Why running analytical benchmark against database for logs? VictoriaLogs is designed as a database for logs. It supports both plaintext and structured logs. Structured logs can be written to a single table with pre-defined columns for every field seen in the structured logs. So rows with ad serving events from ClickBench can be treated as structured logs with many fields. Such logs are known also as wide events [3].

VictoriaLogs has some optimizations for wide events. For example, it stores data per each column in distinct blocks on disk (aka column-oriented storage). This allows reading data only for the needed columns during query execution. That's why I decided giving VictoriaLogs a try at ClickBench [4].

It has been appeared that VictoriaLogs shows good results there:

- it needs 4.5x less disk space than Elasticsearch and PostgreSQL, and 5x less disk space than MongoDB

- it loads data 4.7x faster than Elasticsearch and 21x faster than MongoDB

- it executes queries 9.3x faster than Elasticsearch, 111x faster than PostgreSQL and 51x faster than MongoDB on average

You can easily run and reproduce the benchmark on your hardware by cloning ClickBench [5] and executing benchmark.sh script per each database you want to test (each benchmark.sh script is located in the folder with the corresponding database name).

[1] https://benchmark.clickhouse.com/

[2] https://github.com/ClickHouse/ClickBench/blob/main/clickhouse/create.sql

[3] https://jeremymorrell.dev/blog/a-practitioners-guide-to-wide-events/

[4] https://github.com/ClickHouse/ClickBench/tree/main/victorialogs

[5] https://github.com/ClickHouse/ClickBench