""" Illustrates three strategies for persisting and querying XML documents as represented by ElementTree in a relational database. The techniques do not apply any mappings to the ElementTree objects directly, so are compatible with the native cElementTree as well as lxml, and can be adapted to suit any kind of DOM representation system. Querying along xpath-like strings is illustrated as well. In order of complexity: * ``pickle.py`` - Quick and dirty, serialize the whole DOM into a BLOB column. While the example is very brief, it has very limited functionality. * ``adjacency_list.py`` - Each DOM node is stored in an individual table row, with attributes represented in a separate table. The nodes are associated in a hierarchy using an adjacency list structure. A query function is introduced which can search for nodes along any path with a given structure of attributes, basically a (very narrow) subset of xpath. * ``optimized_al.py`` - Uses the same strategy as ``adjacency_list.py``, but associates each DOM row with its owning document row, so that a full document of DOM nodes can be loaded using O(1) queries - the construction of the "hierarchy" is performed after the load in a non-recursive fashion and is much more efficient. E.g.:: # parse an XML file and persist in the database doc = ElementTree.parse("test.xml") session.add(Document(file, doc)) session.commit() # locate documents with a certain path/attribute structure for document in find_document('/somefile/header/field2[@attr=foo]'): # dump the XML print document """