🚀 Large-scale JSON parsing: which technology to choose?
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When data grows, JSON parsing stops being trivial. This article examines how different Python libraries handle speed, memory, and scalability when processing large volumes of information.

🔧 Highlighted technologies
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⚡ orjson: the fastest and most efficient; written in Rust, memory-safe, and able to serialize advanced types.
🧵 ijson: ideal for streaming huge data; processes JSON without loading it all into memory.
🐍 stdlib/json: the standard library; good enough for small to moderate loads.
📦 ujson: fast, but now maintenance mode.

📊 Comparison of libraries to parse JSON in Python
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Library	Speed	Memory usage	Special capabilities	Limitations	Best use case
stdlib/json	🟡 Medium	🔴 High (loads all in memory)	Supports basic types	Not great for large payloads; does not serialize dataclasses or datetime	Small or medium JSON
ujson	🟢 High	🟡 Medium	C implementation; faster than stdlib	Maintenance mode; does not serialize dataclasses or datetime	Existing systems that already use it
orjson	🟢🟢 Very high	🟢 Efficient	Rust implementation; serializes dataclasses and datetime; returns bytes	Requires handling bytes instead of strings	High-performance and large volumes
ijson	🔴 Low (by design)	🟢 Very low (streaming)	Processes JSON without loading it all; ideal for huge files	Not a “bulk” parser; not speed-competitive	Gigantic payloads, streaming, NDJSON
ndjson	🟡 Medium	🟡 Medium	Converts NDJSON ↔ JSON easily	Only for NDJSON format	Logs, events, line-delimited data

🧠 Quick explanation
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Imagine a JSON file is a box full of thousands of papers.

Some libraries open the box and dump everything on the table (fast, but uses a lot of space).
Others take one paper at a time (slower, but you don’t need a giant table).

Choosing the right library depends on whether you need speed, low memory, or process huge data without crashing your system.