oxidize 0.1.1

High-performance XML to JSON streaming parser built with Rust

Oxidize

High-performance XML to JSON streaming parser built with Rust and PyO3. Specialized for extracting repeated elements from large XML files like API responses, log files, and data exports.

Key Features

• High Performance: 2-3x faster than lxml, built with Rust's quick-xml parser with batch processing in Rayon
• Low Memory Usage: Streaming architecture processes files larger than available RAM
• Specialized Design: Opinionated API and schema design for common data engineering and data analysis workflows

Use Cases

Perfect for extracting structured data from XML files containing repeated elements into newline JSON

• API responses: Extract <record>, <item>, or <entry> elements from REST API responses
• Log files: Parse <event> or <log> entries from XML-formatted logs
• Data exports: Process <row>, <product>, or <transaction> elements from database exports
• Configuration files: Extract <server>, <user>, or similar repeated configuration blocks

Installation

pip install oxidize

Usage

Extract specific elements from XML and convert to JSON-Lines:

import oxidize

# File to file
count = oxidize.parse_xml_file_to_json_file("data.xml", "book", "books.json")

# File to string  
json_lines = oxidize.parse_xml_file_to_json_string("data.xml", "book")

# String to string
result = oxidize.parse_xml_string_to_json_string(xml_content, "book")

# Control batch size (default 1000)
oxidize.parse_xml_file_to_json_file("huge.xml", "record", "out.json", batch_size=500)

Conversion Rules

Uniform arrays: All elements become arrays for consistent schema inference:

<book id="bk101">
    <author>J.K. Rowling</author>
    <title>Harry Potter</title>
</book>

{
  "@id": "bk101",
  "author": ["J.K. Rowling"], 
  "title": ["Harry Potter"]
}

Key behaviors:

• Attributes: Prefixed with @ to avoid conflicts with element names
• Mixed content: Text in elements with children stored as #text entries
• Empty elements: Self-closing/empty tags become null values
• Structure preservation: Element order maintained via IndexMap
• Namespace handling: Prefixes kept in element names, declarations treated as attributes

Ignored features:

• Processing instructions, DTDs, comments (not relevant for data extraction)
• Custom entity definitions (entity references passed through as text)
• Character references automatically unescaped by quick_xml

Data Processing

Pandas

json_lines = oxidize.parse_xml_file_to_json_string("data.xml", "record")
records = [json.loads(line) for line in json_lines.strip().split('\n')]
df = pd.json_normalize(records)

Polars

oxidize.parse_xml_file_to_json_file("data.xml", "record", "temp.json")
# Use infer_schema_length=None and eager loading for unknown schemas
df = pl.read_json("temp.json", infer_schema_length=None)
# Use lazy loading for known schemas
lf = pl.scan_ndjson("temp.json")

DuckDB

oxidize.parse_xml_file_to_json_file("data.xml", "record", "data.json") 
df = duckdb.sql("SELECT * FROM read_json_auto('data.json')").df()

API

parse_xml_file_to_json_file(input_path, target_element, output_path, batch_size=1000) -> int
parse_xml_file_to_json_string(input_path, target_element, batch_size=1000) -> str  
parse_xml_string_to_json_file(xml_content, target_element, output_path, batch_size=1000) -> int
parse_xml_string_to_json_string(xml_content, target_element, batch_size=1000) -> str