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msgspec vs. Pydantic advantages

· 4 min read
Serhii Hrekov
Serhii Hrekov
software engineer, creator, artist, programmer, projects founder

When comparing two things, the advantages are the positive aspects that make one a better choice in a given situation.

Here’s why you should choose msgspec over Pydantic.

msgspec vs. Pydantic: The Case for msgspec​

While Pydantic is a powerful and popular choice, msgspec excels in specific scenarios, primarily due to its focus on performance and strictness. Its advantages are most apparent in high-performance applications where data processing speed is critical.

Performance 🚀​

msgspec is engineered for speed. It consistently outperforms Pydantic in both serialization and deserialization benchmarks, often by a factor of 2x to 5x. This speed advantage comes from several key design decisions:

  • Compiled Core: msgspec is written in Rust, which allows it to compile a highly optimized code path for your data schemas.
  • Static Typing: It leverages Python's type hints to generate static, low-level code for validation and I/O. This means there's less overhead at runtime.
  • Zero-Copy Optimization: For common data types like strings and integers, msgspec can often decode data without creating new Python objects, directly referencing the underlying byte buffer. This drastically reduces memory allocations and CPU cycles.

Strictness and Simplicity​

msgspec has a smaller, more focused API than Pydantic. This deliberate simplicity leads to a strict, unambiguous behavior that is beneficial for performance and predictability.

  • No Implicit Conversions: msgspec is less permissive. If you define a field as an integer, it will not implicitly convert a string like "123" into an integer. This strictness helps prevent subtle bugs and ensures data integrity.
  • Automatic __slots__: Every msgspec.Struct automatically uses __slots__ which makes object instances more memory-efficient. This is a significant advantage when you are working with millions of data objects.
  • Predictable Behavior: The library's strictness makes it behave predictably, which is crucial for building reliable data pipelines and APIs. There are fewer "hidden" features or configurations to manage.

Ideal Use Cases​

You should consider using msgspec over Pydantic in these scenarios:

  • High-Throughput Microservices: APIs that handle a high volume of requests where minimizing latency is a top priority.
  • Data Processing Pipelines: When you need to quickly parse and process large datasets from formats like JSON or MessagePack.
  • Memory-Constrained Environments: Applications running on systems with limited memory, where the efficiency of __slots__ provides a tangible benefit.

In summary, if your primary concern is raw speed and predictable, strict data validation, msgspec is the superior choice. It trades Pydantic's flexibility and rich feature set for a streamlined, high-performance engine.

Sources​

  1. msgspec Documentation: Performance Benchmarks
  2. msgspec Documentation: Why msgspec?