Choosing Java vs Python on Google App Engine

Question

Currently Google App Engine supports both Python & Java. Java support is less mature. However, Java seems to have a longer list of libraries and especially support for J

Answer 1

There's also project Unladen Swallow, which is apparently Google-funded if not Google-owned. They're trying to implement a LLVM-based backend for Python 2.6.1 bytecode, so they can use a JIT and various nice native code/GC/multi-core optimisations. (Nice quote: "We aspire to do no original work, instead using as much of the last 30 years of research as possible.") They're looking for a 5x speed-up to CPython.

Of course this doesn't answer your immediate question, but points towards a "closing of the gap" (if any) in the future (hopefully).

Answer 2

The beauty of python nowdays is how well it communicates with other languages. For instance you can have both python and java on the same table with Jython. Of course jython even though it fully supports java libraries it does not support fully python libraries. But its an ideal solution if you want to mess with Java Libraries. It even allows you to mix it with Java code with no extra coding.

But even python itself has made some steps forwared. See ctypes for example, near C speed , direct accees to C libraries all of this without leaving the comfort of python coding. Cython goes one step further , allowing to mix c code with python code with ease, or even if you dont want to mess with c or c++ , you can still code in python but use statically type variables making your python programms as fast as C apps. Cython is both used and supported by google by the way.

Yesterday I even found tools for python to inline C or even Assembly (see CorePy) , you cant get any more powerful than that.

Python is surely a very mature language, not only standing on itself , but able to coooperate with any other language with easy. I think that is what makes python an ideal solution even in a very advanced and demanding scenarios.

With python you can have acess to C/C++ ,Java , .NET and many other libraries with almost zero additional coding giving you also a language that minimises, simplifies and beautifies coding. Its a very tempting language.

Answer 3

Watch this app for changes in Python and Java performance:

http://gaejava.appspot.com/ (edit: apologies, link is broken now. But following para still applied when I saw it running last)

Currently, Python and using the low-level API in Java are faster than JDO on Java, for this simple test. At least if the underlying engine changes, that app should reflect performance changes.

Answer 4

I'm strongly recommending Java for GAE and here's why:

1. Performance: Java is potentially faster then Python.
2. Python development is under pressure of a lack of third-party libraries. For example, there is no XSLT for Python/GAE at all. Almost all Python libraries are C bindings (and those are unsupported by GAE).
3. Memcache API: Java SDK have more interesting abilities than Python SDK.
4. Datastore API: JDO is very slow, but native Java datastore API is very fast and easy.

I'm using Java/GAE in development right now.

Answer 5

As you've identified, using a JVM doesn't restrict you to using the Java language. A list of JVM languages and links can be found here. However, the Google App Engine does restrict the set of classes you can use from the normal Java SE set, and you will want to investigate if any of these implementations can be used on the app engine.

EDIT: I see you've found such a list

I can't comment on the performance of Python. However, the JVM is a very powerful platform performance-wise, given its ability to dynamically compile and optimise code during the run time.

Ultimately performance will depend on what your application does, and how you code it. In the absence of further info, I think it's not possible to give any more pointers in this area.

Answer 6

An important question to consider in deciding between Python and Java is how you will use the datastore in each language (and most other angles to the original question have already been covered quite well in this topic).

For Java, the standard method is to use JDO or JPA. These are great for portability but are not very well suited to the datastore.

A low-level API is available but this is too low level for day-to-day use - it is more suitable for building 3rd party libraries.

For Python there is an API designed specifically to provide applications with easy but powerful access to the datastore. It is great except that it is not portable so it locks you into GAE.

Fortunately, there are solutions being developed for the weaknesses listed for both languages.

For Java, the low-level API is being used to develop persistence libraries that are much better suited to the datastore then JDO/JPA (IMO). Examples include the Siena project, and Objectify.

I've recently started using Objectify and am finding it to be very easy to use and well suited to the datastore, and its growing popularity has translated into good support. For example, Objectify is officially supported by Google's new Cloud Endpoints service. On the other hand, Objectify only works with the datastore, while Siena is 'inspired' by the datastore but is designed to work with a variety of both SQL databases and NoSQL datastores.

For Python, there are efforts being made to allow the use of the Python GAE datastore API off of the GAE. One example is the SQLite backend that Google released for use with the SDK, but I doubt they intend this to grow into something production ready. The TyphoonAE project probably has more potential, but I don't think it is production ready yet either (correct me if I am wrong).

If anyone has experience with any of these alternatives or knows of others, please add them in a comment. Personally, I really like the GAE datastore - I find it to be a considerable improvement over the AWS SimpleDB - so I wish for the success of these efforts to alleviate some of the issues in using it.