Use case · Databases
ClickHouse hosting
ClickHouse is a column-oriented OLAP database built for fast analytical queries over huge datasets — aggregations, real-time analytics, and log or event data. Its columnar storage and compression let it scan billions of rows quickly, rewarding many CPU cores, ample memory, and fast storage. Open-source and self-hosted on dedicated EU hardware, it gives performance, control, and sovereignty.
Key points
- ClickHouse is a column-oriented database built for analytical queries over huge datasets.
- Columnar storage and compression let it scan billions of rows fast for aggregations.
- It's for OLAP (analytics), not OLTP — not a transactional database for many small updates.
- It rewards many CPU cores, ample memory, and fast storage with capacity for large data.
- Open-source, self-hosted on EU hardware, it gives analytics performance, control, and sovereignty.
What is ClickHouse, and why host it?
ClickHouse is an open-source, column-oriented database built for online analytical processing — running fast analytical queries, such as aggregations and scans, over very large datasets. It is designed for a specific kind of work: analysing large volumes of data quickly, computing aggregates and summaries across billions of rows in a way that would be slow on a general-purpose database. ClickHouse is widely used for real-time analytics, log and event data, observability, and time-series data — anywhere large amounts of data must be queried analytically and quickly. Its speed on analytical queries over big datasets is its defining strength.
Hosting ClickHouse well means providing infrastructure suited to how it works and to the demands of analytical processing over large data. ClickHouse benefits from many CPU cores for its parallel query execution, ample memory, and fast storage with the capacity to hold large datasets, and its columnar design shapes how it uses these. This page focuses on ClickHouse; the general considerations of database hosting apply and are covered on the database-hosting page, while what follows attends to what is particular about running ClickHouse for analytics.
Column-oriented storage: why ClickHouse is fast for analytics
ClickHouse's speed on analytical queries comes largely from its column-oriented storage, which stores data by column rather than by row. Analytical queries typically read a few columns across many rows — computing an aggregate over one or two fields across billions of records — and column-oriented storage suits this exactly, because it can read just the columns a query needs rather than every field of every row. A row-oriented database, storing each record's fields together, would read far more data to answer such a query; ClickHouse, storing each column together, reads only the relevant columns, which is dramatically more efficient for analytics.
This columnar organisation also enables strong compression and efficient processing, compounding the advantage. Data within a column tends to be similar, so it compresses well, reducing the data that must be read from storage; and ClickHouse processes columns in ways that use modern processors efficiently, computing over many values quickly. Together, reading only the needed columns, compressing them well, and processing them efficiently let ClickHouse scan enormous datasets for analytical queries far faster than a row-oriented database could. This columnar foundation is why ClickHouse excels at the analytical work it is built for.
OLAP versus OLTP: what ClickHouse is for
ClickHouse is built for online analytical processing, OLAP, which is a different kind of work from the online transactional processing, OLTP, that transactional databases handle, and understanding this distinction is key to using ClickHouse well. OLAP is about analysing data — running queries that scan and aggregate large amounts of it to produce insights, reports, and analytics. OLTP is about transactions — many small operations that read and write individual records, as an application does when handling users' actions. ClickHouse is designed for the former, and it excels at it, while transactional databases such as PostgreSQL or MySQL are designed for the latter.
This means ClickHouse is not a replacement for a transactional database, and choosing it should follow from having analytical work it suits. ClickHouse is not built for many small transactional updates or point lookups of individual records the way a transactional database is; it is built to scan and aggregate large datasets quickly. The common and sound pattern is to use a transactional database for the application's transactional data and ClickHouse for analytics over large volumes of data, each doing what it is built for. We host ClickHouse for the analytical role it fills, alongside the transactional databases it complements, matching each to its kind of work rather than treating one as a substitute for the other.
Hardware for ClickHouse: cores, memory, and storage
ClickHouse rewards many CPU cores, because it executes analytical queries in parallel across cores to scan and process large data quickly. A query scanning billions of rows is divided across the available cores, so more cores mean faster analytical queries, making high-core-count processors — such as AMD EPYC with its many cores — well suited to ClickHouse. Processing power directly translates into how quickly ClickHouse can churn through large datasets, so core count is a primary lever on its analytical performance.
Ample memory and fast storage with capacity also matter. Memory helps ClickHouse process queries and hold data being worked on, so ample memory supports its performance; and storage must be both fast, to feed the large scans ClickHouse performs, and capacious, to hold the large datasets it works with. Fast storage keeps ClickHouse fed during scans, while capacity accommodates the volume of analytical data. Sizing ClickHouse infrastructure means providing many cores for parallel query execution, ample memory, and fast, capacious storage — a balance suited to scanning and processing large datasets quickly. We size these to the analytical workload, since ClickHouse's performance depends on all of them working together.
Compression and storage efficiency
ClickHouse's columnar storage compresses data well, which reduces the storage needed and speeds queries by reducing the data read from storage. Because values within a column are similar, they compress effectively, so ClickHouse often stores large datasets in considerably less space than their raw size, and reads less compressed data from storage during queries, which is faster. This compression is a meaningful benefit of the columnar design, lowering both the storage capacity required and the amount of data movement during analytical queries.
This storage efficiency has practical consequences for hosting ClickHouse. Effective compression means a given dataset needs less storage than it otherwise would, affecting how much storage capacity a ClickHouse deployment requires; and reading less data during queries contributes to ClickHouse's speed. Fast storage still matters, because even compressed data must be read quickly for large scans, but the volume is reduced by compression. Understanding this clarifies part of why ClickHouse is efficient on large data: it stores it compactly and reads less of it per query. We account for ClickHouse's compression when sizing storage, providing fast storage with capacity suited to the compressed footprint of the analytical data.
Scale: distributed ClickHouse and high ingestion
For datasets and workloads beyond a single server, ClickHouse can be run distributed across multiple servers, spreading data and query processing so that very large analytical workloads are handled across a cluster. Distributing ClickHouse lets it hold and query datasets larger than one server could, and process queries across the combined resources of the cluster, which is how ClickHouse scales to the largest analytical workloads. This distributed capability suits the big-data analytics ClickHouse is often used for, where the data volumes exceed a single machine.
ClickHouse also handles high-volume data ingestion, since analytical data — logs, events, metrics — often arrives continuously and in large quantities that must be inserted efficiently. ClickHouse is built to ingest such data at high rates while remaining queryable, which is important for real-time analytics and observability where data streams in constantly. Hosting ClickHouse for these workloads means providing the infrastructure that distribution and high ingestion require — multiple servers for a distributed cluster, and the throughput to ingest data at the rate it arrives. When a workload's scale or ingestion rate calls for it, we provide the infrastructure for distributed ClickHouse and high-volume ingestion, so its analytical power can be applied at large scale.
Self-hosted, open-source, and sovereign
ClickHouse being open-source makes self-hosting it attractive, giving control over the deployment and, on EU infrastructure, sovereignty over the data it analyses. Being open-source, ClickHouse can be self-hosted without licensing cost, so the cost is the infrastructure and operation, and self-hosting gives full control over the database, its configuration, and its data. This makes self-hosting ClickHouse on infrastructure you control an appealing way to run powerful analytics on your own terms.
Self-hosting on EU infrastructure adds European sovereignty over the analytical data. VV Internet Hosting is incorporated in the Netherlands, within the EU, so a ClickHouse deployment self-hosted with us keeps its data under European jurisdiction and outside the direct reach of the US CLOUD Act, on infrastructure you control. Analytical data can be sensitive — it often includes user behaviour, events, and other data derived from real activity — so keeping it in the EU on controlled infrastructure keeps it under European law. For organisations analysing sensitive or regulated data, or to whom the sovereignty of their analytics matters, self-hosting ClickHouse on EU infrastructure addresses both control and sovereignty.
Where VV Internet Hosting fits — and where it does not
We host dedicated infrastructure for self-hosting ClickHouse: high-core-count servers for its parallel query execution, ample memory, fast storage with capacity for large datasets, and the multiple servers a distributed cluster needs — in EU datacenters under European jurisdiction. This suits organisations self-hosting ClickHouse for analytics that want performance, control, and European data sovereignty, running fast analytical queries over large data on a foundation they command. For that, we are a strong fit, and we will size the cores, memory, and storage to the analytical workload.
We are clear about our limits. We provide the dedicated infrastructure you run ClickHouse on, not a fully managed ClickHouse service that operates it for you. And ClickHouse is for analytical work, not transactional — if you need a transactional database for your application's records, that is a relational or document database's role, which we also host, rather than ClickHouse's. If you want a fully managed analytics service, that is a different offering. We are the dedicated, sovereign infrastructure for self-hosting ClickHouse for analytics — suited to those who want performance, control, and European sovereignty for their analytical workloads, and matched to the analytical role ClickHouse fills. If that fits your needs, we can host it well.
Questions
ClickHouse, answered plainly
Common questions about hosting for ClickHouse.
Why is ClickHouse so fast for analytics?
Largely its column-oriented storage. Analytical queries read a few columns across many rows, and storing data by column lets ClickHouse read just the columns a query needs rather than every field of every row. Columns also compress well and are processed efficiently, so ClickHouse scans enormous datasets for aggregations far faster than a row-oriented database.
Is ClickHouse a replacement for a transactional database?
No. ClickHouse is built for OLAP — analytical queries scanning and aggregating large datasets — not OLTP, the many small transactional operations a transactional database handles. It's not built for small updates or point lookups of individual records. The common pattern is a transactional database for the application's data and ClickHouse for analytics over large volumes.
What hardware does ClickHouse need?
Many CPU cores, because it executes analytical queries in parallel across cores — high-core-count processors like EPYC suit it well. Also ample memory to process queries, and fast storage with capacity to feed large scans and hold large datasets. Its columnar compression reduces the storage footprint, but the cores, memory, and storage all contribute to its analytical speed.
Planning ClickHouse infrastructure?
We host dedicated, EU-sovereign infrastructure sized to your workload — and we will tell you plainly when something else fits better. Tell us what you're building.