XML Matters: Process XML in Python with ElementTree

How does the API stack up against similar libraries?

Fredrik Lundh's ElementTree module is an increasingly popular API for light-weight and fast manipulation of XML documents within Python. In this installment, David contrasts ElementTree with several other libraries devoted to processing XML instances as object trees, especially with his own gnosis.xml.objectify module.

David Mertz (mertz@gnosis.cx), Comparator, Gnosis Software, Inc.

Photo of David MertzFor David Mertz, an atomic object is a combination of facts. David may be reached at mertz@gnosis.cx; his life pored over at http://gnosis.cx/dW/. Suggestions and recommendations on this, past, or future, columns are welcomed. Check out David's new book .



24 June 2003

Also available in Japanese

In previous installments of this column, I have looked at XML libraries whose aim is to emulate the most familiar native operations in a given programming language. The first of these that I covered is my own gnosis.xml.objectify for Python. I also dedicated installments to Haskell's HaXml and Ruby's REXML. Although I have not discussed them here, Java's JDOM and Perl's XML::Grove also have similar goals.

Lately, I have noticed a number of posters to the comp.lang.python newsgroup mentioning Fredrik Lundh's ElementTree as a native XML library for Python. Of course, Python already has several XML API's included in its standard distribution: a DOM module, a SAX module, an expat wrapper, and the deprecated xmllib. Of these, only xml.dom converts an XML document into an in-memory object that you can manipulate with method calls on nodes. Actually, you'll find several different Python DOM implementations, each with somewhat different properties:

  • xml.minidom is a basic one.
  • xml.pulldom builds accessed subtrees only as needed.
  • 4Suite's cDomlette (Ft.Xml.Domlette) builds a DOM tree in C, avoiding Python callbacks for speed.

Of course, appealing to my author's vanity, I am most curious to compare ElementTree to my own gnosis.xml.objectify, to which it is closest in purpose and behavior. The goal of ElementTree is to store representations of XML documents in data structures that behave in much the way you think about data in Python. The focus here is on programming in Python, not on adapting your programming style to XML.

Some benchmarks

My colleague Uche Ogbuji has written a short article on ElementTree for another publication. (See Resources.) One of the tests he ran compared the relative speed and memory consumption of ElementTree to that of DOM. Uche chose to use his own cDomlette for the comparison. Unfortunately, I am unable to install 4Suite 1.0a1 on the Mac OSX machine I use (a workaround is in the works). However, I can use Uche's estimates to guess the likely performance -- he indicates that ElementTree is 30% slower, but 30% more memory-friendly, than cDomlette.

Mostly I was curious how ElementTree compares in speed and memory to gnosis.xml.objectify. I had never actually benchmarked my module very precisely before, since I never had anything concrete to compare it to. I selected two documents that I had used for benchmarking in the past: a 289 KB XML version of Shakespeare's Hamlet and a 3 MB XML Web log. I created scripts that simply parse an XML document into the object models of the various tools, but do not perform any additional manipulation:

Listing 1. Scripts to time XML object models for Python
% cat time_xo.py
import sys
from gnosis.xml.objectify import XML_Objectify,EXPAT
doc = XML_Objectify(sys.stdin,EXPAT).make_instance()
---
% cat time_et.py
import sys
from elementtree import ElementTree
doc = ElementTree.parse(sys.stdin).getroot()
---
% cat time_minidom.py
import sys
from xml.dom import minidom
doc = minidom.parse(sys.stdin)

Creating the program object is quite similar in all three cases, and also with cDomlette. I estimated memory usage by watching the output of top in another window; each test was run three times to make sure that they were consistent, and the median value was used (memory was identical across runs).

Figure 1. Benchmarks of XML object models in Python
Benchmarks of XML object models in Python

One thing that is clear is that xml.minidom quickly becomes quite impractical for moderately large XML documents. The rest stay (fairly) reasonable. gnosis.xml.objectify is the most memory-friendly, but that is not surprising since it does not preserve all the information in the original XML instance (data content is kept, but not all structural information).

I also ran a test of Ruby's REXML, using the following script:

Listing 2. Ruby REXML parsing script (time_rexml.rb)
require "rexml/document"
include REXML
doc = (Document.new File.new ARGV.shift).root

REXML proved about as resource intensive as xml.minidom: parsing Hamlet.xml took 10 seconds and used 14 MB; parsing Weblog.xml took 190 seconds and used 150 MB. Obviously, the choice of programming language usually takes precedence over the comparison of libraries.


Working with an XML document object

A nice thing about ElementTree is that it can be round-tripped. That is, you can read in an XML instance, modify fairly native-feeling data structures, then call the .write() method to re-serialize to well-formed XML. DOM does this, of course, but gnosis.xml.objectify does not. It is not all that difficult to construct a custom output function for gnosis.xml.objectify that produces XML -- but doing so is not automatic. With ElementTree, along with the .write() method of ElementTree instances, individual Element instances can be serialized with the convenience function elementtree.ElementTree.dump(). This lets you write XML fragments from individual object nodes -- including from the root node of the XML instance.

I present a simple task that contrasts the ElementTree and gnosis.xml.objectify APIs. The large weblog.xml document used for benchmark tests contains about 8,500 <entry> elements, each having the same collection of child fields -- a typical arrangement for a data-oriented XML document. In processing this file, one task might be to collect a few fields from each entry, but only if some other fields have particular values (or ranges, or match regexen). Of course, if you really only want to perform this one task, using a streaming API like SAX avoids the need to model the whole document in memory -- but assume that this task is one of several that an application performs on the large data structure. One <entry> element would look something like this:

Listing 3. Sample <entry> element
<entry>
  <host>64.172.22.154</host>
  <referer>-</referer>
  <userAgent>-</userAgent>
  <dateTime>19/Aug/2001:01:46:01</dateTime>
  <reqID>-0500</reqID>
  <reqType>GET</reqType>
  <resource>/</resource>
  <protocol>HTTP/1.1</protocol>
  <statusCode>200</statusCode>
  <byteCount>2131</byteCount>
</entry>

Using gnosis.xml.objectify, I might write a filter-and-extract application as:

Listing 4. Filter-and-extract application (select_hits_xo.py)
from gnosis.xml.objectify import XML_Objectify, EXPAT
weblog = XML_Objectify('weblog.xml',EXPAT).make_instance()
interesting = [entry for entry in weblog.entry
    if entry.host.PCDATA=='209.202.148.31' and 
             entry.statusCode.PCDATA=='200']
for e in interesting:
  print"%s (%s)" % (e.resource.PCDATA,
                     e.byteCount.PCDATA)

List comprehensions are quite convenient as data filters. In essence, ElementTree works the same way:

Listing 5. Filter-and-extract application (select_hits_et.py)
from elementtree import ElementTree
weblog = ElementTree.parse('weblog.xml').getroot()
interesting = [entry for entry in weblog.findall('entry')
    if entry.find('host').text=='209.202.148.31' and 
             entry.find('statusCode').text=='200']
for e in interesting:
  print"%s (%s)" % (e.findtext('resource'),
                    e.findtext('byteCount'))

Note these differences above. gnosis.xml.objectify attaches subelement nodes directly as attributes of nodes (every node is of a custom class named after the tag name). ElementTree, on the other hand, uses methods of the Element class to find child nodes. The .findall() method returns a list of all matching nodes; .find() returns just the first match; .findtext() returns the text content of a node. If you only want the first match on a gnosis.xml.objectify subelement, you just need to index it -- for example, node.tag[0]. But if there is only one such subelement, you can also refer to it without the explicit indexing.

But in the ElementTree example, you do not really need to find all the <entry> elements explicitly; Element instances behave in a list-like way when iterated over. A point to note is that iteration takes place over all child nodes, whatever tags they may have. In contrast, a gnosis.xml.objectify node has no built-in method to step through all of its subelements. Still, it is easy to construct a one-line children() function (I will include one in future releases). Contrast Listing 6:

Listing 6. ElementTree iteration over node list and specific child type
>>> open('simple.xml','w.').write('''<root>
... <foo>this</foo>
... <bar>that</bar>
... <foo>more</foo></root>''')
>>> from elementtree import ElementTree
>>> root = ElementTree.parse('simple.xml').getroot()
>>> for node in root:
...     print node.text,
...
this that more
>>> for node in root.findall('foo'):
...     print node.text,
...
this more

With Listing 7:

Listing 7. gnosis.xml.objectify lossy iteration over all children
>>> children=lambda o: [x for x in o.__dict__ if x!='__parent__']
>>> from gnosis.xml.objectify import XML_Objectify
>>> root = XML_Objectify('simple.xml').make_instance()
>>> for tag in children(root):
...     for node in getattr(root,tag):
...         print node.PCDATA,
...
this more that
>>> for node in root.foo:
...     print node.PCDATA,
...
this more

As you can see, gnosis.xml.objectify currently discards information about the original order of interspersed <foo> and <bar> elements (it could be remembered in another magic attribute, like .__parent__ is, but no one needed or sent a patch to do this).

ElementTree stores XML attributes in a node attribute called .attrib; the attributes are stored in a dictionary. gnosis.xml.objectify puts the XML attributes directly into node attributes of corresponding name. The style I use tends to flatten the distinction between XML attributes and element contents -- to my mind, that is something for XML, not my native data structure, to worry about. For example:

Listing 8. Differences in access to children and XML attributes
>>> xml = '<root foo="this"><bar>that</bar></root>'
>>> open('attrs.xml','w').write(xml)
>>> et = ElementTree.parse('attrs.xml').getroot()
>>> xo = XML_Objectify('attrs.xml').make_instance()
>>> et.find('bar').text, et.attrib['foo']
('that', 'this')
>>> xo.bar.PCDATA, xo.foo
(u'that', u'this')

gnosis.xml.objectify still makes some distinction in between XML attributes that create node attributes containing text, and XML element contents that create node attributes containing objects (perhaps with subnodes that have .PCDATA).


XPaths And tails

ElementTree implements a subset of XPath in its .find*() methods. Using this style can be much more concise than nesting code to look within levels of subnodes, especially for XPaths that contain wildcards. For example, if I were interested in all the timestamps of hits to my Web server, I could examine weblog.xml using:

Listing 9. Using XPath to find nested subelements
>>> from elementtree import ElementTree
>>> weblog = ElementTree.parse('weblog.xml').getroot()
>>> timestamps = weblog.findall('entry/dateTime')
>>> for ts in timestamps:
...     if ts.text.startswith('19/Aug'):
...         print ts.text

Of course, for a standard, shallow document like weblog.xml, it is easy to do the same thing with list comprehensions:

Listing 10. Using list comprehensions to find and filter nested subelements
>>> for ts in [ts.text for e in weblog
...            for ts in e.findall('dateTime')
...            if ts.text.startswith('19/Aug')]:
...     print ts

Prose-oriented XML documents, however, tend to have much more variable document structure, and typically nest tags at least five or six levels deep. For example, an XML schema like DocBook or TEI might have citations in sections, subsections, bibliographies, or sometimes within italics tags, or in blockquotes, and so on. Finding every <citation> element would require a cumbersome (probably recursive) search across levels. Or using XPath, you could just write:

Listing 11. Using XPath to find deeply nested subelements
>>> from elementtree import ElementTree
>>> weblog = ElementTree.parse('weblog.xml').getroot()
>>> cites = weblog.findall('.//citation')

However, XPath support in ElementTree is limited: You cannot use the various functions contained in full XPath, nor can you search on attributes. In what it does, though, the XPath subset in ElementTree greatly aids readability and expressiveness.

I want to mention one more quirk of ElementTree before I wrap up. XML documents can be mixed content. Prose-oriented XML, in particular, tends to intersperse PCDATA and tags rather freely. But where exactly should you store the text that comes between child nodes? Since an ElementTreeElement instance has a single .text attribute -- which contains a string -- that does not really leave space for a broken sequence of strings. The solution ElementTree adopts is to give each node a .tail attribute, which contains all the text after a closing tag but before the next element begins or the parent element is closed. For example:

Listing 12. PCDATA stored in node.tail attribute
>>> xml = '<a>begin<b>inside</b>middle<c>inside</c>end</a>'
>>> open('doc.xml','w').write(xml)
>>> doc = ElementTree.parse('doc.xml').getroot()
>>> doc.text, doc.tail
('begin', None)
>>> doc.find('b').text, doc.find('b').tail
('inside', 'middle')
>>> doc.find('c').text, doc.find('c').tail
('inside', 'end')

Conclusion

ElementTree is a nice effort to bring a much lighter weight object model to XML processing in Python than that provided by DOM. Although I have not addressed it in this article, ElementTree is as good at generating XML documents from scratch as it is at manipulating existing XML data.

As author of a similar library, gnosis.xml.objectify, I cannot be entirely objective in evaluating ElementTree; nonetheless, I continue to find my own approach somewhat more natural in Python programs than that provided by ElementTree. The latter still usually utilizes node methods to manipulate data structures, rather than directly accessing node attributes as one usually does with data structures built within an application.

However, in several areas, ElementTree shines. It is far easier to access deeply nested elements using XPath than with manual recursive searches. Obviously, DOM also gives you XPath, but at the cost of a far heavier and less uniform API. All the Element nodes of ElementTree act in a consistent manner, unlike DOM's panoply of node types.

Resources

  • Find out more about ElementTree at Fredrik Lundh's Element Trees page.
  • Get an additional perspective on the topic with this XML.com article by developerWorks columnist Uche Ogbuji.
  • Read David Mertz's earlier columns on XML libraries.
  • Take a look at another Python XML API/library -- generateDS. Developer Dave Kuhlman has written a very nice essay comparing generateDS with gnosis.xml.objectify. In brief, the idea behind generateDS is to use an XML Schema as the basis for Python classes that properly handle the elements in an XML instance. Rather than handle XML trees generically, generateDS is code generator for Python modules to handle specific XML document schemas; autogenerated code can easily be specialized to quickly form a custom application.
  • Find more XML resources on the developerWorks XML zone.
  • Check out Rational Application Developer for WebSphere Software, an easy-to-use, integrated development environment for building, testing, and deploying J2EE applications, including generating XML documents from DTDs and schemas.

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