This is just a short example that illustrates the use of JSONExtract functions.

The short answer is "no". The key-value workload is among top positions in the list of cases when NOT to use ClickHouse. It’s an OLAP system after all, while there are many excellent key-value storage systems out there.

We can refer to systems like MapReduce as distributed computing systems in which the reduce operation is based on distributed sorting. The most common open-source solution in this class is Apache Hadoop.

We recommend having a maximum of 1000 databases and 5000 tables, 50000 partitions, and 100000 parts across all databases for a service.

OLAP stands for Online Analytical Processing. It is a broad term that can be looked at from two perspectives: technical and business.

First of all, let’s discuss why people ask this question in the first place.

Note: Please see the blog Working with Time series data in ClickHouse for additional examples of using ClickHouse for time series analysis.

ClickHouse is a generic data storage solution for OLAP workloads, while there are many specialized time-series database management systems. Nevertheless, ClickHouse’s focus on query execution speed allows it to outperform specialized systems in many cases. There are many independent benchmarks on this topic out there, so we’re not going to conduct one here. Instead, let’s focus on ClickHouse features that are important to use if that’s your use case.

Being an open-source product makes this question not so straightforward to answer. You do not have to tell anyone if you want to start using ClickHouse, you just go grab source code or pre-compiled packages.