Mypy Frequently Asked Questions

We believe that, in principle, the mypy design makes it possible to run straight statically typed mypy code pretty much as efficiently as Java using the techniques developed in the mypy project, but we hope to have much faster program startup speed and often better-performing libs than in Java. It is always tricky to give precise performance figures, as relative performance can vary a lot from program to program, but let's give it a try. Some programs could run 50 times faster than CPython (or more), but programs that do a lot of I/O or spend most of the time in libraries implemented in C might see little or no benefit. Some big performance boosts might be seen in programs that make heavy use of object-oriented techniques and programs that do a lot of number crunching.

Practical considerations such as the availability of contributors determine how close to the expected performance you can get in the near future. For example, we are planning to implement a C or LLVM back end initially, and although this can save a lot of development time, this may restrict performance somewhat (by a factor of 2, perhaps).

The initial compiler will compile into C or use LLVM for the back end (ahead-of-time compilation to native code, no traditional VM). Later on, we are planning to use a JIT compiler that uses type feedback or trace complation for additional speedups for dynamically typed code, but this won't probably happen any time soon. The first prototypes will have no real back end at all but will translate into Python after type checking. Obviously at this stage there will be no performance benefit over CPython/PyPy yet, but these prototypes allow you to get a feel of how the static type checking will work. The translator to Python is a also an easy way to try mypy if you care more about the potential maintainability, reliability and productivity benefits of static typing instead of improved performance.

A JVM back end would be nice as well, but it's not the initial focus. However, we try to avoid making design decisions that compromise efficiency on the JVM.

We don't believe that it is practical to adapt the CPython VM incrementally for running parallel workloads efficiently. Instead, we are going to get rid of the GIL by having a new VM for running mypy code. The VM will be based on the Alore VM, but it will use compiled native code from the start (the Alore VM uses a bytecode interpeter). It will use a garbage collector instead of reference counting (CPython primarily uses reference counting), and it will also support a modified C Python extension API that will support proper multithreading without a GIL.

The CPython VM will still be used to run Python code and modules, and it will still be limited to running a single thread at a time (most of the time). Objects passed between the VMs will have to be copied or accessed using proxy objects, so there will be some performance overhead compared to native mypy modules. We plan to port commonly used, performance-sensitive Python modules to mypy and the modified C API to minimize performance bottlenecks.

Work is underway to add support for PEP 3107 type annotation syntax syntax to mypy.

Dynamic typing can be flexible, powerful, convenient and easy. But it's not always the best approach; there are good reasons why many developers choose to use staticaly typed languages.

Here are some potential benefits of mypy-style static typing:

• Static typing makes it easy to compile mypy programs to efficient native code. Only performance-critical code needs to be statically typed to get most of the benefits. Also dynamically typed can be faster than in Python, as a side effect of other mypy design decisions.
• Static typing makes it possible to run efficiently on the JVM, .NET and Dalvik/Android (in the future; current development efforts target native code).
• Static typing enables efficient ahead-of-time and method-at-a-time compilation to native code. This means fast program startup, stable performance without unpredictable slowdowns caused the JIT compiler, and predictable performance for complex programs.
• Static typing can make programs easier to understand and maintain. Type declarations serve as machine-checked documentation and automatic runtime assertions. This is important as code is typically read much more often than modified, and this is especially important for code that does complex things.
• Static typing can help you find bugs earlier and with less testing and debugging due to compile-time and runtime type checking. In large and complex projects this can be a major time-saver.
• You can get the benefits of both dynamic and static typing in a single language. Dynamic typing can be perfect for a small project or for writing the UI of your program, for example. As your program grows, you can adapt tricky application logic to static typing to help maintenance and to improve efficiency.
• Static typing makes it practical to build useful development tools such as IDEs with precise and reliable code completion, static analysis tools, etc.

See also the front page.

For many projects dynamic typing is perfectly fine (we think that Python is a great language). But sometimes your projects demand bigger guns, and that's when mypy may come in handy.

If some of these ring true for your projects, mypy (and static typing) may be useful:

• Your project is large or complex.
• Efficiency is important.
• Your codebase must be maintained for a long time.
• Many developers are working on the same code.
• Running tests takes a lot of time or work (type checking may help you find errors early in development, reducing the number of testing iterations).
• Some project members (devs or management) don't like dynamic typing, but others prefer dynamic typing and Python syntax. Mypy could be a solution that everybody finds easy to accept.
• You want to future-proof your project even if currently none of the above really apply.

Yes. Mypy will be available under the MIT license.

It depends. Small programs may run with no modifications, but large programs probably need some changes. We are planning to support importing normal Python modules in mypy programs, so you won't have to migrate your entire program to get benefits. See also the question about differences between mypy and Python.

Mypy currently supports Python 3 syntax. However, we are planning to add Python 2 support in the future.

Adding static typing to a language is a pretty significant change, which requires modifications throughout an implementation. Modifying an existing Python implementation would result in a major redesign (or too many compromises to our liking). Instead, we decided to use the an existing language implementation, which already supports optional static typing, as our basis. This way there's less painful rework, and we can get started much more quickly. We are going to port Python libraries instead of rewriting them, however, as this will save a big chunk of implementation work.

That wasn't really a question, was it? But here is a response anyway. Mypy is not aimed at replacing Python; the goal is to have a more Pythonic alternative when Python is not a realistic option. Many developers have to use Java, C++ or another statically typed language even though they would prefer Python, if it was suitable for the project.

Certainly, mypy has many features that make it a good candidate for developing mobile apps:

• Performance is often important since smartphones and tablets have limited processing power. Wasted cycles also consume the battery quickly.
• Users expect to be able to start mobile apps quickly and competitive user interfaces need to be smooth with very little lag. Mypy supports ahead-of-time compilation, which is a good match for these requirements.
• It should be possible to run mypy programs efficiently on the Dalvik VM in Android. This is important for efficient access to the Android native APIs.

However, mobile platforms are not the initial focus in mypy development — we don't have the resources to do everything at once. Any help is appreciated, though.

Mypy primarily uses nominal subtyping instead of structural subtyping. Some argue that structural subtyping is better suited for languages with duck typing such as Python. Mypy uses nominal subtyping primarily for these reasons:

1. It is easy to generate short and informative error messages when using a nominal type system. This is especially important when using type inference.
2. Nominal subtyping is used in the JVM and .NET. Nominal subtyping makes it relatively easy and efficient to interact with the JVM and Java libraries (and .NET).
3. Python supports basically nominal isinstance tests and they are widely used in programs. It is not clear how to support isinstance in a purely structural type system while remaining compatible with Python.
4. Many programmers are already familiar with nominal subtyping as it is used in languages such as Java, C++ and C#, but only relatively few languages use structural subtyping.

However, structural subtyping can be useful in some contexts. Structural subtyping is a very potential feature to be added to mypy in the future, even though mypy will primarily use nominal subtyping.

The most obvious difference is the availability of static typing. The mypy overview mentions a few more such as the need to make attributes explicit and more explicit protocol representation.

Static typing and the goal of high performance also result in other, more subtle changes. Mypy is unlikely to support arbitrary runtime redefinition of methods and functions, as these features are expensive to support on the JVM and/or with ahead-of-time compilation. Mypy will support modular, efficient type checking, and this seems to rule out some language features, such as arbitrary runtime addition of methods or base types to classes. However, it is likely that many of these features will be supported in a restricted form (for example, runtime modification is only supported for classes or methods tagged as dynamic).

Also some language features that are evaluated at runtime in Python may happen during compilation in mypy. For example, mypy base classes may be bound during compilation (or program loading, before evaluation), unlike Python.

This answer relates to PyPy as a Python implementation. See also the answer related to RPython below.

The main difference between mypy and PyPy is the availability of static typing in mypy. Also while PyPy focuses on efficiency, mypy addresses many concerns, including efficiency, scalability to large and complex projects, maintainability and programmer productivity.

PyPy is an implementation of Python that conforms closely to the main Python implementation, more closely than mypy will. Even though mypy aims to support most Python features at some point, mypy may make changes to features that are in conflict with other goals of mypy (such as support of static typing and high runtime efficiency), whereas PyPy tries to preserve the original Python semantics. Still, many programs written with CPython in mind have to be modified to work properly with PyPy. (See also to the answer to the question above).

Like PyPy, mypy also will also have a new, efficient virtual machine, but it's design will be different from PyPy. Statically typed mypy code is expected to often be significantly more efficient than PyPy; this includes both peak performance and program startup speed. Many developers have reported that PyPy only gives them modest performance gains, or none at all. Dynamically typed mypy code also has the potential to run more efficiently than PyPy due differences in semantics. However, current mypy performance estimates are based on early prototypes, so take them with a grain of salt.

Cython is a variant of Python that supports compilation to efficient C modules. It is an extension to CPython that can give major speedups to certain classes of programs; mypy is a major rethink of the entire language and the implementation. Mypy differs in the following aspects, among others:

• Mypy will have new virtual machine that allows speeding up all major parts of the implementation, including standard libraries and the garbage collector. Cython uses the normal Python VM.
• Mypy will support efficient multithreading without the GIL for a wide range of programs. Cython programs can explicitly release the GIL, but manipulating Python objects is not possible when the GIL is released.
• Mypy has a different set of type system features. (Feature-by-feature analysis will be added soon.)
• The mypy syntax is arguably simpler and more compact (no cdef, etc.) for statically typed code.
• Cython supports accessing C functions directly and many features are defined in terms of translating them to C. Mypy is not bound to any particular target language and can support both C and Java backends, for example. However, accessing C library functionality in mypy will not be as easy as in Cython.

RPython and Shed Skin are basically statically typed subsets of Python. Mypy does the following important things differently:

• Mypy will support both static and dynamic typing. When using dynamic typing, mypy is very similar to Python. Dynamically typed and statically typed code can be freely mixed and can interact seamlessly.
• Mypy will support fast and modular type checking. Both RPython and Shed Skin use whole-program type inference which is very slow, does not scale well to large programs and often produces confusing error messages. Mypy can support modularity since it only uses local type inference; static type checking depends on having type annotatations for functions signatures.
• Mypy will support introspection, dynamic loading of code and many other dynamic language features. RPython and Shed Skin only support a restricted Python subset without several of these features.
• Mypy will support user-defined generic types.

Any help is much appreciated! Contact the developers if you would like to contribute. Any help related to development, design, publicity, documentation, testing, web site maintenance, financing, etc. can be helpful. You can learn a lot by contributing, and anybody can help, even beginners!