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To each customer corresponds a 'repeating group' of transactions. Such a design can be represented in a hierarchical database but not a SQL database, since SQL does not support nested tables. The automated evaluation of any query relating to customers' transactions would broadly involve two stages:
This definition does not preclude columns having sets or relations as values, e.g. nested tables. This is the major difference to first normal form. NoSQL databases like document databases typically does not conform to the relational view. For example, an JSON or XML database might support duplicate records and intrinsic ordering. Such database ...
Unique rows (no duplicate records) [4] Scalar columns (columns cannot contain relations or composite values) [5] Every non-prime attribute has a full functional dependency on each candidate key (attributes depend on the whole of every key) [5]
The following table lists the standard-conforming values - based on SQL:2011. [1] The table's last column shows the part of the standard that defines the row. If it is empty, the definition originates from part 2 Foundation .
When dealing with columnar value sets, there are three types of cardinality: high-cardinality, normal-cardinality, and low-cardinality. High-cardinality refers to columns with values that are very uncommon or unique. High-cardinality column values are typically identification numbers, email addresses, or user names.
In SQL, the unique keys have a UNIQUE constraint assigned to them in order to prevent duplicates (a duplicate entry is not valid in a unique column). Alternate keys may be used like the primary key when doing a single-table select or when filtering in a where clause, but are not typically used to join multiple tables.
The syntax of the SQL programming language is defined and maintained by ISO/IEC SC 32 as part of ISO/IEC 9075.This standard is not freely available. Despite the existence of the standard, SQL code is not completely portable among different database systems without adjustments.
"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.