Struggling with content optimization: Keyword Density Checker too slow?

Hey everyone,

We recently launched our "Keyword Density & Frequency Checker" tool on AdsVolt, and honestly, the traction has been pretty awesome! It's great to see people finding it useful for their on-page SEO. However, we're hitting a pretty significant wall when it comes to performance, especially with larger text inputs.

The main issue is that when users paste texts over, say, ~5000 words (think long-form blog posts or entire articles), the tool becomes incredibly slow. Sometimes it even times out completely, which is obviously a huge pain point for users and directly impacts our conversion rates. People just abandon it if it takes too long.

We haven't just sat on our hands; we've tried a few things:

• Optimized database queries for keyword storage. We initially thought this might be a bottleneck, but profiling showed the processing itself is the main culprit, not the storage.
• Implemented basic caching for frequent checks on the same URLs, which helps for repeat visitors but not for new, large text inputs.
• Increased server resources (RAM, CPU) quite a bit. While this provided a marginal improvement, the problem persists, indicating code-level inefficiencies rather than just throwing more hardware at it.
• Experimented with different text parsing libraries in PHP/Node.js (depending on the microservice handling it), but haven't found a silver bullet for significantly faster tokenizing and counting.

During these long processing times, we consistently see high CPU usage spiking. Here's a simplified log snippet from our monitoring showing a typical scenario when a large text input (around 7000 words) is processed:

[2023-10-27 10:35:12] INFO: Processing request for text_id: 87654
[2023-10-27 10:35:12] INFO: Text length: 7280 words
[2023-10-27 10:35:13] DEBUG: Starting tokenization...
[2023-10-27 10:35:45] DEBUG: Tokenization complete. Elapsed: 32s
[2023-10-27 10:35:46] DEBUG: Starting frequency counting...
[2023-10-27 10:36:18] DEBUG: Frequency counting complete. Elapsed: 32s
[2023-10-27 10:36:19] DEBUG: Calculating density...
[2023-10-27 10:36:25] DEBUG: Density calculation complete. Elapsed: 6s
[2023-10-27 10:36:25] INFO: Total processing time for text_id 87654: 73 seconds.
[2023-10-27 10:36:26] WARNING: Request timed out for text_id: 87654.

As you can see, tokenization and frequency counting are taking up the bulk of the time, leading to timeouts. It's clear that our current approach for handling large volumes of text for content optimization isn't cutting it.

I'm really hoping some of you seasoned pros here might have some insights. Specifically, I'm wondering:

• What are the industry best practices for efficiently processing large volumes of text for content optimization tasks like keyword density and frequency analysis? Are there specific architectural patterns (e.g., message queues, background workers) that are crucial here?
• Are there specific algorithms or open-source libraries (preferably PHP or Node.js, but open to other suggestions) known for their speed and efficiency in tokenizing, stemming, and counting word frequencies?
• Any server-side configuration tips (e.g., Nginx, PHP-FPM, Node.js setups) that can help with CPU-intensive background tasks without blocking the main thread for other users?
• How do larger SEO tools (like Ahrefs or SEMrush) handle this kind of scaling challenge for their keyword analysis features? Are they just throwing massive clusters at it, or is there some underlying magic?

We're really keen to improve this and provide a smoother experience for our users. Any practical advice or pointers to resources would be hugely appreciated. Help a brother out please...