ClickHouse on your server - why does a business need it?
Greetings, friends!
Imagine a standard scenario in a growing business: your online store, CRM system, or mobile application successfully generates millions of transactions, clicks, and logs. A marketer or analyst decides to export a user behavior report for the last six months. They click the button in the dashboard, and… the primary production server freezes completely. This happens quite often when a company is new (although, from personal experience, I can say that this occurs pretty regularly in unoptimized infrastructures). A classical relational database (MySQL or PostgreSQL) goes into deep thought, trying to process hundreds of millions of rows, simultaneously blocking order placements for real customers.
Trying to build complex big data analytics on transactional databases is a classic architectural mistake. In 2026, as the volume of collected information grows exponentially, the column-oriented DBMS ClickHouse has become the standard for fast analytics.
In this article, we will break down in simple terms why ClickHouse executes queries hundreds of times faster than the usual MySQL, what business challenges it solves, and what server hardware is required for its stable operation.
Key Takeaways: Main Points About ClickHouse
Columnar magic: Unlike conventional databases, ClickHouse stores data in columns rather than rows. For an analytical report, it doesn't need to read the entire table from the disk—it only fetches the columns specified in the query.
Ultimate speed: Aggregation queries (sorting, averaging, summing) across billions of rows are executed in fractions of a second.
Colossal compression: Due to the homogeneity of data in columns, ClickHouse compresses information 3 to 10 times more efficiently than traditional DBMS, saving massive amounts of server space.
OLAP nature: The tool was created exclusively for analytics (Online Analytical Processing). It perfectly complements but does not replace classic transactional databases (OLTP).
How to Install ClickHouse on a Server?
We have recorded a guide that clearly demonstrates the process of installing ClickHouse on an Ubuntu 24.04 server:
You will find all the setup commands in the video description and the pinned comment.
Rows vs Columns: Why Is ClickHouse So Fast?
To understand the ClickHouse phenomenon, let's look at a simple example. Imagine a customer table with 50 million rows. We need to calculate the average purchase receipt for users from Chisinau.
How MySQL/PostgreSQL does it (Row-oriented architecture): The server starts reading the entire table from the disk row by row. It reads the client ID, name, surname, address, phone number, registration date, and finally, the receipt amount. A huge volume of redundant information is pushed through the RAM and CPU for the sake of a single number.
How ClickHouse does it (Column-oriented architecture): It physically stores cities in one file and receipt amounts in another. To execute the query, ClickHouse points directly to the city file, finds the necessary indices, and reads only the receipt file. Billions of redundant data bytes are completely untouched by the disk subsystem.
Add to this a vector execution engine (where the processor handles data in batches rather than individually, thanks to SIMD instructions), and you get data processing speeds of dozens of gigabytes per second on a single server. This is why many websites and businesses are increasingly utilizing ClickHouse.
Comparison Table: Relational DBMS vs ClickHouse
| Criterion | Traditional Databases (MySQL / PostgreSQL) | ClickHouse (OLAP Architecture) | Infrastructure Impact |
| Storage Format | Row-oriented (optimal for point lookups/writes). | Column-oriented (optimal for aggregations). | Effective utilization of cache resources. |
| Data Modification (UPDATE/DELETE) | Executed instantly in real time. | Heavy operation, data is written in batches. | ClickHouse is not suitable for frequent row modifications. |
| Disk Data Compression | Minimal (data is heterogeneous). | Massive (up to 5–10 times stronger). | Significant reduction in storage array costs. |
| Primary Business Goal | Order placements, authentication, ACID. | Marketing reports, Big Data, logs. | Role separation prevents the main site from crashing. |
What Business Challenges Does ClickHouse Solve in 2026?
If your project has grown beyond a simple portfolio website, ClickHouse can become the primary core for business decision-making:
Proprietary End-to-End Web Analytics
In an era of strict privacy restrictions, many b2b projects are moving away from public services like Google Analytics. Custom analytical systems are built on top of ClickHouse, collecting every user action on the site (clicks, scrolls, transitions) in real time, generating reports without data sampling.
Security Monitoring and Log Analysis (SIEM / Enterprise Logging)
ClickHouse is capable of digesting terabytes of network logs from intrusion detection systems (Suricata, Zeek) or Nginx load balancers. Security specialists can detect attack or data leak patterns across massive historical data arrays in seconds.
Fintech and E-commerce Analytics
Abandoned cart analysis, LTV (customer lifetime value) calculations, cohort analysis, supply chain and inventory tracking—ClickHouse performs any heavy mathematical operations on the fly, allowing management to see real business charts right "here and now."
FAQ: Quick Summary
Can ClickHouse be used as the sole database for a website?
Categorically no. ClickHouse does not support full transactions (ACID) in the traditional sense and handles frequent updates or deletions of individual rows very poorly. The ideal setup: the website runs on PostgreSQL/MySQL, and from there, data is asynchronously replicated to ClickHouse for analytics.
Is it difficult to set up data migration from MySQL to ClickHouse?
Not at all. There are ready-made open-source tools for this (e.g., Debezium or ClickHouse's built-in integration engines MaterializedPostgreSQL and MaterializedMySQL), which automatically capture changes from the primary database and stream them to the analytical one.
Conclusion
Implementing ClickHouse is a mature step for a business that wants to make decisions based on real numbers rather than intuition. Splitting the infrastructure into transactional and analytical parts saves primary production servers from overloads and gives analysts a powerful tool to process data in milliseconds.
Since ClickHouse performs large-scale scanning of columnar files and heavily relies on parallel computing, its performance directly depends on random disk read speeds (IOPS) and the number of CPU cores.
If you are currently looking for a reliable hosting solution to deploy high-volume analytical databases, explore our Dedicated Server / NVME VPS services at MivoCloud. Our high-frequency AMD Ryzen processors will provide the required compute power for vector calculations, and enterprise-grade local NVMe drives guarantee the highest IOPS for instant Big Data processing.
Article Author — Anatolie Cohaniuc