Which situation best illustrates when row databases are more efficient?

Which situation best illustrates when row databases are more efficient?

• A. When a batch of new row data is inserted
• B. When aggregating data across multiple rows
• C. When a single column in a table is being queried
• D. When many columns of a single row are required simultaneously

Answer: (D)

The most suitable situation illustrating the efficiency of row databases is when many columns of a single row are required simultaneously. This design is particularly beneficial in scenarios involving transactions where the database needs to retrieve all attributes of a specific record efficiently. Row-oriented databases store data in a manner that optimizes the retrieval of complete rows, making them ideal for operations where the entire context of a record is needed. In such cases, fetching multiple columns from a single row becomes a straightforward operation, minimizing the amount of I/O operations needed to retrieve the required data. As a result, row databases excel in transactional systems where operations often involve retrieving detailed information about individual records, such as account details or customer information. Other scenarios, like batch inserting new rows or aggregating data across multiple rows, may favor different database designs, such as columnar databases, which are better suited for analytics and bulk operations. Likewise, querying a single column may be more efficiently handled in columnar databases where data is organized by columns, allowing for faster read access. Understanding these distinctions helps in selecting an appropriate database model based on the specific data access patterns.

The most suitable situation illustrating the efficiency of row databases is when many columns of a single row are required simultaneously. This design is particularly beneficial in scenarios involving transactions where the database needs to retrieve all attributes of a specific record efficiently. Row-oriented databases store data in a manner that optimizes the retrieval of complete rows, making them ideal for operations where the entire context of a record is needed.

In such cases, fetching multiple columns from a single row becomes a straightforward operation, minimizing the amount of I/O operations needed to retrieve the required data. As a result, row databases excel in transactional systems where operations often involve retrieving detailed information about individual records, such as account details or customer information.

Other scenarios, like batch inserting new rows or aggregating data across multiple rows, may favor different database designs, such as columnar databases, which are better suited for analytics and bulk operations. Likewise, querying a single column may be more efficiently handled in columnar databases where data is organized by columns, allowing for faster read access. Understanding these distinctions helps in selecting an appropriate database model based on the specific data access patterns.