Performance Improvement: Optimize Scanning for Large Codebases

Steps to Optimize Scanning for Large Codebases
To improve the performance of AboutCode's scanning process, we can apply the following optimizations:

Implement Parallel Processing

Use multiprocessing or async I/O to scan multiple files concurrently instead of sequentially.

Example: In Python, use concurrent.futures.ThreadPoolExecutor or multiprocessing.Pool.

File Caching & Incremental Scanning

Store hash values of previously scanned files to avoid redundant processing.

Implement a change detection mechanism to only scan modified files.

Batch Processing for File Reads

Instead of reading files one by one, use batch processing for efficiency.

Example: Read multiple files into memory using memory-mapped I/O (mmap).

Optimize Regex & String Matching

Avoid repetitive string operations inside loops by precompiling regex patterns (re.compile()).

Use Aho-Corasick or Trie-based search for license scanning instead of brute force string matching.

Leverage Efficient Data Structures

Replace lists with sets or dictionaries where possible to speed up lookups.

Use NumPy or Pandas for optimized data handling if applicable.

Profile & Benchmark Performance

Identify bottlenecks using cProfile or Py-Spy.

Run benchmarks before and after optimization to measure improvements.

Expected Outcome
50-70% reduction in scan time for large codebases.

No loss in accuracy while improving performance.