# local-docker-dev

**Repository Path**: wuxueshi/local-docker-dev

## Basic Information

- **Project Name**: local-docker-dev
- **Description**: windows本地开发使用docker环境，使mysql以及redis作为多个项目共同使用的服务。提供服务的compose.yml文件，拿来即用，无心智负担。
- **Primary Language**: Docker
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: https://gitee.com/wuxueshi
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-12-26
- **Last Updated**: 2026-06-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# local-docker-dev

#### 介绍
windows本地开发使用docker环境，使mysql以及redis作为多个项目共同使用的服务，避免重复创建容器

#### 软件架构
- docker-desktop 4.24.2
- mysql:8.0
- redis:7.0.4
- elasticsearch:8.11.0
- kibana:8.11.0
- milvus:v2.4.15（向量数据库，单机模式）


#### 安装教程
1. 安装docker
2. cd 本项目更目录
3. 执行 `docker compose up -d`
4. Finish


#### 使用说明
1. 查找当前网络网关，命令行执行 `docker inspect common-redis`，找到下图标记的位置，`172.22.0.1` 就是连接MySql和Redis的Host了。
 - MySql默认密码：`123456`, 端口： `13306`
 - Redis默认密码：`123456`, 端口： `16379`
 - Elasticsearch HTTP 地址：`http://localhost:19200`，用户名：`elastic`，密码：`dev123456`
 - Kibana 管理界面地址：`http://localhost:15601`，登录用户名：`elastic`，密码：`dev123456`
 - Milvus API 地址：`http://localhost:19530`，gRPC 端口，客户端连接用

![输入图片说明](image.png)

2. 自己的docker项目networks修改为`dev_net`

#### Milvus 向量数据库常用操作示例

##### 1. 检查 Milvus 服务状态
```bash
curl http://localhost:19530/health
```

##### 2. 使用 Python SDK 进行向量操作
```python
# pip install pymilvus
from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType, utility

# 连接 Milvus
connections.connect(
    alias="default",
    host="localhost",
    port="19530"
)

# 定义集合结构
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128),
    FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=500)
]
schema = CollectionSchema(fields=fields, description="向量搜索示例集合")
collection = Collection(name="my_vectors", schema=schema)

# 插入向量数据
import random
embeddings = [[random.random() for _ in range(128)] for _ in range(10)]
entities = [embeddings, ["内容1", "内容2", "内容3", "内容4", "内容5",
                        "内容6", "内容7", "内容8", "内容9", "内容10"]]
collection.insert(entities)
collection.flush()

# 搜索相似向量
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
results = collection.search(
    data=[embeddings[0]],
    anns_field="embedding",
    param=search_params,
    limit=5,
    output_fields=["content"]
)
print(results)
```

##### 3. Milvus 连接示例（Python SDK）
```python
# pip install pymilvus
from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType, utility

# 连接 Milvus
connections.connect(
    alias="default",
    host="localhost",
    port="19530"
)

# 定义集合结构
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128),
    FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=500)
]
schema = CollectionSchema(fields=fields, description="向量搜索示例集合")
collection = Collection(name="my_vectors", schema=schema)

# 插入向量数据
import random
embeddings = [[random.random() for _ in range(128)] for _ in range(10)]
entities = [embeddings, ["内容1", "内容2", "内容3", "内容4", "内容5",
                        "内容6", "内容7", "内容8", "内容9", "内容10"]]
collection.insert(entities)
collection.flush()

# 搜索相似向量
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
results = collection.search(
    data=[embeddings[0]],
    anns_field="embedding",
    param=search_params,
    limit=5,
    output_fields=["content"]
)
print(results)
```

##### 4. PHP/Laravel 项目集成示例
```php
// 需要通过 gRPC 调用 Milvus
// 推荐使用第三方封装库或自行封装 gRPC 客户端
$milvusHost = "172.22.0.1";
$milvusPort = "19530";

// 使用 curl 调用 Milvus REST API
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, "http://{$milvusHost}:{$milvusPort}/health");
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);
```


#### Elasticsearch 常用操作示例

##### 1. 查看集群健康状态
```bash
curl -u elastic:dev123456 http://localhost:19200/_cluster/health?pretty
```

##### 2. 创建索引
```bash
curl -u elastic:dev123456 -X PUT "http://localhost:19200/my_index" \
  -H "Content-Type: application/json" \
  -d '{"settings":{"number_of_shards":1,"number_of_replicas":0}}'
```

##### 3. 插入文档
```bash
curl -u elastic:dev123456 -X POST "http://localhost:19200/my_index/_doc/1" \
  -H "Content-Type: application/json" \
  -d '{"title":"Hello Elasticsearch","content":"这是第一条ES数据"}'
```

##### 4. 搜索文档
```bash
curl -u elastic:dev123456 -X GET "http://localhost:19200/my_index/_search?pretty" \
  -H "Content-Type: application/json" \
  -d '{"query":{"match":{"title":"Hello"}}}'
```

##### 5. PHP/Laravel 项目集成示例
```php
// composer require elasticsearch/elasticsearch
use Elasticsearch\ClientBuilder;

$client = ClientBuilder::create()
    ->setHosts(['http://172.22.0.1:19200'])  // 使用网关地址
    ->setBasicAuthentication('elastic', 'dev123456')
    ->build();

$response = $client->search([
    'index' => 'articles',
    'body' => [
        'query' => [
            'multi_match' => [
                'query' => 'Laravel',
                'fields' => ['title^2', 'content']
            ]
        ]
    ]
]);
```

##### 6. Kibana Dev Tools 直接查询（浏览器访问 http://localhost:15601）
```
GET /my_index/_search
{
  "query": {
    "match": {
      "title": "Hello"
    }
  }
}
```


#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request