ClickHouse Reviews

I have experience in ClickHouse, and we also use Apache Druid, which has corporate support from Druid, along with data products in Hadoop. We are currently exploring many platforms such as GMI, TKI, and Vertex.

I use ClickHouse as a merchant side portal, especially when we started exploring how to use the data, which was coming from multiple sources such as logs, mainframe, Teradata, and many file systems that come to the data lake. The real-time challenge was joining the data and providing more analytical queries for our merchants, who work throughout the year to improve GMB, sales, and ensure the right quantity of items is ordered at the right time. That's the challenge for the merchants, and we aim for fast analytical queries on larger databases, which is why we selected ClickHouse as our columnar OLAP database supporting real-time analytics with its own SQL interface.

We have installed both local Docker versions, which are quite scalable, and usually connect with BI tools such as Grafana, Superset, and Tableau while utilizing materialized views, DDLs partitions, and many other connectors with Python, such as ClickHouse connectors and drivers. It's exciting to see how ClickHouse has evolved, and we are evaluating ClickHouse Cloud while also having the on-premises version.

We are already a customer of ClickHouse, with Sam's Club utilizing it on the merchant side while also exploring ClickHouse for consumers, primarily for user analytics, metrics, and streaming data analysis in ad tech. Additionally, we use custom analysis and metrics for fraud detection in payments and ad campaign metrics, with various teams utilizing it for ad campaign management and user behavior analytics, particularly on e-commerce sites focusing on customer behavior. It's extensively used due to its low latency, fast aggregations, and excellent OLAP columnar storage, featuring quick joins and real-time data visibility, making ClickHouse very appealing to us.

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.

ClickHouse deserves a rating of 9 when compared to competitors, particularly Druid, which is stable but comes with higher costs and subpar support. ClickHouse proves to be more lightweight, offering low latency and high throughput, along with joins, making it especially good for log and metrics handling.

The main use case for ClickHouse is as a data warehouse for handling large volumes of data that exceed the capabilities of traditional databases like Postgres. I use it for creating dashboards and performing analytical tasks such as determining the total number of orders, average order value, and evaluations and ratios for various stores. I deploy ClickHouse both on the cloud provided by ClickHouse itself and on-premises for IoT and similar data tasks.

One of the most valuable features of ClickHouse is that it is open source without vendor lock-in, allowing me the freedom to choose any vendor for the database. It offers numerous out-of-the-box analytical functions, eliminating the need for complex coding. The performance of ClickHouse aligns with its claims, being highly efficient and used by large organizations like Uber and Zomato. The deployment process is straightforward, and it is scalable both vertically and in distributed systems via the cloud.

I used ClickHouse to collect data, put it in the database, and then analyze it to find insights. The main advantage is that I can install it on my computer instead of using cloud-based solutions, so I don't have to pay for every query like with Google or Amazon cloud services.

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.

I use ClickHouse to collect and analyze data from Ethereum. We primarily use it for data classification and occasionally for machine learning with GPT, but that's minimal. The primary use case is classification; sometimes, we use it for applications similar to OLTP scenarios. All of our data is stored in ClickHouse. We are customers of ClickHouse, not partners. It's an easy tool to use if you know SQL databases.

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.

Our company had about nine platforms, each with its own database and data. We had to gather all these data in one database and just one table. We used Apache Superset to integrate this database with the business intelligence tool. We had too many choices or options initially for the database engine.

We initially tested a database, and its performance was good. When we tried ClickHouse, we switched to it immediately because the performance was really amazing. When we had a huge amount of data, about five or six gigabytes in just one table, and we used ClickHouse to deduplicate some duplicated entries or records.

Clickhouse helped us to achieve our use cases with simple steps and good performance as mentioned previously

We use the solution not just as an analytics database but also as a data warehouse. A lot of our internal services communicate with the tool. Our analytics and ML teams also use the solution. It's the hub of our company.

It's easier to work with big data and calculations using the product. For example, we can easily calculate metrics around terabytes of data using ClickHouse. The dictionaries help us to make our analysts’ jobs faster and easier and give value to the business faster.

We use it to store live data and for tracking and monitoring every action on a PC, like which websites are opened, through Kubernetes and Google Chrome. Data is sent every second from ActiveMQ, and ClickHouse can insert millions of data points per millisecond.

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.

During my experience Clickhouse was used primary for companies analytics. At the same time I had a chance to apply it for various other use-case, such as log and storage metrics, Clickhouse as an ETL engine, monitorin, alerting and many more