Introduction

If you’ve ever dealt with massive RSS or Atom feeds, you know how quickly memory usage can go up when attempting to parse them. I experienced these headaches firsthand while working on large XML-based projects. In particular, when using XMLFeedSpider, big spiders would often be killed by the Out-Of-Memory (OOM) killer on services like ScrapingHub (now Zyte), where resource limits are strict.

This recurring issue led me to develop XMLStreamer, a Python library designed to handle large XML feeds (even several gigabytes in size) without overloading system memory. In this blog post, I’ll share the story behind XMLStreamer, compare it to XMLFeedSpider, and explain why it could be a game-changer for your web scraping and data aggregation projects.

The Origin of XMLStreamer

While working on large-scale scraping tasks, I noticed that most default XML parsing mechanisms load extensive chunks—or even the entire file—into memory before processing. This becomes problematic when dealing with multi-gigabyte files or continuous data streams. The result is a high memory footprint that can crash your spiders or cause them to be killed in constrained environments.

The catalyst was my personal experience using Scrapy with XMLFeedSpider. It works well for many use cases, but when feeds grow large or are compressed, memory spikes are not uncommon. Determined to tackle this issue, I built XMLStreamer to:

  • Stream the XML rather than store it all in memory.

  • Handle GZIP compression seamlessly, so data can be decompressed on the fly.

  • Tokenize and filter XML items based on attributes.

  • Offer configurable buffers and runtime limits to optimize performance for different needs.

How XMLStreamer Works

XMLStreamer adopts a SAX-based approach, processing each XML element as it streams in from a given URL (HTTP, HTTPS, FTP) or local file. By handling the data in small chunks, XMLStreamer avoids inflating memory usage with large or continuous feeds.

Here’s a quick example demonstrating the streamlined parsing:

from xmlstreamer import StreamInterpreter
import pprint

url = "https://example.com/large-feed.xml"
separator_tag = "item" # The tag to identify individual items
interpreter = StreamInterpreter(
    url=url,
    separator_tag=separator_tag,
    buffer_size=1024 * 128, # 128 KB buffer
    max_running_time=600 # 10 minutes
)

for item in interpreter:
    pprint.pprint(item)

In this script:

  • separator_tag="item" ensures that each <item> is processed separately.

  • buffer_size controls how much data is read at once, which you can tune for your environment.

  • max_running_time sets a limit to safeguard long-running tasks.

Hypothetical Example: Processing a Large Pricing Feed

Imagine you have a huge XML feed with thousands of product entries for an e-commerce platform. Each <product> element contains essential information such as id, name, and price. Instead of loading the entire feed (potentially gigabytes of data) into memory at once, XMLStreamer processes each <product> as it arrives. Here’s how that might look in code:

Below is a simplified representation of how a single <product> element might appear in Python after parsing:

{
  'id': '42',
  'sku' '31337'
  'name': 'Your favorite product',
  'currency': 'USD',
  'price': '19.99'
}

Even if there are thousands—or hundreds of thousands—of such <product> entries, XMLStreamer processes and discards each one before moving on, keeping memory usage to a minimum. This approach is especially beneficial for large pricing feeds where you only need to handle one product at a time, without ever needing to load the entire XML document.

Comparing XMLStreamer with XMLFeedSpider

XMLFeedSpider is a great out-of-the-box solution provided by Scrapy for parsing XML feeds, but it can struggle with extremely large or continuous streams. Below is a simplified comparison:

Feature XMLFeedSpider XMLStreamer
Memory Efficiency Parses bigger chunks (risk of OOM) Streams data in chunks, minimizing memory usage
Compression Handling May require manual decompression Built-in GZIP support
Tokenization Basic item extraction SAX-based approach, custom filters & attributes
Flexible Buffer/Runtime It doesn’t have a buffer User-defined buffer size & max running time
Ease of Integration Native to Scrapy Standalone library (easily used with Scrapy too)

XMLStreamer was built specifically with memory constraints in mind. While XMLFeedSpider can parse XML feeds in a straightforward manner, it’s not always optimized for extremely large data sources or continuous streams that might arrive compressed.

Who Should Use XMLStreamer?

  • News Aggregators and Syndication Services
    If you process massive RSS/Atom feeds from multiple sources, XMLStreamer’s streaming approach keeps servers running smoothly.

  • Data Aggregation and Web Scraping
    For any scenario where you scrape large XML datasets (potentially gigabytes worth of data), the risk of running out of memory is substantially reduced.

  • Developers with Resource Constraints
    Whether you’re on ScrapingHub/Zyte, a limited-memory VPS, or any environment with strict resource caps, XMLStreamer’s fine-grained buffer control and runtime limits help you avoid costly timeouts and OOM errors.

Thanks Ruslan Spivak!

In developing XMLStreamer, I drew inspiration from Ruslan Spivak’s “Let’s Build A Simple Interpreter” tutorial series. His detailed explanations on building interpreters and parsers in Python were instrumental in shaping the design and functionality of XMLStreamer.

Spivak’s tutorials guide readers through the process of constructing a simple interpreter from scratch, introducing fundamental concepts such as lexical analysis, parsing, and abstract syntax trees (ASTs). These concepts are crucial for understanding how to process and interpret structured data formats like XML.

In XMLStreamer, the Tokenizer class is responsible for breaking down the XML feed into manageable chunks, similar to how a lexer (lexical analyzer) tokenizes input in an interpreter. This approach allows XMLStreamer to efficiently process large XML feeds without consuming excessive memory.

Additionally, the StreamInterpreter class in XMLStreamer orchestrates the parsing and interpretation of the tokenized XML data, akin to the parser and interpreter components discussed in Spivak’s tutorials. By adopting a streaming approach, XMLStreamer can handle large XML feeds in a memory-efficient manner, processing each item as it arrives and discarding it before moving on to the next.

For those interested in building interpreters or working with structured data formats, I highly recommend exploring Ruslan Spivak’s tutorial series. His clear explanations and step-by-step approach provide a solid foundation for understanding these concepts.

Let’s Build A Simple Interpreter Series: (https://ruslanspivak.com/lsbasi-part1/)

Conclusion

XMLStreamer grew out of a necessity to handle huge XML feeds efficiently without crashing spiders or overloading servers. Its streaming-based architecture, built-in compression handling, and flexible configuration options make it a robust alternative—or complement—to existing XML parsing solutions like XMLFeedSpider. If you’re juggling massive or continuous feeds, give it a try and see how it improves your data processing workflow.

As a developer who appreciates the UNIX philosophy, I believe XMLStreamer can be improved to fully comply with it. Contributions are welcome!

Explore XMLStreamer on GitHub:
https://github.com/carlosplanchon/xmlstreamer