Row-Oriented Storage

In databases, row-oriented storage is a method of organizing and storing data where the data for each individual row of a table is stored together in contiguous blocks. In other words, all the attributes or columns of a particular row are stored together, one after the other, in the storage medium. This is in contrast to column-oriented storage, where the data for each column is stored together.

Here are some key characteristics and considerations related to row-oriented storage:

Sequential Storage: Row-oriented storage is designed for sequential access of data. When a query requests data from a table, it reads the entire row from storage, even if the query only needs a subset of columns. This can be inefficient if the query only requires a few columns from a large table.
Ideal for Transactional Workloads: Row-oriented storage is well-suited for transactional workloads, where individual rows are frequently read or updated. In transactional scenarios, accessing all the attributes of a single row at once can be advantageous.
Good for Insert and Update Operations: Row-oriented storage tends to perform well for insert and update operations, as new rows or updated rows can be efficiently appended to the end of the table's storage area.
Compression Challenges: Row-oriented storage might have challenges with compression since data for each column in a row may have different characteristics. Some columns may compress well, while others may not, leading to potential inefficiencies in storage usage.
Schema-on-Write: Row-oriented storage is typically associated with a schema-on-write approach, where data is structured and validated before being written to the database. This contrasts with schema-on-read approaches used in some columnar databases.
Better for OLTP: Row-oriented storage is often preferred in Online Transaction Processing (OLTP) databases, where frequent read and write operations are performed on individual records.
Not Ideal for Analytical Queries: Row-oriented storage may not be the best choice for analytical queries that involve aggregations or working with a subset of columns across a large dataset. Columnar storage is generally more suitable for such analytical workloads.

It is commonly used in transactional systems and OLTP databases where frequent read and write operations are performed on individual records. However, for analytical workloads involving large datasets and aggregate queries, columnar storage is often preferred due to its efficiency in working with subsets of columns.