https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
Andrei suggested posting more widely.

On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
> https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
> Andrei suggested posting more widely.

That's flaimbait:

«Many really popular websites use Python. But why is that? Doesn't it affect the performance of the website?»

No. Really popular websites use pre-generated content / front end caches / CDNs or wait for network traffic from distributed databases.

On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
> https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
> Andrei suggested posting more widely.

I was just writing some R code yesterday after playing around with D for a couple weeks. I accomplished more in an afternoon of R coding than I think I had in like a month's worth of playing around with D. The same is true for python.

On Wednesday, 14 October 2015 at 14:32:00 UTC, jmh530 wrote:
> On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
>> https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
>> Andrei suggested posting more widely.
>
> I was just writing some R code yesterday after playing around with D for a couple weeks. I accomplished more in an afternoon of R coding than I think I had in like a month's worth of playing around with D. The same is true for python.

As someone who uses both D and Python every day, I find that - once you are proficient in both - initial productivity is higher in Python and then D starts to overtake as a project gets larger and/or has stricter requirements. I hope never to have to write anything longer than a thousand lines in Python ever again.

On Wednesday, 14 October 2015 at 14:48:22 UTC, John Colvin wrote:
> On Wednesday, 14 October 2015 at 14:32:00 UTC, jmh530 wrote:
>> On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
>>> https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
>>> Andrei suggested posting more widely.
>>
>> I was just writing some R code yesterday after playing around with D for a couple weeks. I accomplished more in an afternoon of R coding than I think I had in like a month's worth of playing around with D. The same is true for python.
>
> As someone who uses both D and Python every day, I find that - once you are proficient in both - initial productivity is higher in Python and then D starts to overtake as a project gets larger and/or has stricter requirements. I hope never to have to write anything longer than a thousand lines in Python ever again.

That's true until you need to connect to other systems.  There are countless clients built for other systems thats are used in real world applications.  With web development the Python code really just becomes glue nowadays and api's.  I understand D is faster until you have to build the clients for systems to connect.  We have an application that uses Postgres, ElasticSearch, Kafka, Redis, etc. This is plenty fast and the productivity of Python is more than D as the clients for Elasticsearch, Postgres and various other systems are unavailable or incomplete.  Sure D is faster but when you have other real world systems to connect to and time constraints on projects how can D be more productive or faster?  Our python code essentially becomes the API and usage of clients to other systems which handle a majority of the hardcore processing.  Once D gets established with those clients and they are battle tested then I will agree.  To me productivity is more than the language itself but also building real world applications in a reasonable time-frame.  D will get there but is nowhere near where Python is.

On Wednesday, 14 October 2015 at 15:25:22 UTC, David DeWitt wrote:
> On Wednesday, 14 October 2015 at 14:48:22 UTC, John Colvin wrote:
>> On Wednesday, 14 October 2015 at 14:32:00 UTC, jmh530 wrote:
>>> On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
>>>> https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
>>>> Andrei suggested posting more widely.
>>>
>>> I was just writing some R code yesterday after playing around with D for a couple weeks. I accomplished more in an afternoon of R coding than I think I had in like a month's worth of playing around with D. The same is true for python.
>>
>> As someone who uses both D and Python every day, I find that - once you are proficient in both - initial productivity is higher in Python and then D starts to overtake as a project gets larger and/or has stricter requirements. I hope never to have to write anything longer than a thousand lines in Python ever again.
>
> That's true until you need to connect to other systems.  There are countless clients built for other systems thats are used in real world applications.  With web development the Python code really just becomes glue nowadays and api's.  I understand D is faster until you have to build the clients for systems to connect.  We have an application that uses Postgres, ElasticSearch, Kafka, Redis, etc. This is plenty fast and the productivity of Python is more than D as the clients for Elasticsearch, Postgres and various other systems are unavailable or incomplete.  Sure D is faster but when you have other real world systems to connect to and time constraints on projects how can D be more productive or faster?  Our python code essentially becomes the API and usage of clients to other systems which handle a majority of the hardcore processing.  Once D gets established with those clients and they are battle tested then I will agree.  To me productivity is more than the language itself but also building real world applications in a reasonable time-frame.  D will get there but is nowhere near where Python is.

Python is inherently quite good for glue and has great library support, so if that's the majority of your work then Python is a good choice. On the other hand, there's plenty of programming out there that isn't like that.

On Wednesday, 14 October 2015 at 15:31:49 UTC, John Colvin wrote:
> On Wednesday, 14 October 2015 at 15:25:22 UTC, David DeWitt wrote:
>> On Wednesday, 14 October 2015 at 14:48:22 UTC, John Colvin wrote:
>>> On Wednesday, 14 October 2015 at 14:32:00 UTC, jmh530 wrote:
>>>> On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
>>>>> https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
>>>>> Andrei suggested posting more widely.
>>>>
>>>> I was just writing some R code yesterday after playing around with D for a couple weeks. I accomplished more in an afternoon of R coding than I think I had in like a month's worth of playing around with D. The same is true for python.
>>>
>>> As someone who uses both D and Python every day, I find that - once you are proficient in both - initial productivity is higher in Python and then D starts to overtake as a project gets larger and/or has stricter requirements. I hope never to have to write anything longer than a thousand lines in Python ever again.
>>
>> That's true until you need to connect to other systems.  There are countless clients built for other systems thats are used in real world applications.  With web development the Python code really just becomes glue nowadays and api's.  I understand D is faster until you have to build the clients for systems to connect.  We have an application that uses Postgres, ElasticSearch, Kafka, Redis, etc. This is plenty fast and the productivity of Python is more than D as the clients for Elasticsearch, Postgres and various other systems are unavailable or incomplete.  Sure D is faster but when you have other real world systems to connect to and time constraints on projects how can D be more productive or faster?  Our python code essentially becomes the API and usage of clients to other systems which handle a majority of the hardcore processing.  Once D gets established with those clients and they are battle tested then I will agree.  To me productivity is more than the language itself but also building real world applications in a reasonable time-frame.  D will get there but is nowhere near where Python is.
>
> Python is inherently quite good for glue and has great library support, so if that's the majority of your work then Python is a good choice. On the other hand, there's plenty of programming out there that isn't like that.

I agree but the quora question ask why it is popular despite being slow and this is the reason.  If you are doing tasks that are computationally expensive in Python then yes it will be slow but Python is popular largely because of their web frameworks and support.  Even something like Pandas is good enough for most peoples data sets.  But still I think most people use it as glue and if they need something done they can pass it off to something else to do the "real" work.  If this wasn't the case then Python would not be as popular.  You pick the right tool for the right job maybe D and maybe Python and this doesn't mean your results will be slow.

On Wednesday, 14 October 2015 at 05:42:12 UTC, Ola Fosheim Grøstad wrote:
> On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
>> https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
>> Andrei suggested posting more widely.
>
> That's flaimbait:
>
> «Many really popular websites use Python. But why is that? Doesn't it affect the performance of the website?»
>
> No. Really popular websites use pre-generated content / front end caches / CDNs or wait for network traffic from distributed databases.

For a long time, Ola, I am done discussing with you.  But I would ask you to take more responsibility for the effect of you words.  The piece you quote is from the question, and not from what I wrote.  You refer to it as flame bait, and don't make this clear in what you write.

On Wednesday, 14 October 2015 at 15:54:49 UTC, Laeeth Isharc wrote:
> For a long time, Ola, I am done discussing with you.  But I would ask you to take more responsibility for the effect of you words.  The piece you quote is from the question, and not from what I wrote.  You refer to it as flame bait, and don't make this clear in what you write.

The question as phrased is flame bait and trolling because the answer is obvious and generates lots of noise for no reason.

Latency is not affected by a script that does very little work compared to all the other causes for latency in a complex architecture. Including memcache access, database retrieval and compression.

What is affected by using Python over Go/D is the number of instances that run the service.

But for a well designed architecture up to 99% of the work is done by specialized infrastructure, implemented in Java/Erlang/C++, that often is too costly to develop for a single project. So you use ready-mades.

Low latency and scaling is a result of architecture, not brute force computation.

On Wed, 2015-10-14 at 14:48 +0000, John Colvin via Digitalmars-d-learn wrote:
> On Wednesday, 14 October 2015 at 14:32:00 UTC, jmh530 wrote:
> > On Tuesday, 13 October 2015 at 23:26:14 UTC, Laeeth Isharc wrote:
> > > https://www.quora.com/Why-is-Python-so-popular-despite-being-so-s
> > > low
> > > Andrei suggested posting more widely.
> > 
> > I was just writing some R code yesterday after playing around with D for a couple weeks. I accomplished more in an afternoon of R coding than I think I had in like a month's worth of playing around with D. The same is true for python.
> 
> As someone who uses both D and Python every day, I find that - once you are proficient in both - initial productivity is higher in Python and then D starts to overtake as a project gets larger and/or has stricter requirements. I hope never to have to write anything longer than a thousand lines in Python ever again.

The thing about Python is NumPy, SciPy, Pandas, Matplotlib, IPython, Jupyter, GNU Radio. The data science, bioinformatics, quant, signal provessing, etc. people do not give a sh!t which language they used, what they want is to get their results as fast as possible. Most of them do not write programs that are to last, they are effectively throw away programs. This leads them to Python (or R) and they are not really interested in learning anything else.

The fact that NumPy sort of sucks in terms of performance, isn't
noticed by them
as they get their results "fast enough" and a lot faster than
sequential Python. The fact that if they used Chapel or even D for
their compute intensive code they would rapidly discover that NumPy
sort of sucks never really occurs to these people as they are focussed
on the results not the means of achieving them.

Polyglot Python/D or Python/Chapel with Matplotlib is the way to go. But that really requires a D replacement for Pandas.

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