Continuing our series of PostgreSQL Data Types today we’re going to introduce the PostgreSQL array data types.
Arrays can be used to denormalize data and avoid lookup tables. A good rule of thumb for using them that way is that you mostly use the array as a whole, even if you might at times search for elements in the array. Heavier processing is going to be more complex than a lookup table.
Continuing our series of PostgreSQL Data Types today we’re going to introduce the PostgreSQL ranges data type.
Range types are a unique feature of PostgreSQL, managing two dimensions of data in a single column, and allowing advanced processing. The main example is the daterange data type, which stores as a single value a lower and an upper bound of the range as a single value. This allows PostgreSQL to implement a concurrent safe check against overlapping ranges, as we’re going to see in this article.
Continuing our series of PostgreSQL Data Types today we’re going to introduce network address types.
PostgreSQL includes support for both cidr, inet, and macaddr data types. Again, those types are bundled with indexing support and advanced functions and operator support.
Continuing our series of PostgreSQL Data Types today we’re going to introduce date and time based processing functions.
Once the application’s data, or rather the user data is properly stored as timestamp with time zone, PostgreSQL allows implementing all the processing you need to. In this article we dive into a set of examples to help you get started with time based processing in your database. Can we boost your reporting skills?
Continuing our series of PostgreSQL Data Types today we’re going to introduce some of the PostgreSQL text processing functions.
There’s a very rich set of PostgreSQL functions to process text — you can find them all in the string functions and operators documentation chapter — with functions such as overlay(), substring(), position() or trim(). Or aggregates such as string_agg(). There are also regular expression functions, including the very powerful regexp_split_to_table(). In this article we see practical example putting them in practice.
