Generic index structure (GiST) to allow R-Tree indexĬombined, PostgreSQL provides a very easy development path to add new spatial types.
No limit on column sizes ("TOAST"able tuples) to support big GIS objects Pluggable type extension and function extension Proven reliability and transactional integrity by default (ACID)Ĭareful support for SQL standards (full SQL92) The list of possible functions is very large, but a common set of functions is defined by the OGC SFSQL and implemented (along with additional useful functions) by PostGIS. Generation: Functions that generate new geometries from others. Retrieval: Functions that retrieve properties and measurements of a Geometry.Ĭomparison: Functions that compare two geometries with respect to their spatial relation.
Management: Functions that manage information about spatial tables and PostGIS administration. The majority of all spatial functions can be grouped into one of the following five categories:Ĭonversion: Functions that convert between geometries and external data formats. These spatial functions serve as the building block for any spatial project. The most common implementations are the R-Tree and Quadtree (used in PostGIS), but there are also grid-based indexes and GeoHash indexes implemented in other spatial databases.įor manipulating data during a query, an ordinary database provides functions such as concatenating strings, performing hash operations on strings, doing mathematics on numbers, and extracting information from dates.Ī spatial database provides a complete set of functions for analyzing geometric components, determining spatial relationships, and manipulating geometries. The actual spatial indexes implemented by various databases vary widely. The question "what lines are inside this polygon?" will be instead interpreted by a spatial index as "what lines have bounding boxes that are contained inside this polygon's bounding box?" So instead of providing exact results, as B-trees do, spatial indexes provide approximate results. Indexes have to perform quickly in order to be useful. Even the most complex polygons and linestrings can be represented by a simple bounding box. Real spatial databases provide a "spatial index" that instead answers the question "which objects are within this particular bounding box?".Ī bounding box is the smallest rectangle - parallel to the coordinate axes - capable of containing a given feature.īounding boxes are used because answering the question "is A inside B?" is very computationally intensive for polygons but very fast in the case of rectangles.
The natural sort order of numbers, strings, and dates is simple to determine - every value is less than, greater than or equal to every other value.īut because polygons can overlap, can be contained in one another, and are arrayed in a two-dimensional (or more) space, a B-tree cannot be used to efficiently index them.
Indexing for standard types (numbers, strings, dates) is usually done with B-tree indexes.Ī B-tree partitions the data using the natural sort order to put the data into a hierarchical tree. An ordinary database provides indexes to allow for fast and random access to subsets of data.
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