I have a working Python web-scraping script that pulls data quickly but bogs down the moment it writes results into my MySQL database. The goal of this upgrade is simple: boost overall run-time by eliminating the storage bottleneck. Here is the current picture: • Language & stack: Python 3.x, standard scraping libraries (requests / BeautifulSoup) feeding a MySQL table. • Pain point: data storage is noticeably slower than retrieval or in-memory processing, turning otherwise fast runs into long waits. • Objective: increase speed—specifically by reworking the way data is written, buffered, or indexed so writes no longer throttle the workflow. What I need from you: 1. Analyse the existing write logic and pinpoint why inserts are slow (inefficient queries, lack of bulk operations, missing indexes, connection handling, etc.). 2. Implement and document the improvements—whether that means batching inserts, optimising table indexes, using transactions properly, or another well-reasoned solution. 3. Provide a clean, well-commented script (or module) that drops into my current codebase plus brief notes on what changed and why. Acceptance criteria • End-to-end scrape completes noticeably faster against the same dataset, with storage no longer the dominant time sink. • All data integrity checks pass exactly as before. • Your changes remain compatible with standard MySQL installations (no proprietary add-ons). If you have deep experience balancing Python data pipelines with relational databases, I’m eager to see how fast we can make this scraper run.