ClickHouse: Self-Hosted Column-Oriented Analytics Database

ClickHouse is a column-oriented database management system designed for online analytical processing (OLAP). With over 45,000 GitHub stars, ClickHouse delivers real-time analytical queries over billions of rows at speeds that traditional databases cannot match. Originally developed at Yandex for web analytics, it has become the standard for high-performance self-hosted analytics across companies handling massive data volumes.

Self-hosting ClickHouse means your analytical data and query infrastructure remain on your own servers, with no per-query costs or data egress fees.

Key Features

• Column-oriented storage engine optimized for analytical queries over large datasets
• Real-time data ingestion handling millions of rows per second per server
• Vectorized query execution using SIMD instructions for maximum CPU efficiency
• SQL-compatible interface with extensions for analytical functions and window operations
• Data compression achieving 5-10x reduction with LZ4 and ZSTD codecs
• Materialized views for pre-aggregating data and accelerating common query patterns
• Distributed query processing across multiple nodes for horizontal scaling
• Integration engines for reading directly from Kafka, PostgreSQL, MySQL, and S3

Why Self-Host ClickHouse?

Query performance at scale. ClickHouse routinely processes billions of rows in under a second on a single server. For analytics workloads — aggregations, time-series analysis, funnel queries — it outperforms traditional row-oriented databases by orders of magnitude.

No per-query pricing. Cloud analytics services charge per query or per byte scanned, making costs unpredictable as data grows. Self-hosted ClickHouse runs on a fixed-cost VPS. Run as many queries as you need — exploratory analysis, dashboard refreshes, ad-hoc investigations — without watching a billing meter.

Data locality and compliance. Analytical data often contains user behavior logs, financial transactions, or operational metrics. Self-hosting ensures this data stays in your infrastructure, satisfying data residency requirements and eliminating the risk of sensitive data flowing through third-party analytics services.

Full infrastructure control. Configure storage engines, compression codecs, merge strategies, and replication topology to match your exact workload. Tune memory limits, thread pools, and cache sizes for your hardware. No managed-service restrictions on schema design or query complexity.

System Requirements

Resource	Minimum	Recommended
CPU	2 vCPUs	4+ vCPUs
RAM	4 GB	8 GB
Storage	20 GB SSD	100 GB SSD
OS	Ubuntu 22.04+	Ubuntu 24.04

ClickHouse is CPU and I/O intensive for analytical workloads. More RAM enables larger query result sets and more effective caching. SSD storage is strongly recommended — ClickHouse's column scanning patterns benefit significantly from fast random reads.

Getting Started

The fastest way to deploy ClickHouse on your VPS is with Docker Compose through Dokploy. Our step-by-step deployment guide walks you through the full setup, including persistent storage, environment configuration, and SSL.

Deploy ClickHouse with Dokploy →

Alternatives

• Apache Druid — Real-time analytics database designed for sub-second OLAP queries on streaming data
• QuestDB — High-performance time-series database with SQL support and fast ingestion
• TimescaleDB — PostgreSQL extension for time-series data with full SQL compatibility
• DuckDB — In-process analytical database for fast local analytics without a server

FAQ

When should I use ClickHouse instead of PostgreSQL? Use ClickHouse for analytical queries — aggregations, reporting, time-series analysis over large datasets. PostgreSQL excels at transactional workloads with frequent inserts, updates, and deletes. Many architectures use both: PostgreSQL for application data and ClickHouse for analytics, syncing data between them.

How much data can ClickHouse handle on a single server? A single ClickHouse server can handle terabytes of data and billions of rows. With proper schema design and compression, a VPS with 100 GB SSD can store far more raw data than that implies. Performance depends on query patterns and how well your schema leverages column-oriented storage.

Does ClickHouse support real-time data ingestion? Yes. ClickHouse can ingest millions of rows per second. It supports batch inserts, Kafka integration for streaming data, and the Buffer engine for absorbing high-frequency writes. Data is queryable immediately after insertion.

Is ClickHouse difficult to operate? For single-node deployments, ClickHouse is straightforward — a single binary with a SQL interface. Complexity increases with distributed clusters and replication. For most self-hosted use cases, a single well-configured node handles analytical workloads that would overwhelm traditional databases.

---

App data sourced from selfh.st open-source directory.