Optimizing text analytics for keyword density: Performance bottlenecks?

Hey everyone,

We're running into some significant performance bottlenecks with our 'Keyword Density & Frequency Checker' web tool, especially when users input large volumes of text. This tool is critical for our users focused on content optimization, helping them fine-tune their on-page SEO strategies.

Our current backend is primarily Python-based, utilizing a standard NLP pipeline. This involves tokenization, filtering for stop words, stemming or lemmatization, and finally, frequency mapping to determine keyword density. For smaller text inputs, it's blazing fast, but the scalability is becoming a real issue.

The core challenge emerges when processing documents that exceed 10,000 words. We're observing unacceptable latency, often ranging from 5 to 10 seconds, or even longer for truly massive inputs. This is particularly problematic during the more complex text analytics operations that are fundamental to providing accurate keyword insights.

We've already tried several optimization strategies:

• Implemented multiprocessing to handle concurrent document analysis, thinking it would distribute the load.
• Optimized data structures, moving to highly efficient options like collections.Counter and defaultdict for frequency mapping.
• Explored different regex engines for tokenization, including Python's built-in re and even external libraries, but with limited success.
• Considered caching common stop word lists and pre-loading stemmer models to reduce runtime overhead.

Profiling consistently points to the tokenization and initial frequency mapping stages as the primary CPU-intensive bottlenecks. It seems these operations, when applied to very large strings, are disproportionately expensive.

Here's a simplified dummy console output from a cProfile run, illustrating where the time is being spent:

         ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    5.120    5.120    9.870    9.870 <string>:1(<module>)
        1    3.450    3.450    6.800    6.800 my_module.py:120(process_document)
        1    2.100    2.100    3.500    3.500 my_module.py:45(tokenize_text)
   120000    0.850    0.000    1.200    0.000 <re#1>:1(<lambda>)
   100000    0.700    0.000    0.900    0.000 my_module.py:70(normalize_word)
        1    0.650    0.650    1.100    1.100 my_module.py:60(map_frequencies)
    50000    0.300    0.000    0.400    0.000 my_module.py:85(is_stop_word)

Given this context, I have a few specific questions for the experts here:

• Are there more efficient algorithms or Python libraries for rapid text analytics on large strings—specifically for tokenization and frequency counting—beyond the standard NLTK/SpaCy approaches for this particular type of task?
• Any practical recommendations for optimizing memory footprint during word frequency calculation for extremely long texts without sacrificing accuracy?
• Could a fundamentally different architectural approach, such as streaming processing or leveraging a specialized in-memory database, yield significantly better results compared to our current Python-centric, batch-like processing?

Eagerly awaiting any expert insights or suggestions on how to tackle this. Thanks in advance!