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Add environment configuration and documentation for various services

Browse Source 
- Created .env.example files for Kafka, Kibana, KodBox, Kong, Langfuse, Logstash, n8n, Nginx, OceanBase, OpenCoze, RocketMQ, TiDB, and TiKV.
- Added README.md and README.zh.md files for OceanBase, RocketMQ, TiDB, and TiKV, detailing usage, configuration, and access instructions.
- Implemented docker-compose.yaml files for OceanBase, RocketMQ, TiDB, and TiKV, defining service configurations, health checks, and resource limits.
- Included broker.conf for RocketMQ to specify broker settings.
- Established a consistent timezone (UTC) across all services.
- Provided optional port overrides in .env.example files for flexibility in deployment.
This commit is contained in:
Sun-ZhenXing
2025-10-22 11:46:50 +08:00
parent 84e8b85990
commit ece59b42bf
49 changed files with 2326 additions and 0 deletions
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# Apache version
APACHE_VERSION=2.4.62-alpine3.20
# Timezone
TZ=UTC
# Apache run user and group
APACHE_RUN_USER=www-data
APACHE_RUN_GROUP=www-data
# Port overrides (optional)
# APACHE_HTTP_PORT_OVERRIDE=80
# APACHE_HTTPS_PORT_OVERRIDE=443

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# Cassandra version
CASSANDRA_VERSION=5.0.2
# Timezone
TZ=UTC
# Cluster configuration
CASSANDRA_CLUSTER_NAME=Test Cluster
CASSANDRA_DC=datacenter1
CASSANDRA_RACK=rack1
CASSANDRA_ENDPOINT_SNITCH=GossipingPropertyFileSnitch
CASSANDRA_NUM_TOKENS=256
CASSANDRA_SEEDS=cassandra
CASSANDRA_START_RPC=false
# JVM heap sizes
MAX_HEAP_SIZE=1G
HEAP_NEWSIZE=100M
# Port overrides (optional)
# CASSANDRA_CQL_PORT_OVERRIDE=9042
# CASSANDRA_THRIFT_PORT_OVERRIDE=9160

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# ClickHouse version
CLICKHOUSE_VERSION=24.11.1.2557
# Timezone
TZ=UTC
# Database configuration
CLICKHOUSE_DB=default
CLICKHOUSE_USER=default
CLICKHOUSE_PASSWORD=clickhouse
CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT=1
# Port overrides (optional)
# CLICKHOUSE_HTTP_PORT_OVERRIDE=8123
# CLICKHOUSE_NATIVE_PORT_OVERRIDE=9000
# CLICKHOUSE_MYSQL_PORT_OVERRIDE=9004
# CLICKHOUSE_POSTGRES_PORT_OVERRIDE=9005

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# ClickHouse
ClickHouse is a fast open-source column-oriented database management system that allows generating analytical data reports in real-time.
## Usage
```bash
docker compose up -d
```
## Configuration
Key environment variables:
- `CLICKHOUSE_DB`: Default database name (default: `default`)
- `CLICKHOUSE_USER`: Default user name (default: `default`)
- `CLICKHOUSE_PASSWORD`: Default user password (default: `clickhouse`)
- `CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT`: Enable SQL-driven access control (default: `1`)
## Ports
- `8123`: HTTP interface
- `9000`: Native TCP protocol
- `9004`: MySQL protocol emulation
- `9005`: PostgreSQL protocol emulation
## Access
### HTTP Interface
```bash
curl 'http://localhost:8123/?user=default&password=clickhouse' -d 'SELECT 1'
```
### ClickHouse Client
```bash
docker compose exec clickhouse clickhouse-client --user default --password clickhouse
```
### MySQL Protocol
```bash
mysql -h127.0.0.1 -P9004 -udefault -pclickhouse
```
### PostgreSQL Protocol
```bash
psql -h127.0.0.1 -p9005 -Udefault
```
## Example Queries
```sql
-- Create a table
CREATE TABLE events (
event_date Date,
event_type String,
user_id UInt32
) ENGINE = MergeTree()
ORDER BY (event_date, event_type);
-- Insert data
INSERT INTO events VALUES ('2024-01-01', 'click', 1), ('2024-01-01', 'view', 2);
-- Query data
SELECT * FROM events;
```
## Notes
- ClickHouse is optimized for OLAP (Online Analytical Processing) workloads
- It excels at aggregating large amounts of data quickly
- For production, consider using a cluster setup with replication
- Custom configurations can be mounted in `/etc/clickhouse-server/config.d/` and `/etc/clickhouse-server/users.d/`
## References
- [ClickHouse Official Documentation](https://clickhouse.com/docs)
- [ClickHouse Docker Hub](https://hub.docker.com/r/clickhouse/clickhouse-server)

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# ClickHouse
ClickHouse 是一个快速的开源列式数据库管理系统，支持实时生成分析数据报告。
## 使用方法
```bash
docker compose up -d
```
## 配置说明
主要环境变量：
- `CLICKHOUSE_DB`：默认数据库名称（默认：`default`）
- `CLICKHOUSE_USER`：默认用户名（默认：`default`）
- `CLICKHOUSE_PASSWORD`：默认用户密码（默认：`clickhouse`）
- `CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT`：启用 SQL 驱动的访问控制（默认：`1`）
## 端口说明
- `8123`：HTTP 接口
- `9000`：Native TCP 协议
- `9004`：MySQL 协议模拟
- `9005`：PostgreSQL 协议模拟
## 访问方式
### HTTP 接口
```bash
curl 'http://localhost:8123/?user=default&password=clickhouse' -d 'SELECT 1'
```
### ClickHouse 客户端
```bash
docker compose exec clickhouse clickhouse-client --user default --password clickhouse
```
### MySQL 协议
```bash
mysql -h127.0.0.1 -P9004 -udefault -pclickhouse
```
### PostgreSQL 协议
```bash
psql -h127.0.0.1 -p9005 -Udefault
```
## 示例查询
```sql
-- 创建表
CREATE TABLE events (
event_date Date,
event_type String,
user_id UInt32
) ENGINE = MergeTree()
ORDER BY (event_date, event_type);
-- 插入数据
INSERT INTO events VALUES ('2024-01-01', 'click', 1), ('2024-01-01', 'view', 2);
-- 查询数据
SELECT * FROM events;
```
## 注意事项
- ClickHouse 专为 OLAP（在线分析处理）工作负载优化
- 擅长快速聚合大量数据
- 生产环境建议使用集群配置和复制功能
- 自定义配置可以挂载到 `/etc/clickhouse-server/config.d/` 和 `/etc/clickhouse-server/users.d/`
## 参考资料
- [ClickHouse 官方文档](https://clickhouse.com/docs)
- [ClickHouse Docker Hub](https://hub.docker.com/r/clickhouse/clickhouse-server)

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
clickhouse:
<<: *default
image: clickhouse/clickhouse-server:${CLICKHOUSE_VERSION:-24.11.1.2557}
hostname: clickhouse
environment:
TZ: ${TZ:-UTC}
CLICKHOUSE_DB: ${CLICKHOUSE_DB:-default}
CLICKHOUSE_USER: ${CLICKHOUSE_USER:-default}
CLICKHOUSE_PASSWORD: ${CLICKHOUSE_PASSWORD:-clickhouse}
CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT: ${CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT:-1}
volumes:
- clickhouse_data:/var/lib/clickhouse
- clickhouse_logs:/var/log/clickhouse-server
# Custom configuration
# - ./config.xml:/etc/clickhouse-server/config.d/config.xml
# - ./users.xml:/etc/clickhouse-server/users.d/users.xml
ports:
- "${CLICKHOUSE_HTTP_PORT_OVERRIDE:-8123}:8123"
- "${CLICKHOUSE_NATIVE_PORT_OVERRIDE:-9000}:9000"
- "${CLICKHOUSE_MYSQL_PORT_OVERRIDE:-9004}:9004"
- "${CLICKHOUSE_POSTGRES_PORT_OVERRIDE:-9005}:9005"
ulimits:
nofile:
soft: 262144
hard: 262144
deploy:
resources:
limits:
cpus: '4.0'
memory: 4G
reservations:
cpus: '1.0'
memory: 1G
healthcheck:
test: ["CMD-SHELL", "wget --no-verbose --tries=1 --spider http://localhost:8123/ping || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
volumes:
clickhouse_data:
clickhouse_logs:

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# Consul version
CONSUL_VERSION=1.20.3
# Timezone
TZ=UTC
# Network interface configuration
CONSUL_BIND_INTERFACE=eth0
CONSUL_CLIENT_INTERFACE=eth0
# Consul local configuration (JSON string)
CONSUL_LOCAL_CONFIG={"datacenter":"dc1","server":true,"ui_config":{"enabled":true},"bootstrap_expect":1,"log_level":"INFO"}
# Port overrides (optional)
# CONSUL_HTTP_PORT_OVERRIDE=8500
# CONSUL_DNS_PORT_OVERRIDE=8600
# CONSUL_SERF_LAN_PORT_OVERRIDE=8301
# CONSUL_SERF_WAN_PORT_OVERRIDE=8302
# CONSUL_SERVER_RPC_PORT_OVERRIDE=8300

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# DuckDB version
DUCKDB_VERSION=v1.1.3
# Timezone
TZ=UTC

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# DuckDB
DuckDB is an in-process SQL OLAP database management system designed to support analytical query workloads. It's embedded, zero-dependency, and extremely fast.
## Usage
```bash
docker compose up -d
```
## Access
### Interactive Shell
Access DuckDB CLI:
```bash
docker compose exec duckdb duckdb /data/duckdb.db
```
### Execute Queries
Run queries directly:
```bash
docker compose exec duckdb duckdb /data/duckdb.db -c "SELECT 1"
```
### Execute SQL File
```bash
docker compose exec duckdb duckdb /data/duckdb.db < query.sql
```
## Example Usage
```sql
-- Create a table
CREATE TABLE users (id INTEGER, name VARCHAR);
-- Insert data
INSERT INTO users VALUES (1, 'Alice'), (2, 'Bob');
-- Query data
SELECT * FROM users;
-- Load CSV file
COPY users FROM '/import/users.csv' (HEADER);
-- Export to CSV
COPY users TO '/data/users_export.csv' (HEADER);
-- Read Parquet file directly
SELECT * FROM '/import/data.parquet';
```
## Features
- **Embeddable**: No separate server process needed
- **Fast**: Vectorized query execution engine
- **Feature-rich**: Full SQL support with window functions, CTEs, etc.
- **File formats**: Native support for CSV, JSON, Parquet
- **Extensions**: PostgreSQL-compatible extensions
## Mounting Data Files
To import data files, mount them as volumes:
```yaml
volumes:
- ./data:/import:ro
```
Then access files in SQL:
```sql
SELECT * FROM '/import/data.csv';
```
## Notes
- DuckDB is designed for analytical (OLAP) workloads, not transactional (OLTP)
- The database file is stored in `/data/duckdb.db`
- Data persists in the named volume `duckdb_data`
- DuckDB can query files directly without importing them
- For production workloads, ensure sufficient memory is allocated
## References
- [DuckDB Official Documentation](https://duckdb.org/docs/)
- [DuckDB Docker Image](https://hub.docker.com/r/davidgasquez/duckdb)

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# DuckDB
DuckDB 是一个进程内 SQL OLAP 数据库管理系统，专为支持分析查询工作负载而设计。它是嵌入式的、零依赖的，并且速度极快。
## 使用方法
```bash
docker compose up -d
```
## 访问方式
### 交互式 Shell
访问 DuckDB CLI：
```bash
docker compose exec duckdb duckdb /data/duckdb.db
```
### 执行查询
直接运行查询：
```bash
docker compose exec duckdb duckdb /data/duckdb.db -c "SELECT 1"
```
### 执行 SQL 文件
```bash
docker compose exec duckdb duckdb /data/duckdb.db < query.sql
```
## 使用示例
```sql
-- 创建表
CREATE TABLE users (id INTEGER, name VARCHAR);
-- 插入数据
INSERT INTO users VALUES (1, 'Alice'), (2, 'Bob');
-- 查询数据
SELECT * FROM users;
-- 加载 CSV 文件
COPY users FROM '/import/users.csv' (HEADER);
-- 导出到 CSV
COPY users TO '/data/users_export.csv' (HEADER);
-- 直接读取 Parquet 文件
SELECT * FROM '/import/data.parquet';
```
## 特性
- **可嵌入**：无需单独的服务器进程
- **快速**：向量化查询执行引擎
- **功能丰富**：完整的 SQL 支持，包括窗口函数、CTE 等
- **文件格式**：原生支持 CSV、JSON、Parquet
- **扩展**：兼容 PostgreSQL 的扩展
## 挂载数据文件
要导入数据文件，将它们作为卷挂载：
```yaml
volumes:
- ./data:/import:ro
```
然后在 SQL 中访问文件：
```sql
SELECT * FROM '/import/data.csv';
```
## 注意事项
- DuckDB 专为分析（OLAP）工作负载设计，而非事务（OLTP）
- 数据库文件存储在 `/data/duckdb.db`
- 数据持久化在命名卷 `duckdb_data` 中
- DuckDB 可以直接查询文件而无需导入
- 生产工作负载需确保分配足够的内存
## 参考资料
- [DuckDB 官方文档](https://duckdb.org/docs/)
- [DuckDB Docker 镜像](https://hub.docker.com/r/davidgasquez/duckdb)

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
duckdb:
<<: *default
image: davidgasquez/duckdb:${DUCKDB_VERSION:-v1.1.3}
command: ["duckdb", "/data/duckdb.db"]
stdin_open: true
tty: true
environment:
TZ: ${TZ:-UTC}
volumes:
- duckdb_data:/data
# Mount additional data files
# - ./data:/import:ro
deploy:
resources:
limits:
cpus: '2.0'
memory: 4G
reservations:
cpus: '0.5'
memory: 512M
healthcheck:
test: ["CMD-SHELL", "duckdb /data/duckdb.db -c 'SELECT 1' || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 10s
volumes:
duckdb_data:

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# Elasticsearch version
ELASTICSEARCH_VERSION=8.16.1
# Timezone
TZ=UTC
# Cluster configuration
ELASTICSEARCH_CLUSTER_NAME=docker-cluster
ELASTICSEARCH_DISCOVERY_TYPE=single-node
# Security settings
ELASTICSEARCH_SECURITY_ENABLED=false
ELASTICSEARCH_SSL_ENABLED=false
# JVM heap size
ELASTICSEARCH_HEAP_SIZE=1g
# Port overrides (optional)
# ELASTICSEARCH_HTTP_PORT_OVERRIDE=9200
# ELASTICSEARCH_TRANSPORT_PORT_OVERRIDE=9300

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# Flink version
FLINK_VERSION=1.20.0-scala_2.12-java11
# Timezone
TZ=UTC
# Port overrides (optional)
# FLINK_JOBMANAGER_RPC_PORT_OVERRIDE=6123
# FLINK_JOBMANAGER_UI_PORT_OVERRIDE=8081

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# Apache Flink
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams.
## Usage
```bash
docker compose up -d
```
## Components
This setup includes:
- **JobManager**: Coordinates Flink jobs and manages resources
- **TaskManager**: Executes tasks and manages data streams
## Configuration
Key environment variables in `FLINK_PROPERTIES`:
- `jobmanager.rpc.address`: JobManager RPC address
- `jobmanager.memory.process.size`: JobManager memory (default: 1600m)
- `taskmanager.memory.process.size`: TaskManager memory (default: 1600m)
- `taskmanager.numberOfTaskSlots`: Number of task slots per TaskManager (default: 2)
## Ports
- `6123`: JobManager RPC port
- `8081`: Flink Web UI
## Access
### Web UI
Access Flink Dashboard at: <http://localhost:8081>
### Submit Jobs
Submit a Flink job:
```bash
docker compose exec jobmanager ./bin/flink run /opt/flink/examples/streaming/WordCount.jar
```
Submit a custom job:
```bash
docker compose exec jobmanager ./bin/flink run /opt/flink/jobs/my-job.jar
```
### Job Management
```bash
# List running jobs
docker compose exec jobmanager ./bin/flink list
# Cancel a job
docker compose exec jobmanager ./bin/flink cancel <job-id>
# Show job details
docker compose exec jobmanager ./bin/flink info /path/to/job.jar
```
## Example: WordCount
Run the built-in WordCount example:
```bash
docker compose exec jobmanager ./bin/flink run /opt/flink/examples/streaming/WordCount.jar
```
## Scaling TaskManagers
To scale TaskManagers for more processing capacity:
```bash
docker compose up -d --scale taskmanager=3
```
## Custom Jobs
Mount your custom Flink jobs by uncommenting the volume in `docker-compose.yaml`:
```yaml
volumes:
- ./jobs:/opt/flink/jobs
```
Then place your JAR files in the `./jobs` directory.
## Notes
- This is a standalone cluster setup suitable for development
- For production, consider using Flink on Kubernetes or YARN
- Adjust memory settings based on your workload requirements
- Task slots determine parallelism; more slots allow more parallel tasks
- Data is persisted in the named volume `flink_data`
## References
- [Apache Flink Official Documentation](https://flink.apache.org/docs/stable/)
- [Flink Docker Setup](https://nightlies.apache.org/flink/flink-docs-stable/docs/deployment/resource-providers/standalone/docker/)
- [Flink Docker Hub](https://hub.docker.com/_/flink)

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# Apache Flink
Apache Flink 是一个框架和分布式处理引擎，用于对无界和有界数据流进行有状态计算。
## 使用方法
```bash
docker compose up -d
```
## 组件说明
此配置包含：
- **JobManager**：协调 Flink 作业并管理资源
- **TaskManager**：执行任务并管理数据流
## 配置说明
`FLINK_PROPERTIES` 中的关键环境变量：
- `jobmanager.rpc.address`：JobManager RPC 地址
- `jobmanager.memory.process.size`：JobManager 内存（默认：1600m）
- `taskmanager.memory.process.size`：TaskManager 内存（默认：1600m）
- `taskmanager.numberOfTaskSlots`：每个 TaskManager 的任务槽数量（默认：2）
## 端口说明
- `6123`：JobManager RPC 端口
- `8081`：Flink Web UI
## 访问方式
### Web UI
访问 Flink Dashboard：<http://localhost:8081>
### 提交作业
提交 Flink 作业：
```bash
docker compose exec jobmanager ./bin/flink run /opt/flink/examples/streaming/WordCount.jar
```
提交自定义作业：
```bash
docker compose exec jobmanager ./bin/flink run /opt/flink/jobs/my-job.jar
```
### 作业管理
```bash
# 列出运行中的作业
docker compose exec jobmanager ./bin/flink list
# 取消作业
docker compose exec jobmanager ./bin/flink cancel <job-id>
# 显示作业详情
docker compose exec jobmanager ./bin/flink info /path/to/job.jar
```
## 示例：WordCount
运行内置的 WordCount 示例：
```bash
docker compose exec jobmanager ./bin/flink run /opt/flink/examples/streaming/WordCount.jar
```
## 扩展 TaskManager
要扩展 TaskManager 以获得更多处理能力：
```bash
docker compose up -d --scale taskmanager=3
```
## 自定义作业
通过取消注释 `docker-compose.yaml` 中的卷来挂载自定义 Flink 作业：
```yaml
volumes:
- ./jobs:/opt/flink/jobs
```
然后将 JAR 文件放在 `./jobs` 目录中。
## 注意事项
- 这是一个独立集群配置，适合开发环境
- 生产环境建议在 Kubernetes 或 YARN 上使用 Flink
- 根据工作负载需求调整内存设置
- 任务槽决定并行度，更多槽允许更多并行任务
- 数据持久化在命名卷 `flink_data` 中
## 参考资料
- [Apache Flink 官方文档](https://flink.apache.org/zh/docs/stable/)
- [Flink Docker 设置](https://nightlies.apache.org/flink/flink-docs-stable/docs/deployment/resource-providers/standalone/docker/)
- [Flink Docker Hub](https://hub.docker.com/_/flink)

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
jobmanager:
<<: *default
image: flink:${FLINK_VERSION:-1.20.0-scala_2.12-java11}
hostname: jobmanager
command: jobmanager
environment:
TZ: ${TZ:-UTC}
FLINK_PROPERTIES: |
jobmanager.rpc.address: jobmanager
jobmanager.memory.process.size: 1600m
taskmanager.memory.process.size: 1600m
taskmanager.numberOfTaskSlots: 2
volumes:
- flink_data:/opt/flink/data
# Custom Flink jobs
# - ./jobs:/opt/flink/jobs
ports:
- "${FLINK_JOBMANAGER_RPC_PORT_OVERRIDE:-6123}:6123"
- "${FLINK_JOBMANAGER_UI_PORT_OVERRIDE:-8081}:8081"
deploy:
resources:
limits:
cpus: '2.0'
memory: 2G
reservations:
cpus: '0.5'
memory: 1G
healthcheck:
test: ["CMD-SHELL", "wget --no-verbose --tries=1 --spider http://localhost:8081 || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
taskmanager:
<<: *default
image: flink:${FLINK_VERSION:-1.20.0-scala_2.12-java11}
command: taskmanager
environment:
TZ: ${TZ:-UTC}
FLINK_PROPERTIES: |
jobmanager.rpc.address: jobmanager
jobmanager.memory.process.size: 1600m
taskmanager.memory.process.size: 1600m
taskmanager.numberOfTaskSlots: 2
volumes:
- flink_data:/opt/flink/data
# Custom Flink jobs
# - ./jobs:/opt/flink/jobs
depends_on:
jobmanager:
condition: service_healthy
deploy:
resources:
limits:
cpus: '2.0'
memory: 2G
reservations:
cpus: '0.5'
memory: 1G
healthcheck:
test: ["CMD-SHELL", "ps aux | grep -v grep | grep -q taskmanager || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
volumes:
flink_data:

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# Halo version
HALO_VERSION=2.21.9
# Timezone
TZ=UTC
# Halo port
HALO_PORT=8090
# External URL (should match your domain)
HALO_EXTERNAL_URL=http://localhost:8090
# Admin credentials
HALO_ADMIN_USERNAME=admin
# HALO_ADMIN_PASSWORD= # Leave empty for random password on first start
# Database configuration
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
POSTGRES_DB=halo
SPRING_R2DBC_URL=r2dbc:pool:postgresql://halo-db:5432/halo
SPRING_SQL_INIT_PLATFORM=postgresql

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# Harbor version
HARBOR_VERSION=v2.12.0
# Timezone
TZ=UTC
# Harbor secrets (generate random strings for production)
HARBOR_CORE_SECRET=
HARBOR_JOBSERVICE_SECRET=
# Database configuration
POSTGRES_PASSWORD=changeit
REDIS_PASSWORD=
# Admin password
HARBOR_ADMIN_PASSWORD=Harbor12345
# External URL
HARBOR_EXTERNAL_URL=http://localhost:80
# Port overrides (optional)
# HARBOR_HTTP_PORT_OVERRIDE=80
# HARBOR_HTTPS_PORT_OVERRIDE=443

12
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# HBase version
HBASE_VERSION=2.6
# Timezone
TZ=UTC
# Port overrides (optional)
# HBASE_MASTER_PORT_OVERRIDE=16000
# HBASE_MASTER_INFO_PORT_OVERRIDE=16010
# HBASE_REGIONSERVER_PORT_OVERRIDE=16020
# HBASE_REGIONSERVER_INFO_PORT_OVERRIDE=16030
# HBASE_ZOOKEEPER_PORT_OVERRIDE=2181

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# HBase
HBase is a distributed, scalable, big data store built on top of Hadoop. It provides random, real-time read/write access to your Big Data.
## Usage
```bash
docker compose up -d
```
## Configuration
This setup runs HBase in standalone mode with embedded ZooKeeper.
## Ports
- `16000`: HBase Master port
- `16010`: HBase Master Web UI
- `16020`: HBase RegionServer port
- `16030`: HBase RegionServer Web UI
- `2181`: ZooKeeper client port
## Access
### HBase Shell
Access HBase shell:
```bash
docker compose exec hbase hbase shell
```
### Web UI
- HBase Master UI: <http://localhost:16010>
- HBase RegionServer UI: <http://localhost:16030>
### Example Commands
```bash
# List tables
echo "list" | docker compose exec -T hbase hbase shell -n
# Create a table
echo "create 'test', 'cf'" | docker compose exec -T hbase hbase shell -n
# Put data
echo "put 'test', 'row1', 'cf:a', 'value1'" | docker compose exec -T hbase hbase shell -n
# Scan table
echo "scan 'test'" | docker compose exec -T hbase hbase shell -n
```
## Notes
- This is a standalone setup suitable for development and testing
- For production, consider using a distributed HBase cluster with external ZooKeeper and HDFS
- Data is persisted in named volumes
## References
- [HBase Official Documentation](https://hbase.apache.org/book.html)
- [HBase Docker Image](https://hub.docker.com/r/harisekhon/hbase)

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# HBase
HBase 是一个构建在 Hadoop 之上的分布式、可扩展的大数据存储系统，提供对大数据的随机、实时读写访问。
## 使用方法
```bash
docker compose up -d
```
## 配置说明
此配置运行 HBase 独立模式，内置 ZooKeeper。
## 端口说明
- `16000`：HBase Master 端口
- `16010`：HBase Master Web UI
- `16020`：HBase RegionServer 端口
- `16030`：HBase RegionServer Web UI
- `2181`：ZooKeeper 客户端端口
## 访问方式
### HBase Shell
访问 HBase shell：
```bash
docker compose exec hbase hbase shell
```
### Web UI
- HBase Master UI：<http://localhost:16010>
- HBase RegionServer UI：<http://localhost:16030>
### 示例命令
```bash
# 列出所有表
echo "list" | docker compose exec -T hbase hbase shell -n
# 创建表
echo "create 'test', 'cf'" | docker compose exec -T hbase hbase shell -n
# 插入数据
echo "put 'test', 'row1', 'cf:a', 'value1'" | docker compose exec -T hbase hbase shell -n
# 扫描表
echo "scan 'test'" | docker compose exec -T hbase hbase shell -n
```
## 注意事项
- 这是一个独立模式配置，适合开发和测试
- 生产环境建议使用分布式 HBase 集群，配合外部 ZooKeeper 和 HDFS
- 数据持久化在命名卷中
## 参考资料
- [HBase 官方文档](https://hbase.apache.org/book.html)
- [HBase Docker 镜像](https://hub.docker.com/r/harisekhon/hbase)

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
hbase:
<<: *default
image: harisekhon/hbase:${HBASE_VERSION:-2.6}
hostname: hbase
environment:
TZ: ${TZ:-UTC}
volumes:
- hbase_data:/hbase-data
- hbase_zookeeper_data:/zookeeper-data
ports:
- "${HBASE_MASTER_PORT_OVERRIDE:-16000}:16000"
- "${HBASE_MASTER_INFO_PORT_OVERRIDE:-16010}:16010"
- "${HBASE_REGIONSERVER_PORT_OVERRIDE:-16020}:16020"
- "${HBASE_REGIONSERVER_INFO_PORT_OVERRIDE:-16030}:16030"
- "${HBASE_ZOOKEEPER_PORT_OVERRIDE:-2181}:2181"
deploy:
resources:
limits:
cpus: '4.0'
memory: 4G
reservations:
cpus: '1.0'
memory: 2G
healthcheck:
test: ["CMD-SHELL", "echo 'status' | hbase shell -n || exit 1"]
interval: 30s
timeout: 10s
retries: 5
start_period: 60s
volumes:
hbase_data:
hbase_zookeeper_data:

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# Jenkins version
JENKINS_VERSION=2.486-lts-jdk17
# Timezone
TZ=UTC
# Jenkins options
JENKINS_OPTS=--httpPort=8080
JAVA_OPTS=-Djenkins.install.runSetupWizard=false -Xmx2g
# Configuration as Code
CASC_JENKINS_CONFIG=/var/jenkins_home/casc_configs
# User and group IDs (adjust to match your host user)
JENKINS_USER_ID=1000
JENKINS_GROUP_ID=1000
# Port overrides (optional)
# JENKINS_HTTP_PORT_OVERRIDE=8080
# JENKINS_AGENT_PORT_OVERRIDE=50000

10
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# Kafka version (Confluent Platform version)
KAFKA_VERSION=7.8.0
# Timezone
TZ=UTC
# Port overrides (optional)
# ZOOKEEPER_CLIENT_PORT_OVERRIDE=2181
# KAFKA_BROKER_PORT_OVERRIDE=9092
# KAFKA_JMX_PORT_OVERRIDE=9999

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# Kibana version
KIBANA_VERSION=8.16.1
# Timezone
TZ=UTC
# Elasticsearch connection
ELASTICSEARCH_HOSTS=http://elasticsearch:9200
ELASTICSEARCH_USERNAME=
ELASTICSEARCH_PASSWORD=
# Security settings
KIBANA_SECURITY_ENABLED=false
KIBANA_ENCRYPTION_KEY=
# Logging
KIBANA_LOG_LEVEL=info
# Port overrides (optional)
# KIBANA_PORT_OVERRIDE=5601

21
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# KodBox version
KODBOX_VERSION=1.62
# Timezone
TZ=UTC
# Port
KODBOX_PORT=80
# MySQL configuration
MYSQL_HOST=kodbox-db
MYSQL_PORT=3306
MYSQL_DATABASE=kodbox
MYSQL_USER=kodbox
MYSQL_PASSWORD=kodbox123
MYSQL_ROOT_PASSWORD=root123
# Redis configuration
REDIS_HOST=kodbox-redis
REDIS_PORT=6379
REDIS_PASSWORD=

26
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# Kong version
KONG_VERSION=3.8.0-alpine
# PostgreSQL version
POSTGRES_VERSION=16.6-alpine3.21
# Timezone
TZ=UTC
# Database password
KONG_DB_PASSWORD=kongpass
# Kong Admin API configuration
KONG_ADMIN_ACCESS_LOG=/dev/stdout
KONG_ADMIN_ERROR_LOG=/dev/stderr
KONG_ADMIN_LISTEN=0.0.0.0:8001
# Kong Proxy configuration
KONG_PROXY_ACCESS_LOG=/dev/stdout
KONG_PROXY_ERROR_LOG=/dev/stderr
# Port overrides (optional)
# KONG_PROXY_PORT_OVERRIDE=8000
# KONG_PROXY_SSL_PORT_OVERRIDE=8443
# KONG_ADMIN_PORT_OVERRIDE=8001
# KONG_ADMIN_SSL_PORT_OVERRIDE=8444

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# Langfuse version
LANGFUSE_VERSION=3.115.0
# Timezone
TZ=UTC
# Port
LANGFUSE_PORT=3000
# Database configuration
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
POSTGRES_DB=langfuse
# NextAuth configuration
NEXTAUTH_URL=http://localhost:3000
NEXTAUTH_SECRET= # Generate with: openssl rand -base64 32
SALT= # Generate with: openssl rand -base64 32
# Feature flags
TELEMETRY_ENABLED=true
LANGFUSE_ENABLE_EXPERIMENTAL_FEATURES=false

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# Logstash version
LOGSTASH_VERSION=8.16.1
# Timezone
TZ=UTC
# Elasticsearch connection
ELASTICSEARCH_HOSTS=http://elasticsearch:9200
ELASTICSEARCH_USERNAME=
ELASTICSEARCH_PASSWORD=
# Monitoring
LOGSTASH_MONITORING_ENABLED=false
# JVM options
LS_JAVA_OPTS=-Xmx1g -Xms1g
# Pipeline configuration
LOGSTASH_PIPELINE_WORKERS=2
LOGSTASH_PIPELINE_BATCH_SIZE=125
LOGSTASH_PIPELINE_BATCH_DELAY=50
# Logging
LOGSTASH_LOG_LEVEL=info
# Port overrides (optional)
# LOGSTASH_BEATS_PORT_OVERRIDE=5044
# LOGSTASH_TCP_PORT_OVERRIDE=5000
# LOGSTASH_UDP_PORT_OVERRIDE=5000
# LOGSTASH_HTTP_PORT_OVERRIDE=9600

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# n8n version
N8N_VERSION=1.114.0
# Timezone
TZ=UTC
GENERIC_TIMEZONE=UTC
# Port
N8N_PORT=5678
# Basic auth
N8N_BASIC_AUTH_ACTIVE=true
N8N_BASIC_AUTH_USER=
N8N_BASIC_AUTH_PASSWORD=
# Host configuration
N8N_HOST=0.0.0.0
N8N_PROTOCOL=http
WEBHOOK_URL=http://localhost:5678/
# Database configuration (SQLite by default, PostgreSQL optional)
DB_TYPE=sqlite
DB_POSTGRESDB_DATABASE=n8n
DB_POSTGRESDB_HOST=n8n-db
DB_POSTGRESDB_PORT=5432
DB_POSTGRESDB_USER=n8n
DB_POSTGRESDB_PASSWORD=
# Execution mode
EXECUTIONS_MODE=regular
# Encryption key (generate with: openssl rand -base64 32)
N8N_ENCRYPTION_KEY=
# PostgreSQL configuration (if using PostgreSQL)
POSTGRES_USER=n8n
POSTGRES_PASSWORD=n8n
POSTGRES_DB=n8n

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# Nginx version
NGINX_VERSION=1.29.2-alpine3.22
# Timezone
TZ=UTC
# Server configuration
NGINX_HOST=localhost
NGINX_PORT=80
# Port overrides (optional)
# NGINX_HTTP_PORT_OVERRIDE=80
# NGINX_HTTPS_PORT_OVERRIDE=443

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# OceanBase version
OCEANBASE_VERSION=4.3.3.1-106000012024110114
# Timezone
TZ=UTC
# Root password
OB_ROOT_PASSWORD=oceanbase
# Cluster configuration
OB_CLUSTER_NAME=obcluster
OB_TENANT_NAME=test
OB_TENANT_PASSWORD=oceanbase
# Resource limits
OB_MEMORY_LIMIT=8G
OB_DATAFILE_SIZE=10G
OB_LOG_DISK_SIZE=6G
# Port overrides (optional)
# OCEANBASE_SQL_PORT_OVERRIDE=2881
# OCEANBASE_RPC_PORT_OVERRIDE=2882

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# OceanBase
OceanBase is a distributed relational database developed by Ant Group. It features high availability, high scalability, and is compatible with MySQL.
## Usage
```bash
docker compose up -d
```
## Configuration
Key environment variables:
- `OB_ROOT_PASSWORD`: Root user password (default: `oceanbase`)
- `OB_TENANT_NAME`: Tenant name (default: `test`)
- `OB_TENANT_PASSWORD`: Tenant password (default: `oceanbase`)
- `OB_MEMORY_LIMIT`: Memory limit (default: `8G`, minimum: `8G`)
- `OB_DATAFILE_SIZE`: Data file size (default: `10G`)
- `OB_LOG_DISK_SIZE`: Log disk size (default: `6G`)
## Ports
- `2881`: MySQL protocol port
- `2882`: RPC port
## Connection
Connect using MySQL client:
```bash
mysql -h127.0.0.1 -P2881 -uroot@test -poceanbase
```
Or connect to sys tenant:
```bash
mysql -h127.0.0.1 -P2881 -uroot -poceanbase
```
## Notes
- OceanBase requires at least 8GB of memory to run properly
- First startup may take several minutes to initialize
- Use `slim` mode for development/testing environments
- For production, consider using `normal` mode and a dedicated cluster
## References
- [OceanBase Official Documentation](https://www.oceanbase.com/docs)
- [OceanBase Docker Hub](https://hub.docker.com/r/oceanbase/oceanbase-ce)

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# OceanBase
OceanBase 是由蚂蚁集团开发的分布式关系型数据库，具有高可用、高扩展性的特点，并兼容 MySQL 协议。
## 使用方法
```bash
docker compose up -d
```
## 配置说明
主要环境变量：
- `OB_ROOT_PASSWORD`：root 用户密码（默认：`oceanbase`）
- `OB_TENANT_NAME`：租户名称（默认：`test`）
- `OB_TENANT_PASSWORD`：租户密码（默认：`oceanbase`）
- `OB_MEMORY_LIMIT`：内存限制（默认：`8G`，最小：`8G`）
- `OB_DATAFILE_SIZE`：数据文件大小（默认：`10G`）
- `OB_LOG_DISK_SIZE`：日志磁盘大小（默认：`6G`）
## 端口说明
- `2881`：MySQL 协议端口
- `2882`：RPC 端口
## 连接方式
使用 MySQL 客户端连接：
```bash
mysql -h127.0.0.1 -P2881 -uroot@test -poceanbase
```
或连接到 sys 租户：
```bash
mysql -h127.0.0.1 -P2881 -uroot -poceanbase
```
## 注意事项
- OceanBase 需要至少 8GB 内存才能正常运行
- 首次启动可能需要几分钟时间进行初始化
- 使用 `slim` 模式适合开发/测试环境
- 生产环境建议使用 `normal` 模式和专用集群
## 参考资料
- [OceanBase 官方文档](https://www.oceanbase.com/docs)
- [OceanBase Docker Hub](https://hub.docker.com/r/oceanbase/oceanbase-ce)

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
oceanbase:
<<: *default
image: oceanbase/oceanbase-ce:${OCEANBASE_VERSION:-4.3.3.1-106000012024110114}
environment:
TZ: ${TZ:-UTC}
MODE: slim
OB_ROOT_PASSWORD: ${OB_ROOT_PASSWORD:-oceanbase}
OB_CLUSTER_NAME: ${OB_CLUSTER_NAME:-obcluster}
OB_TENANT_NAME: ${OB_TENANT_NAME:-test}
OB_TENANT_PASSWORD: ${OB_TENANT_PASSWORD:-oceanbase}
OB_MEMORY_LIMIT: ${OB_MEMORY_LIMIT:-8G}
OB_DATAFILE_SIZE: ${OB_DATAFILE_SIZE:-10G}
OB_LOG_DISK_SIZE: ${OB_LOG_DISK_SIZE:-6G}
volumes:
- oceanbase_data:/root/ob
ports:
- "${OCEANBASE_SQL_PORT_OVERRIDE:-2881}:2881"
- "${OCEANBASE_RPC_PORT_OVERRIDE:-2882}:2882"
deploy:
resources:
limits:
cpus: '4.0'
memory: 10G
reservations:
cpus: '2.0'
memory: 8G
healthcheck:
test: ["CMD-SHELL", "mysql -h127.0.0.1 -P2881 -uroot -p$$OB_ROOT_PASSWORD -e 'SELECT 1' || exit 1"]
interval: 30s
timeout: 10s
retries: 5
start_period: 120s
volumes:
oceanbase_data:

7
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# Timezone
TZ=UTC
# Note: OpenCoze is a complex multi-service platform.
# This is a placeholder configuration.
# For full deployment, please refer to:
# https://github.com/coze-dev/coze-studio/tree/main/docker

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# RocketMQ version
ROCKETMQ_VERSION=5.3.1
ROCKETMQ_DASHBOARD_VERSION=2.0.0
# Timezone
TZ=UTC
# Port overrides (optional)
# ROCKETMQ_NAMESRV_PORT_OVERRIDE=9876
# ROCKETMQ_BROKER_PORT_OVERRIDE=10909
# ROCKETMQ_BROKER_VIP_PORT_OVERRIDE=10911
# ROCKETMQ_BROKER_HA_PORT_OVERRIDE=10912
# ROCKETMQ_DASHBOARD_PORT_OVERRIDE=8080

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# RocketMQ
Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.
## Usage
```bash
docker compose up -d
```
## Components
This setup includes:
- **NameServer**: Manages broker routing information
- **Broker**: Stores and delivers messages
- **Dashboard**: Web UI for monitoring and management
## Configuration
The broker configuration is in `broker.conf`. Key settings:
- `brokerClusterName`: Cluster name
- `brokerName`: Broker name
- `autoCreateTopicEnable`: Auto-create topics (enabled by default)
- `flushDiskType`: Disk flush strategy (`ASYNC_FLUSH` for better performance)
## Ports
- `9876`: NameServer port
- `10909`: Broker listening port (fastRemotingServer)
- `10911`: Broker port (remoting server)
- `10912`: Broker HA port
- `8080`: Dashboard Web UI
## Access
### Dashboard
Access the RocketMQ Dashboard at: <http://localhost:8080>
### Command Line Tools
Execute admin commands:
```bash
# List clusters
docker compose exec broker mqadmin clusterList -n namesrv:9876
# List topics
docker compose exec broker mqadmin topicList -n namesrv:9876
# Create topic
docker compose exec broker mqadmin updateTopic -n namesrv:9876 -c DefaultCluster -t TestTopic
# Query message
docker compose exec broker mqadmin queryMsgById -n namesrv:9876 -i <messageId>
```
## Example: Send and Receive Messages
```bash
# Send messages
docker compose exec broker sh /home/rocketmq/rocketmq/bin/tools.sh org.apache.rocketmq.example.quickstart.Producer
# Consume messages
docker compose exec broker sh /home/rocketmq/rocketmq/bin/tools.sh org.apache.rocketmq.example.quickstart.Consumer
```
## Client Connection
Configure your RocketMQ client to connect to:
- NameServer: `localhost:9876`
## Notes
- This is a single-master setup suitable for development
- For production, use a multi-master or multi-master-multi-slave setup
- Adjust JVM heap sizes in `JAVA_OPT_EXT` based on your needs
- Data is persisted in named volumes
## References
- [RocketMQ Official Documentation](https://rocketmq.apache.org/docs/quick-start/)
- [RocketMQ Docker Hub](https://hub.docker.com/r/apache/rocketmq)
- [RocketMQ Dashboard](https://github.com/apache/rocketmq-dashboard)

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# RocketMQ
Apache RocketMQ 是一个分布式消息和流平台，具有低延迟、高性能和可靠性，万亿级容量和灵活的可扩展性。
## 使用方法
```bash
docker compose up -d
```
## 组件说明
此配置包含：
- **NameServer**：管理 Broker 路由信息
- **Broker**：存储和传递消息
- **Dashboard**：监控和管理的 Web UI
## 配置说明
Broker 配置在 `broker.conf` 文件中，主要设置：
- `brokerClusterName`：集群名称
- `brokerName`：Broker 名称
- `autoCreateTopicEnable`：自动创建主题（默认启用）
- `flushDiskType`：磁盘刷新策略（`ASYNC_FLUSH` 性能更好）
## 端口说明
- `9876`：NameServer 端口
- `10909`：Broker 监听端口（fastRemotingServer）
- `10911`：Broker 端口（remoting server）
- `10912`：Broker HA 端口
- `8080`：Dashboard Web UI
## 访问方式
### Dashboard
访问 RocketMQ Dashboard：<http://localhost:8080>
### 命令行工具
执行管理命令：
```bash
# 列出集群
docker compose exec broker mqadmin clusterList -n namesrv:9876
# 列出主题
docker compose exec broker mqadmin topicList -n namesrv:9876
# 创建主题
docker compose exec broker mqadmin updateTopic -n namesrv:9876 -c DefaultCluster -t TestTopic
# 查询消息
docker compose exec broker mqadmin queryMsgById -n namesrv:9876 -i <messageId>
```
## 示例：发送和接收消息
```bash
# 发送消息
docker compose exec broker sh /home/rocketmq/rocketmq/bin/tools.sh org.apache.rocketmq.example.quickstart.Producer
# 消费消息
docker compose exec broker sh /home/rocketmq/rocketmq/bin/tools.sh org.apache.rocketmq.example.quickstart.Consumer
```
## 客户端连接
配置 RocketMQ 客户端连接到：
- NameServer：`localhost:9876`
## 注意事项
- 这是一个单主配置，适合开发环境
- 生产环境建议使用多主或多主多从配置
- 根据需要在 `JAVA_OPT_EXT` 中调整 JVM 堆大小
- 数据持久化在命名卷中
## 参考资料
- [RocketMQ 官方文档](https://rocketmq.apache.org/docs/quick-start/)
- [RocketMQ Docker Hub](https://hub.docker.com/r/apache/rocketmq)
- [RocketMQ Dashboard](https://github.com/apache/rocketmq-dashboard)

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# Broker configuration
brokerClusterName=DefaultCluster
brokerName=broker-a
brokerId=0
# Network settings
brokerIP1=broker
listenPort=10911
# Storage settings
storePathRootDir=/home/rocketmq/store
storePathCommitLog=/home/rocketmq/store/commitlog
# Auto create topic
autoCreateTopicEnable=true
autoCreateSubscriptionGroup=true
# Delete when
deleteWhen=04
# Flush settings
flushDiskType=ASYNC_FLUSH

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
namesrv:
<<: *default
image: apache/rocketmq:${ROCKETMQ_VERSION:-5.3.1}
command: sh mqnamesrv
environment:
TZ: ${TZ:-UTC}
JAVA_OPT_EXT: "-Xms512m -Xmx512m"
ports:
- "${ROCKETMQ_NAMESRV_PORT_OVERRIDE:-9876}:9876"
volumes:
- namesrv_logs:/home/rocketmq/logs
deploy:
resources:
limits:
cpus: '1.0'
memory: 1G
reservations:
cpus: '0.25'
memory: 512M
healthcheck:
test: ["CMD-SHELL", "mqadmin clusterList -n localhost:9876 || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
broker:
<<: *default
image: apache/rocketmq:${ROCKETMQ_VERSION:-5.3.1}
command: sh mqbroker -n namesrv:9876 -c /home/rocketmq/broker.conf
environment:
TZ: ${TZ:-UTC}
JAVA_OPT_EXT: "-Xms1g -Xmx1g"
ports:
- "${ROCKETMQ_BROKER_PORT_OVERRIDE:-10909}:10909"
- "${ROCKETMQ_BROKER_VIP_PORT_OVERRIDE:-10911}:10911"
- "${ROCKETMQ_BROKER_HA_PORT_OVERRIDE:-10912}:10912"
volumes:
- broker_logs:/home/rocketmq/logs
- broker_store:/home/rocketmq/store
- ./broker.conf:/home/rocketmq/broker.conf:ro
depends_on:
namesrv:
condition: service_healthy
deploy:
resources:
limits:
cpus: '2.0'
memory: 2G
reservations:
cpus: '0.5'
memory: 1G
healthcheck:
test: ["CMD-SHELL", "mqadmin clusterList -n namesrv:9876 | grep -q broker || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
dashboard:
<<: *default
image: apacherocketmq/rocketmq-dashboard:${ROCKETMQ_DASHBOARD_VERSION:-2.0.0}
environment:
TZ: ${TZ:-UTC}
JAVA_OPTS: "-Xms256m -Xmx256m -Drocketmq.namesrv.addr=namesrv:9876"
ports:
- "${ROCKETMQ_DASHBOARD_PORT_OVERRIDE:-8080}:8080"
depends_on:
namesrv:
condition: service_healthy
deploy:
resources:
limits:
cpus: '1.0'
memory: 512M
reservations:
cpus: '0.25'
memory: 256M
healthcheck:
test: ["CMD-SHELL", "wget --no-verbose --tries=1 --spider http://localhost:8080 || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
volumes:
namesrv_logs:
broker_logs:
broker_store:

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# TiDB version (applies to PD, TiKV, and TiDB)
TIDB_VERSION=v8.5.0
# Timezone
TZ=UTC
# Port overrides (optional)
# TIDB_PD_PORT_OVERRIDE=2379
# TIDB_TIKV_PORT_OVERRIDE=20160
# TIDB_PORT_OVERRIDE=4000
# TIDB_STATUS_PORT_OVERRIDE=10080

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# TiDB
TiDB is an open-source, cloud-native, distributed SQL database designed for modern applications. It is MySQL compatible and provides horizontal scalability, strong consistency, and high availability.
## Usage
```bash
docker compose up -d
```
## Components
This setup includes:
- **PD (Placement Driver)**: Manages and schedules TiKV
- **TiKV**: Distributed transactional key-value storage
- **TiDB**: Stateless SQL layer
## Ports
- `4000`: TiDB MySQL protocol port
- `10080`: TiDB status and metrics port
- `2379`: PD client port
- `20160`: TiKV port
## Access
### MySQL Client
TiDB is compatible with MySQL protocol:
```bash
mysql -h127.0.0.1 -P4000 -uroot
```
### Example Usage
```sql
-- Create database
CREATE DATABASE test;
USE test;
-- Create table
CREATE TABLE users (
id INT PRIMARY KEY,
name VARCHAR(50),
email VARCHAR(100)
);
-- Insert data
INSERT INTO users VALUES (1, 'Alice', 'alice@example.com');
-- Query data
SELECT * FROM users;
```
### Status and Metrics
Check TiDB status:
```bash
curl http://localhost:10080/status
```
## Features
- **MySQL Compatible**: Drop-in replacement for MySQL
- **Horizontal Scalability**: Scale out by adding more nodes
- **Strong Consistency**: ACID transactions across distributed data
- **High Availability**: Automatic failover with no data loss
- **Hybrid Transactional/Analytical Processing (HTAP)**: Both OLTP and OLAP workloads
## Notes
- This is a minimal single-node setup for development
- For production, deploy multiple PD, TiKV, and TiDB nodes
- Consider adding TiFlash for analytical workloads
- Monitor using Prometheus and Grafana for production deployments
- Data is persisted in named volumes
## Advanced Configuration
For production deployments, consider:
- Using separate machines for PD, TiKV, and TiDB
- Deploying at least 3 PD nodes for high availability
- Deploying at least 3 TiKV nodes for data replication
- Adding TiFlash for columnar storage and faster analytical queries
- Setting up monitoring with TiDB Dashboard, Prometheus, and Grafana
## References
- [TiDB Official Documentation](https://docs.pingcap.com/tidb/stable)
- [TiDB Quick Start](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb)
- [TiDB Docker Images](https://hub.docker.com/u/pingcap)

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# TiDB
TiDB 是一个开源、云原生、分布式 SQL 数据库，专为现代应用程序设计。它兼容 MySQL 协议，提供水平扩展能力、强一致性和高可用性。
## 使用方法
```bash
docker compose up -d
```
## 组件说明
此配置包含：
- **PD (Placement Driver)**：管理和调度 TiKV
- **TiKV**：分布式事务键值存储
- **TiDB**：无状态 SQL 层
## 端口说明
- `4000`：TiDB MySQL 协议端口
- `10080`：TiDB 状态和指标端口
- `2379`：PD 客户端端口
- `20160`：TiKV 端口
## 访问方式
### MySQL 客户端
TiDB 兼容 MySQL 协议：
```bash
mysql -h127.0.0.1 -P4000 -uroot
```
### 使用示例
```sql
-- 创建数据库
CREATE DATABASE test;
USE test;
-- 创建表
CREATE TABLE users (
id INT PRIMARY KEY,
name VARCHAR(50),
email VARCHAR(100)
);
-- 插入数据
INSERT INTO users VALUES (1, 'Alice', 'alice@example.com');
-- 查询数据
SELECT * FROM users;
```
### 状态和指标
检查 TiDB 状态：
```bash
curl http://localhost:10080/status
```
## 特性
- **MySQL 兼容**：可作为 MySQL 的直接替代品
- **水平扩展**：通过添加更多节点进行扩展
- **强一致性**：分布式数据的 ACID 事务
- **高可用性**：自动故障转移，无数据丢失
- **混合事务/分析处理（HTAP）**：同时支持 OLTP 和 OLAP 工作负载
## 注意事项
- 这是一个最小的单节点配置，适合开发环境
- 生产环境需要部署多个 PD、TiKV 和 TiDB 节点
- 考虑添加 TiFlash 以支持分析工作负载
- 生产部署使用 Prometheus 和 Grafana 进行监控
- 数据持久化在命名卷中
## 高级配置
生产部署建议：
- 为 PD、TiKV 和 TiDB 使用独立的机器
- 部署至少 3 个 PD 节点以实现高可用
- 部署至少 3 个 TiKV 节点以实现数据复制
- 添加 TiFlash 以提供列式存储和更快的分析查询
- 使用 TiDB Dashboard、Prometheus 和 Grafana 设置监控
## 参考资料
- [TiDB 官方文档](https://docs.pingcap.com/zh/tidb/stable)
- [TiDB 快速开始](https://docs.pingcap.com/zh/tidb/stable/quick-start-with-tidb)
- [TiDB Docker 镜像](https://hub.docker.com/u/pingcap)

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
pd:
<<: *default
image: pingcap/pd:${TIDB_VERSION:-v8.5.0}
command:
- --name=pd
- --client-urls=http://0.0.0.0:2379
- --peer-urls=http://0.0.0.0:2380
- --advertise-client-urls=http://pd:2379
- --advertise-peer-urls=http://pd:2380
- --data-dir=/data
environment:
TZ: ${TZ:-UTC}
volumes:
- pd_data:/data
ports:
- "${TIDB_PD_PORT_OVERRIDE:-2379}:2379"
deploy:
resources:
limits:
cpus: '1.0'
memory: 1G
reservations:
cpus: '0.25'
memory: 512M
healthcheck:
test: ["CMD-SHELL", "wget -q -O - http://localhost:2379/health || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
tikv:
<<: *default
image: pingcap/tikv:${TIDB_VERSION:-v8.5.0}
command:
- --addr=0.0.0.0:20160
- --advertise-addr=tikv:20160
- --pd=http://pd:2379
- --data-dir=/data
environment:
TZ: ${TZ:-UTC}
volumes:
- tikv_data:/data
ports:
- "${TIDB_TIKV_PORT_OVERRIDE:-20160}:20160"
depends_on:
pd:
condition: service_healthy
deploy:
resources:
limits:
cpus: '2.0'
memory: 2G
reservations:
cpus: '0.5'
memory: 1G
healthcheck:
test: ["CMD-SHELL", "wget -q -O - http://localhost:20180/status || exit 1"]
interval: 30s
timeout: 10s
retries: 5
start_period: 60s
tidb:
<<: *default
image: pingcap/tidb:${TIDB_VERSION:-v8.5.0}
command:
- --store=tikv
- --path=pd:2379
- --advertise-address=tidb
environment:
TZ: ${TZ:-UTC}
ports:
- "${TIDB_PORT_OVERRIDE:-4000}:4000"
- "${TIDB_STATUS_PORT_OVERRIDE:-10080}:10080"
depends_on:
tikv:
condition: service_healthy
deploy:
resources:
limits:
cpus: '2.0'
memory: 2G
reservations:
cpus: '0.5'
memory: 512M
healthcheck:
test: ["CMD-SHELL", "wget -q -O - http://localhost:10080/status || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
volumes:
pd_data:
tikv_data:

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# TiKV version (applies to PD and TiKV)
TIKV_VERSION=v8.5.0
# Timezone
TZ=UTC
# Port overrides (optional)
# TIKV_PD_PORT_OVERRIDE=2379
# TIKV_PD_PEER_PORT_OVERRIDE=2380
# TIKV_PORT_OVERRIDE=20160
# TIKV_STATUS_PORT_OVERRIDE=20180

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# TiKV
TiKV is an open-source, distributed, transactional key-value database. It provides APIs in multiple languages and is designed to complement or work independently of TiDB.
## Usage
```bash
docker compose up -d
```
## Components
This setup includes:
- **PD (Placement Driver)**: Manages and schedules TiKV clusters
- **TiKV**: Distributed transactional key-value storage engine
## Ports
- `2379`: PD client port
- `2380`: PD peer port
- `20160`: TiKV service port
- `20180`: TiKV status and metrics port
## Access
### Using TiKV Client
TiKV provides client libraries for multiple languages:
- [Rust Client](https://github.com/tikv/client-rust)
- [Go Client](https://github.com/tikv/client-go)
- [Java Client](https://github.com/tikv/client-java)
- [Python Client](https://github.com/tikv/client-py)
### Example (using tikv-client-rust)
```rust
use tikv_client::{RawClient, Config};
#[tokio::main]
async fn main() {
let client = RawClient::new(vec!["127.0.0.1:2379"], None)
.await
.unwrap();
// Put a key-value pair
client.put("key".to_owned(), "value".to_owned()).await.unwrap();
// Get the value
let value = client.get("key".to_owned()).await.unwrap();
println!("Value: {:?}", value);
}
```
### Status and Metrics
Check TiKV status:
```bash
curl http://localhost:20180/status
```
Get metrics in Prometheus format:
```bash
curl http://localhost:20180/metrics
```
## Features
- **Distributed Transactions**: ACID transactions across multiple keys
- **Geo-Replication**: Data replication across data centers
- **Horizontal Scalability**: Scale storage by adding more TiKV nodes
- **Consistent Snapshot**: Snapshot isolation for reads
- **Cloud Native**: Designed for cloud environments
## Use Cases
- **As a key-value store**: Standalone distributed key-value database
- **With TiDB**: Storage layer for TiDB (see `tidb` service)
- **Cache backend**: Distributed cache with persistence
- **Metadata store**: Store metadata for distributed systems
## Notes
- This is a minimal single-node setup for development
- For production, deploy at least 3 TiKV nodes for data replication
- Deploy at least 3 PD nodes for high availability
- Monitor using Prometheus and Grafana
- Data is persisted in named volumes
## References
- [TiKV Official Documentation](https://tikv.org/docs/stable/)
- [TiKV Deep Dive](https://tikv.org/deep-dive/)
- [TiKV Docker Images](https://hub.docker.com/r/pingcap/tikv)
- [TiKV Clients](https://tikv.org/docs/stable/develop/clients/introduction/)

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# TiKV
TiKV 是一个开源、分布式、事务型键值数据库。它提供多种语言的 API，可以独立使用或与 TiDB 配合使用。
## 使用方法
```bash
docker compose up -d
```
## 组件说明
此配置包含：
- **PD (Placement Driver)**：管理和调度 TiKV 集群
- **TiKV**：分布式事务键值存储引擎
## 端口说明
- `2379`：PD 客户端端口
- `2380`：PD 对等端口
- `20160`：TiKV 服务端口
- `20180`：TiKV 状态和指标端口
## 访问方式
### 使用 TiKV 客户端
TiKV 提供多种语言的客户端库：
- [Rust 客户端](https://github.com/tikv/client-rust)
- [Go 客户端](https://github.com/tikv/client-go)
- [Java 客户端](https://github.com/tikv/client-java)
- [Python 客户端](https://github.com/tikv/client-py)
### 示例（使用 tikv-client-rust）
```rust
use tikv_client::{RawClient, Config};
#[tokio::main]
async fn main() {
let client = RawClient::new(vec!["127.0.0.1:2379"], None)
.await
.unwrap();
// 存储键值对
client.put("key".to_owned(), "value".to_owned()).await.unwrap();
// 获取值
let value = client.get("key".to_owned()).await.unwrap();
println!("Value: {:?}", value);
}
```
### 状态和指标
检查 TiKV 状态：
```bash
curl http://localhost:20180/status
```
获取 Prometheus 格式的指标：
```bash
curl http://localhost:20180/metrics
```
## 特性
- **分布式事务**：跨多个键的 ACID 事务
- **地理复制**：跨数据中心的数据复制
- **水平扩展**：通过添加更多 TiKV 节点扩展存储
- **一致性快照**：读取的快照隔离
- **云原生**：专为云环境设计
## 使用场景
- **键值存储**：独立的分布式键值数据库
- **与 TiDB 配合**：作为 TiDB 的存储层（参见 `tidb` 服务）
- **缓存后端**：具有持久化能力的分布式缓存
- **元数据存储**：为分布式系统存储元数据
## 注意事项
- 这是一个最小的单节点配置，适合开发环境
- 生产环境需要部署至少 3 个 TiKV 节点以实现数据复制
- 部署至少 3 个 PD 节点以实现高可用
- 使用 Prometheus 和 Grafana 进行监控
- 数据持久化在命名卷中
## 参考资料
- [TiKV 官方文档](https://tikv.org/docs/stable/)
- [TiKV 深入了解](https://tikv.org/deep-dive/)
- [TiKV Docker 镜像](https://hub.docker.com/r/pingcap/tikv)
- [TiKV 客户端](https://tikv.org/docs/stable/develop/clients/introduction/)

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x-default: &default
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: "3"
services:
pd:
<<: *default
image: pingcap/pd:${TIKV_VERSION:-v8.5.0}
command:
- --name=pd
- --client-urls=http://0.0.0.0:2379
- --peer-urls=http://0.0.0.0:2380
- --advertise-client-urls=http://pd:2379
- --advertise-peer-urls=http://pd:2380
- --data-dir=/data
environment:
TZ: ${TZ:-UTC}
volumes:
- pd_data:/data
ports:
- "${TIKV_PD_PORT_OVERRIDE:-2379}:2379"
- "${TIKV_PD_PEER_PORT_OVERRIDE:-2380}:2380"
deploy:
resources:
limits:
cpus: '1.0'
memory: 1G
reservations:
cpus: '0.25'
memory: 512M
healthcheck:
test: ["CMD-SHELL", "wget -q -O - http://localhost:2379/health || exit 1"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
tikv:
<<: *default
image: pingcap/tikv:${TIKV_VERSION:-v8.5.0}
command:
- --addr=0.0.0.0:20160
- --advertise-addr=tikv:20160
- --status-addr=0.0.0.0:20180
- --pd=http://pd:2379
- --data-dir=/data
- --log-file=/logs/tikv.log
environment:
TZ: ${TZ:-UTC}
volumes:
- tikv_data:/data
- tikv_logs:/logs
ports:
- "${TIKV_PORT_OVERRIDE:-20160}:20160"
- "${TIKV_STATUS_PORT_OVERRIDE:-20180}:20180"
depends_on:
pd:
condition: service_healthy
deploy:
resources:
limits:
cpus: '2.0'
memory: 4G
reservations:
cpus: '0.5'
memory: 2G
healthcheck:
test: ["CMD-SHELL", "wget -q -O - http://localhost:20180/status || exit 1"]
interval: 30s
timeout: 10s
retries: 5
start_period: 60s
volumes:
pd_data:
tikv_data:
tikv_logs: