Distinguished AI Leader at Walmart Global Tech at Walmart
Real User
Top 5
2025-05-08T18:17:52Z
May 8, 2025
The basic challenge for ClickHouse is the documentation, which isn't ideal, but it's mature and stable with more columnar storage, compression, and parallel processing, making it the best for OLAP. In terms of improvements, it's not designed for very frequent small writes, making it less scalable in write-intensive workloads, and it's not flourishing in transactional use cases or when ingesting streaming data, such as batching or buffering, which is something ClickHouse will improve.
A significant area for improvement is the documentation, which is not comprehensive and lacks centralized resources, making it difficult to find information. Additionally, ClickHouse lacks robust support for transactional data, which limits its use as a primary database. My developer experience could be enhanced through better-organized documentation, perhaps by offering a cheat sheet or centralized guide for common setup and usage scenarios.
Backend Software Engineer at a tech vendor with 51-200 employees
Real User
Top 20
2024-07-12T11:11:52Z
Jul 12, 2024
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.
The clusters are not perfect. We had a lot of troubles while deploying a whole cluster. We must tune some sequences, so we must have experience with the product. I worked a lot with bare metal. However, working with the cloud is a little bit harder. When we need to start up and shut down some nodes, we need to start or shut down the whole cluster. It is not so in Databricks.
Software Engineer at Activant Solutions Pvt Ltd, Jaipur
Real User
Top 10
2024-06-13T15:50:00Z
Jun 13, 2024
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 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...
The basic challenge for ClickHouse is the documentation, which isn't ideal, but it's mature and stable with more columnar storage, compression, and parallel processing, making it the best for OLAP. In terms of improvements, it's not designed for very frequent small writes, making it less scalable in write-intensive workloads, and it's not flourishing in transactional use cases or when ingesting streaming data, such as batching or buffering, which is something ClickHouse will improve.
A significant area for improvement is the documentation, which is not comprehensive and lacks centralized resources, making it difficult to find information. Additionally, ClickHouse lacks robust support for transactional data, which limits its use as a primary database. My developer experience could be enhanced through better-organized documentation, perhaps by offering a cheat sheet or centralized guide for common setup and usage scenarios.
ClickHouse has its own concept of database triggers and doesn't support traditional database triggers.
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.
The clusters are not perfect. We had a lot of troubles while deploying a whole cluster. We must tune some sequences, so we must have experience with the product. I worked a lot with bare metal. However, working with the cloud is a little bit harder. When we need to start up and shut down some nodes, we need to start or shut down the whole cluster. It is not so in Databricks.
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.
Some features, like connecting to third-party applications or the cloud, could be better.