mwArray vs MATLAB Data API - Which API should I use when using MATLAB Compiler SDK?

2026 4 6
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When developing libraries with MATLAB Compiler SDK, I see most deployment targets generally have two paths for handling data exchange: the legacy mwArray approach and the modern MATLAB Data API (MDA) approach. Which one should I use?

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MathWorks Support Team
MathWorks Support Team about 20 hours 前
编辑：MathWorks Support Team about 6 hours 前

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For new development—especially if you want the best performance —we strongly recommend using MDA and particularly its strongly-typed approach.
Why choose MDA over mwArray?
mwArray was built on older technology and requires manual memory management and explicit data casting. In contrast, MDA (introduced for C++ in R2018a and expanded to .NET/Java/Python since) offers several modern advantages:
  • Zero/Minimal Data Copying: MDA uses a shared memory model with copy-on-write semantics. This significantly reduces the memory footprint and latency when passing large matrices between your application and the MATLAB Runtime.
  • Thread Safety: Unlike mwArray, MDA is designed for multi-threaded environments, allowing you to call compiled functions across different threads without complex locking.
  • Native Language Experience: Using the Strongly Typed approach with MDA, you don't need to wrap your data in MATLAB-specific classes like MWNumericArray. You simply pass native C++ vectors, C# arrays, or Java primitives.
  • Simplified Lifecycle: Modern APIs utilize smart pointers (C++) and native garbage collection (.NET/Java/Python) to handle the lifecycle of the MATLAB Runtime (and the libraries used with it) automatically.
Target Language
mwArray / MWArray
MDA
C++
Effort: Manual LibInitialize/Terminate and manual mwArray memory management.
Differences: requires deep copies for complex data; not thread-safe.
Effort: Automated lifecycle; uses standard C++11+ types via generateCPP.
Differences: Zero-copy/Copy-on-Write; thread-safe; full compile-time type checking; runtime instance can run in-process or out of process, applications can run synchronously or asynchronously.
C# / .NET
Effort: Heavy casting to MWArray types; must call .Dispose() on every array to avoid leaks.
Differences: marshaling overhead; type errors only caught at runtime.
Effort: Pass native types (double[], string) directly; memory managed by Garbage Collection.
Differences: IntelliSense support for method signatures; faster marshaling; asynchronous call support; runtime instance can run in-process or out of process, applications can run synchronously or asynchronously. Support for cross-platform (Windows, macOS, Linux) development and deployment.
Java
Effort: Explicitly wrap data in MWNumericArray etc.; requires manual .dispose() calls.
Differences: High object overhead; slower data transfer due to heavy wrapping.
Effort: Uses standard Java objects/primitives; lifecycle handled by JVM/Runtime.
Differences: Cleaner API; better memory efficiency; native support for structs and cells.
Python
Has always used MDA with all its associated benefits

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