Faster lxml.objectify attribute access: cache XML element objects with static instantiation

Speed up lxml.objectify attribute access by caching XML element objects via static instantiation. Code, XPath identity checks, and memory vs speed trade-offs.

Speeding up attribute access in lxml.objectify by caching element objects is a pragmatic technique that trades memory for faster lookups. The approach is described in lxml’s performance notes and boils down to statically instantiating Python objects for the entire XML tree so repeated access reuses the same objects instead of creating them on demand.

Code example: reproducing the issue

To illustrate the baseline, consider the following XML used for testing:

<Forms>
    <greeting>Hello, world!</greeting>
    <Form_1>
        <Country>AFG</Country>
        <Country>AFG</Country>
        <Country>IND</Country>
    </Form_1>
    <Form_1>
        <Country>IND</Country>
        <Country>USA</Country>
    </Form_1>
</Forms>

The snippet below prints the identity of an element accessed via attribute lookup before any cache is built:

from lxml import etree, objectify
def show_identity_before_cache(xml_file):
    doc_obj = objectify.parse(xml_file)
    top_node = doc_obj.getroot()
    # Identity and value of an element accessed via attribute lookup
    print(id(top_node.Form_1.Country), top_node.Form_1.Country)

What’s really happening

The optimization relies on a simple cache: you force static instantiation of all element objects by iterating the tree once and storing the result. After that, the same elements obtained through different access patterns point to the same memory address. In practice, this means you improve normal attribute access time by trading memory for speed.

A way to improve the normal attribute access time is static instantiation of the Python objects, thus trading memory for speed

Solution and working code

The following function demonstrates building the cache, verifying object identity, and optionally releasing the cached objects.

from lxml import etree, objectify
def apply_cache_and_compare(xml_file):
    doc_obj = objectify.parse(xml_file)
    top_node = doc_obj.getroot()
    # Before caching: identity of an element via attribute access
    print(id(top_node.Form_1.Country), top_node.Form_1.Country)
    # Build the cache: static instantiation of all elements in the document
    memo_box = {}
    memo_box[top_node] = list(doc_obj.iter())
    # After caching: identity via cache and via attribute access
    print(id(memo_box[top_node][3]), memo_box[top_node][3])
    print(id(top_node.Form_1.Country), top_node.Form_1.Country)
    # Both references point to the same object in memory
    print(top_node.Form_1.Country is memo_box[top_node][3])
    # The object obtained via XPath also resolves to the same cached object
    picked = top_node.xpath('(//Form_1/Country)[1]')[0]
    print(picked is memo_box[top_node][3])
    # Optionally drop the cache entry when you no longer need the speedup
    del memo_box[top_node]

This shows that elements accessed through attribute lookup and XPath resolve to the same object identities after the cache is populated, confirming that the entire document tree is instantiated and reused.

Why it matters

When you traverse or dereference many elements repeatedly, avoiding redundant instantiation helps. By caching the full list of elements once, you reduce overhead on subsequent attribute access, at the cost of holding those Python objects in memory for as long as the cache entry lives.

Practical takeaways

Use caching when you plan to access many elements in hot paths and the memory trade-off is acceptable. Build the cache with cache[root] = list(root.iter()) after parsing, leverage the cached objects during processing, and remove the entry with del cache[root] when you are done. The methods referenced in lxml’s performance notes are also available in the lxml repository under benchmark/bench_objectify.py for further exploration.

Conclusion

Caching with static instantiation is a compact and effective technique in lxml.objectify. If you need faster attribute access, pre-warm the element objects with a one-time iteration, use them across your processing steps, and drop the cache when you no longer need the speed boost. It’s a straightforward way to trade memory for consistent access speed.