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Java PHP Python -- Which is "Faster In General"?

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Java PHP Python -- Which is "Faster In General"?

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Sigh. What a difficult question. There are numerous incarnations on StackOverflow. All nearly unanswerable. The worst part is questions where they add the "in general" qualifier. Which is "faster in general" is essentially impossible to answer. And yet, the question persists.

There are three rules for figuring out which is faster.

And there are three significant problems that make these rules inescapable.

Rule One. Languages don't have speeds. Implementations have speeds.

Info on benchmarking. The idea of a benchmark is to have a single, standard suite of source code, which can be used to compare compilers, run-time libraries or hardware.

Having a standard suite of source is essential because it provides a basis for comparison. A single benchmark source is the fixed reference. We don't compare the top of the Empire State Building with the top of the Stratosphere in Las Vegas without specifying whether we care about height above the ground or height above sea level. There has to be some fixed point of comparison, some common attribute, or the measurements devolve into mere numerosity.

Once we have a basis for comparison (one single body of source code), the other attributes are degrees of freedom; the measurements we make will include the other attributes. This will allow a rational statement of what the experimental results where. We can then compare these various free attributes against each other. For details look at something like the Java Micro Benchmark.

Rule Two. Statistics Aren't a Panacea.

The reason there's no "in general" comparison among languages is because there are too many degrees of freedom to make any kind of rational comparison. We can make irrational comparisons, but that's the trap of numerosity -- throwing numbers around. 1250 vs. 1149, 1300 vs. 3177. What do they mean? Height above ground? Height above sea level? What's being measured?

There's a huge problem with claiming that statistics will yield an answer to which language implementation is faster "in general". We need some population that we can sample and measure. Problem 1: What the population are we measuring? It can't be "programs": we can't compare grep against Apache httpd. Those two programs have almost no common features.

What makes the population of programs difficult to define is the language differences. If we're trying to compare PHP, Python and Java, we need to find a program which somehow -- magically -- is common across all three languages.

The Basis For Comparison

Finding common programs degenerates into Problem 2: what programs could be comparable? For example, we have the Tomcat application, written in Java. We wouldn't want to write Tomcat in Python (since Tomcat is a Java Servlet container). We could probably write something Tomcat-like in PHP, but why try? So we can't just grab programs randomly.

At this point, we devolve to subjectivity. We need to find some kind of problem domain in which these languages overlap. This gets icky. Clearly, big servers aren't a good problem domain. Almost as clearly, command-line applications aren't the best idea. PHP does run from the command-line, but it's always contrived-looking because it doesn't exploit PHP's strengths. So we wind up looking at web applications because that's where PHP excels.

Web applications? Subjective? Correct. PHP is a language plus a web application framework bundled together. Java and Python -- by themselves -- are just languages and require a framework. Which Java (and Python) framework is identical to PHP's framework? Spring, Struts, Django, Pylons? None of these reflects a code base that's even remotely similar. Maybe Java JSP is similar enough to PHP. For Python there are several implementations. Sigh.

Crappy Program Problem

We can't easily compare programs because we're really comparing implementations of an algorithm. This leads to Problem 3: we picked a poor algorithm or did a lousy job of implementing it in the target language.

In order to be "comparable", we don't want to exploit highly-optimized or unique features of a language. So we tried to be generic. This is fraught with risks.

For example, Java and PHP don't have list comprehensions. Do we forbid them from our Python behchmark? In Python, everything is a reference, values cannot be copied. If we pick an algorithm implementation which depends on copying objects, Java may appear to excel. If we pick an algorithm implementation which depends on sharing references, Python may appear to excel.

Somehow we have to get past language differences and programmer mistakes. What to do?

Synthetic Benchmarks

Since we can't easily find comparable programs -- as whole programs -- we're left with the need to create some kind of benchmark based on language primitives. Statements or expressions or something. We can try to follow the Whetstone/ Dhrystone approach of analyzing a bunch of programs to find the primitive constructs and their relative frequency.

Here's the plan. We'll take 100 PHP programs, 100 Java programs and 100 Python programs and analyze them to find the relative frequency of different kinds of statements. What then?

The goal is to create one source that reflects the statements actually used in the 300 programs we analyzed. In three different languages. Hmmm... Okay. We'll need to create a magical mapping among the statement constructs in the three languages. Well, that's hard. The languages aren't terribly comparable. A Python expression using a List Comprehension is the same thing a multi-statement Java loop. Rats.

The languages aren't very comparable at the statement level at all. And if we force them to be comparable, we're not comparing real programs, but an artificial mapping.

Virtual Machine Benchmarks

Since we can't compare the languages at the program level or the statement level, what's left? Clearly, the underlying interpreter is what we care about.

We're really comparing the Java Virtual Machine, the PHP interpreter and the Python interpreter. That's what we really care about.

And life is simple because we can compare Java, The Project Zero PHP Interpreter based on the JVM and Jython. We can look at "compiled" PHP, Java Class Files and Python .PYC files to find the VM primitives used by each language and then -- what? Compare the run-time of the various VM primitives? No, that's silly, since the run-times are all JVM run-times.

What We're Left With

The very best we can can do is to compare the statistical distribution of the VM instructions created by Java, PHP or Jython compilers. We could note that maybe PHP or Python uses too many "slow" VM instructions, where Java used more "fast" VM instructions. That would be an "in general" comparison. Right?

See? You can measure anything.

In this case, the compiler itself is a degree of freedom. Sadly, we're not comparing languages "in general". We're comparing the bytecodes created by various compilers. We're actually comparing compilers and compiler optimizations of the bytecode. Sigh.

That's not what we were hoping for. We were hoping for some kind of "in general" comparison of the language, not the JVM compiler.

Java has pretty sophisticated optimization. Python, however, eschews optimization. PHP has it's own weird issues. See this paper from Rob Nicholson from the CodeZero project on how to implement PHP in the JVM. PHP doesn't fit the JVM as well as Python does. So there's a weird bias.

Rule Three. Benchmarking Is Hard.

There is no "in general" comparison of programming languages. All that we can do is benchmark something specific.

It works like this.

  1. Stop quibbling about language performance "in general".
  2. Find something specific and concrete we plan to implement.
  3. Actually write the performance-critical piece in Java, PHP, Python, Ruby, whatever. Yes. Build it several times. Really. We don't want to use "language-independent" or "common" features. We want to optimize ruthlessly -- use the language the way it was meant to be used. -- use the various unique-to-the language features correctly and completely.
  4. Actually run the performance-critical piece to get actual timings.
  5. Since run-time libraries and hardware are degrees of freedom, we have to use multiple run-time libraries, multiple compiler optimization settings and multiple hardware configurations to make a proper decision on which language to use for our specific problem.

Now we know something about our specific problem domain and the available languages. That's the best we can do.

We can only compare a specific problem, with a specific algorithm. That's the basis for all benchmark comparisons. Since each implementation was well-done and properly optimized, the degree of freedom is the language -- and the run-time implementation of that language -- and the selected OS and hardware.

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