When using qs.stringify to handle large data on a Windows Node.js environment resulted OOM. The problem was identified to occur during the process of concatenating a large number of strings, which consumed significant memory and time.
To resolve this issue, implemented a chunk-based processing approach.
Tested this solution using 20MB of Chinese characters and observed the following improvements:
Memory usage was reduced by approximately 80%, from 1637MB to 327MB.
Processing speed increased by approximately 4 times, from 5256ms to 1318ms.
Summary by CodeRabbit
Refactor
Improved the efficiency of the string encoding process by handling input in segments, enhancing Unicode character support.
Walkthrough
## Walkthrough
The recent update enhances the `encode` function's efficiency and Unicode character handling by processing input strings in segments of 1024 characters. This change optimizes the encoding logic within these segments, ensuring better performance and reliability in data processing tasks.
## Changes
| File | Summary of Changes |
|--------------|--------------------|
| `lib/utils.js` | Updated the `encode` function to process input strings in segments of 1024 characters, improving efficiency and Unicode character handling by updating the encoding logic within these segments. |
Recent Review Details
**Configuration used: CodeRabbit UI**
Commits
Files that changed from the base of the PR and between c4d29f35acec4122a0f6d323e0f4366cd3f3b134 and 6d7df026e6da59b501c3aab4c3af1793a28bfb08.
Files selected for processing (1)
* lib/utils.js (2 hunks)
Files skipped from review as they are similar to previous changes (1)
* lib/utils.js
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When using qs.stringify to handle large data on a Windows Node.js environment resulted OOM. The problem was identified to occur during the process of concatenating a large number of strings, which consumed significant memory and time.
To resolve this issue, implemented a chunk-based processing approach.
Tested this solution using 20MB of Chinese characters and observed the following improvements: Memory usage was reduced by approximately 80%, from 1637MB to 327MB. Processing speed increased by approximately 4 times, from 5256ms to 1318ms.
Summary by CodeRabbit