C++ vs Java vs Python: a first impression

Executive Summary:

I am doing my final year in jaya engg college,as all engineering i has has the confusion of studying what programming language,so most popular language are c/c++,java apart from this we have python . I am not a fan of a particular language (I almost said 'fanboy') but thats a bit inflammatory). I just want to write useful programs and have fun doing it. I know C++ and Java pretty well. I did some beginner work in Python. I then came up with the following conclusions. But before you flame, read the whole article.

Go right to the side-by-side comparison

• C++ vs Java
  Java garbage collection is the big productivity gain
  Java is significantly slower than C++
  C++ is (much) harder to code correctly than any of the others
• Java vs Python
  Python interpreted execution and dynamic typing are big productivity gains over Java.
  Python are slower than Java
  Python programs need less extraneous scaffolding (cleaner code)
  There are two important tradeoffs : [interpreted vs. compiled] and [static vs. dynamic typing]
  

intro

When running various distributions of Linux, I always ran into the choice of KDE or GNOME. There are plenty of advantage on both sides, but there was no overriding authority. Then recently Linus Torvalds came out with a definitive opinion. He took the unequivocal position that KDE is best. Not that he is necessarily the final arbiter of user interfaces, but at least he provides a strong datapoint, since all the other opinions seem to come from biased sources, I can now pick KDE and feel better about it. Paraphrasing the old IBM criteria, 'no one was ever fired for following a Linus directive'. Heh, after all that it turned out that I wanted to use Ubuntu which works best with GNOME so I ended up with that for now. So 'most practical' won out over 'best'.

In the meantime I realized I needed to learn a new language and the current buzz is Python. Again I couldn't find a definitive answer of which one is best. From all the buzz, I came up with a vague impression that python is more pure and is set to win in the long run. And Google uses Python, which is a significant datapoint. They aren't idiots over there.

To see what I could figure out myself, I decided to code up something in Java, then port it to Python and c++ and see how I felt about each, and try to identify where the big wins are for each language.

One caveat. If something significant is missing from a language, like garbage collection, then I don't want to hear a response that says "well, if you use XYZ unsupported library, or you do ABC convoluted technique, then you can do the same thing in [put language name here]". I am trying to evaluate the STANDARD here, since of course you can probably do anything in any language including assembler if you work hard enough. And the problem with using a nonstandard library is not just the extra integration work, it's that you are basing your code on something that may fall by the wayside later on and then you are stuck. Sometimes it's worth it but that has to be proven on a case-by-case basis as far as I am concerned.

the code

The attached code samples are implementations of a Red-Black tree algorithm adapted from descriptions in "Algorithms in C++", Sedgewick and "Introduction To Algorithms",Cormen/Leiserson/Rivest. I picked this because it was short but had some complexity.

code notes and disclaimers:

• commenting is sparser than usual to avoid obscuring code
• I probably made some convention errors in Python and Ruby due to ignorance of the proper idioms
• all these programs compile and/or run without warnings and output the same result
• I believe the programs to be correct. there may be bugs but if so they are in all 3 versions
• Java 5.0 SDK,Python 2.4, Ruby 1.8.3, C++ Microsoft Visual C++ 2005
• 
  
• side-by-side comparison
• Java implementation
• python implementation
• c++ implementation
  

  porting

  It was surprisingly easy to port the Java code to Python . A lot of it was regular expression search and replace, getting some naming conventions right and adapting to a few language differences. During the porting process, the two big gotchas I ran into were Python block indenting errors....

  Porting the Java code to C++ was much more a hassle. I attempted to make use of as much static type checking mechanisms as I could. In Java I used generics for the tree, and in C++ I used templates for the container and 'const' where appropriate. The big gotchas on porting to C++ were:

• The dichotomy between primitive types and objects in C++ is much more pronounced even than Java (and Java is worse off than Python). This dichotomy makes it hard to write a class that supports both primitives and objects. My implementation might need some fixups to work with objects rather than 'int'.
• Javaand Python all use a consistent reference only scheme to refer to objects which are always on the heap or equivalent. In C++, you can have a statically declared objects, a pointer to an object, or a reference to an object, each with features and limitations. A C++ 'reference' is not the same thing as a reference in the other languages. C++ really wants you to use pointers. These alternatives means that when you write something in C++ you have to come up with a consistent strategy for using the 3 types of object access, and your strategy might not be the same as what others prefer. There is 'more than one way to do it'.
• The lack of built-in mechanisms or even just conventions for operations that should be common across types means you have to make things up. Like converting a type to string representation. All the other languages have support of one kind or another but in C++ you have to make up your own convention
• Maybe its just me, but C++ always leaves you wondering what you might have done wrong. Its hard to tell. If you read Meyer's Effective C++ you see that there are numerous detailed infrastructure things like constructors and assignment operators that you have to get exactly right or things fail at runtime. C++ is really hard to get right, and I never feel totally secure that I did it properly

In my opinion (and I have written a lot of C++), use C++ only where you have to for compatibility or performance reasons, or where you arbitrarily decide that you would rather use C++ because its more fun because its harder. As Tom Cargill (a noted C++ guy) said, "If you think C++ is not overly complicated, just what is a protected abstract virtual base pure virtual private destructor and when was the last time you needed one?".

python block indenting

It took me a while to get my editor (JEdit) happy with Python and getting to not use tabs. Fortunately I never screwed up the file so bad that the code didn't work but I always had an unpleasant uncertainty about the indentation simmering in the background. Some (or all) of this may be prejudice. I really liked braces better than either Ruby Do-End or Python indenting, at least when I was coding. On the other hand, a properly indented Python file looks much much cleaner and is easier to read than any of the others because you don't need all the block closing symbols. However the explicit 'self' argument makes it look less clean than it could.

The visitor pattern

One thing I did differently in each language was try to adapt a 'Visitor' pattern (for traversing the tree) to the preferred idiom for each language. You could of course simply code up a Visitor class that is nearly identical for each language, but instead I did the following.

• Java : one scheme : an anonymous class implementing a predefined interface.
• Python : two schemes : a named class similar to Java and just a named function passed in as a parameter.

The Java and C++ approaches give you static type checking but takes a lot more cruft to get going. I found the Python named function parameter very convenient. But it doesn't carry any state so if you need state then you use a class. Surprisingly, I found the Ruby lambda easier to understand and implement than the Ruby 'block'. That is because my traversal algorithm is recursive, and the lambda just gets passed around as a parameter (like the python named function parameter). I didn't exploit the full potential of a lambda closure.

The Ruby block scheme (pun not intended) requires some tricky syntax in the recursive calls, and I could not find a good explanation of how to handle recursive use of blocks in the Ruby documentation. I found a single web hit with an example and after fiddling with it I got it to work. I think I understand them now but it is still a bit fuzzy. I mean, I know what to do now but it takes some concentration to figure out what exactly is happening and why the code looks like it does. I found that viewing a Ruby block as a co-routine (per the documentation) and not as a subroutine to be the best way to understand the whole thing.

All that said about the Visitors, I am a Python/Ruby novice so possibly I did things the hard way :)

interpreted vs. compiled

There has always been this tradeoff. In fact, the Python/Ruby vs. Java performance controversy sounds a lot like the C/Assembler/Forth discussions in the embedded systems world of the early 1980's. Forth was interpreted, it didn't need a compile cycle and it had (supposedly) productivity enhancing features that C didn't have. Development cycles were much shorter with Forth. Performance was not as fast as C or assembler but was close. The drawback of Forth was the weirdness of the language. C won out and Forth went to the dark corner of mostly forgotten languages.

Interpreted languages give you a much quicker development cycle, especially on big programs. There is no doubting that. Its simply a tradeoff of execution speed vs. productivity. Some applications need the speed. I think it is a premature optimization to say the "I like C/Java better than Python/Ruby because they execute faster". Interpreted is better if you can get it. When I was testing the code I experienced the advantage of interpreted. I didn't really measure performance but other sources show the differences. But since Python/Ruby seem to interface to C/C++ pretty readily, I would be very comfortable working in the interpreted world and descending into the netherworld of compiled C/C++ when required. Yes, Python is actually compiled for a VM but you don't have an explicit compile operation so it acts to the user like an interpreted language.

static vs. dynamic type checking

Ok, I like the productivity increase provided by dynamic typing because it eliminates a lot of scaffolding. I found it quite interesting to see errors pop out at runtime that would normally be compile time in C++/Java. These runtime errors were obviously influenced by the paths taken in the test program (or how far it got before it barfed). For a given run, I clearly wasn't seeing all the instances of this class of errors as I would have with static type checking.

Coming from a statically typed language background, my gut says that dynamic typing creates a risk. The Python/Ruby bloggers say that if you just unit test properly, then there is no problem. Brucke Eckel has a well reasoned essay on the issue. I would argue that expecting unit test to catch typing errors has two issues:

• in a really big system, its hard to test exhaustively
• testing for type correctness makes the programmer do the work that a computer could do

We static typers may be wrong. I had a similar experience moving from PVCS to Subversion version control. Oh, My, God, no locking? The code will be completely ruined in a week. But it turned out to be a non-issue and Subversion added so much less friction to the development cycle that productivity was improved maybe 10%. The collective good experience overrode the predjudice based theoretical 'proof'. The same argument can be made for dynamic vs. static typing.

I wouldn't mind a separate 'lint' tool for Python/Ruby (is it possible?). I use lint for C/C++ religiously. The whole compiled language community is moving towards more static type checking (Java Generics, for example) rather than less. Are they all idiots? (don't answer that)

My conclusion is that dynamic 'duck' typing is more productive, more pleasant, gives cleaner looking code but it incurs a risk that you will get a runtime type exception in your application at some later date. The risk is there, quit denying it.

the results

These results are meant to cover issues I noticed in the porting/testing I did. Not an overall evaluation. If I mention stuff that I didn't run into first hand, then throw that out.

C++ vs Java

1. Garbage collection is THE big win for Java.
2. Java simplicity over C++ complexity is a big win for Java.
3. C++ is much harder to write and get right than Java or any of the other choices
4. C/C++ is way faster than Java
5. Language scaffolding requirements are similar for both
6. C/C++ is the only way to go for low level systems programming.

Java vs Python/Ruby

1. interpreted vs. compiled is a big productivity win for Python/Ruby
2. dynamic typing is a big productivity win for Python/Ruby
3. Java is way faster than Python or Ruby
4. minimal scaffolding is a big productivity win for Python/Ruby. Makes programming more pleasant not to have to build all the infrastructure.
5. mostly first class functions a big win for Python/Ruby.
6. built-in lists/arrays and hashes/dictionaries a big win over Java [] and library based collections. Java 5.0 fixes some of this but in Java collections still seem tacked on rather than integrated.
7. dynamic code loading in Python/Ruby is a big win. Yes you can do it in Java but again, the cruft.
8. Ruby OO completeness over Java dichotomy between primitive types vs. objects is a big win for Ruby, less so for Python.
9. There is some weirdness in Python and Ruby lexical scoping of names. The documentation for each has several warnings about edge cases where names don't bind in the expected way. This gives me a queasy feeling although in practice it may not matter. Another win for static type checking.
10. Java 'Comparable' interface ugly compared to Python/Ruby built in comparison mechanisms that require only that a single function be implemented to get the full set of comparison operators. An example of excess Java scaffolding.
11. lack of multiline comments in Python/Ruby was annoying

TRADEOFFS

1. static typing is a correctness win for Java, especially with Generics in 5.0. The C++/Java trend is toward stronger static type checking, not less
2. dynamic typing is a productivity win for Python/Ruby at the cost of some risk
3. interpreted vs. compiled trades off execution speed for shorter development cycles.

Python vs Ruby

none of these are that important

PYTHON WINS

1. Ruby's compiler/runtime error messages were mostly 'syntax error' with no help. in many cases almost useless
2. Why does Ruby use rescue/ensure when the rest of the world has settled on try/catch/finally? I mean, its an arbitrary choice so why not follow the general convention?
3. Once the indentation is correct, a Python program is the cleanest looking

RUBY WINS

1. somewhat uneasy over Python indenting vs. Ruby explicit 'end'. probably a predjudice.
2. Python requirement for explicit 'self' parameter to methods and instance variable access is very annoying
3. Ruby OO completeness is a win over Python.

EQUIVALENT

1. Ruby blocks/lambda/yield seemed more or less equivalent (to me) to Python's named class or function. Didn't seem a big win to be able to write an anonymous function inline. In fact, one could argue that anonymous classes/functions/lambdas reduce testability because they can't be tested independently of the containing code. But on the other hand I wasn't using lambdas in the most complete sense, in which they can act on the containing environment in a way that a named function can't.

A final thought on C++. To me the C++ Standard Template Library is distinguished from the other language libraries in that it seems to be much more mathematically thought out. The containers and algorithms in the STL all have explicit runtime complexity guarantees. There seems to be much more computer science in the STL than in the other language libraries. Java is sort of like that, whereas Ruby and Python libraries seem much more ad-hoc. That probably has a lot to do with their open community driven approach to libraries. I really like how the STL was thought out and designed.

conclusion

Java is more productive than C/C++. Use C/C++ only when speed or bare metal access is called for. Python/Ruby is more productive than Java and more pleasant to code in. There is a big question on static vs. dynamic typing. I contend that static typing has to be better for the purposes of program correctness, but the required cruft reduces productivity. If actual practice in large systems shows that in fact runtime typing errors don't occur often and are worth the productivity tradeoff, then I will bow to dynamic typing. I can't come up with a definitive answer to Python vs. Ruby. They seem very equivalent. Would choose based on practicality in a given situation. My general feeling was that Python annoyed me in ways that Ruby didn't, but I think those annoyances would disappear if I was using Python all the time.

Crap, the whole point was to pick Python or Ruby, but I am back where I started.

Ok, FLAME ON

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