- lxml

- Developing with lxml

- Extending lxml

# Benchmarks and Speed

Author: | Stefan Behnel

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.

Contents

- General notes
- How to read the timings
- Parsing and Serialising
- The ElementTree API
  - Child access
  - Element creation
  - Merging different sources
  - deepcopy
  - Tree traversal
- XPath
- A longer example
- lxml.objectify
  - ObjectPath
  - Caching Elements
  - Further optimisations

# 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 bench_etree.py, bench_xpath.py and bench_objectify.py 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 1.3 (with libxml2 2.6.27) to the ElementTree and cElementTree versions shipped with CPython 2.5 (based on ElementTree 1.2.6). They were run single-threaded on a 1.8GHz Intel Core Duo machine under Ubuntu Linux 7.04 (Feisty).

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 compare.

# Parsing and Serialising

These are areas where lxml excels. The reason is that both parts are executed entirely at the C level, without major interaction with Python code. The results are rather impressive. Compared to cElementTree, lxml is about 20 to 40 times faster on serialisation:

    lxe: tostring_utf16  (SATR T1)   21.9206 msec/pass
    cET: tostring_utf16  (SATR T1)  461.9428 msec/pass
    ET : tostring_utf16  (SATR T1)  486.8946 msec/pass

    lxe: tostring_utf16  (UATR T1)   22.7508 msec/pass
    cET: tostring_utf16  (UATR T1)  526.3446 msec/pass
    ET : tostring_utf16  (UATR T1)  496.0767 msec/pass

    lxe: tostring_utf16  (S-TR T2)   23.8452 msec/pass
    cET: tostring_utf16  (S-TR T2)  537.9200 msec/pass
    ET : tostring_utf16  (S-TR T2)  504.4273 msec/pass

    lxe: tostring_utf8   (S-TR T2)   18.2550 msec/pass
    cET: tostring_utf8   (S-TR T2)  528.3908 msec/pass
    ET : tostring_utf8   (S-TR T2)  549.7071 msec/pass

    lxe: tostring_utf8   (U-TR T3)    2.5497 msec/pass
    cET: tostring_utf8   (U-TR T3)   49.8495 msec/pass
    ET : tostring_utf8   (U-TR T3)   62.6927 msec/pass

For parsing, the difference between the libraries is smaller. The (c)ET libraries use the expat parser, which is known to be extremely fast:

    lxe: parse_stringIO  (SAXR T1)  150.2380 msec/pass
    cET: parse_stringIO  (SAXR T1)   25.9311 msec/pass
    ET : parse_stringIO  (SAXR T1)  222.9431 msec/pass

    lxe: parse_stringIO  (S-XR T3)    5.9490 msec/pass
    cET: parse_stringIO  (S-XR T3)    5.4519 msec/pass
    ET : parse_stringIO  (S-XR T3)   76.4120 msec/pass

    lxe: parse_stringIO  (UAXR T3)   29.3601 msec/pass
    cET: parse_stringIO  (UAXR T3)   28.9941 msec/pass
    ET : parse_stringIO  (UAXR T3)  163.5361 msec/pass

The expat parser allows cET to be up to 80% faster than lxml on plain parser performance. Similar timings can be observed for the `iterparse()` function. However, if you take a complete input-output cycle, the numbers will look similar to these:

    lxe: write_utf8_parse_stringIO  (S-TR T1)  166.3210 msec/pass
    cET: write_utf8_parse_stringIO  (S-TR T1)  581.2099 msec/pass
    ET : write_utf8_parse_stringIO  (S-TR T1)  803.5331 msec/pass

    lxe: write_utf8_parse_stringIO  (UATR T2)  184.4249 msec/pass
    cET: write_utf8_parse_stringIO  (UATR T2)  671.5119 msec/pass
    ET : write_utf8_parse_stringIO  (UATR T2)  924.3481 msec/pass

    lxe: write_utf8_parse_stringIO  (S-TR T3)    9.1329 msec/pass
    cET: write_utf8_parse_stringIO  (S-TR T3)   77.9850 msec/pass
    ET : write_utf8_parse_stringIO  (S-TR T3)  157.0492 msec/pass

    lxe: write_utf8_parse_stringIO  (SATR T4)    1.3900 msec/pass
    cET: write_utf8_parse_stringIO  (SATR T4)   12.6081 msec/pass
    ET : write_utf8_parse_stringIO  (SATR T4)   16.2580 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 3-4 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: 0.1181 0.1080 0.1074 0.1088 0.1087 0.1099
         T2: 0.1103 0.1109 0.1164 0.1241 0.1203 0.1231
         T3: 0.0297 0.0309 0.0297 0.0716 0.0704 0.0703
         T4: 0.0005 0.0004 0.0004 0.0014 0.0014 0.0014
    cET:       --     S-     U-     -A     SA     UA
         T1: 0.0290 0.0271 0.0275 0.0297 0.0273 0.0274
         T2: 0.0280 0.0280 0.0281 0.0285 0.0283 0.0286
         T3: 0.0071 0.0072 0.0071 0.0113 0.0096 0.0096
         T4: 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
    ET :       --     S-     U-     -A     SA     UA
         T1: 0.1362 0.1985 0.2300 0.1344 0.2672 0.1335
         T2: 0.3107 0.1386 0.3581 0.3886 0.1388 0.4277
         T3: 0.0334 0.0332 0.0320 0.0367 0.3769 0.0375
         T4: 0.0006 0.0005 0.0008 0.0007 0.0007 0.0006

While lxml is still faster than ET in most cases (30-60%), 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 `getchildren()` 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_getchildren          (--TR T2)    0.1960 msec/pass
    cET: root_getchildren          (--TR T2)    0.0150 msec/pass
    ET : root_getchildren          (--TR T2)    0.0091 msec/pass

When accessing single children, however, e.g. by index, this handicap is negligible:

    lxe: first_child               (--TR T2)    0.2289 msec/pass
    cET: first_child               (--TR T2)    0.2048 msec/pass
    ET : first_child               (--TR T2)    0.9291 msec/pass

    lxe: last_child                (--TR T1)    0.2310 msec/pass
    cET: last_child                (--TR T1)    0.2148 msec/pass
    ET : last_child                (--TR T1)    0.9191 msec/pass

... unless you add the time to find a child index in a bigger list, as 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)    0.2759 msec/pass
    cET: middle_child              (--TR T1)    0.2069 msec/pass
    ET : middle_child              (--TR T1)    0.9291 msec/pass

    lxe: middle_child              (--TR T2)    1.7111 msec/pass
    cET: middle_child              (--TR T2)    0.2089 msec/pass
    ET : middle_child              (--TR T2)    0.9360 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)    3.7301 msec/pass
    cET: create_elements           (--TC T2)    0.1960 msec/pass
    ET : create_elements           (--TC T2)    1.4279 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)    2.3680 msec/pass
    cET: makeelement               (--TC T2)    0.3128 msec/pass
    ET : makeelement               (--TC T2)    1.6940 msec/pass

    lxe: create_subelements        (--TC T2)    2.2051 msec/pass
    cET: create_subelements        (--TC T2)    0.2370 msec/pass
    ET : create_subelements        (--TC T2)    3.2189 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)    4.3468 msec/pass
    cET: append_from_document      (--TR T1,T2)    0.2608 msec/pass
    ET : append_from_document      (--TR T1,T2)    1.2310 msec/pass

    lxe: append_from_document      (--TR T3,T4)    0.0679 msec/pass
    cET: append_from_document      (--TR T3,T4)    0.0148 msec/pass
    ET : append_from_document      (--TR T3,T4)    0.0880 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)    6.3150 msec/pass
    cET: insert_from_document      (--TR T1,T2)    0.4039 msec/pass
    ET : insert_from_document      (--TR T1,T2)    1.4770 msec/pass

Or replacing the child slice by a new element:

    lxe: replace_children_element  (--TC T1)    0.2608 msec/pass
    cET: replace_children_element  (--TC T1)    0.0238 msec/pass
    ET : replace_children_element  (--TC T1)    0.1628 msec/pass

You should keep this difference in mind when you merge very large trees.

## deepcopy

Deep copying a tree is fast in lxml:

    lxe: deepcopy_all              (--TR T1)   11.0400 msec/pass
    cET: deepcopy_all              (--TR T1)  119.6141 msec/pass
    ET : deepcopy_all              (--TR T1)  451.2160 msec/pass

    lxe: deepcopy_all              (-ATR T2)   13.5410 msec/pass
    cET: deepcopy_all              (-ATR T2)  135.2482 msec/pass
    ET : deepcopy_all              (-ATR T2)  476.1350 msec/pass

    lxe: deepcopy_all              (S-TR T3)    4.2889 msec/pass
    cET: deepcopy_all              (S-TR T3)   36.0429 msec/pass
    ET : deepcopy_all              (S-TR T3)  113.4322 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 T2)    6.4790 msec/pass
    cET: getiterator_all      (--TR T2)   28.2831 msec/pass
    ET : getiterator_all      (--TR T2)   26.0720 msec/pass

    lxe: getiterator_islice   (--TR T2)    0.0892 msec/pass
    cET: getiterator_islice   (--TR T2)    0.2460 msec/pass
    ET : getiterator_islice   (--TR T2)   26.6550 msec/pass

    lxe: getiterator_tag      (--TR T2)    0.3850 msec/pass
    cET: getiterator_tag      (--TR T2)    9.3720 msec/pass
    ET : getiterator_tag      (--TR T2)   22.8221 msec/pass

    lxe: getiterator_tag_all  (--TR T2)    0.7222 msec/pass
    cET: getiterator_tag_all  (--TR T2)   27.2939 msec/pass
    ET : getiterator_tag_all  (--TR T2)   22.8271 msec/pass

This translates directly into similar timings for `Element.findall()`:

    lxe: findall              (--TR T2)    6.8321 msec/pass
    cET: findall              (--TR T2)   28.8639 msec/pass
    ET : findall              (--TR T2)   27.1060 msec/pass

    lxe: findall              (--TR T3)    1.3590 msec/pass
    cET: findall              (--TR T3)    8.9881 msec/pass
    ET : findall              (--TR T3)    6.4890 msec/pass

    lxe: findall_tag          (--TR T2)    0.9229 msec/pass
    cET: findall_tag          (--TR T2)   27.2651 msec/pass
    ET : findall_tag          (--TR T2)   22.7208 msec/pass

    lxe: findall_tag          (--TR T3)    0.1700 msec/pass
    cET: findall_tag          (--TR T3)    6.4540 msec/pass
    ET : findall_tag          (--TR T3)    5.4770 msec/pass

Note that all three libraries currently use the same Python implementation for `findall()`, except for their native tree iterator.

# XPath

The following timings are based on the benchmark script bench_xpath.py.

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)    1.0180 msec/pass
    lxe: xpath_method         (--TC T2)   20.3521 msec/pass
    lxe: xpath_method         (--TC T3)    0.1259 msec/pass
    lxe: xpath_method         (--TC T4)    1.0169 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)    0.1891 msec/pass
    lxe: xpath_class          (--TC T2)    3.0179 msec/pass
    lxe: xpath_class          (--TC T3)    0.0570 msec/pass
    lxe: xpath_class          (--TC T4)    0.1910 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)    0.4089 msec/pass
    lxe: xpath_element        (--TR T2)    5.9960 msec/pass
    lxe: xpath_element        (--TR T3)    0.1230 msec/pass
    lxe: xpath_element        (--TR T4)    0.3440 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)    1.0259 msec/pass
    lxe: xpath_class_repeat   (--TC T2)   20.4861 msec/pass
    lxe: xpath_class_repeat   (--TC T3)    0.1280 msec/pass
    lxe: xpath_class_repeat   (--TC T4)    1.0269 msec/pass

# A longer example

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 way to do this is obviously `iterparse()`, as most of the data is not of any interest.

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:
                result.append(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:
                    result.append(text)
            v.clear()
        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:
                result.append(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:
                result.append(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...

# lxml.objectify

The following timings are based on the benchmark script bench_objectify.py.

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 meme.

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

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)   10.6189 msec/pass
    lxe: attribute                  (--TR T2)   53.7431 msec/pass
    lxe: attribute                  (--TR T4)   10.3359 msec/pass

    lxe: objectpath                 (--TR T1)    5.8351 msec/pass
    lxe: objectpath                 (--TR T2)   48.1579 msec/pass
    lxe: objectpath                 (--TR T4)    5.6930 msec/pass

    lxe: attributes_deep            (--TR T1)   58.7430 msec/pass
    lxe: attributes_deep            (--TR T2)   63.0901 msec/pass
    lxe: attributes_deep            (--TR T4)   17.4620 msec/pass

    lxe: objectpath_deep            (--TR T1)   52.1719 msec/pass
    lxe: objectpath_deep            (--TR T2)   52.9201 msec/pass
    lxe: objectpath_deep            (--TR T4)    7.5650 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 speed:

    lxe: attribute_cached           (--TR T1)    7.9739 msec/pass
    lxe: attribute_cached           (--TR T2)   50.9331 msec/pass
    lxe: attribute_cached           (--TR T4)    7.8540 msec/pass

    lxe: attributes_deep_cached     (--TR T1)   51.1391 msec/pass
    lxe: attributes_deep_cached     (--TR T2)   55.7129 msec/pass
    lxe: attributes_deep_cached     (--TR T4)   10.7968 msec/pass

    lxe: objectpath_deep_cached     (--TR T1)   47.6151 msec/pass
    lxe: objectpath_deep_cached     (--TR T2)   48.0802 msec/pass
    lxe: objectpath_deep_cached     (--TR T4)    4.0281 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 random.