The main use case for ClickHouse is as a data warehouse for handling large volumes of data that exceed the capabilities of traditional databases like Postgres. I use it for creating dashboards and performing analytical tasks such as determining the total number of orders, average order value, and evaluations and ratios for various stores. I deploy ClickHouse both on the cloud provided by ClickHouse itself and on-premises for IoT and similar data tasks.

One of the most valuable features of ClickHouse is that it is open source without vendor lock-in, allowing me the freedom to choose any vendor for the database. It offers numerous out-of-the-box analytical functions, eliminating the need for complex coding. The performance of ClickHouse aligns with its claims, being highly efficient and used by large organizations like Uber and Zomato. The deployment process is straightforward, and it is scalable both vertically and in distributed systems via the cloud.

A significant area for improvement is the documentation, which is not comprehensive and lacks centralized resources, making it difficult to find information. Additionally, ClickHouse lacks robust support for transactional data, which limits its use as a primary database. My developer experience could be enhanced through better-organized documentation, perhaps by offering a cheat sheet or centralized guide for common setup and usage scenarios.

I have known ClickHouse for more than two years, but I have used it for about one year.

Deployment is quite straightforward, though not all resources are directly on the official site. While it is not hard to find deployment information, having a cheat sheet on their site would be beneficial. Overall, I can figure out the deployment process within an hour or so.

ClickHouse is stable and performs exceptionally well with large data sets. It does not slow down under the volume of data that was problematic for Postgres.

ClickHouse is highly scalable. It is vertically scalable and can be used in distributed systems through their cloud service, managing scalability for large data volumes.

I have not directly contacted ClickHouse's support team but have joined their Slack channel where I asked a few questions.

I previously used Postgres, which started slowing down with massive amounts of data. I evaluated over twelve databases, starting with TiDB, but found ClickHouse to be the best fit after considering options like DuckDB. I initially preferred Postgres for its comprehensive features, but it couldn't handle the data scale.

Initial setup is straightforward and not hard at all. I can figure out the process within an hour or so.

ClickHouse is open source without direct fees, unlike other databases that have hidden fees or restrict hosting to their platforms. The open-source nature of ClickHouse allows for complete flexibility without licensing constraints.

I evaluated over twelve databases, including TiDB and DuckDB, but I opted for ClickHouse based on its performance in benchmarks compared to others.

For the right use cases, I would rate ClickHouse eight to eight point five out of ten. However, it is not suitable as a primary database for startups due to the lack of transactional support. For companies with massive data struggling with query speed and facing high costs from vendor lock-ins, ClickHouse is an excellent choice.

Our company had about nine platforms, each with its own database and data. We had to gather all these data in one database and just one table. We used Apache Superset to integrate this database with the business intelligence tool. We had too many choices or options initially for the database engine.

We initially tested a database, and its performance was good. When we tried ClickHouse, we switched to it immediately because the performance was really amazing. When we had a huge amount of data, about five or six gigabytes in just one table, and we used ClickHouse to deduplicate some duplicated entries or records.

Clickhouse helped us to achieve our use cases with simple steps and good performance as mentioned previously

The main feature of ClickHouse is the optimizer, if we had too many records to deduplicate, the optimizer took this task by itself. The second valuable feature of the solution is its performance. It's not easy when we talk about five or six gigabytes of one table of data.

Then, if you have to generate too many KPIs, charts, lines, and reports, it's not easy to deal with all of these with just one engine and tool. ClickHouse was really nice in this respect, and we had no problem with its performance.

ClickHouse has its own concept of database triggers and doesn't support traditional database triggers.

11 months

We haven’t faced any stability issues with ClickHouse.

ClickHouse is a scalable solution.

I rate the solution’s scalability a nine out of ten.

We used the free, self-hosted community version of ClickHouse.

For about six gigabytes, we took about two seconds to fetch all data at the maximum performance. Otherwise, it was really nice to have a medium CPU or database engine and resources. We don't have a really huge server; it's just traditional servers and traditional resources.

ClickHouse is not a straightforward tool for anyone to use. Users need some time to switch from traditional things to study new concepts.

We had just one client, Apache Superset. Apache Superset connects with just one connection but with too many requests. We had about 20 to 30 reports on the same page, and they work concurrently.

The solution’s documentation is amazing.

I would recommend the solution to other users. ClickHouse is the first step to the next generation of databases. When we deal with this amount of data and this performance, I think it's a respected technology.

Overall, I rate the solution a nine out of ten.

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 used ClickHouse to collect data, put it in the database, and then analyze it to find insights. The main advantage is that I can install it on my computer instead of using cloud-based solutions, so I don't have to pay for every query like with Google or Amazon cloud services.

The best thing about the tool is that I can set it up on my computer and run queries without depending on the cloud. This is why I use it every day.

There aren't too many improvements I'd suggest for ClickHouse as it covers all my needs. There are just a few technical issues. For example, sometimes, when you want to get unique values and use certain tables, they don't work as expected. But it's not a major problem.

I have been using the product for two years.

The solution is stable and works well. However, it consumes a lot of memory, so you need plenty of RAM in your computer or cloud solution to run it effectively. This could be a problem because you need to think about how much memory you have for calculations.

In my company, many people used ClickHouse—over 1000 people could access it. About 10-20 people used it at a professional level, creating tables and maintaining the database.

I have talked to the ClickHouse support team before. They have a support group on Telegram messenger where you can ask technical questions. I often asked about working with tables and views and making sophisticated calculations. But now, I don't have any issues, so I don't need to ask for support.

I was satisfied with the support. Many people in the support group try to really understand your problem and help, not just dismiss it. If something isn't possible due to database limitations, they try to help you look at the situation differently.

Installing the tool was easy. I used a Windows laptop with WSL and followed the documentation instructions. I didn't have any issues with the installation.

The tool is open-source.

We chose ClickHouse because we needed to move away from cloud-based solutions due to risks in Russia. We considered other options, such as Postgres, NoSQL databases, Hadoop, and Hive, before deciding on ClickHouse.

The tool is open-source, so you don't need to pay for the software itself. However, you need to consider hardware costs and maintenance. A small company can install it on a company computer. For larger companies, you might need to hire a team for maintenance and consider data safety and privacy issues.

Integrating ClickHouse with other tools in our data stack was easy. It has native connections to many tools, such as Google and Amazon cloud solutions, and can easily connect with other databases.

For beginners, the ease of use depends on your background. If you're familiar with relational databases, it's easy. If not, you might need to read the documentation or ask for support.

I use ClickHouse to collect and analyze data from Ethereum. We primarily use it for data classification and occasionally for machine learning with GPT, but that's minimal. The primary use case is classification; sometimes, we use it for applications similar to OLTP scenarios. All of our data is stored in ClickHouse. We are customers of ClickHouse, not partners. It's an easy tool to use if you know SQL databases.

The aggregation capability is a valuable feature. It's highly efficient, allowing us to review entire transaction histories and user activities in the market. We've tried MongoDB, Postgres, MariaDB, and BigQuery, but ClickHouse is the most cost-efficient solution for collecting data at high speeds with minimal cost. We even used ClickHouse Cloud for a month, and it proved to be a great setup, especially for startups looking to handle big data. For example, if there is a need for 2-4 terabytes of data and around 40 billion rows with reasonable computing speed and latency, ClickHouse is ideal.

Regarding the real-time query performance of ClickHouse, when using an API server to query it, I achieved query results in less than twenty milliseconds in some of my experiments with one billion rows. However, it depends on the scenario since ClickHouse has limitations in handling mutations.

Additionally, one of ClickHouse's strengths is its compression capability. Our experimental server has only four terabytes, and ClickHouse effectively compresses data, allowing us to store large amounts of data at high speed. This compression efficiency is a significant advantage of using ClickHouse.

We faced a challenge with deploying ClickHouse onto Kubernetes. Recently, we've been using ClickHouse Cloud, and the main issue is the high cost of the cloud service. The pricing isn't very competitive, especially for startups. I would instead buy a server and self-host if I have enough disk space. Besides that, ClickHouse has done very well, with clear goals and effective execution.

I have been using Clickhouse for the past one and a half years.

Based on stability, I would rate ClickHouse around nine out of ten.

The cloud services support is excellent. Their support team is very timely and helpful, and even if you encounter any bugs, they assist you quickly. Compared to other services I've used, ClickHouse's support is very helpful. Even if you don't know much about databases or ClickHouse, their support will help resolve any issues.

For pricing, if you use the self-hosted version, it would be free. Cloud services pricing would be an eight out of ten. I try to minimize costs but still have to monitor usage.

I would recommend ClickHouse to others. If they have large datasets, ClickHouse is much more cost-effective and efficient than BigQuery. For example, running a query on one billion rows in BigQuery took a few minutes and was very expensive, whereas ClickHouse could do it in less than five seconds at a much lower cost.

I don't use AI in ClickHouse, but I use full-text search, and it's mighty. There's no significant gap when migrating from other SQL databases to ClickHouse, though you must learn some specific syntax. If you are familiar with databases and know how to code and design systems, using ClickHouse should be straightforward.

Overall, I would rate ClickHouse an eight 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.