What is ClickHouse, and how does it differ from a traditional relational database?

ClickHouse is an open-source column-oriented database management system for online analytical processing (OLAP). Unlike row-oriented databases such as MySQL and PostgreSQL, it stores data by column and reads only the columns a query needs. This makes it well suited to analytical aggregations.

What are common uses for ClickHouse in big data systems?

ClickHouse is used for clickstream and user behavior analysis, log and server-metric processing, financial analysis, online advertising and business intelligence dashboards.

What is the difference between OLTP and OLAP databases?

OLTP databases handle operational transactions and row-level updates. OLAP systems such as ClickHouse are designed to read and aggregate large datasets for analysis.

How do I connect ClickHouse to dashboard tools?

After deploying ClickHouse on Deplomo, configure the HTTP (8123) and native (9000) ports and create a user. You can then connect tools such as Metabase, Superset and Grafana to run SQL queries.

What should I consider when hosting ClickHouse in the cloud?

Plan ClickHouse around its CPU, RAM and persistent-storage requirements. Containers use the platform network and storage configuration, while your selected zone package sets the monthly resource ceiling.

Deplomo

Analytics and big data database

ClickHouse analytical database

Deploy ClickHouse on Deplomo, an open-source columnar database for online analytical processing (OLAP) and reports on large datasets.

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Key benefits of deploying ClickHouse on Deplomo

Column-oriented analytics, persistent storage and a fixed monthly zone package.

Columnar analytics

Columnar storage and parallel query execution help analyze large datasets. Performance depends on the data, query and available resources.

Data compression

Built-in compression reduces disk usage and can help limit storage needs.

SQL query support

Use SQL for queries, aggregations and statistical functions.

OLAP and business intelligence

Use ClickHouse for analytical dashboards, log analysis, traffic statistics and large reporting workloads.

ClickHouse FAQs

Answers to common questions about the ClickHouse columnar database:

ClickHouse guide: columnar storage and analytics for large datasets

As user activity, application events and system transactions accumulate, heavy analytical queries can affect operational databases. ClickHouse uses columnar storage and compression to support analytical aggregations over large datasets. Deploy it on Deplomo as a container and plan the resources your workload needs.

What is ClickHouse, and what is different about its columnar architecture?

To understand ClickHouse, compare how databases store data. Row-oriented databases keep each record together, which suits individual transactions. ClickHouse stores each column separately, so an analytical query can read only the columns it needs. The performance difference depends on the query and dataset.

For business intelligence projects, connect ClickHouse to data tools such as Metabase to build charts and explore data. To compare relational, NoSQL and in-memory databases, visit our Cloud database database guides.

Manual ClickHouse setup compared with cloud deployment

Running ClickHouse on a traditional server can involve configuring XML files, ZooKeeper or ClickHouse Keeper for clusters, memory settings and storage paths. Misconfiguration can cause problems under heavy workloads.

Deplomo deploys ClickHouse in a container with platform storage and network settings. Configure resources for your workload and review the database setup before sending data.

Compare ClickHouse on Deplomo with row-oriented databases and manual setup

The table compares ClickHouse with other database approaches:

Feature	ClickHouse on Deplomo	Traditional row-oriented databases (OLTP)	Manual setup on a VPS
Aggregation query workload	Designed for aggregations over large datasets	May require additional planning for analytical workloads	Performance depends on the workload and hardware configuration
Data compression	Columnar storage and compression can reduce disk usage; results depend on the data.	May use more disk space for some workloads	Compression settings require manual configuration
Deployment and setup	Deploy from a template	Not designed for heavy analytical workloads	Requires configuration of settings files and Keeper
Pricing and resource scaling	Fixed monthly EUR package pricing	May require substantial hardware resources	Fixed monthly fee with limited options to scale immediately

ClickHouse’s columnar storage and compression can help with analytical queries and disk use. Results depend on the workload and configuration.

ClickHouse deployment and connection guide

Follow these steps to connect to ClickHouse:

Deploy from the marketplace Open the Deplomo console and choose ClickHouse from the database apps.
Set RAM and NVMe storage Choose resources based on the size of your analytical workload.
Find your connection details When the service is ready, find the username, password and HTTP and native connection ports in the dashboard.
Connect and run queries Use a graphical client such as DBeaver, or Python and Go libraries, to create MergeTree tables and analyze data.

Deploy ClickHouse on Deplomo

ClickHouse is designed for analyzing large volumes of logs, events and business data. Choose resources and storage based on your workload, and configure network access for your needs.

Deploy ClickHouse on Deplomo for your analytical workload.

Deploy ClickHouse

Deploy ClickHouse from a template and configure its storage and compute resources. Availability depends on your selected package and configuration.

Get started with ClickHouse