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. 

Our use cases are for data analytics, both real-time and batch, and also for logging Clickstream data.

We use it in our organization. We have it in our production environment.

The query engine is super fast. We deploy ClickHouse on our Kubernetes cluster, not as a cloud subscription, so it's easy to scale with the deployment.

Some features, like connecting to third-party applications or the cloud, could be better.

I have been using it for one year. 

One issue is that you need persistent volumes. Otherwise, if one system goes down, you lose data in that cluster. 

Another issue is performance. You have to make sure you have the right configurations; otherwise, it will lead to queuing where all your jobs get queued.

It is a scalable product. 

You only get technical support when you take the cloud subscription. If you have it in-house, you won't get any support. If you have a cloud subscription, then the support is pretty good. You can raise a ticket from the UI, and they will respond within 24 hours.

So, the support team is pretty good but there is a little room for improvement. 

Neutral

The initial setup is pretty difficult since we deployed it in-house. We didn't use the cloud subscription, so we have to handle the deployment very carefully.

The challenge was deploying it and having the replication concept working. Another challenging feature is persistent volumes. You have to make sure the data is available on all clusters; otherwise, if one cluster goes down, you'll lose all your data. It's better to have it replicated.

We first used the cloud subscription, but we saw a possibility to reduce costs, so we tried deploying the open-source ClickHouse on-premises. That saved us money, but we didn't get all the features that come with the subscription.

We did it in-house.

Pricing for the cloud version is alright, not very costly or cheap. 

But if you have an in-house deployment on Kubernetes or something, it's going to be very cheap since you'll be managing everything.

I would tell other users to do a POC because it depends upon the business use case and the data. They can explore first. There's another open-source option called Apache Druid, which is a little better than ClickHouse. If that doesn't fit the use case, then they could go for ClickHouse.

Overall, I would rate the solution a seven out of ten. 

If you have a real-time basis, you should take a look at ClickHouse because it works on a vector database, and the querying is super fast compared to traditional databases. So, if your use case is real-time or logging or real-time dashboarding, then ClickHouse is a tool to consider. Otherwise, if it's batch processing and you can expect some latency, then you should go for other databases.

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.