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# Benchmarks and Speed

Author: | Stefan Behnel

lxml.etree is a very fast XML library. Most of this is due to the speed of libxml2, e.g. the parser and serialiser, or the XPath engine. Other areas of lxml were specifically written for high performance in high-level operations, such as the tree iterators.

On the other hand, the simplicity of lxml sometimes hides internal operations that are more costly than the API suggests. If you are not aware of these cases, lxml may not always perform as you expect. A common example in the Python world is the Python list type. New users often expect it to be a linked list, while it actually is implemented as an array, which results in a completely different complexity for common operations.

Similarly, the tree model of libxml2 is more complex than what lxml's ElementTree API projects into Python space, so some operations may show unexpected performance. Rest assured that most lxml users will not notice this in real life, as lxml is very fast in absolute numbers. It is definitely fast enough for most applications, so lxml is probably somewhere between 'fast enough' and 'the best choice' for yours. Read some messages from happy users to see what we mean.

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 presented below compare lxml 4.6.3 (with libxml2 2.9.10) to the latest released versions of ElementTree (with cElementTree as accelerator module) in the standard library of CPython 3.8.10. They were run single-threaded on a 2.3GHz 64bit double core Intel i5 machine under Ubuntu Linux 20.04 (Focal).

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

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 (which was part of the standard library before Python 2.7/3.2), lxml is still several times faster than the much improved ElementTree 1.3 in recent Python versions:

    lxe: tostring_utf16  (S-TR T1)    5.9340 msec/pass
    cET: tostring_utf16  (S-TR T1)   38.3270 msec/pass

    lxe: tostring_utf16  (UATR T1)    6.2032 msec/pass
    cET: tostring_utf16  (UATR T1)   37.7944 msec/pass

    lxe: tostring_utf16  (S-TR T2)    6.1841 msec/pass
    cET: tostring_utf16  (S-TR T2)   40.2577 msec/pass

    lxe: tostring_utf8   (S-TR T2)    4.6697 msec/pass
    cET: tostring_utf8   (S-TR T2)   30.5173 msec/pass

    lxe: tostring_utf8   (U-TR T3)    1.2085 msec/pass
    cET: tostring_utf8   (U-TR T3)   9.0246 msec/pass

The difference is somewhat smaller for plain text serialisation:

    lxe: tostring_text_ascii     (S-TR T1)    2.6727 msec/pass
    cET: tostring_text_ascii     (S-TR T1)    2.9683 msec/pass

    lxe: tostring_text_ascii     (S-TR T3)    0.6952 msec/pass
    cET: tostring_text_ascii     (S-TR T3)    1.0073 msec/pass

    lxe: tostring_text_utf16     (S-TR T1)    2.7366 msec/pass
    cET: tostring_text_utf16     (S-TR T1)   7.3647 msec/pass

    lxe: tostring_text_utf16     (U-TR T1)    3.0322 msec/pass
    cET: tostring_text_utf16     (U-TR T1)   7.5922 msec/pass

The `tostring()` function also supports serialisation to a Python unicode string object, which is currently faster in ElementTree under CPython 3.8:

    lxe: tostring_text_unicode   (S-TR T1)    2.7645 msec/pass
    cET: tostring_text_unicode   (S-TR T1)    1.1806 msec/pass

    lxe: tostring_text_unicode   (U-TR T1)    2.9871 msec/pass
    cET: tostring_text_unicode   (U-TR T1)    1.1659 msec/pass

    lxe: tostring_text_unicode   (S-TR T3)    0.7446 msec/pass
    cET: tostring_text_unicode   (S-TR T3)    0.4532 msec/pass

    lxe: tostring_text_unicode   (U-TR T4)    0.0048 msec/pass
    cET: tostring_text_unicode   (U-TR T4)    0.0134 msec/pass

For parsing, lxml.etree and cElementTree compete for the medal. Depending on the input, either of the two can be faster. The (c)ET libraries use a very thin layer on top of the expat parser, which is known to be very fast. Here are some timings from the benchmarking suite:

    lxe: parse_bytesIO   (SAXR T1)   14.2074 msec/pass
    cET: parse_bytesIO   (SAXR T1)    7.9336 msec/pass

    lxe: parse_bytesIO   (S-XR T3)    1.4477 msec/pass
    cET: parse_bytesIO   (S-XR T3)    2.1925 msec/pass

    lxe: parse_bytesIO   (UAXR T3)    8.4128 msec/pass
    cET: parse_bytesIO   (UAXR T3)   12.2926 msec/pass

And another couple of timings from a benchmark that Fredrik Lundh used to promote cElementTree, comparing a number of different parsers. First, parsing a 274KB XML file containing Shakespeare's Hamlet:

    xml.etree.ElementTree.parse done in 0.006 seconds
    xml.etree.cElementTree.parse done in 0.007 seconds
    xml.etree.cElementTree.XMLParser.feed(): 6636 nodes read in 0.006 seconds
    lxml.etree.parse done in 0.004 seconds
    drop_whitespace.parse done in 0.004 seconds
    lxml.etree.XMLParser.feed(): 6636 nodes read in 0.004 seconds
    minidom tree read in 0.066 seconds

And a 3.4MB XML file containing the Old Testament:

    xml.etree.ElementTree.parse done in 0.037 seconds
    xml.etree.cElementTree.parse done in 0.036 seconds
    xml.etree.cElementTree.XMLParser.feed(): 25317 nodes read in 0.036 seconds
    lxml.etree.parse done in 0.025 seconds
    drop_whitespace.parse done in 0.022 seconds
    lxml.etree.XMLParser.feed(): 25317 nodes read in 0.026 seconds
    minidom tree read in 0.194 seconds

Here are the same benchmarks again, but including the memory usage of the process in KB before and after parsing (using os.fork() to make sure we start from a clean state each time). For the 274KB hamlet.xml file:

    Memory usage: 9256
    xml.etree.ElementTree.parse done in 0.006 seconds
    Memory usage: 12764 (+3508)
    xml.etree.cElementTree.parse done in 0.007 seconds
    Memory usage: 12764 (+3508)
    xml.etree.cElementTree.XMLParser.feed(): 6636 nodes read in 0.006 seconds
    Memory usage: 12720 (+3464)
    lxml.etree.parse done in 0.004 seconds
    Memory usage: 15052 (+5796)
    drop_whitespace.parse done in 0.004 seconds
    Memory usage: 14040 (+4784)
    lxml.etree.XMLParser.feed(): 6636 nodes read in 0.004 seconds
    Memory usage: 15812 (+6556)
    minidom tree read in 0.066 seconds
    Memory usage: 15332 (+6076)

And for the 3.4MB Old Testament XML file:

    Memory usage: 12456
    xml.etree.ElementTree.parse done in 0.037 seconds
    Memory usage: 23288 (+10832)
    xml.etree.cElementTree.parse done in 0.036 seconds
    Memory usage: 23288 (+10832)
    xml.etree.cElementTree.XMLParser.feed(): 25317 nodes read in 0.036 seconds
    Memory usage: 23644 (+11220)
    lxml.etree.parse done in 0.025 seconds
    Memory usage: 31404 (+18948)
    drop_whitespace.parse done in 0.022 seconds
    Memory usage: 28752 (+16296)
    lxml.etree.XMLParser.feed(): 25317 nodes read in 0.026 seconds
    Memory usage: 33924 (+21500)
    minidom tree read in 0.194 seconds
    Memory usage: 31284 (+18828)

As can be seen from the sizes, both lxml.etree and cElementTree are rather memory friendly and fast. Comparing to older CPython versions, the memory footprint of the minidom library was considerably reduced in CPython 3.3, by about a factor of 4 in this case.

For plain parser performance, lxml.etree and cElementTree tend to stay rather close to each other, usually within a factor of two, with winners well distributed over both sides. Similar timings can be observed for the `iterparse()` function:

    lxe: iterparse_bytesIO   (SAXR T1)   20.3598 msec/pass
    cET: iterparse_bytesIO   (SAXR T1)   10.8948 msec/pass

    lxe: iterparse_bytesIO   (UAXR T3)    10.1640 msec/pass
    cET: iterparse_bytesIO   (UAXR T3)   12.9926 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_bytesIO   (S-TR T1)   18.9857 msec/pass
    cET: write_utf8_parse_bytesIO   (S-TR T1)   35.7475 msec/pass

    lxe: write_utf8_parse_bytesIO   (UATR T2)   22.4853 msec/pass
    cET: write_utf8_parse_bytesIO   (UATR T2)   42.6254 msec/pass

    lxe: write_utf8_parse_bytesIO   (S-TR T3)    3.3801 msec/pass
    cET: write_utf8_parse_bytesIO   (S-TR T3)   11.2493 msec/pass

    lxe: write_utf8_parse_bytesIO   (SATR T4)    0.4263 msec/pass
    cET: write_utf8_parse_bytesIO   (SATR T4)    1.0326 msec/pass

For applications that require a high parser throughput of large files, and that do little to no serialization, both cET and lxml.etree are a good choice. The cET library is particularly fast for iterparse applications that extract small amounts of data or aggregate information from large XML data sets that do not fit into memory. If it comes to round-trip performance, however, lxml is multiple times faster in total. So, whenever the input documents are not considerably larger than the output, lxml is the clear winner.

Regarding HTML parsing, Ian Bicking has done some benchmarking on lxml's HTML parser, comparing it to a number of other famous HTML parser tools for Python. lxml wins this contest by quite a length. To give an idea, the numbers suggest that lxml.html can run a couple of parse-serialise cycles in the time that other tools need for parsing alone. The comparison even shows some very favourable results regarding memory consumption.

Liza Daly has written an article that presents a couple of tweaks to get the most out of lxml's parser for very large XML documents. She quite favourably positions `lxml.etree` as a tool for high-performance XML parsing.

Finally, xml.com has a couple of publications about XML parser performance. Farwick and Hafner have written two interesting articles that compare the parser of libxml2 to some major Java based XML parsers. One deals with event-driven parser performance, the other one presents benchmark results comparing DOM parsers. Both comparisons suggest that libxml2's parser performance is largely superior to all commonly used Java parsers in almost all cases. Note that the C parser benchmark results are based on xmlbench, which uses a simpler setup for libxml2 than lxml does.

# 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 and full XPath support, 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: 0.0219 0.0254 0.0257 0.0216 0.0259 0.0259
         T2: 0.0234 0.0279 0.0283 0.0271 0.0318 0.0307
         T3: 0.0051 0.0050 0.0058 0.0218 0.0233 0.0231
         T4: 0.0001 0.0001 0.0001 0.0004 0.0004 0.0004
    cET:       --     S-     U-     -A     SA     UA
         T1: 0.0035 0.0029 0.0078 0.0031 0.0031 0.0029
         T2: 0.0047 0.0051 0.0053 0.0046 0.0055 0.0048
         T3: 0.0016 0.0216 0.0027 0.0021 0.0023 0.0026
         T4: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

The timings are somewhat close to each other, although cET can be several times faster than lxml for larger trees. One of the reasons is that lxml must encode incoming string data and tag names into UTF-8, and additionally discard the created Python elements after their use, when they are no longer referenced. ElementTree represents the tree itself through these objects, which reduces the overhead in creating them.

## Child access

The same tree overhead makes operations like collecting children as in `list(element)` more costly in lxml. Where 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)    0.0036 msec/pass
    cET: root_list_children        (--TR T1)    0.0005 msec/pass

    lxe: root_list_children        (--TR T2)    0.0634 msec/pass
    cET: root_list_children        (--TR T2)    0.0086 msec/pass

This handicap is also visible when accessing single children:

    lxe: first_child               (--TR T2)    0.0601 msec/pass
    cET: first_child               (--TR T2)    0.0548 msec/pass

    lxe: last_child                (--TR T1)    0.0570 msec/pass
    cET: last_child                (--TR T1)    0.0534 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)    0.0892 msec/pass
    cET: middle_child              (--TR T1)    0.0510 msec/pass

    lxe: middle_child              (--TR T2)    2.3038 msec/pass
    cET: middle_child              (--TR T2)    0.0508 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)    0.8032 msec/pass
    cET: create_elements           (--TC T2)    0.0675 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)    0.8030 msec/pass
    cET: makeelement               (--TC T2)    0.0625 msec/pass

    lxe: create_subelements        (--TC T2)    0.8621 msec/pass
    cET: create_subelements        (--TC T2)    0.0923 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)    1.3800 msec/pass
    cET: append_from_document      (--TR T1,T2)    0.0513 msec/pass

    lxe: append_from_document      (--TR T3,T4)    0.0150 msec/pass
    cET: append_from_document      (--TR T3,T4)    0.0026 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)    5.2345 msec/pass
    cET: insert_from_document         (--TR T1,T2)    0.0732 msec/pass

or replacing the child slice by a newly created element:

    lxe: replace_children_element   (--TC T1)    0.0720 msec/pass
    cET: replace_children_element   (--TC T1)    0.0105 msec/pass

as opposed to replacing the slice with an existing element from the same document:

    lxe: replace_children           (--TC T1)    0.0060 msec/pass
    cET: replace_children           (--TC T1)    0.0050 msec/pass

While these numbers are too small to provide a major performance impact in practice, you should keep this difference in mind when you merge very large trees. Note that Elements have a `makeelement()` method that allows to create an Element within the same document, thus avoiding the merge overhead when inserting it into that tree.

## deepcopy

Deep copying a tree is fast in lxml:

    lxe: deepcopy_all              (--TR T1)    4.1246 msec/pass
    cET: deepcopy_all              (--TR T1)   2.5451 msec/pass

    lxe: deepcopy_all              (-ATR T2)    4.7867 msec/pass
    cET: deepcopy_all              (-ATR T2)   2.7504 msec/pass

    lxe: deepcopy_all              (S-TR T3)    1.0097 msec/pass
    cET: deepcopy_all              (S-TR T3)   0.6278 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 important area in XML processing 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, the `.iter()` method is a good choice:

    lxe: iter_all             (--TR T1)    1.3661 msec/pass
    cET: iter_all             (--TR T1)    0.2670 msec/pass

    lxe: iter_islice          (--TR T2)    0.0122 msec/pass
    cET: iter_islice          (--TR T2)    0.0033 msec/pass

    lxe: iter_tag             (--TR T2)    0.0098 msec/pass
    cET: iter_tag             (--TR T2)    0.0086 msec/pass

    lxe: iter_tag_all         (--TR T2)    0.6840 msec/pass
    cET: iter_tag_all         (--TR T2)    0.4323 msec/pass

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

    lxe: findall              (--TR T2)    3.9611 msec/pass
    cET: findall              (--TR T2)    0.9227 msec/pass

    lxe: findall              (--TR T3)    0.3989 msec/pass
    cET: findall              (--TR T3)    0.2670 msec/pass

    lxe: findall_tag          (--TR T2)    0.7420 msec/pass
    cET: findall_tag          (--TR T2)    0.4942 msec/pass

    lxe: findall_tag          (--TR T3)    0.1099 msec/pass
    cET: findall_tag          (--TR T3)    0.1748 msec/pass

Note that all three libraries currently use the same Python implementation for `.findall()`, except for their native tree iterator (`element.iter()`). In general, lxml is very fast for iteration, but loses ground against cET when many Elements are found and need to be instantiated. So, the more selective your search is, the faster lxml will run.

# 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 straightforward way is to call the `xpath()` method on an Element or ElementTree:

    lxe: xpath_method         (--TC T1)    0.2828 msec/pass
    lxe: xpath_method         (--TC T2)    5.4705 msec/pass
    lxe: xpath_method         (--TC T3)    0.0324 msec/pass
    lxe: xpath_method         (--TC T4)    0.2804 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)    0.0570 msec/pass
    lxe: xpath_class          (--TC T2)    0.6924 msec/pass
    lxe: xpath_class          (--TC T3)    0.0148 msec/pass
    lxe: xpath_class          (--TC T4)    0.0446 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)    0.0684 msec/pass
    lxe: xpath_element        (--TR T2)    1.0865 msec/pass
    lxe: xpath_element        (--TR T3)    0.0174 msec/pass
    lxe: xpath_element        (--TR T4)    0.0665 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   )    0.2813 msec/pass
    lxe: xpath_class_repeat           (--TC T2   )    5.4042 msec/pass
    lxe: xpath_class_repeat           (--TC T3   )    0.0339 msec/pass
    lxe: xpath_class_repeat           (--TC T4   )    0.2706 msec/pass

Note that tree iteration can be substantially faster than XPath if your code short-circuits after the first couple of elements were found. The XPath engine will always return the complete result set, regardless of how much of it will actually be used.

Here is an example where only the first matching element is being searched, a case for which XPath has syntax support as well:

    lxe: find_single                (--TR T2)    0.0031 msec/pass
    cET: find_single                (--TR T2)    0.0026 msec/pass

    lxe: iter_single                (--TR T2)    0.0019 msec/pass
    cET: iter_single                (--TR T2)    0.0002 msec/pass

    lxe: xpath_single               (--TR T2)    0.0861 msec/pass

When looking for the first two elements out of many, the numbers explode for XPath, as restricting the result subset requires a more complex expression:

    lxe: iterfind_two               (--TR T2)    0.0050 msec/pass
    cET: iterfind_two               (--TR T2)    0.0036 msec/pass

    lxe: iter_two                   (--TR T2)    0.0021 msec/pass
    cET: iter_two                   (--TR T2)    0.0014 msec/pass

    lxe: xpath_two                  (--TR T2)    0.0916 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.

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 incredibly 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 developers 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)    2.4018 msec/pass
    lxe: attribute                  (--TR T2)   16.3755 msec/pass
    lxe: attribute                  (--TR T4)    2.3725 msec/pass

    lxe: objectpath                 (--TR T1)    1.1816 msec/pass
    lxe: objectpath                 (--TR T2)   14.4675 msec/pass
    lxe: objectpath                 (--TR T4)    1.2276 msec/pass

    lxe: attributes_deep            (--TR T1)    3.7086 msec/pass
    lxe: attributes_deep            (--TR T2)   17.5436 msec/pass
    lxe: attributes_deep            (--TR T4)    3.8407 msec/pass

    lxe: objectpath_deep            (--TR T1)    1.4980 msec/pass
    lxe: objectpath_deep            (--TR T2)   14.7266 msec/pass
    lxe: objectpath_deep            (--TR T4)    1.4834 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)    1.9207 msec/pass
    lxe: attribute_cached           (--TR T2)   15.6903 msec/pass
    lxe: attribute_cached           (--TR T4)    1.8718 msec/pass

    lxe: attributes_deep_cached     (--TR T1)    2.6512 msec/pass
    lxe: attributes_deep_cached     (--TR T2)   16.7937 msec/pass
    lxe: attributes_deep_cached     (--TR T4)    2.5539 msec/pass

    lxe: objectpath_deep_cached     (--TR T1)    0.8519 msec/pass
    lxe: objectpath_deep_cached     (--TR T2)   13.9337 msec/pass
    lxe: objectpath_deep_cached     (--TR T4)    0.8645 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 descendants 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.