Cassandra vs ClickHouse comparison

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Cassandra

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Executive SummaryUpdated on Mar 1, 2026

Apache Cassandra and ClickHouse are open-source database management systems. ClickHouse appears to have an advantage due to its superior analytical capabilities, although Cassandra offers excellent value and support.

Features: Cassandra provides peer-to-peer architecture, distributed scalability, and high availability. ClickHouse benefits from a columnar storage system, real-time queries, and efficient data compression.

Room for Improvement: Cassandra could improve in query performance and user interface friendliness, as well as providing more comprehensive query language support. ClickHouse could be enhanced in handling transactional workloads, simplifying integration with other systems, and better managing very high concurrency.

Ease of Deployment and Customer Service: Cassandra involves more complexity in setup and requires significant expertise, whereas ClickHouse benefits from simpler installation and a responsive community-driven support system, making deployment smoother.

Pricing and ROI: Cassandra is usually lower in initial costs and delivers high ROI with large datasets. ClickHouse may involve higher setup costs but offers significant performance benefits for analytical tasks, leading to a high return on analytical investments.

To learn more, read our detailed Cassandra vs. ClickHouse Report (Updated: September 2026).

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Cassandra vs. ClickHouse

September 2026

Helped 920,769 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Cassandra

Ranking in Vector Databases

11th

Average Rating

8.0

Reviews Sentiment

6.0

Number of Reviews

Ranking in other categories

NoSQL Databases (5th)

ClickHouse

Ranking in Vector Databases

8th

Average Rating

8.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Open Source Databases (3rd)

Mindshare comparison

As of November 2026, in the Vector Databases category, the mindshare of Cassandra is 4.0%, up from 2.0% compared to the previous year. The mindshare of ClickHouse is 5.6%, up from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases Mindshare Distribution
Product	Mindshare (%)
ClickHouse	5.6%
Cassandra	4.0%
Other	90.4%

Vector Databases

Featured Reviews

Monirul Islam Khan

Head, Data Integration & Management at a non-profit with 10,001+ employees

Has maintained secure document storage and efficient data distribution with peer-to-peer architecture

The functions or features in Cassandra that I have found most valuable are that it is a distributed system similar to Mongo. It's good enough for comparison with another SQL database, so it's smooth and organized for distributed database system. The peer-to-peer architecture in Cassandra is helpful for network decentralization, and I have already introduced that feature. Cassandra features in peer-to-peer as well as another monitoring, so basically, it's good enough for our service. The tunable consistency level in Cassandra is good, and we are using that feature already. In terms of built-in caching and lightweight transactions in Cassandra, the transaction level is good, and it's optimized, so there are no more issues in that database. Based on my experience, Cassandra is good for document management system, as well as distributed database system, and the automatic recovery process is there. Additionally, the database monitoring system or auditing system is well-comparable with other database systems, so we are actually happy to be using this Cassandra database.

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reviewer2785038

Senior Data Engineer at a transportation company with 501-1,000 employees

Data observability has enabled real‑time analytics and cost savings but needs smoother inserts and cleanup

ClickHouse could be improved concerning data insertion, especially given the high amount of data handled. Constant efforts are made to optimize the features on its own, but with merges and inserts, only a single insert query can be performed allowing for the input of only 100,000 rows per second. It would be beneficial to insert more data and have configurations that are less user-operated. Ideally, ClickHouse would optimize itself to handle these processes automatically, reducing the need to contact the ClickHouse support team for infrastructure optimization. Additionally, delays are experienced when trying to delete databases with corrupt data, taking too much time and causing major outages, which necessitate contacting multiple teams across continents for resolution. The community surrounding ClickHouse also seems limited, providing a reliance on documentation, and there is a scarcity of developers working with ClickHouse, which hinders growth. If ClickHouse were more user-friendly and technically feasible, it would likely see greater expansion in usage.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The most valuable features of this solution are its speed and distributed nature."

"The most valuable features of Cassandra are the NoSQL database, high performance, and zero-copy streaming."

"The most valuable features are the counter features and the NoSQL schema, and it also has good scalability because you can scale Cassandra to any infinite level."

"I am getting much better performance than relational databases."

"Overall, I would rate Cassandra as nine because of its fast writes, which really suit our use cases mostly."

"Our primary use case for the solution is testing."

"We can add almost one million columns to the solution."

"Cassandra has some features that are more useful for specific use cases where you have time series where you have huge amounts of writes. That should be quick, but not specifically the reads. We needed to have quicker reads and writes and this is why we are using Cassandra right now."

More Cassandra pros

"The tool is column-based and infinitely scalable."

"ClickHouse has reduced our storage cost and improved our 99th percentile latency by 40%."

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

"ClickHouse provides great query speeds because it is an OLAP database, so naturally, it provides higher speeds."

"ClickHouse is very easy to use; one of the good features is that it has joins, which were not present in Druid, and Druid was quite expensive, especially with our applications at Sam's Club utilizing ClickHouse very quickly."

"With ClickHouse, since data is stored in a columnar way, we get aggregation functions that are much faster than transactional databases, such as SQL Server, and the cost efficiency is also much reduced compared to Cosmos DB since we use it on-premises, the cost is nearly cut, which is very useful for us."

"ClickHouse is much faster than traditional databases like MySQL and MongoDB. Its column-row searching strategy makes it very efficient. With ClickHouse, we can manage multiple databases, automatically insert data from other databases and delete data as needed. It supports real-time query performance, allowing simultaneous data insertion and retrieval. ClickHouse has improved significantly over the past two years, adding more functions and queries, as well as top functionality."

"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."

More ClickHouse pros

Cons

"There were challenges with the query language and the development interface. The query language, in particular, could be improved for better optimization. These challenges were encountered while using the Java SDK."

"Interface is not user friendly."

"Depending upon our schema, we can't make ORDER BY or GROUP BY clauses in the product."

"The solution doesn't have joins between tables so you need other tools for that."

"The secondary index in Cassandra was a bit problematic and could be improved."

"The interface could definitely be improved. It's a technical database and for me the features are not user friendly."

"Cassandra can improve by adding more built-in tools. For example, if you want to do some maintenance activities in the cluster, we have to depend on third-party tools. Having these tools build-in would be e benefit."

"I want Cassandra to update its open-source version more quickly. It's already feature-rich, but I'd appreciate better integration with other NoSQL databases like MariaDB or MongoDB. If I ever need to work with customers or vendors using different NoSQL databases, having native integration in Cassandra would make managing and interacting with their databases much easier."

More Cassandra cons

"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."

"<p>ClickHouse could be improved with self-hosting capabilities and better documentation for how to host it at scale.</p>"

"The open-source version of ClickHouse is not very scalable."

"In terms of needed improvements, some enhancements in documentation are necessary."

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

"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."

"Additionally, delays are experienced when trying to delete databases with corrupt data, taking too much time and causing major outages, which necessitate contacting multiple teams across continents for resolution."

"Initially, I faced challenges integrating ClickHouse, particularly with inserting data from ActiveMQ, which caused duplicates. However, after adjusting the ClickHouse settings, the issue was resolved and there were no more duplicates."

More ClickHouse cons

Pricing and Cost Advice

"I use the tool's open-source version."

"Cassandra is a free open source solution, but there is a commercial version available called DataStax Enterprise."

"We pay for a license."

"There are licensing fees that must be paid, but I'm not sure if they are paid monthly or yearly."

"I don't have the specific numbers on pricing, but it was fairly priced."

"We are using the open-source version of Cassandra, the solution is free."

"ClickHouse Cloud is not expensive compared to other databases, costing a few dollars per month while providing fast performance."

"ClickHouse has an open-source version, which is free to use and has almost all the features."

"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."

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

"The tool is open-source."

"We used the free, community version of ClickHouse."

"The tool is free."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Comms Service Provider

10%

Construction Company

Manufacturing Company

Financial Services Firm

15%

Comms Service Provider

11%

Outsourcing Company

11%

Computer Software Company

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	14

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	5
Large Enterprise	8

Questions from the Community

What is your experience regarding pricing and costs for Cassandra?

The pricing for Cassandra is a little bit high, so it would be better for our community services if they consider community pricing for any non-profit organization like an NGO or other things. It w...

What needs improvement with Cassandra?

Regarding areas of improvement for Cassandra, currently, we are not facing significant issues. Some issues arise from our vendors like Apache slowness and distribution or load balancing from HAProx...

What is your primary use case for Cassandra?

My use case for Cassandra is for a document and other unstructured data management system as well as structured data for ultra-poor member community edition, community members' PII information, so ...

What is your experience regarding pricing and costs for ClickHouse?

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 abstract...

What needs improvement with ClickHouse?

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 ...

What is your primary use case for ClickHouse?

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 u...

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Product Reports

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Cassandra

October 2026

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October 2026

Overview

Cassandra is a distributed and scalable database management system used for real-time data processing.

It is highly valued for its ability to handle large amounts of data, scalability, high availability, fault tolerance, and flexible data model.

It is commonly used in finance, e-commerce, and social media industries.

Apache

ClickHouse is renowned for its speed, scalability, and real-time query performance. Its compatibility with SQL standards enhances flexibility while enabling integration with popular tools.

ClickHouse leverages a column-based architecture for efficient data compression and real-time analytics. It seamlessly integrates with tools like Kafka and Tableau and is effective in handling large datasets due to its cost-efficient aggregation capabilities. With robust data deduplication and strong community backing, users can access comprehensive documentation and up-to-date functionality. However, improvements in third-party integration, cloud deployment, and handling of SQL syntax differences are noted, impacting ease-of-use and migration from other databases.

What features make ClickHouse outstanding?

Speed and Scalability: Enables rapid deployment and scalability, particularly on Kubernetes.
SQL Compatibility: Aligns with SQL standards for flexible database management.
Column-based Structure: Offers efficient data compression and quick query performance.
Integration Support: Works with Kafka, Tableau, and other popular platforms.
Data Deduplication: Manages complex data duplication efficiently.

What benefits should users consider?

Cost-Efficiency: Reduces expenses while managing large datasets effectively.
Real-Time Analytics: Provides fast data processing for real-time insights.
Flexibility: Offers multiple integration points and compatibility options.
Robust Documentation: Backed by an active development community, ensuring comprehensive support.

ClickHouse is deployed in sectors like telecommunications for passive monitoring and is beneficial for data analytics, logging Clickstream data, and as an ETL engine. Organizations harness it for machine learning applications when combined with GPT. With the ability to be installed independently, it's an attractive option for avoiding cloud service costs.

Sample Customers

1. Apple 2. Netflix 3. Facebook 4. Instagram 5. Twitter 6. eBay 7. Spotify 8. Uber 9. Airbnb 10. Adobe 11. Cisco 12. IBM 13. Microsoft 14. Yahoo 15. Reddit 16. Pinterest 17. Salesforce 18. LinkedIn 19. Hulu 20. Airbnb 21. Walmart 22. Target 23. Sony 24. Intel 25. Cisco 26. HP 27. Oracle 28. SAP 29. GE 30. Siemens 31. Volkswagen 32. Toyota

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