5 ms·

> the performance available when using Python The honest thing would be to say "yes, Python is slow, but you can use some fast modules written in C". You can

> the performance available when using Python The honest thing would be to say "yes, Python is slow, but you can use some fast modules written in C". You can even go farther and say that if you're going to use Python for non-trivial projects you better learn C (or some C generator like Cython).

pekk 13y ago
The ubiquitous implementation of Python is written in C. So, for example, the list-sorting algorithm is written in C. If you want to eliminate C from the equation completely, I guess we now have to rewrite the C sort algorithm to be slower to show off "how slow Python really is" in isolation from C. But this is a benchmark of a fantasy world. That Python isn't the real Python we use. In the real world Python is not in isolation from C, by design, and this works well.
- stefantalpalaru 13y ago
  No. The language in which the interpreter is written is not the point here. The point is the interpretation overhead of the best (or "only" if we're being pedantic since Python is de facto whatever CPython does) interpreter. That overhead is the price you pay for using Python so it's what matters when you compare languages for CPU bound projects.
  - maxerickson 13y ago
    It's a little strange to insist that CPython is the only Python interpreter and then reject all the C libraries that you can access for performance (because those C libraries are the big loss when shifting from CPython 2.7 to Pypy). I think interpreter overhead and language ecosystem performance are probably best looked at as separate questions.
calpaterson 13y ago
You only need to know C to get fast Python code for a tiny minority of situations. For one, most interesting C wrappers have already been written and second, the majority of C code you will use in normal business software contexts isn't used by FFI. For a normal web application the request flow goes something like: nginx (WSGI) -> uwsgi -> web application code -> psycopg2 driver -> postgres. Only the web application part is written in Python, so for practical purposes you actually have a C stack that uses Python for business logic. Loads of libraries, from json parsers to templating libraries include optional C code for speedups.
- stefantalpalaru 13y ago
  Guess where the bottleneck is when you need to generate a dynamic page for each and every request.
  - calpaterson 13y ago
    In the database? About 90% of bottlenecks boil down to something along the lines of "this query is slow" or "there are a lot of queries here". This has been true since the dotcom boom
    - stefantalpalaru 13y ago
      Guess again. All these benchmarks use the same database: http://www.techempower.com/benchmarks/#section=data-r8&hw=i7&test=query http://www.techempower.com/benchmarks/#section=data-r8&hw=i7...
      - calpaterson 13y ago
        Those are fairly uselss synthetic benchmarks. How many apps do you have in production that are purely key value stores with a random access profile? And if you really have these performance characteristics (hint: it's unlikely), maybe it's time to get a edge caching CDN? Cutting 400ms of latency as you cross from Amsterdam to California is going to be the easiest performance boost you ever got
        tethis 13y ago
        The point of these benchmarks is that, given the same database and the same queries, languages have different performance profiles. If I build an application in Java and Python, with the same database backend, running the exact same set of SQL queries, the Java application should perform better.
        BugBrother 13y ago
deleted 13y ago
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pekk 13y ago

The ubiquitous implementation of Python is written in C. So, for example, the list-sorting algorithm is written in C. If you want to eliminate C from the equation completely, I guess we now have to rewrite the C sort algorithm to be slower to show off "how slow Python really is" in isolation from C. But this is a benchmark of a fantasy world. That Python isn't the real Python we use. In the real world Python is not in isolation from C, by design, and this works well.

stefantalpalaru 13y ago
No. The language in which the interpreter is written is not the point here. The point is the interpretation overhead of the best (or "only" if we're being pedantic since Python is de facto whatever CPython does) interpreter. That overhead is the price you pay for using Python so it's what matters when you compare languages for CPU bound projects.
- maxerickson 13y ago
  It's a little strange to insist that CPython is the only Python interpreter and then reject all the C libraries that you can access for performance (because those C libraries are the big loss when shifting from CPython 2.7 to Pypy). I think interpreter overhead and language ecosystem performance are probably best looked at as separate questions.

calpaterson 13y ago

You only need to know C to get fast Python code for a tiny minority of situations. For one, most interesting C wrappers have already been written and second, the majority of C code you will use in normal business software contexts isn't used by FFI. For a normal web application the request flow goes something like: nginx (WSGI) -> uwsgi -> web application code -> psycopg2 driver -> postgres. Only the web application part is written in Python, so for practical purposes you actually have a C stack that uses Python for business logic. Loads of libraries, from json parsers to templating libraries include optional C code for speedups.

stefantalpalaru 13y ago
Guess where the bottleneck is when you need to generate a dynamic page for each and every request.
- calpaterson 13y ago
  In the database? About 90% of bottlenecks boil down to something along the lines of "this query is slow" or "there are a lot of queries here". This has been true since the dotcom boom
  - stefantalpalaru 13y ago
    Guess again. All these benchmarks use the same database: http://www.techempower.com/benchmarks/#section=data-r8&hw=i7&test=query http://www.techempower.com/benchmarks/#section=data-r8&hw=i7...
    - calpaterson 13y ago
      Those are fairly uselss synthetic benchmarks. How many apps do you have in production that are purely key value stores with a random access profile? And if you really have these performance characteristics (hint: it's unlikely), maybe it's time to get a edge caching CDN? Cutting 400ms of latency as you cross from Amsterdam to California is going to be the easiest performance boost you ever got
      - tethis 13y ago
        The point of these benchmarks is that, given the same database and the same queries, languages have different performance profiles. If I build an application in Java and Python, with the same database backend, running the exact same set of SQL queries, the Java application should perform better.
        BugBrother 13y ago

deleted 13y ago

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