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digitalmars.D.learn - OT: why do people use python when it is slow?

reply Laeeth Isharc <laeethnospam nospamlaeeth.com> writes:
https://www.quora.com/Why-is-Python-so-popular-despite-being-so-slow
Andrei suggested posting more widely.
Oct 13 2015
next sibling parent reply Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
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.
Oct 13 2015
next sibling parent reply Laeeth Isharc <nospamlaeeth nospamlaeeth.com> writes:
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.
Oct 14 2015
parent Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
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.
Oct 14 2015
prev sibling parent reply Mengu <mengukagan gmail.com> writes:
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.
really popular portals, news sites? yes. really popular websites? nope. like booking.com, airbnb.com, reddit.com are popular websites that have many parts which have to be dynamic and responsive as hell and they cannot use caching, pre-generated content, etc. using python affect the performance of your website. if you were to use ruby or php your web app would be slower than it's python version. and python version would be slower than go or d version.
Oct 14 2015
next sibling parent reply Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Wednesday, 14 October 2015 at 18:37:40 UTC, Mengu wrote:
 websites? nope. like booking.com, airbnb.com, reddit.com are 
 popular websites that have many parts which have to be dynamic 
 and responsive as hell and they cannot use caching, 
 pre-generated content, etc.
They can if they know what they are doing. E.g. Reddit can push indexes to their CDN (Cloudflare?) for commonly requested topics. There are many many layers and options for caching or delaying recomputation (e.g. eventual consistency strategies).
 using python affect the performance of your website.
No. You can do a lot of computation in Python in 10ms. Using Python over C++/Go affects the number of instances, not the performance. You don't handle one request at the time, so if you use Python you handle fewer concurrent requests than in Go. That is the only difference unless you are doing something real time (like a game server).
Oct 14 2015
parent Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Wednesday, 14 October 2015 at 18:55:28 UTC, Ola Fosheim 
Grøstad wrote:
 On Wednesday, 14 October 2015 at 18:37:40 UTC, Mengu wrote:
 websites? nope. like booking.com, airbnb.com, reddit.com are 
 popular websites that have many parts which have to be dynamic 
 and responsive as hell and they cannot use caching, 
 pre-generated content, etc.
They can if they know what they are doing. E.g. Reddit can push indexes to their CDN (Cloudflare?) for commonly requested topics.
And that is exactly what reddit does: « cache-control:max-age=0, must-revalidate cf-cache-status:HIT cf-ray:23558623ce9b231e-FRA » As you can see my request for /r/programming at reddit.com found a hit in their CDN cache Cloudflare... CDNs may allow explicit preloading and removal of outdated resources from caches (contrary to HTTP).
Oct 14 2015
prev sibling parent reply Chris <wendlec tcd.ie> writes:
On Wednesday, 14 October 2015 at 18:37:40 UTC, Mengu wrote:
 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.
really popular portals, news sites? yes. really popular websites? nope. like booking.com, airbnb.com, reddit.com are popular websites that have many parts which have to be dynamic and responsive as hell and they cannot use caching, pre-generated content, etc. using python affect the performance of your website. if you were to use ruby or php your web app would be slower than it's python version. and python version would be slower than go or d version.
Yep. This occurred to me too. Sorry Ola, but I think you don't know how sausages are made. Do you really think that all the websites out there are performance tuned by network programming specialists? You'd be surprised!
Oct 15 2015
parent reply Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Thursday, 15 October 2015 at 09:24:52 UTC, Chris wrote:
 Yep. This occurred to me too. Sorry Ola, but I think you don't 
 know how sausages are made.
I most certainly do. I am both doing backend programming and we have a farm... :-)
 Do you really think that all the websites out there are 
 performance tuned by network programming specialists? You'd be 
 surprised!
If they are to scale, then they have to pick algorithms and architectures that scale. This is commodity nowadays. You want to get as close to O(1) as possible for requests. This is how you build scalable systems. No point in having 1ms response time under low load and 10000ms response time when the incoming link is saturated. You'd rather have 100ms response under low load and 120ms response time when saturated + 99.9999% availability/uptime. Robustness and scaling costs latency, but you want acceptable and stable QoS, not brilliant QoS under low load and horrible QoS under high load. Scalable websites aren't designed like sportcars, they are designed like trains.
Oct 15 2015
parent reply Chris <wendlec tcd.ie> writes:
On Thursday, 15 October 2015 at 09:47:56 UTC, Ola Fosheim Grøstad 
wrote:
 On Thursday, 15 October 2015 at 09:24:52 UTC, Chris wrote:
 Yep. This occurred to me too. Sorry Ola, but I think you don't 
 know how sausages are made.
I most certainly do. I am both doing backend programming and we have a farm... :-)
Well, you know how gourmet sausages are made (100% meat), because you make them yourself apparently. But I was talking about the sausages you get out there ;) A lot of websites are not "planned". They are quickly put together to promote an idea. The code/architecture is not important at that stage. The idea is important. The website has to have dynamic content that can be edited by non-programmers (Not even PHP! HTML at most!). If you designed a website from a programming point of view first, you'd never get the idea out in time.
Oct 15 2015
next sibling parent Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Thu, 2015-10-15 at 10:00 +0000, Chris via Digitalmars-d-learn wrote:
=20
[=E2=80=A6]
 Well, you know how gourmet sausages are made (100% meat), because=20
 you make them yourself apparently. But I was talking about the=20
 sausages you get out there ;) A lot of websites are not=20
 "planned". They are quickly put together to promote an idea. The=20
 code/architecture is not important at that stage. The idea is=20
 important. The website has to have dynamic content that can be=20
 edited by non-programmers (Not even PHP! HTML at most!). If you=20
 designed a website from a programming point of view first, you'd=20
 never get the idea out in time.#
And most commercial websites selling things are truly appalling: slow performance, atrocious usability/UX. Who cares if the site is brilliantly tuned if it is unusable? --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Oct 15 2015
prev sibling parent Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Thursday, 15 October 2015 at 10:00:21 UTC, Chris wrote:
 about the sausages you get out there ;) A lot of websites are 
 not "planned". They are quickly put together to promote an idea.
They are WordPress sites... :-(
 If you designed a website from a programming point of view 
 first, you'd never get the idea out in time.
It's not that bad, but modelling data for nosql databases is a bigger challenge than getting decent performance from the code. There is another issue with using languages like Rust/C++/D and that is: if it crashes you loose all the concurrent requests, perhaps even without a reasonable log trace. What I'd want for handling requests is something less fragile where only the single request that went bad crash out. Pure Python and Java provide this property.
Oct 15 2015
prev sibling next sibling parent reply jmh530 <john.michael.hall gmail.com> writes:
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.
Oct 14 2015
parent reply John Colvin <john.loughran.colvin gmail.com> writes:
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.
Oct 14 2015
next sibling parent reply David DeWitt <dkdewitt gmail.com> writes:
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.
Oct 14 2015
next sibling parent reply John Colvin <john.loughran.colvin gmail.com> writes:
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.
Oct 14 2015
parent reply David DeWitt <dkdewitt gmail.com> writes:
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.
Oct 14 2015
parent Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Wednesday, 14 October 2015 at 15:49:20 UTC, David DeWitt wrote:
 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.
If it is slow then one can configure the load balancer to give more resources to that URL, or even run it on an insanely fast compute node with lots of memory for caching/memoing. Or just implement that single high frequency URL handler in a different language if it scaling up resources become costly.
 You pick the right tool for the right job maybe D and maybe 
 Python and this doesn't mean your results will be slow.
Yes, you pick the right tool for the job, and in a web context that means picking a tool that has infrastructure support and makes it possible to cover future unexpected needs in a cost efficient and timely manner where you offload as much maintenance costs as possible to the infrastructure maintainers rather than the application maintainers. I use AppEngine and Python is by far the most pleasant choice when choosing between Java, Go, Php and Python. For many very good reasons. But you can use whatever language you want for any particular URL so using Python is not a lock-in solution. Using Rust or D for web developement do imply lockin on many dimensions (infrastructure, libraries, people, maintenance) compared to Python, Java and Ruby.
Oct 14 2015
prev sibling parent Laeeth Isharc <spamnolaeeth nospamlaeeth.com> writes:
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.
Few thoughts: 1. It's easy to embed Python in your D applications. I do this for things like web scraping and when I want to write something quick to read simple XML (I just convert to JSON). 2. Of course there is a Redis client. Elasticsearch is an amazing product, but hardly requires much work to have a complete API. I made a start on this, and if I use Elasticsearch more then I'll have one done and will release it. I don't know the finer aspects of Postgres to know what is involved. 3. That raises a broader point, which is that it depends on the ultimate aim of your project and what it is about the right tradeoff between different things. It will ultimately be much more productive for me to do things in D for the reasons John alludes to. A little work to get started is neither here nor there in the major scheme of things. Adam Ruppe made the same point - it's not all that much work to put a foundation that suits you in place. You do it once (and maybe add things when something like Elasticsearch comes out), and that's it, apart from minor updates. The dollar expenditure on building these things is not enormous given the stakes involved for me. But that doesn't mean that you should get to the same answer, as it depends. 4. I am not sure that all web development is just glue, or will be going forward given what might be on the horizon, but time will tell. Laeeth.
Oct 15 2015
prev sibling parent reply Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
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=20
 wrote:
 https://www.quora.com/Why-is-Python-so-popular-despite-being-so-s
 low
 Andrei suggested posting more widely.
=20 I was just writing some R code yesterday after playing around=20 with D for a couple weeks. I accomplished more in an afternoon=20 of R coding than I think I had in like a month's worth of=20 playing around with D. The same is true for python.
=20 As someone who uses both D and Python every day, I find that -=20 once you are proficient in both - initial productivity is higher=20 in Python and then D starts to overtake as a project gets larger=20 and/or has stricter requirements. I hope never to have to write=20 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. --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Oct 14 2015
next sibling parent Chris <wendlec tcd.ie> writes:
On Wednesday, 14 October 2015 at 18:17:29 UTC, Russel Winder 
wrote:

 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.
Scary, but I agree with you again. In science this is exactly what usually happens. Throw away programs, a list here, a loop there, clumsy, inefficient code. And that's fine, in a way that's what scripting is for. The problems start to kick in when the same guys get the idea to go public and write a program that everyone can use. Then you have a mess of slow code (undocumented) in a slow language. This is why I always say "Use C, C++ or D from the very beginning" or at least document your code in a way that it can easily be rewritten in D or C. But well, you know, results, papers, conferences ... This is why many innovations live in an eternal Matlab or Python limbo.
Oct 15 2015
prev sibling parent Laeeth Isharc <laeeth nospamlaeeth.com> writes:
On Wednesday, 14 October 2015 at 18:17:29 UTC, Russel Winder 
wrote:
 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.
Russell, thanks for your thoughts - I appreciate it. What would a Pandas replacement look like in D?
Oct 17 2015
prev sibling next sibling parent reply data pulverizer <data.pulverizer gmail.com> writes:
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 am coming at D by way of R, C++, Python etc. so I speak as a statistician who is interested in data science applications. It's about programmer time. You have to weight the time it takes you to do the task in each programming language, if you are doing statistical analysis now, R and Python come out streaks ahead. The scope roughly speaking is Research -> Deployment. R and Python sit on the research side, and Python/JVM technologies sit on the deployment side (broadly speaking). The question is where does D sit? What should D's data science strategy be? To sit on the deployment side, D needs to grow it's big data/noSQL infrastructure for a start, then hook into a whole ecosystem of analytic tools in an easy and straightforward manner. This will take a lot of work! I believe it is easier and more effective to start on the research side. D will need: 1. A data table structure like R's data.frame or data.table. This is a dynamic data structure that represents a table that can have lots of operations applied to it. It is the data structure that separates R from most programming languages. It is what pandas tries to emulate. This includes text file and database i/o from mySQL and ODBC for a start. 2. Formula class : the ability to talk about statistical models using formulas e.g. y ~ x1 + x2 + x3 etc and then use these formulas to generate model matrices for input into statistical algorithms. 3. Solid interface to a big data database, that allows a D data table <-> database easily 4. Functional programming: especially around data table and array structures. R's apply(), lapply(), tapply(), plyr and now data.table(,, by = list()) provides powerful tools for data manipulation. 5. A factor data type:for categorical variables. This is easy to implement! This ties into the creation of model matrices. 6. Nullable types makes talking about missing data more straightforward and gives you the opportunity to code them into a set value in your analysis. D is streaks ahead of Python here, but this is built into R at a basic level. If D can get points 1, 2, 3 many people would be all over D because it is a fantastic programming language and is wicked fast.
Oct 14 2015
next sibling parent reply jmh530 <john.michael.hall gmail.com> writes:
On Wednesday, 14 October 2015 at 22:11:56 UTC, data pulverizer 
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 believe it is easier and more effective to start on the research side. D will need: [snip]
Great list, but tons of work!
Oct 14 2015
parent reply data pulverizer <data.pulverizer gmail.com> writes:
On Thursday, 15 October 2015 at 02:20:42 UTC, jmh530 wrote:
 On Wednesday, 14 October 2015 at 22:11:56 UTC, data pulverizer 
 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 believe it is easier and more effective to start on the research side. D will need: [snip]
Great list, but tons of work!
A journey of a thousand miles ... I tried to start creating a data table type object by investigating variantArray: http://forum.dlang.org/thread/hhzavwrkbrkjzfohczyq forum.dlang.org but hit the snag that D is a static programming language and may not allow the kind of behaviour you need for creating the same kind of behaviour you need in data table - like objects. I envisage such an object as being composed of arrays of vectors where each vector represents a column in a table as in R - easier for model matrix creation. Some people believe that you should work with arrays of tuple rows - which may be more big data friendly. I am not overly wedded to either approach. Anyway it seems I have hit an inherent limitation in the language. Correct me if I am wrong. The data frame needs to have dynamic behaviour bind rows and columns and return parts of itself as a data table etc and since D is a static language we cannot do this.
Oct 14 2015
parent reply Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Thu, 2015-10-15 at 06:48 +0000, data pulverizer via Digitalmars-d-
learn wrote:
=20
[=E2=80=A6]
 A journey of a thousand miles ...
Exactly.
 I tried to start creating a data table type object by=20
 investigating variantArray:=20
 http://forum.dlang.org/thread/hhzavwrkbrkjzfohczyq forum.dlang.org
  but hit the snag that D is a static programming language and may not
 allow the kind of behaviour you need for creating the same kind of
 behaviour you need in data table - like objects.
=20
 I envisage such an object as being composed of arrays of vectors=20
 where each vector represents a column in a table as in R - easier=20
 for model matrix creation. Some people believe that you should=20
 work with arrays of tuple rows - which may be more big data=20
 friendly. I am not overly wedded to either approach.
=20
 Anyway it seems I have hit an inherent limitation in the=20
 language. Correct me if I am wrong. The data frame needs to have=20
 dynamic behaviour bind rows and columns and return parts of=20
 itself as a data table etc and since D is a static language we=20
 cannot do this.
Just because D doesn't have this now doesn't mean it cannot. C doesn't have such capability but R and Python do even though R and CPython are just C codes. Pandas data structures rely on the NumPy n-dimensional array implementation, it is not beyond the bounds of possibility that that data structure could be realized as a D module. Is R's data.table written in R or in C? In either case, it is not beyond the bounds of possibility that that data structure could be realized as a D module. The core issue is to have a seriously efficient n-dimensional array that is amenable to data parallelism and is extensible. As far as I am aware currently (I will investigate more) the NumPy array is a good native code array, but has some issues with data parallelism and Pandas has to do quite a lot of work to get the extensibility. I wonder how the R data.table works. I have this nagging feeling that like NumPy, data.table seems a lot better than it could be. From small experiments D is (and also Chapel is even more) hugely faster than Python/NumPy at things Python people think NumPy is brilliant for. Expectations of Python programmers are set by the scale of Python performance, so NumPy seems brilliant. Compared to the scale set by D and Chapel, NumPy is very disappointing. I bet the same is true of R (I have never really used R). This is therefore an opportunity for D to step in. However it is a journey of a thousand miles to get something production worthy. Python/NumPy/Pandas have had a very large number of programmer hours expended on them. =C2=A0Doing this poorly as a D modules is likely worse than not doing it at all. --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Oct 15 2015
next sibling parent reply Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Thursday, 15 October 2015 at 07:57:51 UTC, Russel Winder wrote:
 lot better than it could be. From small experiments D is (and 
 also Chapel is even more) hugely faster than Python/NumPy at 
 things Python people think NumPy is brilliant for. Expectations
Have you had a chance to look at PyOpenCL and PYCUDA?
Oct 15 2015
parent reply Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Thu, 2015-10-15 at 09:35 +0000, Ola Fosheim Gr=C3=B8stad via Digitalmars=
-
d-learn wrote:
 On Thursday, 15 October 2015 at 07:57:51 UTC, Russel Winder wrote:
 lot better than it could be. From small experiments D is (and=20
 also Chapel is even more) hugely faster than Python/NumPy at=20
 things Python people think NumPy is brilliant for. Expectations
=20 Have you had a chance to look at PyOpenCL and PYCUDA?
Yes.=C2=A0 CUDA is of course doomed in the long run as Intel put GPGPU on the processor chip. OpenCL will eventually be replaced with Vulkan (assuming they can get the chips made). --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Oct 15 2015
parent reply jmh530 <john.michael.hall gmail.com> writes:
On Thursday, 15 October 2015 at 10:33:54 UTC, Russel Winder wrote:
 CUDA is of course doomed in the long run as Intel put GPGPU on 
 the processor chip. OpenCL will eventually be replaced with 
 Vulkan (assuming they can get the chips made).
I thought Vulkan was meant to replace OpenGL.
Oct 15 2015
parent Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Thu, 2015-10-15 at 17:00 +0000, jmh530 via Digitalmars-d-learn
wrote:
 On Thursday, 15 October 2015 at 10:33:54 UTC, Russel Winder wrote:
=20
 CUDA is of course doomed in the long run as Intel put GPGPU on=20
 the processor chip. OpenCL will eventually be replaced with=20
 Vulkan (assuming they can get the chips made).
=20 I thought Vulkan was meant to replace OpenGL.
True, but there is an intent to try and have Vulkan allow for replacing both OpenGL and OpenCL. --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Oct 15 2015
prev sibling next sibling parent data pulverizer <data.pulverizer gmail.com> writes:
On Thursday, 15 October 2015 at 07:57:51 UTC, Russel Winder wrote:
 On Thu, 2015-10-15 at 06:48 +0000, data pulverizer via 
 Digitalmars-d- learn wrote:
 Just because D doesn't have this now doesn't mean it cannot. C 
 doesn't have such capability but R and Python do even though R 
 and CPython are just C codes.
I think the way R does this is that its dynamic runtime environment is used bind together native C basic type arrays. I wander if we could simulate dynamic behaviour by leveraging D's short compilation time to dynamically write/update data table source file(s) containing the structure of new/modified data tables?
 Pandas data structures rely on the NumPy n-dimensional array 
 implementation, it is not beyond the bounds of possibility that 
 that data structure could be realized as a D module.
Julia's DArray object is an interested take on this: https://github.com/JuliaParallel/DistributedArrays.jl I believe that parallelism on arrays and data tables are different challenges. Data tables are easier since we can parallelise by row, thus the preference of having row-based tuples.
 The core issue is to have a seriously efficient n-dimensional 
 array that is amenable to data parallelism and is extensible. 
 As far as I am aware currently (I will investigate more) the 
 NumPy array is a good native code array, but has some issues 
 with data parallelism and Pandas has to do quite a lot of work 
 to get the extensibility. I wonder how the R data.table works.
R's data table is not currently parallelised
 I have this nagging feeling that like NumPy, data.table seems a 
 lot better than it could be. From small experiments D is (and 
 also Chapel is even more) hugely faster than Python/NumPy at 
 things Python people think NumPy is brilliant for. Expectations 
 of Python programmers are set by the scale of Python 
 performance, so NumPy seems brilliant. Compared to the scale 
 set by D and Chapel, NumPy is very disappointing. I bet the 
 same is true of R (I have never really used R).
Thanks for notifying me about Chapel - something else interesting to investigate. When it comes to speed R is very strange. Basic math (e.g. *, +, /) operation on an R array can be fast but for-looping will kill speed by hundreds of times - most things are slow in R unless they are directly baked into its base operations. You can write code in C and C++ can call it very easily in R though using its Rcpp interface.
 This is therefore an opportunity for D to step in. However it 
 is a journey of a thousand miles to get something production 
 worthy. Python/NumPy/Pandas have had a very large number of 
 programmer hours expended on them.  Doing this poorly as a D 
 modules is likely worse than not doing it at all.
I think D has a lot to offer the world of data science.
Oct 15 2015
prev sibling parent Laeeth Isharc <Laeeth.nospam nospam-laeeth.com> writes:
On Thursday, 15 October 2015 at 07:57:51 UTC, Russel Winder wrote:
 On Thu, 2015-10-15 at 06:48 +0000, data pulverizer via 
 Digitalmars-d- learn wrote:
 
[…]
 A journey of a thousand miles ...
Exactly.
 I tried to start creating a data table type object by
 investigating variantArray:
 http://forum.dlang.org/thread/hhzavwrkbrkjzfohczyq forum.dlang.org
  but hit the snag that D is a static programming language and 
 may not
 allow the kind of behaviour you need for creating the same 
 kind of
 behaviour you need in data table - like objects.
 
 I envisage such an object as being composed of arrays of 
 vectors where each vector represents a column in a table as in 
 R - easier for model matrix creation. Some people believe that 
 you should work with arrays of tuple rows - which may be more 
 big data friendly. I am not overly wedded to either approach.
 
 Anyway it seems I have hit an inherent limitation in the 
 language. Correct me if I am wrong. The data frame needs to 
 have dynamic behaviour bind rows and columns and return parts 
 of itself as a data table etc and since D is a static language 
 we cannot do this.
Just because D doesn't have this now doesn't mean it cannot. C doesn't have such capability but R and Python do even though R and CPython are just C codes. Pandas data structures rely on the NumPy n-dimensional array implementation, it is not beyond the bounds of possibility that that data structure could be realized as a D module. Is R's data.table written in R or in C? In either case, it is not beyond the bounds of possibility that that data structure could be realized as a D module. The core issue is to have a seriously efficient n-dimensional array that is amenable to data parallelism and is extensible. As far as I am aware currently (I will investigate more) the NumPy array is a good native code array, but has some issues with data parallelism and Pandas has to do quite a lot of work to get the extensibility. I wonder how the R data.table works. I have this nagging feeling that like NumPy, data.table seems a lot better than it could be. From small experiments D is (and also Chapel is even more) hugely faster than Python/NumPy at things Python people think NumPy is brilliant for. Expectations of Python programmers are set by the scale of Python performance, so NumPy seems brilliant. Compared to the scale set by D and Chapel, NumPy is very disappointing. I bet the same is true of R (I have never really used R). This is therefore an opportunity for D to step in. However it is a journey of a thousand miles to get something production worthy. Python/NumPy/Pandas have had a very large number of programmer hours expended on them.  Doing this poorly as a D modules is likely worse than not doing it at all.
I think it's much better to start, which means solving your own problems in a way that is acceptable to you rather than letting perfection be the enemy of the good. It's always easier to do something a second time too, as you learn from successes and mistakes and you have a better idea about what you want. Of course it's better to put some thought into design early on, but that shouldn't end up in analysis paralysis. John Colvin and others are putting quite a lot of thought into dlang science, it seems to me, but he is also getting stuff done. Running D in a Jupyter notebook is something very useful. It doesn't matter that it's cosmetically imperfect at this stage, and it won't stay that way. And that's just a small step towards the bigger goal.
Oct 15 2015
prev sibling parent reply Laeeth Isharc <Laeeth.nospam nospam-laeeth.com> writes:
On Wednesday, 14 October 2015 at 22:11:56 UTC, data pulverizer 
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 am coming at D by way of R, C++, Python etc. so I speak as a statistician who is interested in data science applications.
Welcome... Looks like we have similar interests.
 To sit on the deployment side, D needs to grow it's big 
 data/noSQL infrastructure for a start, then hook into a whole 
 ecosystem of analytic tools in an easy and straightforward 
 manner. This will take a lot of work!
Indeed. The dlangscience project managed by John Colvin is very interesting. It is not a pure stats project, but there will be many shared areas of need. He has some v interesting ideas, and being able to mix Python and D in a Jupyter notebook is rather nice (you can do this already).
 I believe it is easier and more effective to start on the 
 research side. D will need:

 1. A data table structure like R's data.frame or data.table. 
 This is a dynamic data structure that represents a table that 
 can have lots of operations applied to it. It is the data 
 structure that separates R from most programming languages. It 
 is what pandas tries to emulate. This includes text file and 
 database i/o from mySQL and ODBC for a start.
I fully agree, and have made a very simple start on this. See github. It's usable for my needs as they stand, although far from production ready or elegant. You can read and write to/from CSV and HDF5. I guess mysql and ODBC wouldn't be hard to add, but I don't myself need for now and won't have time to do myself. If I have space I may channel some reesources in that direction some time next year.
 2. Formula class : the ability to talk about statistical models 
 using formulas e.g. y ~ x1 + x2 + x3 etc and then use these 
 formulas to generate model matrices for input into statistical 
 algorithms.
Sounds interesting. Take a look at Colvin's dlang science draft white paper, and see what you would add. It's a chance to shape things whilst they are still fluid.
 3. Solid interface to a big data database, that allows a D data 
 table <-> database easily
Which ones do you have in mind for stats? The different choices seem to serve quite different needs. And when you say big data, how big do you typically mean ?
 4. Functional programming: especially around data table and 
 array structures. R's apply(), lapply(), tapply(), plyr and now 
 data.table(,, by = list()) provides powerful tools for data 
 manipulation.
Any thoughts on what the design should look like? To an extent there is a balance between wanting to explore data iteratively (when you don't know where you will end up), and wanting to build a robust process for production. I have been wondering myself about using LuaJIT to strap together D building blocks for the exploration (and calling it based on a custom console built around Adam Ruppe's terminal).
 5. A factor data type:for categorical variables. This is easy 
 to implement! This ties into the creation of model matrices.

 6. Nullable types makes talking about missing data more 
 straightforward and gives you the opportunity to code them into 
 a set value in your analysis. D is streaks ahead of Python 
 here, but this is built into R at a basic level.
So matrices with nullable types within? Is nan enough for you ? If not then could be quite expensive if back end is C.
 If D can get points 1, 2, 3 many people would be all over D 
 because it is a fantastic programming language and is wicked 
 fast.
What do you like best about it ? And in your own domain, what have the biggest payoffs been in practice?
Oct 15 2015
parent data pulverizer <data.pulverizer gmail.com> writes:
On Thursday, 15 October 2015 at 21:16:18 UTC, Laeeth Isharc wrote:
 On Wednesday, 14 October 2015 at 22:11:56 UTC, data pulverizer 
 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 am coming at D by way of R, C++, Python etc. so I speak as a statistician who is interested in data science applications.
Welcome... Looks like we have similar interests.
That's good to know
 To sit on the deployment side, D needs to grow it's big 
 data/noSQL infrastructure for a start, then hook into a whole 
 ecosystem of analytic tools in an easy and straightforward 
 manner. This will take a lot of work!
Indeed. The dlangscience project managed by John Colvin is very interesting. It is not a pure stats project, but there will be many shared areas of need. He has some v interesting ideas, and being able to mix Python and D in a Jupyter notebook is rather nice (you can do this already).
Thanks for bringing my attention to this, this looks interesting.
 Sounds interesting.  Take a look at Colvin's dlang science 
 draft white paper, and see what you would add.  It's a chance 
 to shape things whilst they are still fluid.
Good suggestion.
 3. Solid interface to a big data database, that allows a D 
 data table <-> database easily
Which ones do you have in mind for stats? The different choices seem to serve quite different needs. And when you say big data, how big do you typically mean ?
What I mean is to start by tapping into current big data technologies. HDFS and Cassandra have C APIs which we can wrap for D.
 4. Functional programming: especially around data table and 
 array structures. R's apply(), lapply(), tapply(), plyr and 
 now data.table(,, by = list()) provides powerful tools for 
 data manipulation.
Any thoughts on what the design should look like?
Yes, I think this is easy to implement but still important. The real devil is my point #1 the dynamic data table object.
 To an extent there is a balance between wanting to explore data 
 iteratively (when you don't know where you will end up), and 
 wanting to build a robust process for production.  I have been 
 wondering myself about using LuaJIT to strap together D 
 building blocks for the exploration (and calling it based on a 
 custom console built around Adam Ruppe's terminal).
Sounds interesting
 6. Nullable types makes talking about missing data more 
 straightforward and gives you the opportunity to code them 
 into a set value in your analysis. D is streaks ahead of 
 Python here, but this is built into R at a basic level.
So matrices with nullable types within? Is nan enough for you ? If not then could be quite expensive if back end is C.
I am not suggesting that we pass nullable matrices to C algorithms, yes nan is how this is done in practice but you wouldn't have nans in your matrix at the point of modeling - they'll just propagate and trash your answer. Nullable types are useful in data acquisition and exploration - the more practical side of data handling. I was quite shocked to see them in D, when they are essentially absent from "high level" programming languages like Python. Real data is messy and having nullable types is useful in processing, storing and summarizing raw data. I put in as #6 because I think it is possible to do practical statistics working around them by using notional hacks. Nullables are something that C#, and R have and Python's pandas has struggled with. The great news is that they are available in D so we can use them.
 If D can get points 1, 2, 3 many people would be all over D 
 because it is a fantastic programming language and is wicked 
 fast.
What do you like best about it ? And in your own domain, what have the biggest payoffs been in practice?
I am playing with D at the moment. To become useful to me the data table structure is a must. I previously said points 1, 2, and 3 would get data scientists sucked into D. But the data table structure is the seed. A dynamic structure like that in D would catalyze the rest. Everything else is either wrappers, routine and maybe a lot of work but straightforward to implement. The data table structure for me is the real enigma. The way that R's data types are structured around SEXPs is the key to all of this. I am currently reading through R's internal documentation to get my head around this. https://cran.r-project.org/doc/manuals/r-release/R-ints.html
Oct 18 2015
prev sibling parent reply Namespace <rswhite4 gmail.com> writes:
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.
Maybe also interesting: https://docs.google.com/presentation/d/1LO_WI3N-3p2Wp9PDWyv5B6EGFZ8XTOTNJ7Hd40WOUHo/mobilepresent?pli=1&slide=id.g70b0035b2_1_168
Oct 18 2015
parent reply Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Sunday, 18 October 2015 at 12:50:43 UTC, Namespace 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.
Maybe also interesting: https://docs.google.com/presentation/d/1LO_WI3N-3p2Wp9PDWyv5B6EGFZ8XTOTNJ7Hd40WOUHo/mobilepresent?pli=1&slide=id.g70b0035b2_1_168
What I got out of that is that someone at Mozilla were writing a push service (stateful connections, which more demanding than regular http) and found that jitted Python was more suitable than Go for productivity reasons. Then they speculate that their own Rust will be better suited than Go for such services in the future, apparently not yet. To the poster further up in the thread: turns out that reddit.com is implemented in Python and a little bit of C: https://github.com/reddit/reddit So there we have it. Python gives higher productive at the cost of efficiency, but does not have a significant impact on effectiveness, for regular web services that are built to scale.
Oct 18 2015
next sibling parent reply Namespace <rswhite4 gmail.com> writes:
On Sunday, 18 October 2015 at 13:29:50 UTC, Ola Fosheim Grøstad 
wrote:
 On Sunday, 18 October 2015 at 12:50:43 UTC, Namespace 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.
Maybe also interesting: https://docs.google.com/presentation/d/1LO_WI3N-3p2Wp9PDWyv5B6EGFZ8XTOTNJ7Hd40WOUHo/mobilepresent?pli=1&slide=id.g70b0035b2_1_168
What I got out of that is that someone at Mozilla were writing a push service (stateful connections, which more demanding than regular http) and found that jitted Python was more suitable than Go for productivity reasons. Then they speculate that their own Rust will be better suited than Go for such services in the future, apparently not yet.
I liked the fact that Python with PyPy is more performant than Go (in contrast to the title "Python is slow") and that Go-Routines leak.
 To the poster further up in the thread: turns out that 
 reddit.com is implemented in Python and a little bit of C: 
 https://github.com/reddit/reddit

 So there we have it. Python gives higher productive at the cost 
 of efficiency, but does not have a significant impact on 
 effectiveness, for regular web services that are built to scale.
Oct 18 2015
parent Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Sunday, 18 October 2015 at 13:57:40 UTC, Namespace wrote:
 I liked the fact that Python with PyPy is more performant than 
 Go (in contrast to the title "Python is slow") and that 
 Go-Routines leak.
Yes, Python apparently used less memory, which is rather important when you write a service with persistent websocket connections, like a webchat or game. Old school stackless coroutines probably would be better than fibers like D and Go uses. An alternative to writing such code for the application is to get persistent connections by "ready made" server-infrastructure (which probably is more reliable anyway). On AppEngine you have something called channels which basically allows you to send messages to a connected client push-style: https://cloud.google.com/appengine/docs/python/channel/ As far as I can tell that means that the application server can die without loosing the connection.
Oct 18 2015
prev sibling parent reply Mengu <mengukagan gmail.com> writes:
On Sunday, 18 October 2015 at 13:29:50 UTC, Ola Fosheim Grøstad 
wrote:
 On Sunday, 18 October 2015 at 12:50:43 UTC, Namespace 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.
Maybe also interesting: https://docs.google.com/presentation/d/1LO_WI3N-3p2Wp9PDWyv5B6EGFZ8XTOTNJ7Hd40WOUHo/mobilepresent?pli=1&slide=id.g70b0035b2_1_168
What I got out of that is that someone at Mozilla were writing a push service (stateful connections, which more demanding than regular http) and found that jitted Python was more suitable than Go for productivity reasons. Then they speculate that their own Rust will be better suited than Go for such services in the future, apparently not yet. To the poster further up in the thread: turns out that reddit.com is implemented in Python and a little bit of C: https://github.com/reddit/reddit So there we have it. Python gives higher productive at the cost of efficiency, but does not have a significant impact on effectiveness, for regular web services that are built to scale.
that's the pylons guy. he also has many python libraries for web development. reddit is built with pylons btw and pylons is now pyramid. i've seen the presentation and i can't stop thinking how it'd be if they had chosen D instead of Go.
Oct 18 2015
parent Ola Fosheim =?UTF-8?B?R3LDuHN0YWQ=?= writes:
On Sunday, 18 October 2015 at 20:44:44 UTC, Mengu wrote:
 i've seen the presentation and i can't stop thinking how it'd 
 be if they had chosen D instead of Go.
Not much better, probably worse, given that Go has stack protection for fibers and D doesn't. So in Go you can get away with 2K growable stacks, in D you would need a lot more to stay on the safe side. IIRC he claims that CPython would fast enough for their application and that the application was memory limited and not computation limited.
Oct 18 2015