As time-series applications are designed to store massive amounts of time-related information, designing the underlying data storage is essential. The three major design options for hypertables in a TimeSeries database are narrow, wide, and medium table layouts. Each layout has its pros and cons, and the choice depends on the use case. Narrow tables are great for low cardinality metrics with known data types, making it easy to extend with additional metrics. Medium tables offer a practical design choice for uncertain future data types, providing ease of use and extensibility. Wide tables are suitable for systems with well-known metrics up front, but can become complicated to manage and extend over time. Hypertable compression plays a role in each layout's performance, with narrow tables compressing well due to uniform records, medium tables also benefiting from compression, and wide tables having potential issues with adding columns. Ultimately, the choice of table design depends on the specific use case, considering factors such as ease of use, extensibility, cost of up-front design, and multi-tenancy requirements.