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Benchmarks and Speed
Stefan Behnel
.. meta::
:description: Performance evaluation of lxml and ElementTree
:keywords: lxml performance, lxml.etree, lxml.objectify, benchmarks, ElementTree
As an XML library, lxml.etree is very fast. It is also slow. As with all
software, it depends on what you do with it. Rest assured that lxml is fast
enough for most applications, so lxml is probably somewhere between 'fast
enough' and 'the best choice' for yours.
This text describes where lxml.etree (abbreviated to 'lxe') excels, gives
hints on some performance traps and compares the overall performance to the
original ElementTree_ (ET) and cElementTree_ (cET) libraries by Fredrik Lundh.
The cElementTree library is a fast C-implementation of the original
.. _ElementTree:
.. _cElementTree:
.. contents::
1 How to read the timings
2 Bad things first
3 Parsing and Serialising
4 The ElementTree API
5 Tree traversal
6 XPath
7 lxml.objectify
General notes
First thing to say: there *is* an overhead involved in having a DOM-like C
library mimic the ElementTree API. As opposed to ElementTree, lxml has to
generate Python representations of tree nodes on the fly when asked for them,
and the internal tree structure of libxml2 results in a higher maintenance
overhead than the simpler top-down structure of ElementTree. What this means
is: the more of your code runs in Python, the less you can benefit from the
speed of lxml and libxml2. Note, however, that this is true for most
performance critical Python applications. No one would implement fourier
transformations in pure Python when you can use NumPy.
The up side then is that lxml provides powerful tools like tree iterators,
XPath and XSLT, that can handle complex operations at the speed of C. Their
pythonic API in lxml makes them so flexible that most applications can easily
benefit from them.
How to read the timings
The statements made here are backed by the (micro-)benchmark scripts
``_, ``_ and ``_ that come with
the lxml source distribution. They are distributed under the same BSD license
as lxml itself, and the lxml project would like to promote them as a general
benchmarking suite for all ElementTree implementations. New benchmarks are
very easy to add as tiny test methods, so if you write a performance test for
a specific part of the API yourself, please consider sending it to the lxml
mailing list.
The timings cited below compare lxml 2.0alpha (with libxml2 2.6.30) to
the December 2007 SVN trunk versions of ElementTree (1.3) and
cElementTree (1.2.7). They were run single-threaded on a 1.8GHz Intel
Core Duo machine under Ubuntu Linux 7.10 (Gutsy). The C libraries
were compiled with the same platform specific optimisation flags. The
Python interpreter (2.5.1) was used as provided by the distribution.
.. _``:
.. _``:
.. _``:
The scripts run a number of simple tests on the different libraries, using
different XML tree configurations: different tree sizes (T1-4), with or
without attributes (-/A), with or without ASCII string or unicode text
(-/S/U), and either against a tree or its serialised XML form (T/X). In the
result extracts cited below, T1 refers to a 3-level tree with many children at
the third level, T2 is swapped around to have many children below the root
element, T3 is a deep tree with few children at each level and T4 is a small
tree, slightly broader than deep. If repetition is involved, this usually
means running the benchmark in a loop over all children of the tree root,
otherwise, the operation is run on the root node (C/R).
As an example, the character code ``(SATR T1)`` states that the benchmark was
running for tree T1, with plain string text (S) and attributes (A). It was
run against the root element (R) in the tree structure of the data (T).
Note that very small operations are repeated in integer loops to make them
measurable. It is therefore not always possible to compare the absolute
timings of, say, a single access benchmark (which usually loops) and a 'get
all in one step' benchmark, which already takes enough time to be measurable
and is therefore measured as is. An example is the index access to a single
child, which cannot be compared to the timings for ``getchildren()``. Take a
look at the concrete benchmarks in the scripts to understand how the numbers
Parsing and Serialising
Serialisation is an area where lxml excels. The reason is that it
executes entirely at the C level, without any interaction with Python
code. The results are rather impressive, especially for UTF-8, which
is native to libxml2. While 20 to 40 times faster than (c)ElementTree
1.2, lxml is still more than 5 times as fast as the much improved
ElementTree 1.3::
lxe: tostring_utf16 (SATR T1) 23.4821 msec/pass
cET: tostring_utf16 (SATR T1) 129.8430 msec/pass
ET : tostring_utf16 (SATR T1) 136.1301 msec/pass
lxe: tostring_utf16 (UATR T1) 23.4859 msec/pass
cET: tostring_utf16 (UATR T1) 130.1570 msec/pass
ET : tostring_utf16 (UATR T1) 136.3101 msec/pass
lxe: tostring_utf16 (S-TR T2) 24.2729 msec/pass
cET: tostring_utf16 (S-TR T2) 136.9388 msec/pass
ET : tostring_utf16 (S-TR T2) 143.9550 msec/pass
lxe: tostring_utf8 (S-TR T2) 18.4860 msec/pass
cET: tostring_utf8 (S-TR T2) 137.0859 msec/pass
ET : tostring_utf8 (S-TR T2) 144.3110 msec/pass
lxe: tostring_utf8 (U-TR T3) 2.7399 msec/pass
cET: tostring_utf8 (U-TR T3) 52.1040 msec/pass
ET : tostring_utf8 (U-TR T3) 53.1070 msec/pass
For parsing, on the other hand, the advantage is clearly with
cElementTree. The (c)ET libraries use a very thin layer on top of the
expat parser, which is known to be extremely fast::
lxe: parse_stringIO (SAXR T1) 144.1851 msec/pass
cET: parse_stringIO (SAXR T1) 14.4269 msec/pass
ET : parse_stringIO (SAXR T1) 245.9190 msec/pass
lxe: parse_stringIO (S-XR T3) 5.6100 msec/pass
cET: parse_stringIO (S-XR T3) 5.3229 msec/pass
ET : parse_stringIO (S-XR T3) 82.4831 msec/pass
lxe: parse_stringIO (UAXR T3) 23.4420 msec/pass
cET: parse_stringIO (UAXR T3) 30.2689 msec/pass
ET : parse_stringIO (UAXR T3) 165.7169 msec/pass
While about as fast for smaller documents, the expat parser allows cET
to be up to 10 times faster than lxml on plain parser performance for
large input documents. Similar timings can be observed for the
``iterparse()`` function::
lxe: iterparse_stringIO (SAXR T1) 160.3689 msec/pass
cET: iterparse_stringIO (SAXR T1) 19.1891 msec/pass
ET : iterparse_stringIO (SAXR T1) 274.8971 msec/pass
lxe: iterparse_stringIO (UAXR T3) 24.9629 msec/pass
cET: iterparse_stringIO (UAXR T3) 31.7740 msec/pass
ET : iterparse_stringIO (UAXR T3) 173.8000 msec/pass
However, if you benchmark the complete round-trip of a serialise-parse
cycle, the numbers will look similar to these::
lxe: write_utf8_parse_stringIO (S-TR T1) 160.0718 msec/pass
cET: write_utf8_parse_stringIO (S-TR T1) 207.6778 msec/pass
ET : write_utf8_parse_stringIO (S-TR T1) 450.2120 msec/pass
lxe: write_utf8_parse_stringIO (UATR T2) 173.5830 msec/pass
cET: write_utf8_parse_stringIO (UATR T2) 253.0849 msec/pass
ET : write_utf8_parse_stringIO (UATR T2) 519.2261 msec/pass
lxe: write_utf8_parse_stringIO (S-TR T3) 8.4269 msec/pass
cET: write_utf8_parse_stringIO (S-TR T3) 75.7639 msec/pass
ET : write_utf8_parse_stringIO (S-TR T3) 156.1930 msec/pass
lxe: write_utf8_parse_stringIO (SATR T4) 1.2100 msec/pass
cET: write_utf8_parse_stringIO (SATR T4) 6.4859 msec/pass
ET : write_utf8_parse_stringIO (SATR T4) 9.9051 msec/pass
For applications that require a high parser throughput and do little
serialization, cET is the best choice. Also for iterparse
applications that extract small amounts of data from large XML data
sets. If it comes to round-trip performance, however, lxml tends to
be between 30% and multiple times faster in total. So, whenever the
input documents are not considerably bigger than the output, lxml is
the clear winner.
The ElementTree API
Since all three libraries implement the same API, their performance is easy to
compare in this area. A major disadvantage for lxml's performance is the
different tree model that underlies libxml2. It allows lxml to provide parent
pointers for elements, but also increases the overhead of tree building and
restructuring. This can be seen from the tree setup times of the benchmark
(given in seconds)::
lxe: -- S- U- -A SA UA
T1: 0.0914 0.0875 0.0872 0.0892 0.0882 0.0900
T2: 0.0894 0.0897 0.0892 0.0988 0.0978 0.0974
T3: 0.0219 0.0194 0.0189 0.0570 0.0570 0.0573
T4: 0.0004 0.0003 0.0003 0.0012 0.0012 0.0012
cET: -- S- U- -A SA UA
T1: 0.0272 0.0264 0.0267 0.0268 0.0261 0.0265
T2: 0.0280 0.0274 0.0273 0.0273 0.0276 0.0275
T3: 0.0065 0.0066 0.0065 0.0111 0.0088 0.0088
T4: 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
ET : -- S- U- -A SA UA
T1: 0.1302 0.1903 0.2208 0.1265 0.2542 0.1267
T2: 0.2994 0.1301 0.3402 0.3746 0.1326 0.4170
T3: 0.0301 0.0310 0.0302 0.0348 0.3654 0.0349
T4: 0.0006 0.0005 0.0008 0.0006 0.0007 0.0006
While lxml is still faster than ET in most cases (10-70%), cET can be up to
three times faster than lxml here. One of the reasons is that lxml must
additionally discard the created Python elements after their use, when they
are no longer referenced. ET and cET represent the tree itself through these
objects, which reduces the overhead in creating them.
Child access
The same reason makes operations like collecting children as in
``list(element)`` more costly in lxml. Where ET and cET can quickly
create a shallow copy of their list of children, lxml has to create a
Python object for each child and collect them in a list::
lxe: root_list_children (--TR T1) 0.0169 msec/pass
cET: root_list_children (--TR T1) 0.0081 msec/pass
ET : root_list_children (--TR T1) 0.0541 msec/pass
lxe: root_list_children (--TR T2) 0.2339 msec/pass
cET: root_list_children (--TR T2) 0.0319 msec/pass
ET : root_list_children (--TR T2) 0.4420 msec/pass
This handicap is also visible when accessing single children::
lxe: first_child (--TR T2) 0.2470 msec/pass
cET: first_child (--TR T2) 0.2170 msec/pass
ET : first_child (--TR T2) 0.9968 msec/pass
lxe: last_child (--TR T1) 0.2482 msec/pass
cET: last_child (--TR T1) 0.2291 msec/pass
ET : last_child (--TR T1) 0.9830 msec/pass
... unless you also add the time to find a child index in a bigger
list. ET and cET use Python lists here, which are based on arrays.
The data structure used by libxml2 is a linked tree, and thus, a
linked list of children::
lxe: middle_child (--TR T1) 0.2789 msec/pass
cET: middle_child (--TR T1) 0.2229 msec/pass
ET : middle_child (--TR T1) 1.0030 msec/pass
lxe: middle_child (--TR T2) 1.9610 msec/pass
cET: middle_child (--TR T2) 0.2229 msec/pass
ET : middle_child (--TR T2) 0.9930 msec/pass
Element creation
As opposed to ET, libxml2 has a notion of documents that each element must be
in. This results in a major performance difference for creating independent
Elements that end up in independently created documents::
lxe: create_elements (--TC T2) 3.1691 msec/pass
cET: create_elements (--TC T2) 0.1929 msec/pass
ET : create_elements (--TC T2) 1.3590 msec/pass
Therefore, it is always preferable to create Elements for the document they
are supposed to end up in, either as SubElements of an Element or using the
explicit ``Element.makeelement()`` call::
lxe: makeelement (--TC T2) 2.2650 msec/pass
cET: makeelement (--TC T2) 0.3211 msec/pass
ET : makeelement (--TC T2) 1.6358 msec/pass
lxe: create_subelements (--TC T2) 1.9531 msec/pass
cET: create_subelements (--TC T2) 0.2351 msec/pass
ET : create_subelements (--TC T2) 3.2270 msec/pass
So, if the main performance bottleneck of an application is creating large XML
trees in memory through calls to Element and SubElement, cET is the best
choice. Note, however, that the serialisation performance may even out this
advantage, especially for smaller trees and trees with many attributes.
Merging different sources
A critical action for lxml is moving elements between document contexts. It
requires lxml to do recursive adaptations throughout the moved tree structure.
The following benchmark appends all root children of the second tree to the
root of the first tree::
lxe: append_from_document (--TR T1,T2) 3.8681 msec/pass
cET: append_from_document (--TR T1,T2) 0.2699 msec/pass
ET : append_from_document (--TR T1,T2) 1.2650 msec/pass
lxe: append_from_document (--TR T3,T4) 0.0570 msec/pass
cET: append_from_document (--TR T3,T4) 0.0169 msec/pass
ET : append_from_document (--TR T3,T4) 0.0820 msec/pass
Although these are fairly small numbers compared to parsing, this easily shows
the different performance classes for lxml and (c)ET. Where the latter do not
have to care about parent pointers and tree structures, lxml has to deep
traverse the appended tree. The performance difference therefore increases
with the size of the tree that is moved.
This difference is not always as visible, but applies to most parts of the
API, like inserting newly created elements::
lxe: insert_from_document (--TR T1,T2) 5.8019 msec/pass
cET: insert_from_document (--TR T1,T2) 0.4041 msec/pass
ET : insert_from_document (--TR T1,T2) 1.4789 msec/pass
or replacing the child slice by a newly created element::
lxe: replace_children_element (--TC T1) 0.2480 msec/pass
cET: replace_children_element (--TC T1) 0.0238 msec/pass
ET : replace_children_element (--TC T1) 0.1600 msec/pass
as opposed to replacing the slice with an existing element from the
same document::
lxe: replace_children (--TC T1) 0.0188 msec/pass
cET: replace_children (--TC T1) 0.0119 msec/pass
ET : replace_children (--TC T1) 0.0739 msec/pass
You should keep this difference in mind when you merge very large trees.
Deep copying a tree is fast in lxml::
lxe: deepcopy_all (--TR T1) 10.9420 msec/pass
cET: deepcopy_all (--TR T1) 120.6188 msec/pass
ET : deepcopy_all (--TR T1) 902.6880 msec/pass
lxe: deepcopy_all (-ATR T2) 12.5830 msec/pass
cET: deepcopy_all (-ATR T2) 136.9810 msec/pass
ET : deepcopy_all (-ATR T2) 944.2801 msec/pass
lxe: deepcopy_all (S-TR T3) 4.1170 msec/pass
cET: deepcopy_all (S-TR T3) 36.1221 msec/pass
ET : deepcopy_all (S-TR T3) 221.6041 msec/pass
So, for example, if you have a database-like scenario where you parse in a
large tree and then search and copy independent subtrees from it for further
processing, lxml is by far the best choice here.
Tree traversal
Another area where lxml is very fast is iteration for tree traversal. If your
algorithms can benefit from step-by-step traversal of the XML tree and
especially if few elements are of interest or the target element tag name is
known, lxml is a good choice::
lxe: getiterator_all (--TR T1) 5.8582 msec/pass
cET: getiterator_all (--TR T1) 39.9489 msec/pass
ET : getiterator_all (--TR T1) 23.0000 msec/pass
lxe: getiterator_islice (--TR T2) 0.0780 msec/pass
cET: getiterator_islice (--TR T2) 0.3440 msec/pass
ET : getiterator_islice (--TR T2) 0.2429 msec/pass
lxe: getiterator_tag (--TR T2) 0.3119 msec/pass
cET: getiterator_tag (--TR T2) 14.1001 msec/pass
ET : getiterator_tag (--TR T2) 7.4241 msec/pass
lxe: getiterator_tag_all (--TR T2) 0.6540 msec/pass
cET: getiterator_tag_all (--TR T2) 40.7901 msec/pass
ET : getiterator_tag_all (--TR T2) 21.0390 msec/pass
This translates directly into similar timings for ``Element.findall()``::
lxe: findall (--TR T2) 8.1239 msec/pass
cET: findall (--TR T2) 44.5340 msec/pass
ET : findall (--TR T2) 27.1149 msec/pass
lxe: findall (--TR T3) 1.6870 msec/pass
cET: findall (--TR T3) 12.9611 msec/pass
ET : findall (--TR T3) 8.6131 msec/pass
lxe: findall_tag (--TR T2) 0.7660 msec/pass
cET: findall_tag (--TR T2) 40.6358 msec/pass
ET : findall_tag (--TR T2) 21.4581 msec/pass
lxe: findall_tag (--TR T3) 0.2160 msec/pass
cET: findall_tag (--TR T3) 9.6831 msec/pass
ET : findall_tag (--TR T3) 5.2109 msec/pass
Note that all three libraries currently use the same Python implementation for
``findall()``, except for their native tree iterator.
The following timings are based on the benchmark script ``_.
This part of lxml does not have an equivalent in ElementTree. However, lxml
provides more than one way of accessing it and you should take care which part
of the lxml API you use. The most straight forward way is to call the
``xpath()`` method on an Element or ElementTree::
lxe: xpath_method (--TC T1) 1.8251 msec/pass
lxe: xpath_method (--TC T2) 23.3159 msec/pass
lxe: xpath_method (--TC T3) 0.1378 msec/pass
lxe: xpath_method (--TC T4) 1.1270 msec/pass
This is well suited for testing and when the XPath expressions are as diverse
as the trees they are called on. However, if you have a single XPath
expression that you want to apply to a larger number of different elements,
the ``XPath`` class is the most efficient way to do it::
lxe: xpath_class (--TC T1) 0.6981 msec/pass
lxe: xpath_class (--TC T2) 3.6111 msec/pass
lxe: xpath_class (--TC T3) 0.0591 msec/pass
lxe: xpath_class (--TC T4) 0.1979 msec/pass
Note that this still allows you to use variables in the expression, so you can
parse it once and then adapt it through variables at call time. In other
cases, where you have a fixed Element or ElementTree and want to run different
expressions on it, you should consider the ``XPathEvaluator``::
lxe: xpath_element (--TR T1) 0.4342 msec/pass
lxe: xpath_element (--TR T2) 11.9958 msec/pass
lxe: xpath_element (--TR T3) 0.1690 msec/pass
lxe: xpath_element (--TR T4) 0.3510 msec/pass
While it looks slightly slower, creating an XPath object for each of the
expressions generates a much higher overhead here::
lxe: xpath_class_repeat (--TC T1) 1.7619 msec/pass
lxe: xpath_class_repeat (--TC T2) 21.9102 msec/pass
lxe: xpath_class_repeat (--TC T3) 0.1330 msec/pass
lxe: xpath_class_repeat (--TC T4) 1.0631 msec/pass
A longer example
... based on lxml 1.3.
A while ago, Uche Ogbuji posted a `benchmark proposal`_ that would
read in a 3MB XML version of the `Old Testament`_ of the Bible and
look for the word *begat* in all verses. Apparently, it is contained
in 120 out of almost 24000 verses. This is easy to implement in
ElementTree using ``findall()``. However, the fastest and most memory
friendly way to do this is obviously ``iterparse()``, as most of the
data is not of any interest.
.. _`benchmark proposal`:
.. _`Old Testament`:
Now, Uche's original proposal was more or less the following::
def bench_ET():
tree = ElementTree.parse("ot.xml")
result = []
for v in tree.findall("//v"):
text = v.text
if 'begat' in text:
return len(result)
which takes about one second on my machine today. The faster ``iterparse()``
variant looks like this::
def bench_ET_iterparse():
result = []
for event, v in ElementTree.iterparse("ot.xml"):
if v.tag == 'v':
text = v.text
if 'begat' in text:
return len(result)
The improvement is about 10%. At the time I first tried (early 2006), lxml
didn't have ``iterparse()`` support, but the ``findall()`` variant was already
faster than ElementTree. This changes immediately when you switch to
cElementTree. The latter only needs 0.17 seconds to do the trick today and
only some impressive 0.10 seconds when running the iterparse version. And
even back then, it was quite a bit faster than what lxml could achieve.
Since then, lxml has matured a lot and has gotten much faster. The iterparse
variant now runs in 0.14 seconds, and if you remove the ``v.clear()``, it is
even a little faster (which isn't the case for cElementTree).
One of the many great tools in lxml is XPath, a swiss army knife for finding
things in XML documents. It is possible to move the whole thing to a pure
XPath implementation, which looks like this::
def bench_lxml_xpath_all():
tree = etree.parse("ot.xml")
result = tree.xpath("//v[contains(., 'begat')]/text()")
return len(result)
This runs in about 0.13 seconds and is about the shortest possible
implementation (in lines of Python code) that I could come up with. Now, this
is already a rather complex XPath expression compared to the simple "//v"
ElementPath expression we started with. Since this is also valid XPath, let's
try this instead::
def bench_lxml_xpath():
tree = etree.parse("ot.xml")
result = []
for v in tree.xpath("//v"):
text = v.text
if 'begat' in text:
return len(result)
This gets us down to 0.12 seconds, thus showing that a generic XPath
evaluation engine cannot always compete with a simpler, tailored solution.
However, since this is not much different from the original findall variant,
we can remove the complexity of the XPath call completely and just go with
what we had in the beginning. Under lxml, this runs in the same 0.12 seconds.
But there is one thing left to try. We can replace the simple ElementPath
expression with a native tree iterator::
def bench_lxml_getiterator():
tree = etree.parse("ot.xml")
result = []
for v in tree.getiterator("v"):
text = v.text
if 'begat' in text:
return len(result)
This implements the same thing, just without the overhead of parsing and
evaluating a path expression. And this makes it another bit faster, down to
0.11 seconds. For comparison, cElementTree runs this version in 0.17 seconds.
So, what have we learned?
* Python code is not slow. The pure XPath solution was not even as fast as
the first shot Python implementation. In general, a few more lines in
Python make things more readable, which is much more important than the last
5% of performance.
* It's important to know the available options - and it's worth starting with
the most simple one. In this case, a programmer would then probably have
started with ``getiterator("v")`` or ``iterparse()``. Either of them would
already have been the most efficient, depending on which library is used.
* It's important to know your tool. lxml and cElementTree are both very fast
libraries, but they do not have the same performance characteristics. The
fastest solution in one library can be comparatively slow in the other. If
you optimise, optimise for the specific target platform.
* It's not always worth optimising. After all that hassle we got from 0.12
seconds for the initial implementation to 0.11 seconds. Switching over to
cElementTree and writing an ``iterparse()`` based version would have given
us 0.10 seconds - not a big difference for 3MB of XML.
* Take care what operation is really dominating in your use case. If we split
up the operations, we can see that lxml is slightly slower than cElementTree
on ``parse()`` (both about 0.06 seconds), but more visibly slower on
``iterparse()``: 0.07 versus 0.10 seconds. However, tree iteration in lxml
is increadibly fast, so it can be better to parse the whole tree and then
iterate over it rather than using ``iterparse()`` to do both in one step.
Or, you can just wait for the lxml authors to optimise iterparse in one of
the next releases...
The following timings are based on the benchmark script ``_.
Objectify is a data-binding API for XML based on lxml.etree, that was added in
version 1.1. It uses standard Python attribute access to traverse the XML
tree. It also features ObjectPath, a fast path language based on the same
Just like lxml.etree, lxml.objectify creates Python representations of
elements on the fly. To save memory, the normal Python garbage collection
mechanisms will discard them when their last reference is gone. In cases
where deeply nested elements are frequently accessed through the objectify
API, the create-discard cycles can become a bottleneck, as elements have to be
instantiated over and over again.
ObjectPath can be used to speed up the access to elements that are deep in the
tree. It avoids step-by-step Python element instantiations along the path,
which can substantially improve the access time::
lxe: attribute (--TR T1) 9.8128 msec/pass
lxe: attribute (--TR T2) 53.2899 msec/pass
lxe: attribute (--TR T4) 9.6800 msec/pass
lxe: objectpath (--TR T1) 5.4898 msec/pass
lxe: objectpath (--TR T2) 48.4819 msec/pass
lxe: objectpath (--TR T4) 5.3761 msec/pass
lxe: attributes_deep (--TR T1) 56.3290 msec/pass
lxe: attributes_deep (--TR T2) 62.4361 msec/pass
lxe: attributes_deep (--TR T4) 15.8000 msec/pass
lxe: objectpath_deep (--TR T1) 49.0060 msec/pass
lxe: objectpath_deep (--TR T2) 52.5169 msec/pass
lxe: objectpath_deep (--TR T4) 7.1371 msec/pass
Note, however, that parsing ObjectPath expressions is not for free either, so
this is most effective for frequently accessing the same element.
Caching Elements
A way to improve the normal attribute access time is static instantiation of
the Python objects, thus trading memory for speed. Just create a cache
dictionary and run::
cache[root] = list(root.iter())
after parsing and::
del cache[root]
when you are done with the tree. This will keep the Python element
representations of all elements alive and thus avoid the overhead of repeated
Python object creation. You can also consider using filters or generator
expressions to be more selective. By choosing the right trees (or even
subtrees and elements) to cache, you can trade memory usage against access
lxe: attribute_cached (--TR T1) 7.6170 msec/pass
lxe: attribute_cached (--TR T2) 50.7941 msec/pass
lxe: attribute_cached (--TR T4) 7.4880 msec/pass
lxe: attributes_deep_cached (--TR T1) 49.9220 msec/pass
lxe: attributes_deep_cached (--TR T2) 55.9340 msec/pass
lxe: attributes_deep_cached (--TR T4) 10.0131 msec/pass
lxe: objectpath_deep_cached (--TR T1) 44.9121 msec/pass
lxe: objectpath_deep_cached (--TR T2) 48.2371 msec/pass
lxe: objectpath_deep_cached (--TR T4) 3.9630 msec/pass
Things to note: you cannot currently use ``weakref.WeakKeyDictionary`` objects
for this as lxml's element objects do not support weak references (which are
costly in terms of memory). Also note that new element objects that you add
to these trees will not turn up in the cache automatically and will therefore
still be garbage collected when all their Python references are gone, so this
is most effective for largely immutable trees. You should consider using a
set instead of a list in this case and add new elements by hand.
Further optimisations
Here are some more things to try if optimisation is required:
* A lot of time is usually spent in tree traversal to find the addressed
elements in the tree. If you often work in subtrees, do what you would also
do with deep Python objects: assign the parent of the subtree to a variable
or pass it into functions instead of starting at the root. This allows
accessing its descendents more directly.
* Try assigning data values directly to attributes instead of passing them
through DataElement.
* If you use custom data types that are costly to parse, try running
``objectify.annotate()`` over read-only trees to speed up the attribute type
inference on read access.
Note that none of these measures is guaranteed to speed up your application.
As usual, you should prefer readable code over premature optimisations and
profile your expected use cases before bothering to apply optimisations at
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