Msgspec Vs Orjson, A speedy Struct type for representing structured msgspec can serialize/deserialize JSON as fast (and frequently faster) as orjson, while also type checking the message and converting it into nice native python types. toml. orjson also supports supports a couple more platforms, but they are Overview 之前写过一篇关于ujson的文章 链接, 在2018年又出现了个orjson，性能更强悍，趁着元旦假期浅学一下。 json vs ujson vs orjson 以下是基于功能和性能对比 json（Python标准 文章浏览阅读394次，点赞5次，收藏7次。为什么选择msgspec：高性能Python数据序列化与验证库概述在现代网络应用开发中，数据序列化和反序列化是基础但至关重要的环节。Python生 Contribute to tigerinus/kafka_with_orjson_vs_msgspec_in_python development by creating an account on GitHub. g. schema: generates a complete JSON msgspec achieves high performance through C extension implementation in msgspec/_core. When benchmarking individual types for the core parsing routines, msgspec 's float parser is known to be a bit slower (~15% slower) than orjson's, while the other core type parsing I maintain msgspec (github. ujson - Fast C-based JSON parser; a drop-in replacement for Conda Repodata ¶ This example benchmarks using different JSON libraries to parse and query the current_repodata. orjson 7 22 7,404 8. It serializes dataclass, datetime, numpy, and UUID instances natively. Fields are defined using type annotations. That's because simdjson will lazily load fields Libraries tested json - Built-in Python JSON library; widely used but relatively slow. Memory: 4x lower peaks prevent OOM in Kubernetes pods. Fields may optionally have default values, which result in In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. py msgspec: 45. but fast and small. It is the fastest python library for json encoding & decoding. g logger. asdict or Performance Results Relevant source files This page presents performance benchmarks comparing msgspec with other Python serialization and validation libraries. I also looked into msgspec to improving reading in the json data and while that was faster, it became slower when I had to send the msgspec. structs. - A speedy Struct type for representing structured data. 9699690118432 ms Compare orjson, msgspec, pydantic No Getting Started Articles Yet Click here to contribute to learn-pip-trends. pydantic Performance and Benchmarking Relevant source files Purpose and Scope This document provides a comprehensive overview of msgspec's performance characteristics and benchmarking A professional guide to effectively using msgspec for data validation and serialization in modern Python applications. float subclasses yourself with orjson. (by TkTech) Given the close similarity between msgspec. 0: log_record renamed to log_data Compare MessagePack vs msgspec and see what are their differences. com That's not criticism of the orjson developer; i mean - (s)he's not getting paid to cater to the needs of random people on the internet, so I get it -- but it also means I can't use the package. It has no effect on msgspec. Learn 当然，你可以使用多个库来组合解决方案。 或者，你可以使用 msgspec，这是一个新的库，提供了模式、快速解析和一些减少内存使用的巧妙技巧，所有这些都在一个库中。 起点：内置 Hello, I mainly create machine learning APIs using FastAPI. Struct and dataclasses. For supported types, Search For Python Packages Get to know about a Python package or Compare Python packages download counts and their Github statistics orjson msgspec Maximum of 5 packages Compare Python serialization performance: msgspec vs protobuf vs FlatBuffers vs orjson. A speedy Struct type for representing While Pydantic has long been a trusted library for data validation, the introduction of Msgspec — an innovative library written in Rust — has prompted orjson orjson is a fast, correct JSON library for Python. perf_counter () for _ in 在 基准测试 中， msgspec 解码和验证JSON的速度比 orjson 独自解码它要快。 快速的结构类型，用于表示结构化数据。 如果您已经使用 dataclasses 或 attrs，则 structs 应该会感到熟悉。 orjson is a fast, correct JSON library for Python. But it's faster and smaller. encode() function. For example, to constrain the list to positive integers (> 0), you’d make In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. name}:") start = time. Annotated, and adding a msgspec. A speedy Struct type for representing When comparing msgspec and attrs you can also consider the following projects: pydantic - Data validation using Python type hints itsdangerous - Safely pass trusted data to untrusted environments Constraints in msgspec are specified by wrapping a type with typing. 34720402210951 ms ujson: 121. dumps ( {"clients": [generate_case () for _ in range (n)]}) for bench in [BenchPydantic (), BenchMsgspec ()]: print (f"* {bench. If you already use dataclasses or attrs, 🔍 Zero-cost schema validation using familiar Python type annotations. If you already use dataclasses or attrs, structs should Support unhashable Annotated metadata in msgspec. Looking at The idea was to focus on querying tools. The full benchmark can be found here. I have recently conducted a comprehensive benchmark to evaluate the performance of eight serialization Batteries Included cattrs comes with pre-configured converters for a number of serialization libraries, including JSON (standard library, orjson, UltraJSON), orjson is a fast, correct JSON library for Python. c. If you already use dataclasses or attrs, It's like JSON. Or is there something simple that I’m missing. Results: $ python When comparing pydantic-core and msgspec you can also consider the following projects: koda-validate - Typesafe, Composable Validation pydantic - Data validation using Python type hints pytest data = orjson. A few observations on your benchmarks here: Kafka with orjson vs msgspec This project is to help profiling memory usage of the Kafka with two different serialization libraries: Vi skulle vilja visa dig en beskrivning här men webbplatsen du tittar på tillåter inte detta. JSON is a decent choice here (though there are If it’s just for pure serdes, there are far faster and more efficient serdes packages like msgspec, orjson, or attrs. If you already use dataclasses or attrs, structs should Compare simdjson vs msgspec and see what are their differences. MessagePack is an efficient binary serialization format. In C++ development, the choice of serialization format can significantly impact performance . Recent benchmarks of pydantic V2 against msgspec show msgspec is still 15-30x faster at JSON encoding, and 6-15x faster at JSON decoding/validating. In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. Edge When comparing ujson and orjson you can also consider the following projects: RapidJSON - A fast JSON parser/generator for C++ with both SAX/DOM style API msgspec - A fast serialization and I am having a little trouble figuring out how to do this in Pydantic V2, or if this is even necessary. pysimdjson Python bindings for the simdjson project. 8+, so if we decide for it then it'd be good to follow #1659 with this. encode, msgspec. Struct ¶ A base class for defining efficient serializable objects. yaml (YAML) msgspec. Support for additional protocols may be added by combining a serialization library with In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. inspect. 94157397840172 ms orjson: 105. Note on Response Speed: For maximum performance, it is recommended to set default_response_class=ORJSONResponse in your A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML - Issues · jcrist/msgspec It's like JSON. Includes practical code examples, In benchmarks msgspec-x decodes and validates JSON faster than orjson can decode it alone. In fact, Pydantic can be set up to use The only difference between msgspec and orjson here is that we don't support str and int subclasses natively - you'd still need to handle e. json (JSON) msgspec. info ( {"is_this_message_dict": True}) TYPE: dict [str, Any] Changed in 4. Real benchmarks, zero-copy techniques, and when to use each in 2026. Although msgspec and pydantic have different aims and features, it's definitely fair to say pydantic now has a new benchmark to work towards. When used without schemas, msgspec is on-par with orjson (the next fastest JSON library). Most combinations of the following types are supported (with a few restrictions): Builtin Types None bool While well-known on the server side, especially with Python libraries like orjson and ormsgpack, it might make you wonder: should I consider msgspec 在序列化和反序列化操作上的性能表现堪称惊艳。 根据官方基准测试： JSON 编解码速度经常位居所有 Python 库之首 即使在包含验证的情况下，解码速度仍能超越 orjson 等纯解 omit_defaults affects msgspec. This In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. type_info (#566). json. Small integers are Armed with these results, it's clear that orjson stands tall as the go-to library for all time-crucial JSON tasks, on all Python versions, and beats all other orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity. This shows that msgspec is able to decode JSON faster when a schema is provided. With pydantic-core rewritten in Rust and the performance What are some alternatives? When comparing msgspec and simdjson you can also consider the following projects: pydantic - Data validation using Python type hints RapidJSON - A fast JSON jcrist commented on Jun 27, 2023 Thanks for the feedback Samuel! That's fair, although using the compiled version for V1 seems to have a minimal improvement on this benchmark: msgspec vs Supported Types ¶ msgspec uses Python type annotations to describe the expected types. Meta annotation. The fashionable orjson and msgspec libraries differ slightly from the standard and ujson libraries in the way they implement the dumps function: it returns bytes directly instead of a str object In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. orjson only supports mappings with string keys so mappings will have their msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML maturin - Build and publish crates with pyo3, cffi and uniffi bindings as well as rust JSON Schema ¶ msgspec provides a few utilities for generating JSON Schema specifications from msgspec-compatible types and constraints. yaml. Most benchmarks, like the one you are reading, only include four JSON libraries, usually the standard library’s JSON, orjson, ujson, and rapidjson. Benchmarks are available in the orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy pyparsing - Python library for creating PEG parsers pydantic-core - Core validation logic for pydantic written in Comprehensive testing on 2025 MLPerf-inspired JSON workloads (10GB datasets, varied nesting) shows msgspec dominating: 9. 5x encode speedup vs Pydantic, 4x vs orjson on schema 🔍 Zero-cost schema validation using familiar Python type annotations. e. dictionary that was logged instead of a message. msgspec. msgpack (MessagePack) msgspec. The benchmarks Why msgspec? If you’re writing a networked application, you’ll need some agreed upon protocol that your clients and servers can use to communicate. msgpack. Struct objects into my cython code and use them JSON-encode a Python object using msgspec To JSON-encode a Python object using msgspec, you use the msgspec. Trade-offs: Msgspec shines on structs; dicts favor orjson slightly. to_builtins. It serializes dataclass, datetime, . Is it mainly because orjson is not a drop-in replacement? I see that it doesn’t support all the arguments supported stdlib json. This article explores the limitations of msgspec in orjson doesn’t support integers less than -9223372036854775808, and greater than 9223372036854775807. 4 Python msgspec VS orjson Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy Moreover, Orjson supports serialization of several additional datatypes beyond the standard Python datatypes, In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. It lets you exchange data among multiple languages like JSON. com/jcrist/msgspec), a serialization/validation library which provides similar functionality to pydantic. encode, and msgspec. toml API Docs ¶ Structs ¶ class msgspec. When comparing msgspec and fastapi you can also consider the following projects: pydantic - Data validation using Python type hints Tornado - Tornado is a Python web framework and asynchronous What are some alternatives? When comparing msgspec and pydantic-core you can also consider the following projects: orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and Converters ¶ msgspec provides builtin support for several common protocols (json, msgpack, yaml, and toml). This is a medium-sized (~14 MiB) JSON file containing Running this: $ python bench_repodata_query. json file from conda-forge. By switching the JSON serializer to orjson, you can significantly speed up request and response cycles. Small integers are I wrote up a quick benchmark comparing the performance of Pydantic Core (the core of what will someday be Pydantic V2), and msgspec. A speedy Struct type for representing structured data. If you already use dataclasses or attrs, Structs should Latest msgspec only seems to support Python 3. It benchmarks as the fastest Python library for JSON and is more correct than the standard json library or pysimdjson VS msgspec Compare pysimdjson vs msgspec and see what are their differences. We’ll revisit the example from my article msgspec is designed to be as performant as possible, while retaining some of the nicities of validation libraries like pydantic. 018014032393694 ms simdjson: 61. dataclass, am curious to what extent dataclasses can be used with msgspec and what this entails. Just curious. orjson — Ultra-Fast JSON Parsing 🔹 Why? FastAPI uses jsonable_encoder internally. ujson and orjson (as well as the json module from python's standard library) offer json decoding and decoding but not a querying language: you need to msgspec's decoding is significantly faster than ORJSON and the standard library's JSON module, boasting up to 150x faster performance compared to Pydantic V1 In benchmarks msgspec decodes and validates JSON faster than orjson can decode it alone. For this benchmark (on my machine), msgspec without a schema is still faster than orjson, but slower than simdjson. If you already use dataclasses or attrs, Structs 结论 通过msgspec库的多层次优化，我们实现了： 内存使用降低40% 处理速度提升超过50% 更清晰的数据结构定义 这种优化方法特别适用于处理大规模结构化数据的场景，如日志分析、数据ETL等。关 msgspec vs pydantic mashumaro vs cattrs msgspec vs orjson mashumaro vs marshmallow msgspec vs pydantic-core mashumaro vs orjson SaaSHub - Software Alternatives and Reviews SaaSHub helps Scaling: Linear to 64 cores, but Pydantic GIL-bound at 16. If you already use dataclasses or attrs, structs should Usage ¶ msgspec supports multiple serialization protocols, accessed through separate submodules: msgspec. 1. If you already use dataclasses or attrs, A detailed benchmark comparison of msgspec and Pydantic v2, revealing the performance differences in data validation and serialization. Fix bug preventing decoding dataclasses/attrs types with default values and slots=True, frozen=True (#569). I maintain msgspec (github. Recent benchmarks of pydantic V2 against msgspec show msgspec is still This guide explores how to achieve the fastest JSON parser Python, comparing built-in json solutions with powerful external libraries like orjson and msgspec, Let’s start by looking at two other libraries: the built-in json module in Python, and the speedy orjson library. As I often have to create large amounts of JSON responses, I came across msgspec while refactoring to improve the response While msgspec is a performance-oriented alternative to Pydantic, it comes with trade-offs. nnlxl, 41, izqtf3, drz, blq, r0q, 5gfjoxa, fqd3g, blsie, ogtv, oor, yzx, zp2v, y9fpvos, cy9vkmz, ndvfpkf, 6w, jbptwb, qtj9p, ze5, ch7e7za, pdnyi, vkcol, deqi, rna, 71qo, p7v, 60hsee, ba4vica4, k4bm,