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. 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.


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 2.0 final (with libxml2 2.6.31) to the January 2008 SVN trunk versions of ElementTree (1.3alpha) 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 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, lxml is still more than 5 times as fast as the much improved ElementTree 1.3:

lxe: tostring_utf16  (SATR T1)   19.0921 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)   20.4630 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)   18.8632 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)   14.4310 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)    2.6381 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: 0.0783 0.0777 0.0774 0.0787 0.0781 0.0783
     T2: 0.0799 0.0796 0.0799 0.0879 0.0882 0.0886
     T3: 0.0245 0.0216 0.0217 0.0577 0.0575 0.0572
     T4: 0.0003 0.0003 0.0003 0.0011 0.0011 0.0011
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)    0.0160 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)    0.2100 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)    0.2429 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)    0.2470 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)    0.2759 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)    1.7071 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)    2.8961 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)    1.9000 msec/pass
cET: makeelement               (--TC T2)    0.3211 msec/pass
ET : makeelement               (--TC T2)    1.6358 msec/pass

lxe: create_subelements        (--TC T2)    1.7891 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)    3.3841 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)    0.0441 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)    5.7020 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)    0.2210 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)    0.0179 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)    9.7558 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)   12.3210 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)    8.3981 msec/pass
cET: deepcopy_all              (S-TR T3)   35.6541 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)    5.7251 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)    0.0830 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)    0.3011 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)    0.6340 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)    7.8950 msec/pass
cET: findall              (--TR T2)   44.5340 msec/pass
ET : findall              (--TR T2)   27.1149 msec/pass

lxe: findall              (--TR T3)    1.7281 msec/pass
cET: findall              (--TR T3)   12.9611 msec/pass
ET : findall              (--TR T3)    8.6131 msec/pass

lxe: findall_tag          (--TR T2)    0.7720 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)    0.2050 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)    1.7459 msec/pass
lxe: xpath_method         (--TC T2)   22.0850 msec/pass
lxe: xpath_method         (--TC T3)    0.1309 msec/pass
lxe: xpath_method         (--TC T4)    1.0772 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)    0.6740 msec/pass
lxe: xpath_class          (--TC T2)    3.1760 msec/pass
lxe: xpath_class          (--TC T3)    0.0548 msec/pass
lxe: xpath_class          (--TC T4)    0.1700 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)    0.4151 msec/pass
lxe: xpath_element        (--TR T2)   11.6129 msec/pass
lxe: xpath_element        (--TR T3)    0.1299 msec/pass
lxe: xpath_element        (--TR T4)    0.3409 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)    1.6699 msec/pass
lxe: xpath_class_repeat   (--TC T2)   20.4420 msec/pass
lxe: xpath_class_repeat   (--TC T3)    0.1230 msec/pass
lxe: xpath_class_repeat   (--TC T4)    0.9859 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:
    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?


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 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 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)    9.4581 msec/pass
lxe: attribute                  (--TR T2)   52.5560 msec/pass
lxe: attribute                  (--TR T4)    9.1729 msec/pass

lxe: objectpath                 (--TR T1)    4.8690 msec/pass
lxe: objectpath                 (--TR T2)   47.8780 msec/pass
lxe: objectpath                 (--TR T4)    4.7870 msec/pass

lxe: attributes_deep            (--TR T1)   54.7471 msec/pass
lxe: attributes_deep            (--TR T2)   62.7451 msec/pass
lxe: attributes_deep            (--TR T4)   15.1050 msec/pass

lxe: objectpath_deep            (--TR T1)   48.2810 msec/pass
lxe: objectpath_deep            (--TR T2)   51.3949 msec/pass
lxe: objectpath_deep            (--TR T4)    6.1419 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)    7.5061 msec/pass
lxe: attribute_cached           (--TR T2)   50.1881 msec/pass
lxe: attribute_cached           (--TR T4)    7.4170 msec/pass

lxe: attributes_deep_cached     (--TR T1)   48.7239 msec/pass
lxe: attributes_deep_cached     (--TR T2)   55.2199 msec/pass
lxe: attributes_deep_cached     (--TR T4)    9.9740 msec/pass

lxe: objectpath_deep_cached     (--TR T1)   43.4160 msec/pass
lxe: objectpath_deep_cached     (--TR T2)   47.6480 msec/pass
lxe: objectpath_deep_cached     (--TR T4)    3.4680 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.