Struggling with 'Keyword Density & Frequency Checker' parsing large JSON, affecting keyword analysis algorithms accuracy
we're running into a bottleneck with our Keyword Density & Frequency Checker, specifically when it processes very large text inputs, often nested JSON or complex HTML.
the core issue isn't the keyword analysis algorithms themselves for density and frequency, but the initial parsing phase for documents over 5MB. it's causing unacceptable latency and sometimes outright crashes, which is pretty bad.
i'm trying to figure out if there's a more performant streaming parser approach in Python or Go that maintain context for accurate keyword extraction, or if chunking strategies are the only way to go without sacrificing data integrity. what's the recommended best practice here?
2 Answers
Maryam Rahman
Answered 5 days agoParsing those monstrous JSON/HTML files for keyword density and frequency can be a real headache, I get it.
- For Python, look into
ijsonfor streaming JSON andlxml'siterparsefor HTML to handle large document processing efficiently without loading everything into memory. - In Go, leverage
encoding/json.DecoderwithToken()for JSON andgolang.org/x/net/html'sTokenizerfor HTML text analysis. Both maintain context for accurate keyword extraction.
Chunking is also a viable strategy if you need more granular control over memory or specific parts of the document. What's your average file size you're dealing with?
Aarti Kumar
Answered 5 days agoThat's some really solid advice on ijson and lxml, thanks Maryam! Definitely bookmarking this thread to come back to when I dive into it.