High-Performance JSON Streaming Techniques
2025-12-15#Performance#Streaming#Node.js
Why Streaming?
When JSON files exceed memory limits (e.g., 1GB+), JSON.parse() causes OOM (Out of Memory). Streaming allows processing nodes one by one with low memory footprint.
Common Solutions
1. NDJSON (Newline Delimited JSON)
One JSON object per line, naturally suited for streaming.
{"id": 1, "data": "..."}
{"id": 2, "data": "..."}
2. Streaming Libraries (e.g., stream-json)
Event-driven parsing for single large arrays or objects.
const { parser } = require('stream-json');
const { streamArray } = require('stream-json/streamers/StreamArray');
fs.createReadStream('large.json')
.pipe(parser())
.pipe(streamArray())
.on('data', ({ value }) => process(value));
Browser-side Streaming
Leverage ReadableStream and TextDecoder, combined with WebAssembly or custom parsers, to handle large file imports on the client.
Summary
Streaming is essential for heavy JSON applications. Choosing the right format (like NDJSON) is often more effective than optimizing parsing algorithms.
Related articles
Working with Large JSON Files - A Practical Guide
Techniques and tools for handling JSON files that exceed memory limits or browser constraints.
JSON Performance Optimization Techniques
Speed up JSON parsing, serialization, and processing with these proven optimization strategies.
JSON Lines and Streaming JSON - Efficient Line-Based Data Processing
Learn how to use JSON Lines format for streaming, logging, and large-scale data processing.
Practical Guide to JSON Performance Optimization
Actionable tips across serialization, parsing, and transport layers
JSON Lines 与流式处理:可靠的数据管道实践
利用 JSONL 构建高可靠的数据管道,兼顾生产与消费的容错与监控。
JSON 性能调优:解析、内存与网络的协同优化
面向大文件与高并发的 JSON 性能实践,从解析、内存到网络传输全链路优化。