Optimizing Regex for Log Parsing in a High-Volume Node.js Application
I'm building a feature where This might be a silly question, but Currently developing a log parsing module for a Node.js application that handles thousands of log entries per second. I've noticed that the regex I'm using to extract specific fields is causing performance bottlenecks. The goal is to match patterns like `ERROR [timestamp] [component]: message` and pull out the timestamp and message efficiently. Here's my current implementation: ```javascript const logRegex = /ERROR \[(.*?)\] \[(.*?)\]: (.*)/; function parseLog(line) { const match = logRegex.exec(line); if (match) { return { timestamp: match[1], component: match[2], message: match[3] }; } return null; } ``` I've tested this with a sample log file, and while it works, the performance degrades significantly with larger files. With logs of around 100,000 lines, I noticed that the processing time can reach upwards of several seconds. I’ve tried using the `String.prototype.matchAll()` method, as suggested in various performance articles, but it didn't yield a noticeable improvement. The regex might be too complex, or perhaps there’s a more efficient way to structure the parsing without regex altogether. Additionally, I've contemplated breaking the log entries into smaller chunks before applying regex, thinking that smaller strings could speed up the matching process. However, this adds extra complexity and hasn't been tested extensively yet. If anyone has experience optimizing regex in high-load scenarios or alternative approaches to efficiently parse structured logs in Node.js, your insights would be incredibly valuable. What strategies can I adopt to enhance performance here? Are there specific regex patterns or practices that I should avoid for such use cases? Any advice on profiling or debugging tools specifically for regex would also be appreciated! Any feedback is welcome!