Choosing Java vs Python on Google App Engine

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Choosing Java vs Python on Google App Engine

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 Java bytecode regardless of the languages used to write that code. Which language will give better performance and more power? Please advise. Thank you!


By “power” I mean better expandability and inclusion of available libraries outside the framework. Python allows only pure Python libraries, though.

Asked By: Viet


Answer #1:

I’m biased (being a Python expert but pretty rusty in Java) but I think the Python runtime of GAE is currently more advanced and better developed than the Java runtime — the former has had one extra year to develop and mature, after all.

How things will proceed going forward is of course hard to predict — demand is probably stronger on the Java side (especially since it’s not just about Java, but other languages perched on top of the JVM too, so it’s THE way to run e.g. PHP or Ruby code on App Engine); the Python App Engine team however does have the advantage of having on board Guido van Rossum, the inventor of Python and an amazingly strong engineer.

In terms of flexibility, the Java engine, as already mentioned, does offer the possibility of running JVM bytecode made by different languages, not just Java — if you’re in a multi-language shop that’s a pretty large positive. Vice versa, if you loathe Javascript but must execute some code in the user’s browser, Java’s GWT (generating the Javascript for you from your Java-level coding) is far richer and more advanced than Python-side alternatives (in practice, if you choose Python, you’ll be writing some JS yourself for this purpose, while if you choose Java GWT is a usable alternative if you loathe writing JS).

In terms of libraries it’s pretty much a wash — the JVM is restricted enough (no threads, no custom class loaders, no JNI, no relational DB) to hamper the simple reuse of existing Java libraries as much, or more, than existing Python libraries are similarly hampered by the similar restrictions on the Python runtime.

In terms of performance, I think it’s a wash, though you should benchmark on tasks of your own — don’t rely on the performance of highly optimized JIT-based JVM implementations discounting their large startup times and memory footprints, because the app engine environment is very different (startup costs will be paid often, as instances of your app are started, stopped, moved to different hosts, etc, all trasparently to you — such events are typically much cheaper with Python runtime environments than with JVMs).

The XPath/XSLT situation (to be euphemistic…) is not exactly perfect on either side, sigh, though I think it may be a tad less bad in the JVM (where, apparently, substantial subsets of Saxon can be made to run, with some care). I think it’s worth opening issues on the Appengine Issues page with XPath and XSLT in their titles — right now there are only issues asking for specific libraries, and that’s myopic: I don’t really care HOW a good XPath/XSLT is implemented, for Python and/or for Java, as long as I get to use it. (Specific libraries may ease migration of existing code, but that’s less important than being able to perform such tasks as “rapidly apply XSLT transformation” in SOME way!-). I know I’d star such an issue if well phrased (especially in a language-independent way).

Last but not least: remember that you can have different version of your app (using the same datastore) some of which are implemented with the Python runtime, some with the Java runtime, and you can access versions that differ from the “default/active” one with explicit URLs. So you could have both Python and Java code (in different versions of your app) use and modify the same data store, granting you even more flexibility (though only one will have the “nice” URL such as — which is probably important only for access by interactive users on browsers, I imagine;-).

Answered By: Alex Martelli

Answer #2:

Watch this app for changes in Python and Java performance:
(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.

Answered By: Richard Watson

Answer #3:

Based on experience with running these VMs on other platforms, I’d say that you’ll probably get more raw performance out of Java than Python. Don’t underestimate Python’s selling points, however: The Python language is much more productive in terms of lines of code – the general agreement is that Python requires a third of the code of an equivalent Java program, while remaining as or more readable. This benefit is multiplied by the ability to run code immediately without an explicit compile step.

With regards to available libraries, you’ll find that much of the extensive Python runtime library works out of the box (as does Java’s). The popular Django Web framework ( is also supported on AppEngine.

With regards to ‘power’, it’s difficult to know what you mean, but Python is used in many different domains, especially the Web: YouTube is written in Python, as is Sourceforge (as of last week).

Answered By: Judy2K

Answer #4:

June 2013: This video is a very good answer by a google engineer:

TLDR; is:

  • Pick the language that you and your team is most productive with
  • If you want to build something for production: Java or Python (not Go)
  • If you have a big team and a complex code base: Java (because of static code analysis and refactoring)
  • Small teams that iterate quickly: Python (although Java is also okay)
Answered By: Bijan

Answer #5:

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.

Answered By: Tom

Answer #6:

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.

Answered By: Paul

Answer #7:

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.

Answered By: Brian Agnew

Answer #8:

I’ve been amazed at how clean, straightforward, and problem free the Python/Django SDK is. However I started running into situations where I needed to start doing more JavaScript and thought I might want to take advantage of the GWT and other Java utilities. I’ve gotten just half way through the GAE Java tutorial, and have had one problem after another: Eclipse configuration issues, JRE versionitis, the mind-numbing complexity of Java, and a confusing and possibly broken tutorial. Checking out this site and others linked from here clinched it for me. I’m going back to Python, and I’ll look into Pyjamas to help with my JavaScript challenges.

Answered By: mjhm

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