Database storage structures

Database tables and indexes may be stored on disk in one of a number of forms, including ordered/unordered flat files, ISAM, heap files, hash buckets, or B+ trees. Each form has its own particular advantages and disadvantages. The most commonly used forms are B-trees and ISAM. Such forms or structures are one aspect of the overall schema used by a database engine to store information.

Unordered

Unordered storage typically stores the records in the order they are inserted. Such storage offers good insertion efficiency ( $O\left(1\right)$ ), but inefficient retrieval times ( $O\left(n\right)$ ). Typically these retrieval times are better, however, as most databases use indexes on the primary keys, resulting in retrieval times of $O\left(\log n\right)$ or $O\left(1\right)$ for keys that are the same as the database row offsets within the storage system.^{[citation needed]}

Ordered

Ordered storage typically stores the records in order and may have to rearrange or increase the file size when a new record is inserted, resulting in lower insertion efficiency. However, ordered storage provides more efficient retrieval as the records are pre-sorted, resulting in a complexity of $O\left(\log n\right)$ .^{[citation needed]}

Structured files

Heap files

Heap files are lists of unordered records of variable size. Although sharing a similar name, heap files are widely different from in-memory heaps. In-memory heaps are ordered, as opposed to heap files.

Simplest and most basic method
- insert efficient, with new records added at the end of the file, providing chronological order
- retrieval efficient when the handle to the memory is the address of the memory
- search inefficient, as searching has to be linear
- deletion is accomplished by marking selected records as "deleted"
- requires periodic reorganization if file is very volatile (changed frequently)
Advantages
- efficient for bulk loading data
- efficient for relatively small relations as indexing overheads are avoided
- efficient when retrievals involve large proportion of stored records
Disadvantages
- not efficient for selective retrieval using key values, especially if large
- sorting may be time-consuming
- not suitable for volatile tables

Hash buckets

Hash functions calculate the address of the page in which the record is to be stored based on one or more fields in the record
- hashing functions chosen to ensure that addresses are spread evenly across the address space
- ‘occupancy’ is generally 40% to 60% of the total file size
- unique address not guaranteed so collision detection and collision resolution mechanisms are required
Open addressing
Chained/unchained overflow
Pros and cons
- efficient for exact matches on key field
- not suitable for range retrieval, which requires sequential storage
- calculates where the record is stored based on fields in the record
- hash functions ensure even spread of data
- collisions are possible, so collision detection and restoration is required

B+ trees

These are the most commonly used in practice.

Time taken to access any record is the same because the same number of nodes is searched
Index is a full index so data file does not have to be ordered
Pros and cons
- versatile data structure – sequential as well as random access
- access is fast
- supports exact, range, part key and pattern matches efficiently.
- volatile files are handled efficiently because index is dynamic – expands and contracts as table grows and shrinks
- less well suited to relatively stable files – in this case, ISAM is more efficient

Data orientation

Most conventional relational databases use "row-oriented" storage, meaning that all data associated with a given row is stored together. By contrast, column-oriented DBMS store all data from a given column together in order to more quickly serve data warehouse-style queries. Correlation databases are similar to row-based databases, but apply a layer of indirection to map multiple instances of the same value to the same numerical identifier.