Database

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 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.

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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.

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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.