> For the complete documentation index, see [llms.txt](https://docs.gaiodataos.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.gaiodataos.com/fundamentals/database.md).

# Database

<figure><img src="/files/1VnxngD4cGhOCGdx50E8" alt=""><figcaption></figcaption></figure>

All data imported into **Gaio DataOS** is stored internally in a high-performance local database.\
Behind the scenes, Gaio uses the **ClickHouse** database engine to store, manage, and query data across your flows, dashboards, and APIs.

### What is ClickHouse?

[ClickHouse](https://clickhouse.com/) is a **column-oriented database management system (DBMS)** designed for **real-time analytical processing (OLAP)**. It was built to deliver **lightning-fast queries** on **massive volumes of data**, making it ideal for modern data platforms like Gaio.

***

### Benefits of ClickHouse in Gaio

| Feature                       | Description                                                                                                       |
| ----------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| **Blazing-fast performance**  | ClickHouse is optimized for analytical queries and can process billions of rows per second on commodity hardware. |
| **Columnar storage**          | Stores data by columns instead of rows, reducing disk I/O and improving query speed dramatically.                 |
| **Real-time analytics**       | Supports real-time data ingestion and instant query execution — perfect for dashboards and live monitoring.       |
| **Efficient compression**     | Automatically compresses data, reducing storage footprint while preserving performance.                           |
| **Scalability**               | Easily scales horizontally with distributed architecture — useful for enterprise-scale workloads.                 |
| **SQL-compatible**            | Uses familiar SQL syntax, making it accessible for analysts, engineers, and data scientists.                      |
| **Fault-tolerant & reliable** | Includes support for replication, backup, and high availability across nodes.                                     |

***

#### How It Works in Gaio

* When data is uploaded or ingested into a **Data Project**, it is stored in your project’s isolated **ClickHouse schema**.
* All **flows**, **dashboards**, and **AI features** run queries directly on top of ClickHouse, ensuring high performance without additional infrastructure.
* Every action, from filtering a table to exploring data distributions (as shown in the image above), is powered by ClickHouse’s real-time query engine.

### Performance Benchmarks: ClickHouse vs Other Databases

ClickHouse consistently outperforms traditional and cloud-native databases in OLAP scenarios. Below is a comparison of query performance based on public benchmarks and real-world use cases.

> **Note:** Benchmarks may vary depending on infrastructure, dataset size, and query complexity. The table below reflects averages from public sources like ClickBench, Altinity, and independent performance tests.

| Database       | Type             | Query Latency (on 1B rows)  | Data Compression Ratio | OLAP Optimization | Real-Time Capabilities |
| -------------- | ---------------- | --------------------------- | ---------------------- | ----------------- | ---------------------- |
| **ClickHouse** | Columnar, OLAP   | **<1 second**               | **3–8x**               | ✅ **Yes**         | ✅ **Yes**              |
| PostgreSQL     | Row-based, OLTP  | \~30–60 seconds             | 1–2x                   | ❌ No              | ❌ Limited              |
| BigQuery       | Serverless OLAP  | \~3–10 seconds (cold start) | 2–4x                   | ✅ Yes             | ❌ No (batch-based)     |
| Redshift       | Columnar, OLAP   | \~5–15 seconds              | 2–4x                   | ✅ Yes             | ❌ No (cached)          |
| Snowflake      | Cloud-based OLAP | \~5–20 seconds              | 3–6x                   | ✅ Yes             | ❌ No (batch)           |
| MySQL          | Row-based, OLTP  | \~50–120 seconds            | \~1x                   | ❌ No              | ❌ No                   |

#### Why Gaio Chose ClickHouse

Gaio DataOS was designed for **speed, flexibility, and scalability**. ClickHouse was the natural choice for the following reasons:

* It's **open-source**, robust, and proven in production at scale (used by Yandex, Cloudflare, and others).
* It allows Gaio to deliver **low-latency queries** across huge datasets with **minimal resource overhead**.
* Its **native SQL support**, **built-in compression**, and **distributed architecture** perfectly match Gaio's mission: **democratizing enterprise analytics without compromising on performance**.