Question 1

How does simdjson compare to RapidJSON in terms of speed?

Accepted Answer

simdjson is significantly faster, parsing JSON 4x quicker than RapidJSON and up to 25x faster than JSON for Modern C++ in benchmarks, reaching over 1 GB/s on common datasets like the Rome JSON suite. It uses SIMD instructions to reduce instructions by three-quarters.

Question 2

How to install and use simdjson in a C++ project on Linux?

Accepted Answer

Download simdjson.h and simdjson.cpp files, then compile with a C++17 compatible compiler like g++ or clang++. The README's Quick Start shows a simple example loading a JSON file and parsing it in a few lines of code with ondemand::parser.

Question 3

Does simdjson support parsing JSON streams or large files?

Accepted Answer

Yes, simdjson offers a parse_many function for NDJSON (newline-delimited JSON) that can exceed 3 GB/s with multithreading, making it suitable for streaming and large-scale data ingestion. It handles files from tiny to multi-megabyte sizes efficiently.

Question 4

What are the minimum system requirements for simdjson?

Accepted Answer

simdjson requires a 64-bit system with a modern CPU supporting SIMD instructions, and a C++17 compiler like g++ 7+ or clang++ 6+. It automatically adapts to different CPU architectures at runtime, but performance is optimal on newer hardware.

Question 5

How to handle errors when using simdjson's on-demand API?

Accepted Answer

Error handling can be done via exceptions or without exceptions, as shown in the Godbolt examples. The library provides robust error reporting for invalid JSON or UTF-8, but users need to manage try-catch blocks or error codes based on their coding style.

Question 6

Are there Python bindings for simdjson and how do they perform?

Accepted Answer

Yes, there are multiple Python bindings like pysimdjson and cysimdjson that wrap the C++ library, offering high-speed JSON parsing in Python. Performance is close to native C++ but may have slight overhead due to the binding layer.

simdjson

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