ClickHouse vs Milvus comparison

"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."

"ClickHouse is a user-friendly solution that tries to be compatible with SQL standards."

"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."

"It's easier to work with big data and calculations using the product."

"The tool's most valuable feature is a database. It supports portal APIs and offers good flexibility."

"The main feature of ClickHouse is the optimizer because we had too many records to deduplicate, and the optimizer took this task by itself."

"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."

"The best feature of Milvus was finding the closest chunk from a huge amount of data."

"Milvus offers multiple methods for calculating similarities or distances between vectors, such as L2 norm and cosine similarity. These methods help in comparing different vectors based on specific use cases. For instance, in our use case, we find that the L2 distance works best, but you can experiment with different methods to find the most suitable one for your needs."

"Milvus has good accuracy and performance."

"I like the accuracy and usability."

"The solution is well containerized, and since containerization is quick and easy for me, I can scale it up quickly."

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

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

"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."

"We had a lot of troubles while deploying a whole cluster."

"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."

"I would like ClickHouse to work more on integration with third-party tools."

"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."

"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."

"Milvus could make it simpler. Simplifying the requirements and making it more accessible. It could be more user-friendly."

"Milvus has higher resource consumption, which introduces complexity in implementation."

"Milvus' documentation is not very user-friendly and doesn't help me get started quickly."

"I've heard that when we store too much data in Milvus, it becomes slow and does not work properly."

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

"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."

"The tool is open-source."

"The tool is free."

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

"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."

"Milvus is an open-source solution."

"Milvus is an open-source solution."