ClickHouse Reviews & Product Details

I think the best thing about it is the query speed. We have a lot of analytics data, and queries on our existing platforms used to take a lot of time, but with ClickHouse it’s pretty fast. The platform is simple and has a UI that’s very easy to navigate. On top of that, the documentation is very good, and the support team was helpful during onboarding. Review collected by and hosted on G2.com.

It’s a little more costly than other products. Integrations with many products worked well, but with some they were difficult and painful to set up. They also have an AI SQL assistant, which can be helpful, but on our larger schema it doesn’t work well. Review collected by and hosted on G2.com.

What I like most about ClickHouse is its ability to query massive volumes of data with extremely low latency, making it ideal for real-time analytics and observability. Its column-oriented storage, efficient compression, and vectorized query execution help deliver fast analytical queries while keeping storage and compute costs relatively low. Review collected by and hosted on G2.com.

What I dislike about ClickHouse is that complex updates and deletes are less straightforward and efficient than in traditional OLTP databases like PostgreSQL. Also, query optimization and distributed cluster management can become challenging as data volume and workload complexity grow. Review collected by and hosted on G2.com.

I like that ClickHouse is fast and can scan large volumes of data by reading only the required data or columns. It cut our risk calculations from 5 minutes to 30 seconds, so the business team can make quick calls on liquidity and margins. I also like that it’s open source, works in any cloud ecosystem, and supports observability for reading logs and monitoring issues, as well as replication to help with data loss and fault tolerance. We moved from PostHog because ClickHouse cost 40% less and improved speed and real-time processing. Review collected by and hosted on G2.com.

ClickHouse is fast for analytics, but I don’t find it as good or reliable as OLTP databases, which are comparatively faster for their design and use case. It’s only as good as the data and the modeling behind it, and that modeling takes close work between engineers and business analysts. The initial setup wasn’t very straightforward; it needed experienced engineers with real-time data and data-modeling knowledge, along with many teams to set up and maintain the environment. Review collected by and hosted on G2.com.

What I like most about ClickHouse is its performance when working with large volumes of data. Review collected by and hosted on G2.com.

The main downside is that ClickHouse can take a while to fully understand and tune properly, especially when it comes to data modeling, choosing the right table engine, setting up partitioning, and optimizing queries. Review collected by and hosted on G2.com.

I like ClickHouse’s speed and real-time analytics. It handles large datasets efficiently and returns query results in seconds, helping me track key metrics, troubleshoot issues faster, and make informed decisions without delays. It scales as our data grows and works well with our reporting and visualization tools, making analytics more efficient across our workflows. Getting started was easier than I expected: installation and basic configuration were simple, and we could begin working with our data quickly. Review collected by and hosted on G2.com.

I found that new users may need some time to get familiar with ClickHouse’s setup and advanced features. Advanced tuning also took extra effort. More beginner-friendly documentation and easier management tools would make adoption smoother. Review collected by and hosted on G2.com.

I like the lower cost: I can keep the same GKE and Cloud Run logs and search them without paying GCP’s price for every query and retained day. SQL over the logs is straightforward. I can filter by service and time and pull the exact lines I need to trace a production issue. I also like using the ClickStack MCP to query ClickHouse from my editor, list log sources, check schemas, and run SQL without opening another tool. Scoped searches by service and time come back quickly, and it saves us a lot of time. Review collected by and hosted on G2.com.

I don't have any major issues with the database itself, and narrow queries by service and time are quite fast. The only friction is that broad searches time out instead of telling me to tighten the time range, so I have to retry with a smaller window. Review collected by and hosted on G2.com.

I like ClickHouse’s query performance on large datasets, its efficient columnar storage, and how it handles complex aggregations with low latency and cost. I value the columnar storage, MergeTree engines, partitioning, materialized views, and parallel query execution for fast aggregations and improved performance at scale. It supports real-time monitoring, reporting, and operational insights, and I use it with Kafka for data ingestion and Grafana for monitoring and visualization. The initial setup was relatively straightforward, and basic deployment and integration were easy. Review collected by and hosted on G2.com.

I find ClickHouse challenging to optimize for complex queries, frequent updates, and joins across large datasets. Production tuning, schema design, and performance optimization also took some effort. Better query execution plans, clearer bottleneck diagnostics, workload-based resource isolation, and improved monitoring and alerts would help me identify performance issues faster and make operations at scale simpler. Review collected by and hosted on G2.com.

I like ClickHouse’s speed, especially on large datasets. Aggregations over billions of rows keep our dashboards responsive, and it gives us real-time visibility into data. It can handle large batches and millions of events, with our data compressed together. I also like that it integrates with tools like Kafka and S3 and fits into our existing data stack without requiring us to rebuild it. We can build dashboards directly on top of the data, which shortens the path from raw data to insight and reduces engineering effort. Review collected by and hosted on G2.com.

I found ClickHouse operationally complex at first. It takes time for a newcomer to understand table engines, sorting keys, and partitioning. A production cluster took more effort to set up than a basic single-node setup, and replication, sharding, and ZooKeeper required some learning. Cluster management was also difficult and needed some rework before we got good performance and could handle updates. These challenges mostly affected onboarding and early adoption, though the impact was manageable once we adapted. Guided tools for schema and query optimization and clearer documentation for common patterns would help. Review collected by and hosted on G2.com.

What I like best about ClickHouse is its fast query performance when working with large datasets. It is particularly useful for analytical workloads where I need to aggregate data, run complex SQL queries, and analyze trends without waiting too long for results. Its column-oriented architecture makes it well suited for reporting, dashboards, and exploring large volumes of data. I also appreciate that it supports SQL, which makes it easier to work with familiar query patterns. Overall, ClickHouse can make data analysis faster and help teams get insights from large datasets more efficiently. Review collected by and hosted on G2.com.

One thing I find challenging about ClickHouse is that getting the best performance can require a good understanding of table design, partitioning, sorting keys, and query optimization. While basic SQL queries are straightforward, managing complex analytical workloads can take some learning and experimentation. Troubleshooting performance issues and choosing the right data structure may also require additional technical knowledge. Better guidance for beginners and simpler tools for query optimization and workload management would make the overall experience easier. Review collected by and hosted on G2.com.

What makes ClickHouse stand out is its core engine, which is optimised for append-heavy, time-ordered data, enabling high-speed analytical queries on large datasets. Its vectorised execution engine really speeds things up when it comes to integrations, filtering and scanning. We also heavily rely on its Distributed architecture, which supports horizontal scaling through Shards and Replicas with distributed tables for high availability. To be honest, these features make a real difference for us. Review collected by and hosted on G2.com.

At first, joining operations was a bit of a challenge. We didn't have a full query optimiser, so we had to be really careful when it came to filtering, especially before large table joins, and we also had to use dictionaries. It took our data engineers about six months to get a handle on all this. It was a bit of a mess to begin with. Now, complex joins flow smoothly. Not ideal. Review collected by and hosted on G2.com.