ClickHouse vs Pinecone comparison

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

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

"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."

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

"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."

"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 Pinecone is its private local host feature. It displays all the maintenance parameters and lets us view the data sent to the database. We can also see the status of the CD and which application it corresponds to."

"We chose Pinecone because it covers most of the use cases."

"The semantic search capability is very good."

"The most valuable feature of Pinecone is its managed service aspect. There are many vector databases available, but Pinecone stands out in the market. It is very flexible, allowing us to input any kind of data dimensions into the platform. This makes it easy to use for both technical and non-technical users."

"The most valuable features of the solution are similarity search and maximal marginal relevance search for retrieval purposes."

"The product's setup phase was easy."

"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."

"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."

"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."

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

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

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

"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."

"The tool does not confirm whether a file is deleted or not."

"Onboarding could be better and smoother."

"The product fails to offer a serverless type of storage capacity."

"For testing purposes, the product should offer support locally as it is one area where the tool has shortcomings."

"I want to suggest that Pinecone requires a login and API key, but I would prefer not to have a login system and to use the environment directly."

"Pinecone can be made more budget-friendly."

"The tool is free."

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

"The tool is open-source."

"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."

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

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

"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 have experience with the tool's free version."

"The solution is relatively cheaper than other vector DBs in the market."

"I think Pinecone is cheaper to use than other options I've explored. However, I also remember that they offer a paid version."

"Pinecone is not cheap; it's actually quite expensive. We find that using Pinecone can raise our budget significantly. On the other hand, using open-source options is more budget-friendly."