Questions and answers about analytics in YDB

Can YDB be used for analytical workloads (OLAP)?

Yes, it can. If this is the primary type of workload for a given table, make sure it is column-oriented.

How to choose between row-oriented and column-oriented tables?

Similarly to choosing between transactional (OLTP) and analytical (OLAP) database management systems, this question comes to a number of trade-offs that need to be considered:

• What's the main use case for the table? For mostly transactional (OLTP) workloads, use row-oriented tables. For analytical workloads (OLAP), use column-oriented tables. Transactional workloads are characterized by a high rate of queries affecting a small number of rows each. Analytical workloads are characterized by processing large volumes of data to produce relatively small query results.
• How is the table modified? As a rule of thumb, row-oriented tables work better when data is frequently modified in place, while column-oriented tables work better when data is mostly appended by adding new rows. Thus, row-oriented tables usually reflect the current state of a dataset, while column-oriented tables often store a history of some sort of immutable events.
• Which features are needed? Even though YDB strives for feature parity between row-oriented and column-oriented tables, there might be current limitations to consider. Check the documentation for details on specific features intended to be used with a given table.

Unlike most other database management systems, YDB supports both row-oriented and column-oriented tables in the same database. However, keep in mind that transactional and analytical workloads have different resource consumption patterns and might affect each other when the cluster is overloaded.