Passage: Whether or not a column-oriented system will be more efficient in operation depends heavily on the workload being automated.  Operations that retrieve all the data for a given object (the entire row) are slower.  A row-based system can retrieve the row in a single disk read, whereas numerous disk operations to collect data from multiple columns are required from a columnar database.  However, these whole-row operations are generally rare.  In the majority of cases, only a limited subset of data is retrieved.  In a rolodex application, for instance, collecting the first and last names from many rows to build a list of contacts is far more common than reading all data for any single address.  This is even more true for writing data into the database, especially if the data tends to be "sparse" with many optional columns. For this reason, column stores have demonstrated excellent real-world performance in spite of many theoretical disadvantages.Partitioning, indexing, caching, views, OLAP cubes, and transactional systems such as write-ahead logging or multiversion concurrency control all dramatically affect the physical organization of either system. That said, online transaction processing (OLTP)-focused RDBMS systems are more row-oriented, while online analytical processing (OLAP)-focused systems are a balance of row-oriented and column-oriented.

Question: What orientation are most OLTP focused RDBMS systems?
row-oriented