ClickHouse Reviews

We use ClickHouse for a passive monitoring system in telecommunications. It is used to record primary data from the mobile network technology.

The tool is column-based and infinitely scalable. 

There are some areas where ClickHouse could improve. Specifically, we encountered incompatibilities with its SQL syntax when migrating queries from MySQL or SQL to ClickHouse. This difference in details made it challenging to figure out the exact issues. Additionally, we faced difficulties due to the lack of a proper Django driver for ClickHouse, unlike MySQL, which Django supports out of the box.

I have been working with the product for one and a half years. 

My company has ten product users. 

The company decided to use ClickHouse because mobile networks produce enormous amounts of data—millions of timestamped vectors, each representing a measurement, which total billions of rows per month. Initially, they used MySQL, but as the data volume grew, MySQL couldn't handle the load. Therefore, they switched to ClickHouse.

If you're considering using ClickHouse for the first time, my advice would depend on how much data you need to handle. For most scenarios where big data isn't involved, I don't think it's a good idea to use ClickHouse. SQL Server, MySQL, or PostgreSQL are well-documented and supported. The software you need to access these databases will be readily available. So, I don't see any reason to use ClickHouse for small to medium-scale scenarios.

I don't think you'll find it any more difficult than other databases, apart from the SQL syntax, which is a bit different. The most challenging part with ClickHouse is dealing with the large amounts of data it handles without overloading your server. I don't think the database itself is difficult to use. However, I was primarily accessing data from it and don't have much experience with setting it up or feeding it data. 

I rate the overall solution a nine out of ten. 

I do not use the tool myself. Our developers and data analysts use it. 

The tool's most valuable feature is a database. It supports portal APIs and offers good flexibility. While it may not be the best on the market, it is the best open-source solution we have tried. It has a development community and good documentation, though not all is published. 

The tool's integration with other tools is not complex. We use it alongside Kafka and Tableau. 

I have been using the product for four years. 

Every customer I've worked with over the past few years uses ClickHouse, including many Russian companies and those related to Russia.

I have some experience talking with the tech support team. It was an open-source project at one point, so I used community resources for help. The best way to communicate with them was through their program channel, which had support available in both English and Russian.

Regarding the initial installation, setup, and deployment, I can say it's easy for someone with my engineering skills. I prefer managing the installation myself rather than relying on out-of-the-box solutions.

ClickHouse is good for analytics. Using ClickHouse is beneficial if you understand its specific purpose and advantages. Many engineers and developers mistakenly think it is an alternative to AWS databases like Postgres or MySQL, but it's not. ClickHouse has a different architecture and purpose, primarily excelling at analytical queries rather than traditional CRUD operations.

If you join our team, it should be easy for you to use ClickHouse, especially if you are a developer. However, you need to read the documentation and understand the problems you are trying to solve. As an infrastructure engineer, it shouldn't be hard either.

I rate the overall solution an eight out of ten.