ClickHouse Reviews

My main use case for ClickHouse is that I used it earlier at my previous companies for transactional data of blockchain, so we could provide analytical data to our clients.

A specific example of how I used ClickHouse for blockchain transactional data is that our clients would not directly interact with ClickHouse database, but we had our entire SaaS application that used to do that. We had a lot of ETLs that wrote into ClickHouse database that we built, with ClickHouse being one of the primary databases we used for real-time calculations of different account balances or tracking entities through multiple channels.

The unique aspect about my main use case with ClickHouse is that we made it into multiple shards and had multiple replicating instances, alongside using replicated merge trees and neat materialized views. However, the most painful part was the cold starts of new chains, as materialized views need to be filled, deleted, and rebuilt again because they only track upserts, not deletions, which wasted significant amounts of my time.

The best feature ClickHouse offers is speed, no question.

I would add that the connectors and ClickPipes are really cool tools, and the ETL capabilities around ClickHouse are also improving, with good Kafka integrations, making ClickHouse excellent, particularly with many connectors to ClickHouse and CDC systems.

ClickHouse positively impacts my organization by potentially saving costs if you are migrating from an OLAP database or OLTP database to an OLAP database, but cost-wise, it depends on how you set it up and how many replicating systems you need, with bare-metal costs depending on that. There is no better option than ClickHouse in all OLAP-based databases, so I think it is best to use ClickHouse in that regard.

ClickHouse can be improved, primarily in terms of the materialized views that do not change based on the base table, which is painful to keep reusing due to the demonstrated architecture.

I would add that we could benefit from more connection options with ClickHouse, as the connections are good, and while the documentation is solid, it could use more graphics to help explain things, particularly since I have not seen the documentation in the last year and a half. Support-wise, the team is really solid, and I had a chat with them a few times—they are good product folks.

My experience with ClickHouse's documentation is that it needs improvement; I think it can be made more beginner-friendly, while the community support is really good.

I have been using ClickHouse for three years.

ClickHouse is stable.

ClickHouse's scalability is a ten.

I did not use customer support that much.

My experience with ClickHouse's security features is limited, as I have not used many of them.

I am satisfied with the monitoring and observability features in ClickHouse, rating them as good, not great.

Negative

The ease of setting up and configuring ClickHouse for my needs is a nine.

The setup and configuration process is a nine for me because I think the onboarding could be better.

In terms of return on investment, my focus was more on developer productivity than on money saved per se, but compared to other databases we used, it was fairly in a similar range. The number of employees needed did not change initially, but we could reduce the amount of employees needed when we migrated to ClickHouse Cloud.

My experience with pricing, setup cost, and licensing indicates that it is very expensive—ClickHouse is the most expensive option.

After switching to ClickHouse, I noticed specific outcomes such as cost savings if we put the right engines in place, and our metrics indicated at least 500 requests per second at peak times, with more typical requests being around 20 or 30 per second, handled adequately by our systems. We started with about 200 GB of data during testing, which grew to about five or six TB in production, indicating our capacity to handle a huge dataset.

My advice for others looking into using ClickHouse is that it is a good system that remains stable. I would rate this review as a nine.

My main use case for ClickHouse is that it primarily drives our analytics and reports. I use ClickHouse for product analytics, and that mostly drives product decisions.

We moved away from Redshift to ClickHouse because of the integration and the flexibility that it provides, which best suited our use case. Most of the teams in my company use it as a central resource where all teams have their separate accesses to the databases that they work on within ClickHouse.

The best features ClickHouse offers are seamless integrations, data exports, and data imports, which fit well because we use Postgres as our primary database for our transactional databases. Seamless integrations help our workflow by allowing us to integrate data sources more easily, and the data exports and imports compare favorably to our previous solution.

ClickHouse has positively impacted my organization by driving our products because we use it for our product analytics, and the integrations make it easier to integrate new data sources.

ClickHouse could be improved with self-hosting capabilities and better documentation for how to host it at scale. I do not have anything in particular to add about the needed improvements around performance, UI, or anything else.

I have been using ClickHouse for about a year.

In my experience, ClickHouse is stable.

The scalability of ClickHouse is great. I would like to add that performance and the scalability needs are important aspects of ClickHouse.

The customer support for ClickHouse is fine, and I have used it. I would rate the customer support as ten out of ten.

Positive

I previously used Redshift, as it did not fit our use case.

I purchased ClickHouse through the AWS Marketplace since that was how I could deploy it on AWS.

In terms of specific outcomes, I have noticed faster report generation, but I cannot really say the cost has reduced much. I have already mentioned what returns I got in terms of driving our product.

My experience with pricing, setup cost, and licensing was straightforward, as it is open-source.

Before choosing ClickHouse, I evaluated all the major cloud database providers that have something analogous to ClickHouse.

I would rate ClickHouse as a nine or an eight on a scale of one to ten. I chose nine because there are certain improvements, as I previously mentioned, that prevent me from giving it a ten. My advice for others looking into using ClickHouse is to understand your use case and choose accordingly, as it is good at many things and may fit well with analytics use cases. I appreciate the team behind ClickHouse; it is a really great product. My overall review rating for ClickHouse is nine.

My main use case for ClickHouse at Infoblox involves receiving detection data, which we attempt to perform aggregation on and store in ClickHouse for faster query and access.

For the kind of detection data I work with, I use ClickHouse, specifically utilizing Aggregate MergeTree and deleting tables, along with indexing and sharding techniques for faster access.

In handling detection data with ClickHouse, we write two queries to retrieve data, but for storing, we use different Kafka table engine and Aggregate MergeTree to keep our data structured.

The best features ClickHouse offers in my experience include its performance and the various table engines it provides, allowing me to avoid writing large queries to access my shaped or fine-tuned data.

I mostly use the Kafka table engine, SummingMergeTree, and AggregatingMergeTree, which enhance performance because we work with streaming data, using Kafka as our input.

The features of ClickHouse that stand out for me are primarily centered around performance.

ClickHouse has positively impacted my organization by replacing PostgreSQL, which required complex foreign tables for queries. With ClickHouse, we now have Cube.js for easier data visualization.

I have seen specific improvements such as faster query times. For instance, queries that took 10 milliseconds on PostgreSQL are now approximately 50% faster due to improved storage and query performance.

ClickHouse can be improved, and the main challenge I see is its operational complexity.

One improvement I think ClickHouse needs, apart from operational complexity, would be around its documentation, which is already quite great.

I started using ClickHouse in my current company about one year ago.

In my experience, ClickHouse is stable.

ClickHouse's scalability is good as we manage it through Kubernetes, allowing us easy scaling up and down with ClickHouse operator and installation resources.

I have not interacted with ClickHouse's customer support, as I focus mainly on query work, and any issues go through a separate team that contacts support.

Previously, I used Snowflake and BigQuery in a different company, but ClickHouse is now in use. The reason for the switch is uncertain to me.

I can vouch for time as a return on investment with ClickHouse, but I am uncertain about the financial aspect.

Regarding pricing, setup cost, and licensing, I have not been involved in the pricing part and am not fully certain about it.

Before choosing ClickHouse, I think my organization evaluated other options such as CockroachDB, though I am not entirely certain.

My advice for others considering ClickHouse is to opt for it due to its scalability, performance, and deployment ease, especially with Kubernetes.

I believe ClickHouse is a great product that is maturing well, and although it may have flaws, it will overcome them and continue to serve users worldwide. I would rate this product an 8 out of 10.

My main use case for ClickHouse is data ingestion and for its OLAP properties, as we had use cases where database locks were slowing us down and because ClickHouse does not have that, we chose to use it.

I could give you a quick, specific example of how I'm using ClickHouse for data ingestion where the lack of database locks helped us when we were parsing IOCs among other things, as a lot of that data has to be processed really quickly and ingested into the database for further processing and identifying which IOCs are compromised.

ClickHouse helped us solve the problem that we were having and it's one of the two databases we used at Cyware—one was Postgres, the other was ClickHouse.

The best features ClickHouse offers are its OLAP features because, given that there are no database locks and its eventual consistency, that is the biggest feature that we have or that solved our problems.

The eventual consistency and lack of database locks specifically benefit my team in terms of speed and reliability, as once data is ingested, we have to quickly process it and then show the outputs to the user, say there are ten indicators of compromise, and we have our own database where we tally whether these IPs or IOCs that we are scanning right now are marked or red flagged before or not, so we have to quickly scan them, process them and then give an output, and that helped us with the reliability part, the speed part, while eventual consistency is used on a different side of the product.

ClickHouse has positively impacted my organization, as there was an entire exercise done on which database we were supposed to use for solving our problems, and we found ClickHouse was the one performing the best, which is when we adopted it.

ClickHouse can be improved on the documentation side, and there is one small constraint that is mentioned in ClickHouse documentation, which is a partition limit of ten thousand that we hit, so if that can be increased or there are workarounds around it, that would be great.

I chose nine out of ten because, as I mentioned, the improvement side and the ten thousand partition limit created issues that we were hitting quite frequently, but with some schema manipulations we did manage to find a workaround, although that could have been avoided had things been better documented on how we could have solved this problem in a different approach, which took some bandwidth.

I do not have any other improvements I think ClickHouse needs, besides the documentation and partition limit.

I have been using ClickHouse for about a year, maybe slightly more than that.

ClickHouse is stable, as we did not encounter stability issues in production, but in the dev environment, one of the seniors did flag one specific point where we found some inconsistencies, although I think they did find a workaround around it, but it was stable for us.

ClickHouse's scalability is good.

We previously used Postgres, and we encountered issues with Postgres, which was again, as I mentioned, why we did a study on switching.

I have seen a return on investment, as I can share that on the engineering side we had improvements in database performance, but for the metrics asked, time saved, fewer employees needed, I do not have them.

My experience with pricing, setup cost, and licensing was such that the setup costs were just my own bandwidth, while licensing and pricing were done by other members of the team so it was abstracted away from me, and I am not aware of it.

Before choosing ClickHouse, we evaluated other options such as Apache Druid and Pinot from Apache, and then there was a study.

The advice I would give to others looking into using ClickHouse is that on the engineering side, if there is some OLAP use case or anywhere where data needs to be ingested at very high rates or there is a use case for eventual consistency, then perhaps it can be used. I gave this review a rating of nine out of ten.

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.

Positive

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 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.

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.

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.