Learn how to optimize Map column lookups in ClickHouse for better query performance by materializing specific keys as standalone columns.

The short answer is "yes". ClickHouse has multiple mechanisms that allow freeing up disk space by removing old data. Each mechanism is aimed for different scenarios.

Learn how to check if a projection is used in ClickHouse queries by testing with sample data and using EXPLAIN to verify projection usage.

An example using Python and requests module to write and read to ClickHouse

How to enforce limit on max query execution time

The short answer is yes. However, we recommend keeping latency between all regions/datacenters in two-digit range, otherwise write performance will suffer as it goes through distributed consensus protocol.

If you use Oracle as a source of ClickHouse external dictionaries via Oracle ODBC driver, you need to set the correct value for the `NLS_LANG` environment variable in `/etc/default/clickhouse`.

A columnar database stores the data of each column independently. This allows reading data from disk only for those columns that are used in any given query.

Curious what ClickHouse means? Take a look inside this knowledge base article to find out!

ClickHouse is an open-source project developed on GitHub. As customary, contribution instructions are published in CONTRIBUTING file in the root of the source code repository.