# Apache Superset — Open Source Data Visualization & Exploration

> Apache Superset is a modern data exploration and visualization platform. Connect any SQL database, build interactive dashboards with 40+ chart types, no coding required.

## Install

Save as a script file and run:

## Quick Use

```bash
docker run -d --name superset -p 8088:8088 
  -e SUPERSET_SECRET_KEY=your-secret-key 
  apache/superset:latest
```

Then initialize:
```bash
docker exec -it superset superset fab create-admin 
  --username admin --firstname Admin --lastname User 
  --email admin@example.com --password admin
docker exec -it superset superset db upgrade
docker exec -it superset superset init
```

Open `http://localhost:8088` — login and connect your database.

## Intro

**Apache Superset** is a modern, enterprise-ready data exploration and visualization platform. It provides an intuitive interface for exploring data, building interactive dashboards with 40+ chart types, and sharing insights — all through a web browser without writing code. Power users can write SQL for advanced analysis.

With 72.3K+ GitHub stars and Apache-2.0 license, Superset is the most popular open-source BI platform under the Apache Software Foundation umbrella, used by Airbnb, Dropbox, Lyft, Netflix, and thousands of organizations worldwide.

## What Superset Does

- **40+ Chart Types**: Bar, line, area, scatter, pie, heatmap, map, treemap, sunburst, pivot table, and more
- **SQL Editor**: Full SQL IDE with autocomplete, saved queries, and result visualization
- **Dashboard Builder**: Drag-and-drop dashboard creation with cross-filtering and drill-down
- **Data Exploration**: No-code exploration with visual query builder for slicing and dicing data
- **30+ Database Connectors**: PostgreSQL, MySQL, ClickHouse, BigQuery, Snowflake, Redshift, Trino, Druid, etc.
- **Access Control**: Role-based access with row-level security and dataset permissions
- **Caching**: Query result caching with configurable TTL for dashboard performance
- **Alerts & Reports**: Scheduled reports and threshold-based alerts via email/Slack
- **Embedding**: Embed dashboards and charts into external applications

## Architecture

```
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  Browser     │────▶│  Superset    │────▶│  Your Data   │
│  Dashboards  │     │  Server      │     │  PostgreSQL  │
└──────────────┘     │  (Python/    │     │  ClickHouse  │
                     │   Flask)     │     │  BigQuery    │
                     └──────┬───────┘     │  Snowflake   │
                            │             │  30+ more    │
                  ┌─────────┼─────────┐   └──────────────┘
                  │         │         │
           ┌──────┴──┐ ┌───┴───┐ ┌───┴───┐
           │  Redis  │ │Celery │ │Metadata│
           │ (Cache) │ │(Async)│ │  DB    │
           └─────────┘ └───────┘ └────────┘
```

## Self-Hosting

### Docker Compose

```yaml
services:
  superset:
    image: apache/superset:latest
    ports:
      - "8088:8088"
    environment:
      SUPERSET_SECRET_KEY: your-long-secret-key
      DATABASE_URL: postgresql+psycopg2://superset:superset@db:5432/superset
      REDIS_URL: redis://redis:6379/0
    depends_on:
      - db
      - redis

  db:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: superset
      POSTGRES_PASSWORD: superset
      POSTGRES_DB: superset
    volumes:
      - pg-data:/var/lib/postgresql/data

  redis:
    image: redis:7-alpine

volumes:
  pg-data:
```

## Key Features

### No-Code Exploration

```
1. Select dataset (table/view)
2. Choose chart type (bar, line, etc.)
3. Drag metrics (SUM, COUNT, AVG)
4. Add dimensions (group by)
5. Apply filters
6. Customize colors, labels, formatting
7. Save to dashboard
```

### SQL Lab (SQL IDE)

```sql
-- Full SQL editor with schema browser
SELECT
  DATE_TRUNC('month', order_date) as month,
  product_category,
  SUM(revenue) as total_revenue,
  COUNT(DISTINCT customer_id) as unique_customers
FROM orders
WHERE order_date >= '2024-01-01'
GROUP BY 1, 2
ORDER BY 1, 3 DESC

-- Results can be instantly visualized as any chart type
-- Save queries for reuse
-- Share with team members
```

### Dashboard Features

- **Cross-filtering**: Click on one chart to filter all others
- **Filter bar**: Global filters affecting all charts
- **Drill-down**: Click data points to explore deeper
- **Annotations**: Mark events on time-series charts
- **CSS customization**: Custom styling per dashboard
- **Auto-refresh**: Configurable refresh intervals

### Jinja Templating

```sql
-- Dynamic queries with user context
SELECT * FROM orders
WHERE region = '{{ current_user().region }}'
  AND order_date >= '{{ from_dttm }}'
  AND order_date <= '{{ to_dttm }}'

{% if filter_values('status') %}
  AND status IN ({{ "'" + "', '".join(filter_values('status')) + "'" }})
{% endif %}
```

## Superset vs Alternatives

| Feature | Superset | Metabase | Grafana | Looker | Tableau |
|---------|----------|----------|---------|--------|---------|
| Open Source | Yes (Apache-2.0) | Yes (AGPL) | Yes (AGPL) | No | No |
| Chart types | 40+ | 15+ | 20+ | LookML | Extensive |
| SQL IDE | Yes | Basic | No | No | No |
| No-code explore | Yes | Yes | Limited | LookML | Yes |
| Databases | 30+ | 20+ | 100+ (metrics) | BigQuery focus | Any |
| Row-level security | Yes | Enterprise | No | Yes | Yes |
| Best for | Data teams | Business users | DevOps metrics | Enterprises | Enterprises |

## 常见问题

**Q: Superset 和 Metabase 怎么选？**
A: Metabase 更适合非技术用户（可视化查询构建器更友好）。Superset 更适合数据团队（SQL Lab 更强大、图表类型更多、性能更好）。如果团队习惯写 SQL，选 Superset。

**Q: 可以处理大数据吗？**
A: Superset 本身不存储数据，性能取决于底层数据库。搭配 ClickHouse、Druid 或 Trino 等 OLAP 引擎可以查询数十亿行数据。Superset 的缓存层（Redis）也能显著提升仪表盘加载速度。

**Q: 自托管难度大吗？**
A: Docker 部署相对简单。生产环境需要配置 Redis（缓存）、Celery（异步任务）和 PostgreSQL（元数据）。Apache 官方提供详细的生产部署指南和 Helm chart。

## 来源与致谢

- GitHub: [apache/superset](https://github.com/apache/superset) — 72.3K+ ⭐ | Apache-2.0
- 官网: [superset.apache.org](https://superset.apache.org)

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Source: https://tokrepo.com/en/workflows/58b28055-34e1-11f1-9bc6-00163e2b0d79
Author: Script Depot