When are columnar databases more efficient than row databases?

When are columnar databases more efficient than row databases?

• A. When many columns of a single row are needed
• B. When data needs to be stored with high integrity
• C. When aggregations are computed across many rows
• D. When only single-row transactions are executed

Answer: (C)

Columnar databases are particularly efficient when it comes to scenarios that involve aggregations across many rows. This is because, in a columnar database, data is stored in columns rather than rows. When aggregating data, such as calculating sums, averages, or counts, having the data organized by column allows for faster access and processing. For example, if an application needs to compute the total sales for a particular product across multiple transactions, a columnar database can quickly access just the 'sales' column, processing the necessary calculations without needing to load entire rows of data. This leads to improved performance, especially with large datasets where accessing only a subset of columns speeds up queries significantly. On the other hand, columnar databases may not perform well for use cases where entire rows of data are needed frequently, as they require reconstructive effort to collect data across rows. This highlights the advantage of columnar databases in analytical and reporting environments where read-heavy operations and complex queries are common.

Columnar databases are particularly efficient when it comes to scenarios that involve aggregations across many rows. This is because, in a columnar database, data is stored in columns rather than rows. When aggregating data, such as calculating sums, averages, or counts, having the data organized by column allows for faster access and processing.

For example, if an application needs to compute the total sales for a particular product across multiple transactions, a columnar database can quickly access just the 'sales' column, processing the necessary calculations without needing to load entire rows of data. This leads to improved performance, especially with large datasets where accessing only a subset of columns speeds up queries significantly.

On the other hand, columnar databases may not perform well for use cases where entire rows of data are needed frequently, as they require reconstructive effort to collect data across rows. This highlights the advantage of columnar databases in analytical and reporting environments where read-heavy operations and complex queries are common.