices under /geoserver/cloud, PgConfig vs datadir backends, RabbitMQ config-bus sync, Helm chart geoservercloud 3.0.2, and diagnosing 502s via actuator/health and Consul registration.

Overview

Publisherznlgis
Repositoryopengis-skills
Skill namegeoserver-cloud
Stars
76
Forks
12
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by znlgis on GitHub. Read the source before you install it.

Installation

Install the Geoserver Cloud AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/znlgis/opengis-skills/tree/main/gis/geoserver-cloud
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/znlgis/opengis-skills.git /tmp/opengis-skills
mkdir -p .claude/skills
cp -r /tmp/opengis-skills/gis/geoserver-cloud .claude/skills/geoserver-cloud
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Geoserver Cloud in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Geoserver Cloud on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Geoserver Cloud is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

项目地址: https://github.com/geoserver/geoserver-cloud

官方文档: https://geoserver.org/geoserver-cloud/

Docker Hub: https://hub.docker.com/u/geoservercloud(镜像标签与 GeoServer 主版本对应,如 3.0.2)

最新版本: 3.0.2(2026-10-20,GitHub Releases)

许可证: GPL-2.0

概述

GeoServer Cloud(GS Cloud):

  • 微服务架构:每种 OGC 服务为独立 Spring Boot 应用
  • 配置同步:Spring Cloud Bus + RabbitMQ 实时同步(GEOSERVER_BUS_ENABLED)
  • 配置后端:本地目录 / PgConfig(推荐) / JDBCConfig
  • 可观测:Actuator + Prometheus + Micrometer Tracing
  • 容器友好:官方镜像、Helm Chart、docker-compose
  • 完全兼容社区版:相同 Web UI、SLD、数据存储
  • 默认路径前缀:/geoserver/cloud(网关 http://localhost:9090/geoserver/cloud/...)

服务拆分

服务镜像用途
gatewaygeoservercloud/geoserver-cloud-gateway网关
discovery—(3.x 不再发布该镜像)服务发现:官方 compose/K8s 改用 hashicorp/consul,geoservercloud/geoserver-cloud-discovery:3.0.2 在 Docker Hub 已 404
configgeoservercloud/geoserver-cloud-config配置中心
web-uigeoservercloud/geoserver-cloud-webui管理界面
restgeoservercloud/geoserver-cloud-restREST API
wmsgeoservercloud/geoserver-cloud-wmsWMS
wfsgeoservercloud/geoserver-cloud-wfsWFS
wcsgeoservercloud/geoserver-cloud-wcsWCS
wpsgeoservercloud/geoserver-cloud-wpsWPS
gwcgeoservercloud/geoserver-cloud-gwc瓦片缓存

环境准备与快速启动(docker-compose)

bash
# 官方 Quick Start:下载稳定版 compose 文件(仓库内的 compose/ 目录仅供开发调试)
wget "https://geoserver.org/geoserver-cloud/deploy/docker-compose/stable/pgconfig/compose.yml"
docker compose pull
docker compose up -d
docker compose ps

# Gateway: http://localhost:9090/geoserver/cloud   (Web UI)
# Consul : http://localhost:8500                   (服务发现与控制台)
curl -u admin:geoserver "http://localhost:9090/geoserver/cloud/rest/workspaces.json"

配置后端

后端适用场景
datadir共享卷(NFS/PVC),简单
pgconfig生产推荐,强一致
jdbcconfig旧方案,已被替代

启用 PgConfig:

yaml
SPRING_PROFILES_ACTIVE: "pgconfig"   # 启用 PgConfig 后端
PGCONFIG_HOST: "geoserverdb"
PGCONFIG_PORT: "5432"
PGCONFIG_DATABASE: "geoserver"
PGCONFIG_USERNAME: "geoserver"
PGCONFIG_PASSWORD: "geoserver"
PGCONFIG_SCHEMA: "pgconfig"          # 启动时自动创建 schema

注意: 走 Spring Cloud Config Server 集中下发时,以上变量加前缀 SPRING_CLOUD_CONFIG_SERVER_OVERRIDES_(如 SPRING_CLOUD_CONFIG_SERVER_OVERRIDES_PGCONFIG_HOST)配在 config 服务上;standalone profile(Kubernetes 常用)则直接写在各服务上。


Kubernetes(Helm)

bash
# Chart:camptocamp/helm-geoserver-cloud(chart 名 geoservercloud,最新 3.0.2)
helm repo add geoserver-cloud https://camptocamp.github.io/helm-geoserver-cloud
helm repo update

官方要求把该 chart 作为**子 chart(依赖)**引入自己的 umbrella chart(不直接 helm install),副本数等参数写在子 chart 键 geoservercloud 下:

yaml
# Chart.yaml
dependencies:
  - name: geoservercloud
    repository: https://camptocamp.github.io/helm-geoserver-cloud
    version: 3.0.2
yaml
# values.yaml
geoservercloud:
  global:
    profile: standalone,pgconfig
  geoserver:
    services:
      wms:
        replicaCount: 3
bash
helm dependency update && helm install my-gsc . -n gis --create-namespace -f values.yaml
# 临时扩缩容也可直接改 Deployment(chart 默认 nameOverride 为 gsc)
kubectl -n gis get deploy | grep wms

核心 API:网关路由与端点

路径转发到
/geoserver/cloud/wmswms
/geoserver/cloud/wfswfs
/geoserver/cloud/wcswcs
/geoserver/cloud/wpswps
/geoserver/cloud/gwcgwc
/geoserver/cloud/restrest
/geoserver/cloud/webweb-ui

前缀由 GEOSERVER_BASE_PATH(默认 compose 中为 /geoserver/cloud)控制,网关的 StripBasePath 过滤器去掉前缀后再转发(/geoserver/cloud/wms → 下游 /wms)。


配置同步

任意修改 → REST/web-ui 发送 RemoteApplicationEvent 到 RabbitMQ → 所有副本订阅刷新本地 Catalog,秒级一致。

yaml
# 环境变量形式(compose 中使用)
GEOSERVER_BUS_ENABLED: "true"
RABBITMQ_HOST: rabbitmq
RABBITMQ_PORT: "5672"

监控

yaml
management:
  endpoints.web.exposure.include: "*"
  metrics.export.prometheus.enabled: true
  • Prometheus 抓 /actuator/prometheus(Micrometer 指标,另有 /actuator/metrics)
  • 日志 STDOUT,配合 EFK / Loki
  • 分布式追踪非默认开启:可接 OpenTelemetry / Zipkin / Jaeger

与单体兼容

直接挂载现有 GeoServer 数据目录平滑迁移:

bash
docker run -e SPRING_PROFILES_ACTIVE=datadir \
  -v /mnt/gsdata:/opt/app/data_directory \
  geoservercloud/geoserver-cloud-wms:3.0.2

性能优化

  1. WMS 多副本 + Gateway 轮询;GWC 单独部署
  2. PgConfig 数据库独立 + 连接池调优
  3. RabbitMQ 设 delivery_limit 避免风暴
  4. 关闭未用服务(不部署 WPS/WCS)
  5. 配合 Caffeine + Redis 缓存(按需)
  6. 影像金字塔与瓦片预切片


典型工作流

工作流 1:从零部署高可用 GeoServer Cloud

bash
# 下载稳定版 pgconfig compose(仓库内 compose/ 目录仅供开发调试)
wget "https://geoserver.org/geoserver-cloud/deploy/docker-compose/stable/pgconfig/compose.yml"

# 启动基础设施 + 所有微服务
docker compose pull
docker compose up -d

# 验证 Gateway 可访问
curl -u admin:geoserver http://localhost:9090/geoserver/cloud/rest/about/version.json

# 扩容 WMS 服务(3 副本)
docker compose up -d --scale wms=3

工作流 2:Kubernetes 生产部署 + 自动伸缩

bash
# 添加 Helm 仓库(chart 名 geoservercloud,由 camptocamp 维护)
helm repo add geoserver-cloud https://camptocamp.github.io/helm-geoserver-cloud
helm repo update

# 在自己的 umbrella chart 中把 geoservercloud 声明为依赖(RabbitMQ/PostgreSQL 同样以依赖引入)
# values.yaml 里写 geoservercloud.geoserver.services.wms.replicaCount 等参数
helm dependency update
helm upgrade --install gs . -n gis --create-namespace -f values.yaml

# 手动伸缩 WMS(先查出 Deployment 名)
kubectl -n gis get deploy | grep wms
kubectl -n gis scale deploy <wms-deployment> --replicas=10

常见问题

问题解决
配置不同步检查 RabbitMQ 可达 + GEOSERVER_BUS_ENABLED=true
Web UI 改了 wms 没生效检查事件订阅
启动顺序错乱depends_on + 健康检查;K8s 用 initContainers
OOM调整 -Xmx,按服务独立资源
Gateway 502 / 服务不可达检查目标服务健康状态与 Consul 注册(服务名解析:wms、wfs 等)
瓦片 404 / 缓存未命中确认 gwc 服务已部署、GEOSERVER_BASE_PATH 与请求路径一致,必要时重新 seed

AI 使用建议

  • 执行环境:命令在终端 / shell 中执行(Windows 无 wget,改 curl.exe -LO);先用 docker version、helm version、kubectl version --client 确认工具在 PATH。
  • 先探测版本:curl.exe -s -u admin:geoserver http://localhost:9090/geoserver/cloud/rest/about/version.json 读 GeoServer 版本;curl.exe -s http://localhost:9090/actuator/health 取 {"status":"UP"}(被 profile 关掉时改用 kubectl -n gis get pods 判活)。
  • 验证产物:部署后逐服务探活,再经网关 .../rest/workspaces.json 与一次 WMS GetMap 走通全链路;docker compose ps 显示 Up 不等于 OGC 服务可用。
  • 排障顺序:502 → 查 Consul 注册与目标服务健康(kubectl -n gis logs deploy/<wms>);配置不生效 → 查 RabbitMQ 与 GEOSERVER_BUS_ENABLED;瓦片 404 → 查 gwc 是否部署、GEOSERVER_BASE_PATH 前缀是否一致。
  • 安全与权限:admin:geoserver、PGCONFIG_PASSWORD、RabbitMQ 口令不要回显到报告;docker compose down -v、helm uninstall、删 PVC 或 PgConfig schema 会丢配置,先向用户确认并备份。
  • 先本地复现:能在 docker compose 复现的问题再上 K8s;镜像标签与 chart 版本保持同一版本号(3.0.2),避免网关与服务错配。

相关技能

参考资源

Frequently asked questions

What does the Geoserver Cloud AI skill do?

Use when deploying GeoServer Cloud 3.0.2 on Docker Compose or Kubernetes — gateway serving WMS/WFS/WCS/WPS/GWC microservices under /geoserver/cloud, PgConfig vs datadir backends, RabbitMQ config-bus sync, Helm chart geoservercloud 3.0.2, and diagnosing 502s via actuator/health and Consul registration.

Why use Geoserver Cloud on TypingMind?

Because you install it once and use it with any model. Geoserver Cloud is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Geoserver Cloud in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/znlgis/opengis-skills/tree/main/gis/geoserver-cloud. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Geoserver Cloud?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Geoserver Cloud?

As many as you like. As long as a model supports skills, you can use Geoserver Cloud with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Geoserver Cloud AI skill free?

Yes. It is published on GitHub by znlgis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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