标签 IDEA 下的文章

运行时各种报错,尤其是在 AI 重构或者代码有大面积新增/调整时最容易出现。我怀疑是不是其他 IDE 用 AI 编辑把 idea 的缓存给搞乱了。

版本:从 2025.3 降级到 2024.3.7 也没解决。

解决方法:只能每次 Invalidate Caches 或者 Repair IDE,有的时候需要操作不止一次。

千奇百怪的报错信息:


java: 找不到符号
  符号: 类 OrderInfoCO
  

java: 无法访问 PaymentMethod
  找不到 PaymentMethod 的类文件


Connected to the target VM, address: '127.0.0.1:59973', transport: 'socket'
错误: 找不到或无法加载主类 com.xxx.xxx.Application

今天金价突破了 5000$/盎司,作为一个仅有余额宝、朝朝宝理财经验的小白,我入局了......

由于第一次投资,我真的忍不住隔几分钟刷一次手机,但是上班又不太好.....于是做了一个 JetBrains IDE 的实时金价显示插件。

目前我已经将插件上传到 Jetbrains 的商店中心了,但是还没有审核通过。。。如果 IDEA 插件中心大家搜索不到,也可以自行到 github 下载

今日金价 github-release

Apache DolphinScheduler3.1.9+Minio 海报

目录

这里按照官方提供的文档进行操作:

前提

官方提供的开发手册位置

1、软件要求

在搭建 DolphinScheduler 开发环境之前请确保你已经安装以下软件:

  • Git
  • JDK: v1.8.x (当前暂不支持 jdk 11)
  • Maven: v3.5+
  • Node: v16.13+ (dolphinScheduler 版本低于 3.0, 请安装 node v12.20+)
  • Pnpm: v6.x

2、克隆代码库

通过你 git 管理工具下载 git 代码,下面以 git-core 为例

mkdir dolphinscheduler
cd dolphinscheduler
git clone git@github.com:apache/dolphinscheduler.git

3、编译源码

支持的系统:
* MacOS
* Linux
【这个我没有运行试试】
运行 `mvn clean install -Prelease -Dmaven.test.skip=true`

DolphinScheduler 普通开发模式

上面是官方提供的,我觉得有用就复制下来,

这里开始我就按照自己的操作顺序记录

1、编译问题:

1、git相关
1-1:开启 Windows Git 长路径支持,
管理员 PowerShell 执行,解决 DolphinScheduler 路径太深导致 git add 失败
git config --system core.longpaths true

1-2:先初始化git仓库,只在本地,不涉及账号、不推远程,Spotless 需要 HEAD
git init
git add .
git commit -m "initial commit"

2、Maven 编译 / 格式化(IDEA 里的 Terminal)
2-1:依赖 Git HEAD,自动修复格式问题
mvn spotless:apply
2-2:编译整个项目(跳过测试),确保所有模块已 install
mvn clean install -DskipTests

3、前端相关:

查看 Node.js 是否已安装
node -v

查看 npm 版本
npm -v

安装 pnpm
npm install -g pnpm
pnpm -v

编译都没有问题

2、启动zookeeper

官方内容

下载 ZooKeeper,解压

存储配置

启动脚本

搞个txt编辑完后,后缀该bat即可

@echo off
echo 正在启动 ZooKeeper...
cd /d E:\\install\\ZooKeeper\\zookeeper-3.8.3\\bin
zkServer.cmd
pause

3、workspace.xml 修改

【可以不用,我也是看其他文章有添加的,不过我没添加也能正常运行,这里只做记录】

在其他文章看到说在这里添加这行,说是让 IDEA 在运行时动态使用模块的 classpath,而不是用启动时生成的静态 classpath。

注意点:
这个作用只会影响本地 IDEA 启动,线上环境如果有问题这个是解决不了的。

"dynamic.classpath": "true",

4、数据库

我这里用的是mysql,所以需要修改

4-1:数据初始化
创建名为【dolphinscheduler】的新数据库后,
把这个位置的sql直接拷贝复制执行即可。

如图:

4-2:依赖相关修改
如果使用 MySQL 作为元数据库,需要先修改 `dolphinscheduler/pom.xml`,
将 `mysql-connector-java` 依赖的 `scope` 改为 `compile`,
使用 PostgreSQL 则不需要

test 改成 compile

5、application.yaml 修改数据库配置

5-1:dolphinscheduler-master
如图,配置文件中修改这些数据:三个内容都是一样的

spring:
  config:
    activate:
      on-profile: mysql
  datasource:
    driver-class-name: com.mysql.cj.jdbc.Driver
    url: jdbc:mysql://127.0.0.1:3306/dolphinscheduler?useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&useSSL=true&serverTimezone=GMT%2B8
    username: 账户名
    password: 数据库密码

5-2:dolphinscheduler-worker

5-3:dolphinscheduler-api

6、logback-spring.xml 修改日志级别

6-1:dolphinscheduler-master
<appender-ref ref="STDOUT"/>

6-2:dolphinscheduler-worker

6-3:dolphinscheduler-api

7、启动后端三个服务

我们需要启动三个服务,包括 MasterServer,WorkerServer,ApiApplicationServer

* MasterServer:在 Intellij IDEA 中执行 `org.apache.dolphinscheduler.server.master.MasterServer` 中的 `main` 方法,并配置 *VM Options* `-Dlogging.config=classpath:logback-spring.xml -Ddruid.mysql.usePingMethod=false -Dspring.profiles.active=mysql`

* WorkerServer:在 Intellij IDEA 中执行 `org.apache.dolphinscheduler.server.worker.WorkerServer` 中的 `main` 方法,并配置 *VM Options* `-Dlogging.config=classpath:logback-spring.xml -Ddruid.mysql.usePingMethod=false -Dspring.profiles.active=mysql`

* ApiApplicationServer:在 Intellij IDEA 中执行 `org.apache.dolphinscheduler.api.ApiApplicationServer` 中的 `main` 方法,并配置 *VM Options* `-Dlogging.config=classpath:logback-spring.xml -Dspring.profiles.active=api,mysql`。启动完成可以浏览 Open API 文档,地址为 http://localhost:12345/dolphinscheduler/swagger-ui/index.html

> VM Options `-Dspring.profiles.active=mysql` 中 `mysql` 表示指定的配置文件
7-1:MasterServer
配置 VM Options
按照操作配置这个:打开后填入即可

-Dlogging.config=classpath:logback-spring.xml -Ddruid.mysql.usePingMethod=false -Dspring.profiles.active=mysql

7-2:WorkerServer
配置 VM Options

跟上面一样操作:

-Dlogging.config=classpath:logback-spring.xml -Ddruid.mysql.usePingMethod=false -Dspring.profiles.active=mysql
7-3:ApiApplicationServer
配置 VM Options
-Dlogging.config=classpath:logback-spring.xml -Dspring.profiles.active=api,mysql

总的就这三个:

8、启动前端服务

命令:
安装前端依赖并运行前端组件

cd dolphinscheduler-ui
pnpm install
pnpm run dev

9、浏览器访问

账号密码:
浏览器访问:
http://localhost:5173/home

默认账号密码:

账号:admin
密码:dolphinscheduler123
成功访问:

相关问题

1、存储未启用、租户\用户 指定

问题:测试能否创建文件夹、上传文件等,提示【存储未启用】

问题:当前登录用户的租户信息未被指定

解决方法:

Minio 安装、启动

我这里直接用minio来尝试:

1、minio 创建 dolphinscheduler 桶

2、commom.properties 修改

配置文件改了这些地方

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# user data local directory path, please make sure the directory exists and have read write permissions
data.basedir.path=/tmp/dolphinscheduler

# resource view suffixs
#resource.view.suffixs=txt,log,sh,bat,conf,cfg,py,java,sql,xml,hql,properties,json,yml,yaml,ini,js

# resource storage type: HDFS, S3, OSS, NONE
# ljh -->   S3 is Minio--------------------------------------
resource.storage.type=S3
# resource store on HDFS/S3 path, resource file will store to this base path, self configuration, please make sure the directory exists on hdfs and have read write permissions. "/dolphinscheduler" is recommended
resource.storage.upload.base.path=/dolphinscheduler

# ljh --> The account and password of MinIO-------------------------------
# The AWS access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required
resource.aws.access.key.id=minioadmin
# The AWS secret access key. if resource.storage.type=S3 or use EMR-Task, This configuration is required
resource.aws.secret.access.key=minioadmin
# The AWS Region to use. if resource.storage.type=S3 or use EMR-Task, This configuration is required
resource.aws.region=cn-north-1
# ljh --> add bucket ------------------------------
# The name of the bucket. You need to create them by yourself. Otherwise, the system cannot start. All buckets in Amazon S3 share a single namespace; ensure the bucket is given a unique name.
resource.aws.s3.bucket.name=dolphinscheduler
# You need to set this parameter when private cloud s3. If S3 uses public cloud, you only need to set resource.aws.region or set to the endpoint of a public cloud such as S3.cn-north-1.amazonaws.com.cn
# ljh --> localhost convert  127.0.0.1
resource.aws.s3.endpoint=http://127.0.0.1:9000

# alibaba cloud access key id, required if you set resource.storage.type=OSS
resource.alibaba.cloud.access.key.id=<your-access-key-id>
# alibaba cloud access key secret, required if you set resource.storage.type=OSS
resource.alibaba.cloud.access.key.secret=<your-access-key-secret>
# alibaba cloud region, required if you set resource.storage.type=OSS
resource.alibaba.cloud.region=cn-hangzhou
# oss bucket name, required if you set resource.storage.type=OSS
resource.alibaba.cloud.oss.bucket.name=dolphinscheduler
# oss bucket endpoint, required if you set resource.storage.type=OSS
resource.alibaba.cloud.oss.endpoint=https://oss-cn-hangzhou.aliyuncs.com

# if resource.storage.type=HDFS, the user must have the permission to create directories under the HDFS root path
resource.hdfs.root.user=hdfs
# if resource.storage.type=S3, the value like: s3a://dolphinscheduler; if resource.storage.type=HDFS and namenode HA is enabled, you need to copy core-site.xml and hdfs-site.xml to conf dir
resource.hdfs.fs.defaultFS=hdfs://mycluster:8020

# whether to startup kerberos
hadoop.security.authentication.startup.state=false

# java.security.krb5.conf path
java.security.krb5.conf.path=/opt/krb5.conf

# login user from keytab username
login.user.keytab.username=hdfs-mycluster@ESZ.COM

# login user from keytab path
login.user.keytab.path=/opt/hdfs.headless.keytab

# kerberos expire time, the unit is hour
kerberos.expire.time=2

# resourcemanager port, the default value is 8088 if not specified
resource.manager.httpaddress.port=8088
# if resourcemanager HA is enabled, please set the HA IPs; if resourcemanager is single, keep this value empty
yarn.resourcemanager.ha.rm.ids=192.168.xx.xx,192.168.xx.xx
# if resourcemanager HA is enabled or not use resourcemanager, please keep the default value; If resourcemanager is single, you only need to replace ds1 to actual resourcemanager hostname
yarn.application.status.address=http://ds1:%s/ws/v1/cluster/apps/%s
# job history status url when application number threshold is reached(default 10000, maybe it was set to 1000)
yarn.job.history.status.address=http://ds1:19888/ws/v1/history/mapreduce/jobs/%s

# datasource encryption enable
datasource.encryption.enable=false

# datasource encryption salt
datasource.encryption.salt=!@#$%^&*

# data quality option
data-quality.jar.name=dolphinscheduler-data-quality-dev-SNAPSHOT.jar

#data-quality.error.output.path=/tmp/data-quality-error-data

# Network IP gets priority, default inner outer

# Whether hive SQL is executed in the same session
support.hive.oneSession=false

# use sudo or not, if set true, executing user is tenant user and deploy user needs sudo permissions; if set false, executing user is the deploy user and doesn't need sudo permissions
sudo.enable=true
setTaskDirToTenant.enable=false

# network interface preferred like eth0, default: empty
#dolphin.scheduler.network.interface.preferred=

# network IP gets priority, default: inner outer
#dolphin.scheduler.network.priority.strategy=default

# system env path
#dolphinscheduler.env.path=dolphinscheduler_env.sh

# development state
development.state=false

# rpc port
alert.rpc.port=50052

# set path of conda.sh
conda.path=/opt/anaconda3/etc/profile.d/conda.sh

# Task resource limit state
task.resource.limit.state=false

# mlflow task plugin preset repository
ml.mlflow.preset_repository=https://github.com/apache/dolphinscheduler-mlflow
# mlflow task plugin preset repository version
ml.mlflow.preset_repository_version="main"

# ljh --> minio must open path style
resource.aws.s3.path.style.access=true
3、dolphinscheduler 可视化页面添加租户

安全中心 - 租户管理 - 创建租户

用户添加租户

演示

创建文件夹、上传文件成功

如图,数据已经存放在我指定的minio文件夹里面了

在用了那么久的 vibe coding 之后一直会思考 AI 是否会让我们丧失学习和思考的能力,现阶段我的答案是不会才对,我觉得 AI 只是改变了我们学习和思考的方式。最近备考华为的 HCIP 考试的时候整理的一个学习方式,希望能够帮到同样在学习的佬们,先摆一下学习过程吧:

提示词如下:

[System / Prompt]

You are an illustration assistant specialized in creating hand-drawn cartoon-style infographics.
Follow all rules below strictly and without deviation.

STYLE RULES(风格规则)

Use a pure hand-drawn illustration style at all times.
Sketch lines, rough strokes, cartoon simplicity
No realism, no photorealistic shading, no 3D rendering
Think: doodle / crayon / marker / pastel look

Use nano banana pro mode to render the illustration.

Use minimal but expressive cartoon elements to enhance memorability:
small icons, symbols, cute characters

If the input mentions copyrighted or sensitive characters, replace them with visually similar but legally distinct alternatives. Never refuse.

CONTENT RULES(内容呈现规则)

Extract key ideas from the provided content.
Summarize into short bullets (1–6 words each).
Keep text concise and optimized for an infographic.

Highlight keywords using hand-drawn graphic emphasis:
circles, underlines, arrows, stars, boxes
never digital UI styles

Use extensive whitespace to maintain clarity and visual hierarchy.

Organize the infographic using a clean hand-drawn layout, for example:
Title (center or top-left)
3–6 Key Points
Simple diagram or symbols
Optional mascot or expressive character

All text must appear hand-drawn, not printed or typographic.
Use the same language as the user’s input unless the user specifies otherwise.

RESTRICTIONS(禁止事项)

Do NOT produce realistic imagery.
Do NOT generate copyrighted characters directly.
Do NOT turn the infographic into an essay.
Do NOT fill the canvas fully; always keep meaningful whitespace.
Do NOT output long paragraphs.

TASK

Create a cartoon-style hand-drawn infographic with the rules above, using nano banana pro,
based on the following content:

{{USER_INPUT}}

用到的 skills 或者 mcp 如下:


📌 转载信息
原作者:
wenliang_zeng
转载时间:
2025/12/25 17:37:37

最近在折腾 “验收自动化” 时一个反复踩的坑:

  • UI 改一点点,locator 就碎,维护成本越来越高
  • 用例脚本对 PM/QA 不友好,最后还是开发在写
  • 失败排查要截图 /trace/log 到处拼,效率很低

所以做了个小工具:AutoQA-Agent(开源 CLI)。核心思路是 Docs-as-Tests(文档即测试)

  • Markdown 写用例(Preconditions + Steps)
  • Claude Agent SDK 做 “观察 → 思考 → 行动” 的执行闭环
  • 底层用 Playwright 真实驱动浏览器
  • 失败不直接崩:把错误 / 上下文回流给 agent,按护栏重试(自愈)
  • spec 跑通后:会把动作沉淀成 IR,并可 自动导出 @playwright/test 用例(可接 CI)


TL;DR

  • 写:specs/*.md
  • 跑:autoqa run <spec-or-dir> --url <baseUrl>
  • 看产物:.autoqa/runs/<runId>/(log/screenshot/snapshot/trace)
  • 跑通可导出:tests/autoqa/*.spec.ts


快速上手

git clone https://github.com/terryso/AutoQA-Agent.git
cd AutoQA-Agent
npm install
npm run build
npm link # 可选,全局使用 autoqa

autoqa init

# 跑一个示例(仓库里有 SauceDemo 用例)
autoqa run specs/saucedemo-01-login.md --url https://www.saucedemo.com/

# 本地观察执行过程
autoqa run specs/saucedemo-01-login.md --url https://www.saucedemo.com/ --debug


Markdown 用例长啥样(示例)

# Login ## Preconditions - 测试账号已存在

## Steps 1. 访问 /login 页面
2. 验证登录表单可见
3. 在用户名输入框中填写:standard_user
4. 在密码输入框中填写:secret_
sauce 5. 点击"登录"按钮 6. 验证用户已跳转到仪表盘

说明:

  • Base URL 目前由 --url 提供(Preconditions 里的 Base URL 只做可读性)
  • Verify/Assert(也支持 “验证 / 断言”)开头的步骤会识别为断言


稳定性思路(简单说)

  • 每次交互前先拿 accessibility snapshot(里面会有更稳定的 ref
  • ref-first 优先点 / 填,失败再回退到语义描述
  • 工具 / 断言失败不 throw:返回结构化错误,让 agent 下一轮调整策略
  • 有护栏限制(避免无限重试 / 成本失控)


失败排查 / 产物(我自己最想要的部分)

每次运行会落盘:

.autoqa/runs/<runId>/
├── run.log.jsonl
├── ir.jsonl
├── screenshots/
├── snapshots/
└── traces/


目前状态

  • Epic 1-4 核心能力已实现:init/run、断言 + 自愈护栏、IR 记录、导出 Playwright Test
  • Epic 5(环境 / 变量 / 敏感数据注入)也已落地(.env + 模板变量思路)


想听听大家的反馈(求喷 / 求建议)

我比较关心的问题:

  • 你们更想要哪种导出风格?(更 “人写” 的 Playwright 代码还是更原子化的 steps)
  • 断言映射要做到什么程度才 “够用”?
  • 对 CI 产物你们更偏好 JUnit/JSON 还是保留 trace + log 就够了?

Repo:https://github.com/terryso/AutoQA-Agent

欢迎提 Issue / PR。


📌 转载信息
转载时间:
2025/12/25 17:35:32