December 2016 - Pharo-users

Re: [Pharo-users] NeoJSON

by Sven Van Caekenberghe

> On 16 Dec 2016, at 15:10, stepharong <stepharong(a)free.fr> wrote: > > I looked at the Neo code and I do not see how I can specify a mapping at the class level > because I cannot control the json writer creation. See my previous message (sent at the same time ;-) << add it to the class side of your model objects as #neoJsonMapping: (search for implementors as examples). >> > So I should probably shortcut everything at the neoJsonOn: level. > > Stef > > >> Hi sven >> >> I'm trying to adapt the teapot library example to get a simple item collector (as a project for a future book) and >> so that I can learn and use it for my PS2/PS3 game collection :) >> >> Now when I set up teapot to emit JSON I get an NeoJSONMappingNotFound >> In the library example attila does not manipulate objects but dictionaries. >> So I imagine that I have to do something :) >> I read the NeoJSON chapter but I did not find the solution. >> >> Should I implement >> neoJsonOn: >> on my domain? >> >> Stef >> > > > -- > Using Opera's mail client: http://www.opera.com/mail/ >

Dec. 16, 2016

Re: [Pharo-users] NeoJSON

by Sven Van Caekenberghe

Stef, > On 16 Dec 2016, at 15:00, stepharong <stepharong(a)free.fr> wrote: > > Hi sven > > I'm trying to adapt the teapot library example to get a simple item collector (as a project for a future book) and > so that I can learn and use it for my PS2/PS3 game collection :) > > Now when I set up teapot to emit JSON I get an NeoJSONMappingNotFound > In the library example attila does not manipulate objects but dictionaries. > So I imagine that I have to do something :) > I read the NeoJSON chapter but I did not find the solution. > > Should I implement > neoJsonOn: > on my domain? Section 5 of https://ci.inria.fr/pharo-contribution/job/EnterprisePharoBook/lastSuccessf… explains most of this. You either add the mapping to the writer, builder style, or you add it to the class side of your model objects as #neoJsonMapping: (search for implementors as examples). I see that this second aspect is not well explained in the book. In the simplest case, the following is enough: neoJsonMapping: mapper mapper for: self do: [ :mapping | mapping mapInstVars: #(id width height data) ] But is gets a bit more complicated with inheritance. In a last resort you could also overwrite #neoJsonOn: Now, this is all for the writer side. Reading is harder because JSON has no type info (that what STON adds, among others), so you have to tell the reader what (static) type you want the parser to create. This is based on the same mapping. This is what #nextAs: does. If you have a more complex graph with collection values, you need to type all of them 'statically'. There are some examples in the unit tests. Sven > Stef > > -- > Using Opera's mail client: http://www.opera.com/mail/ >

Dec. 16, 2016

Re: [Pharo-users] NeoJSON

by stepharong

I looked at the Neo code and I do not see how I can specify a mapping at the class level because I cannot control the json writer creation. So I should probably shortcut everything at the neoJsonOn: level. Stef > Hi sven > > I'm trying to adapt the teapot library example to get a simple item > collector (as a project for a future book) and > so that I can learn and use it for my PS2/PS3 game collection :) > > Now when I set up teapot to emit JSON I get an NeoJSONMappingNotFound > In the library example attila does not manipulate objects but > dictionaries. > So I imagine that I have to do something :) > I read the NeoJSON chapter but I did not find the solution. > > Should I implement > neoJsonOn: > on my domain? > > Stef > -- Using Opera's mail client: http://www.opera.com/mail/

Dec. 16, 2016

Re: [Pharo-users] real world pharo web application set ups

by phil＠highoctane.be

I have been doing lots of Tomcat as well and helped some people at Orange scale some of their mobile provisioning stuff. It scales. But one would scale Pharo just the same. I remember some AJP module for Pharo/Squeak and http://book.seaside.st/book/advanced/deployment/deployment-apache/mod-proxy… Lets of parms in there but can help scale things and do session affinity. https://tomcat.apache.org/tomcat-7.0-doc/config/ajp.html Phil On Fri, Dec 16, 2016 at 1:50 PM, Esteban Lorenzano <estebanlm(a)gmail.com> wrote: > Hi, > > On 16 Dec 2016, at 10:41, Norbert Hartl <norbert(a)hartl.name> wrote: > > We are talking about really high numbers of requests/s. The odds you are > getting in this kind of scaling trouble are usually close to zero. It means > you need to generate an application that has really many users. Most > projects we know end up using a single image for everything. > > > amen to everything, but this in particular. 1000 /concurrent/ requests are > a HUGE amount of requests most applications will never need. > > Remember concurrent does not means simultaneous but in the same lapseâ¦ > which means in any fraction of time you measures you can count 1000 > requests being processed (no matter if thatâs 1ms, 1s or 1m)â¦ when I was > designing web-applications all the time, the count I usually was doing was: > the number of users I expect to have, grouped by time-picks then I was > dividing that per 50/s (this was an âobscureâ heuristic I got from some > even more obscure general observation that have much to do with the fact > that people spend much more time looking a monitor than clicking a mouse). > > For example: to serve an application to 1000 users, > > - letâs consider 80% are connected at pick times = 800 users who I need to > serve > - = roughly 40 requests per secondâ¦ > > so in general a couple of tomcats would be ok (because at the time I was > working in java). > â¦ or 4 pharos. > > now, as I always said at the time: this are estimations that are meant to > calme the stress of customers (the ones who pays for the projects) or my > project managers (who didnât know much about systems anyway)â¦ > and they just worked as âpain-killersâ because since I really cannot know > how much will take a request I cannot measure anything. > Even worst: Iâm assuming all request take same time, which absolutely a > non sense. > > But well, since people (both customers and managers) always made that > question, I made up that number based in my own observation (20 years of > experience, not so bad) that âin general, a Tomcat can handle about 40 > req/s and Seaside can handle something around 15â¦ and you always need to > calculate a bit more because of murphyâs lawâ. Fun fact: the estimation was > in general correct :P > > In conclusion: if you *really* need to serve 1000 concurrent users, youâll > probably have the budget to make it right :) > > Esteban >

Dec. 16, 2016

NeoJSON

by stepharong

Hi sven I'm trying to adapt the teapot library example to get a simple item collector (as a project for a future book) and so that I can learn and use it for my PS2/PS3 game collection :) Now when I set up teapot to emit JSON I get an NeoJSONMappingNotFound In the library example attila does not manipulate objects but dictionaries. So I imagine that I have to do something :) I read the NeoJSON chapter but I did not find the solution. Should I implement neoJsonOn: on my domain? Stef -- Using Opera's mail client: http://www.opera.com/mail/

Dec. 16, 2016

Re: [Pharo-users] real world pharo web application set ups

by Esteban Lorenzano

Hi, > On 16 Dec 2016, at 10:41, Norbert Hartl <norbert(a)hartl.name> wrote: > > We are talking about really high numbers of requests/s. The odds you are getting in this kind of scaling trouble are usually close to zero. It means you need to generate an application that has really many users. Most projects we know end up using a single image for everything. amen to everything, but this in particular. 1000 /concurrent/ requests are a HUGE amount of requests most applications will never need. Remember concurrent does not means simultaneous but in the same lapseâ¦ which means in any fraction of time you measures you can count 1000 requests being processed (no matter if thatâs 1ms, 1s or 1m)â¦ when I was designing web-applications all the time, the count I usually was doing was: the number of users I expect to have, grouped by time-picks then I was dividing that per 50/s (this was an âobscureâ heuristic I got from some even more obscure general observation that have much to do with the fact that people spend much more time looking a monitor than clicking a mouse). For example: to serve an application to 1000 users, - letâs consider 80% are connected at pick times = 800 users who I need to serve - = roughly 40 requests per secondâ¦ so in general a couple of tomcats would be ok (because at the time I was working in java). â¦ or 4 pharos. now, as I always said at the time: this are estimations that are meant to calme the stress of customers (the ones who pays for the projects) or my project managers (who didnât know much about systems anyway)â¦ and they just worked as âpain-killersâ because since I really cannot know how much will take a request I cannot measure anything. Even worst: Iâm assuming all request take same time, which absolutely a non sense. But well, since people (both customers and managers) always made that question, I made up that number based in my own observation (20 years of experience, not so bad) that âin general, a Tomcat can handle about 40 req/s and Seaside can handle something around 15â¦ and you always need to calculate a bit more because of murphyâs lawâ. Fun fact: the estimation was in general correct :P In conclusion: if you *really* need to serve 1000 concurrent users, youâll probably have the budget to make it right :) Esteban

Dec. 16, 2016

Re: [Pharo-users] real world pharo web application set ups

by phil＠highoctane.be

That, just that. There is something in Pharo that I just do not experience elsewhere. Phil On Fri, Dec 16, 2016 at 10:05 AM, Sven Van Caekenberghe <sven(a)stfx.eu> wrote: > > I choose to do that in Pharo because I like it so much. It is perfectly > fine by me that 99.xx % of the world makes other decisions, for whatever > reason. > >

Dec. 16, 2016

Re: [Pharo-users] [Pharo-dev] Pharo poster

by Vitor Medina Cruz

That is soo cool!! Database shouldn't be join as subset of Data? On Sat, Dec 10, 2016 at 1:09 PM, stepharong <stepharong(a)free.fr> wrote: > > **tx** > I added the two files to the media folder on file.pharo.org > > On Sat, 10 Dec 2016 12:12:36 +0100, Cyril Ferlicot D. < > cyril.ferlicot(a)gmail.com> wrote: > > On 10/12/2016 12:08, stepharong wrote: >> >>> Hi >>> >>> I'm brainstorming about a pharo poster and I remember that one person >>> produce a poster with all the pharo technologies. >>> I'm that I saved it somewhere but I do not know where. I tried to find >>> it with google but no chance for now. >>> >>> Stef >>> >>> >>> >> Hi! >> >> See join files: >> >> http://forum.world.st/Pharo-family-update-td4857661.html >> >> > > -- > Using Opera's mail client: http://www.opera.com/mail/ > >

Dec. 16, 2016

Re: [Pharo-users] real world pharo web application set ups

by Norbert Hartl

> Am 16.12.2016 um 11:50 schrieb Sven Van Caekenberghe <sven(a)stfx.eu>: > > >> On 16 Dec 2016, at 11:33, Volkert <volkert(a)nivoba.de> wrote: >> >> come on, i am only interested in what set up Pharo is currently used (as mentioned in my initial question). >> This gives me a feeling, if my requirements are nearby the requirement found in current pharo >> based systems ... if i am complete out of population of current pharo systems, this is for me a good >> indication to bet not on it ... > > Well, yes. > > Norbert's conclusion (last two points) was spot on. > > You can do in the order of 1K req/s on a single image. If you want more, you need to scale (horizontally). Either you don't share state and you can do that easily. Or you do share state and you will have to build something custom (perfectly doable, but you will have to architect/design for that, preferably upfront). > > Note that the initial versions of all successful web apps that now serve millions of people on thousands of servers all started with very simple, inferior technology stacks. Make something great first, scale later. So true! Because there is nothing that just scales. The perfect solution for 100 concurrent requests is likely to be very different to the perfect solution for 1000 concurrent requests etc. And expect to discover bottlenecks you couldn't really anticipate. Norbert > >> On 16.12.2016 10:41, Norbert Hartl wrote: >>> I'm still not sure about what we are talking. There are some many opinions regarding totally different things. >>> >>> These benchmark don't say much. As Sven and you did the benchmark on different machines they are hard to compare in numbers. It is not that important because you can not make many conclusions from a micro benchmark. So what Sven has proven is the fact that there is no limit for pharo per se to handle 1k requests per second and more. >>> >>> From these 1k req/s to Joachims 5 reqs/s is big difference. You can always assume there is something blocking the vm or a synchron I/O call takes a lot of time. But it is not helpful either because that is an edge case like Svens test with an app that does nothing. I would even state that it is not that easy to produce a situation like Joachim describes. If you have that kind of a problem than I'm pretty sure the reasons are mostly not pharo related. Sure if it comes to blocking I/O then it is pharo's fault because it cannot do async I/O, yet. But a slow database query is not the fault of pharo and you will experience the exact same thing in any other runtime. >>> >>> Whatever it will be there is no other way then to measure your exact use case and find the bottlenecks that prevent your app from being able to handle 1000 concurrent requests. While I agree with a lot of points mentioned in this thread I cannot share the general notion of saying that you reduce the number of requests per image and "just" use more images and more machines. That is not true. >>> The moment you cannot deal with all your requests in a single image you are in trouble. As soon as there is a second image you need to make sure there is no volatile shared state between those images. You need to take caution then. Scaling up using more images and more machines shifts problem to the database because it is a central component that is not easy to scale. But again it is not pharo's fault either. >>> >>> So I would state two things: >>> >>> - We are talking about really high numbers of requests/s. The odds you are getting in this kind of scaling trouble are usually close to zero. It means you need to generate an application that has really many users. Most projects we know end up using a single image for everything. >>> - Whenever you have performance problems in your application architecture I'm pretty sure pharo is not in the top of the list of bottlenecks. >>> >>> So yes, you can handle pretty huge numbers using pharo. >>> >>> Norbert >>> >>>> Am 16.12.2016 um 09:57 schrieb volkert <volkert(a)nivoba.de>: >>>> >>>> Sven, >>>> >>>> compare with an erlang vm (Cowboy) on a standard pc, i5-4570 CPU @ 3.20GHz Ã 4, on linux ... >>>> >>>> Conncurrent request: 8 >>>> >>>> $ ab -k -c 8 -n 10240 http://127.0.0.1:8080/ >>>> This is ApacheBench, Version 2.3 <$Revision: 1706008 $> >>>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>>> >>>> Benchmarking 127.0.0.1 (be patient) >>>> Completed 1024 requests >>>> Completed 2048 requests >>>> Completed 3072 requests >>>> Completed 4096 requests >>>> Completed 5120 requests >>>> Completed 6144 requests >>>> Completed 7168 requests >>>> Completed 8192 requests >>>> Completed 9216 requests >>>> Completed 10240 requests >>>> Finished 10240 requests >>>> >>>> >>>> Server Software: >>>> Server Hostname: 127.0.0.1 >>>> Server Port: 8080 >>>> >>>> Document Path: / >>>> Document Length: 7734 bytes >>>> >>>> Concurrency Level: 8 >>>> Time taken for tests: 0.192 seconds >>>> Complete requests: 10240 >>>> Failed requests: 0 >>>> Keep-Alive requests: 10143 >>>> Total transferred: 80658152 bytes >>>> HTML transferred: 79196160 bytes >>>> Requests per second: 53414.29 [#/sec] (mean) >>>> Time per request: 0.150 [ms] (mean) >>>> Time per request: 0.019 [ms] (mean, across all concurrent requests) >>>> Transfer rate: 410871.30 [Kbytes/sec] received >>>> >>>> Connection Times (ms) >>>> min mean[+/-sd] median max >>>> Connect: 0 0 0.0 0 0 >>>> Processing: 0 0 0.2 0 3 >>>> Waiting: 0 0 0.2 0 3 >>>> Total: 0 0 0.2 0 3 >>>> >>>> Percentage of the requests served within a certain time (ms) >>>> 50% 0 >>>> 66% 0 >>>> 75% 0 >>>> 80% 0 >>>> 90% 0 >>>> 95% 1 >>>> 98% 1 >>>> 99% 1 >>>> 100% 3 (longest request) >>>> >>>> >>>> And here with 1000 concurrent request ... >>>> >>>> $ab -k -c 1000 -n 10240 http://127.0.0.1:8080/ >>>> This is ApacheBench, Version 2.3 <$Revision: 1706008 $> >>>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>>> >>>> Benchmarking 127.0.0.1 (be patient) >>>> Completed 1024 requests >>>> Completed 2048 requests >>>> Completed 3072 requests >>>> Completed 4096 requests >>>> Completed 5120 requests >>>> Completed 6144 requests >>>> Completed 7168 requests >>>> Completed 8192 requests >>>> Completed 9216 requests >>>> Completed 10240 requests >>>> Finished 10240 requests >>>> >>>> >>>> Server Software: >>>> Server Hostname: 127.0.0.1 >>>> Server Port: 8080 >>>> >>>> Document Path: / >>>> Document Length: 7734 bytes >>>> >>>> Concurrency Level: 1000 >>>> Time taken for tests: 0.225 seconds >>>> Complete requests: 10240 >>>> Failed requests: 0 >>>> Keep-Alive requests: 10232 >>>> Total transferred: 80660288 bytes >>>> HTML transferred: 79196160 bytes >>>> Requests per second: 45583.23 [#/sec] (mean) >>>> Time per request: 21.938 [ms] (mean) >>>> Time per request: 0.022 [ms] (mean, across all concurrent requests) >>>> Transfer rate: 350642.85 [Kbytes/sec] received >>>> >>>> Connection Times (ms) >>>> min mean[+/-sd] median max >>>> Connect: 0 1 3.3 0 23 >>>> Processing: 0 6 16.1 0 198 >>>> Waiting: 0 6 16.1 0 198 >>>> Total: 0 7 18.0 0 211 >>>> >>>> Percentage of the requests served within a certain time (ms) >>>> 50% 0 >>>> 66% 2 >>>> 75% 6 >>>> 80% 10 >>>> 90% 21 >>>> 95% 32 >>>> 98% 47 >>>> 99% 108 >>>> 100% 211 (longest request) >>>> >>>> >>>> >>>> Am 15.12.2016 um 15:00 schrieb Sven Van Caekenberghe: >>>>> Joachim, >>>>> >>>>>> On 15 Dec 2016, at 11:43, jtuchel(a)objektfabrik.de wrote: >>>>>> >>>>>> Victor, >>>>>> >>>>>> Am 14.12.16 um 19:23 schrieb Vitor Medina Cruz: >>>>>>> If I tell you that my current estimate is that a Smalltalk image with Seaside will not be able to handle more than 20 concurrent users, in many cases even less. >>>>>>> >>>>>>> Seriously? That is kinda a low number, I would expect more for each image. Certainly it depends much on many things, but it is certainly very low for a rough estimate, why you say that? >>>>>> seriously, I think 20 is very optimistic for several reasons. >>>>>> >>>>>> One, you want to be fast and responsive for every single user, so there is absolutely no point in going too close to any limit. It's easy to lose users by providing bad experience. >>>>>> >>>>>> Second, in a CRUD Application, you mostly work a lot with DB queries. And you connect to all kinds of stuff and do I/O. Some of these things simply block the VM. Even if that is only for 0.3 seconds, you postpone processing for each "unaffected" user by these 0.3 seconds, so this adds to significant delays in response time. And if you do some heavy db operations, 0.3 seconds is not a terribly bad estimate. Add to that the materialization and stuff within the Smalltalk image. >>>>>> >>>>>> Seaside adapters usually start off green threads for each request. But there are things that need to be serialized (like in a critical Block). So in reality, users block each other way more often than you'd like. >>>>>> >>>>>> So if you asked me to give a more realistic estimation, I'd correct myself down to a number between 5 and probably a maximum of 10 users. Everything else means you must use all those fancy tricks and tools people mention in this thread. >>>>>> So what you absolutely need to do is start with an estimate of 5 concurrent users per image and look for ways to distribute work among servers/images so that these blocking situations are down to a minimum. If you find your software works much better, congratulate yourself and stack up new machines more slowly than initially estimated. >>>>>> >>>>>> >>>>>> Before you turn around and say: Smalltalk is unsuitable for the web, let's take a brief look at what concurrent users really means. Concurrent users are users that request some processing from the server at they very same time (maybe within an interval of 200-400msec). This is not the same as 5 people being currently logged on to the server and requesting something sometimes. 5 concurrent users can be 20, 50, 100 users who are logged in at the same time. >>>>>> >>>>>> Then there is this sad "share all vs. share nothing" argument. In Seaside you keep all your objects alive (read from db and materialized) between web requests. IN share nothing, you read everything back from disc/db whenever a request comes in. This also takes time and ressources (and pssibly blocks the server for the blink of an eye or two). You exchange RAM with CPU cycles and I/O. It is extremely hard to predict what works better, and I guess nobody ever made A/B tests. It's all just theoretical bla bla and guesses of what definitely must be better in one's world. >>>>>> >>>>>> Why do I come up with this share everything stuff? Because it usually means that each user that is logged on holds onto a load of objects on the server side (session storage), like their user account, shopping card, settings, last purchases, account information and whatnot. That's easily a list of a few thousand objects (and be it only Proxies) that take up space and want to be inspected by the garbage collector. So each connected user not only needs CPU cycles whenever they send a request to the server, but also uses RAM. In our case, this can easily be 5-10 MB of objects per user. Add to that the shadow copies that your persistence mechanism needs for undo and stuff, and all the data Seaside needs for Continuations etc, and each logged on users needs 15, 20 or more MB of object space. Connect ten users and you have 150-200 MB. That is not a problem per se, but also means there is some hard limit, especially in a 32 bit world. You don't want your server to slow down because it cannot allocate new memory or can't find contiguous slots for stuff and GCs all the time. >>>>>> >>>>>> To sum up, I think the number of influencing factors is way too high to really give a good estimate. Our experience (based on our mix of computation and I/O) says that 5 concurrent users per image is doable without negative impact on other users. Some operations take so much time that you really need to move them out of the front-facing image and distribute work to backend servers. More than 5 is probably possible but chances are that there are operations that will affect all users and with every additional user there is a growing chance that you have 2 or more requesting the yery same operation within a very short interval. This will make things worse and worse. >>>>>> >>>>>> So I trust in you guys having lots of cool tools around and knowing loads of tricks to wrench out much more power of a single Smalltalk image, but you also need to take a look at your productivity and speed in creating new features and fixing bugs. Sometimes throwing hardware at a problem like growth and starting with a clever architecture to scale on multiple layers is just the perfect thing to do. To me, handling 7 instead of 5 concurrent users is not such a big win as long as we are not in a posotion where we have so many users that this really matters. For sites like Amazon, Google, Facebook etc. saving 40% in server cost by optimizing the software (investing a few man years) is significant. I hope we'll soon change our mind about this question ;-) >>>>>> >>>>>> So load balancing and services outsourced to backend servers are key to scalability. This, btw, is not smalltalk specific (some people seem to think you won't get these problems in Java or Ruby because they are made for the web...). >>>>>> >>>>>> Joachim >>>>> Everything you say, all your considerations, especially the last paragraph is/are correct and I agree. >>>>> >>>>> But some people will only remember the very low number you seem to be suggesting (which is more of a worse case scenario, with Seaside+blocking/slow connections to back end systems). >>>>> >>>>> One the other hand, plain HTTP access to a Pharo image can be quite fast. Here is quick & dirty benchmark I just did on one of our modern/big machines (inside an LXD container, light load) using a single stock image on Linux. >>>>> >>>>> >>>>> $ pharo Pharo.image printVersion >>>>> [version] 4.0 #40626 >>>>> >>>>> $ pharo Pharo.image eval 'ZnServer startDefaultOn: 1701. 1 hour wait' & >>>>> >>>>> $ ab -k -c 8 -n 10240 http://127.0.0.1:1701/bytes/32 >>>>> This is ApacheBench, Version 2.3 <$Revision: 1638069 $> >>>>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>>>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>>>> >>>>> Benchmarking 127.0.0.1 (be patient) >>>>> Completed 1024 requests >>>>> Completed 2048 requests >>>>> Completed 3072 requests >>>>> Completed 4096 requests >>>>> Completed 5120 requests >>>>> Completed 6144 requests >>>>> Completed 7168 requests >>>>> Completed 8192 requests >>>>> Completed 9216 requests >>>>> Completed 10240 requests >>>>> Finished 10240 requests >>>>> >>>>> >>>>> Server Software: Zinc >>>>> Server Hostname: 127.0.0.1 >>>>> Server Port: 1701 >>>>> >>>>> Document Path: /bytes/32 >>>>> Document Length: 32 bytes >>>>> >>>>> Concurrency Level: 8 >>>>> Time taken for tests: 1.945 seconds >>>>> Complete requests: 10240 >>>>> Failed requests: 0 >>>>> Keep-Alive requests: 10240 >>>>> Total transferred: 2109440 bytes >>>>> HTML transferred: 327680 bytes >>>>> Requests per second: 5265.17 [#/sec] (mean) >>>>> Time per request: 1.519 [ms] (mean) >>>>> Time per request: 0.190 [ms] (mean, across all concurrent requests) >>>>> Transfer rate: 1059.20 [Kbytes/sec] received >>>>> >>>>> Connection Times (ms) >>>>> min mean[+/-sd] median max >>>>> Connect: 0 0 0.0 0 2 >>>>> Processing: 0 2 8.0 2 309 >>>>> Waiting: 0 1 8.0 1 309 >>>>> Total: 0 2 8.0 2 309 >>>>> >>>>> Percentage of the requests served within a certain time (ms) >>>>> 50% 2 >>>>> 66% 2 >>>>> 75% 2 >>>>> 80% 2 >>>>> 90% 2 >>>>> 95% 3 >>>>> 98% 3 >>>>> 99% 3 >>>>> 100% 309 (longest request) >>>>> >>>>> >>>>> More than 5K req/s (10K requests, 8 concurrent clients). >>>>> >>>>> Granted, this is only for just 32 bytes payload and the loopback network interface. But this is the other end of the interval, the maximum speed. >>>>> >>>>> A more realistic payload (7K HTML) gives the following: >>>>> >>>>> >>>>> $ ab -k -c 8 -n 10240 http://127.0.0.1:1701/dw-bench >>>>> This is ApacheBench, Version 2.3 <$Revision: 1638069 $> >>>>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>>>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>>>> >>>>> Benchmarking 127.0.0.1 (be patient) >>>>> Completed 1024 requests >>>>> Completed 2048 requests >>>>> Completed 3072 requests >>>>> Completed 4096 requests >>>>> Completed 5120 requests >>>>> Completed 6144 requests >>>>> Completed 7168 requests >>>>> Completed 8192 requests >>>>> Completed 9216 requests >>>>> Completed 10240 requests >>>>> Finished 10240 requests >>>>> >>>>> >>>>> Server Software: Zinc >>>>> Server Hostname: 127.0.0.1 >>>>> Server Port: 1701 >>>>> >>>>> Document Path: /dw-bench >>>>> Document Length: 7734 bytes >>>>> >>>>> Concurrency Level: 8 >>>>> Time taken for tests: 7.874 seconds >>>>> Complete requests: 10240 >>>>> Failed requests: 0 >>>>> Keep-Alive requests: 10240 >>>>> Total transferred: 80988160 bytes >>>>> HTML transferred: 79196160 bytes >>>>> Requests per second: 1300.46 [#/sec] (mean) >>>>> Time per request: 6.152 [ms] (mean) >>>>> Time per request: 0.769 [ms] (mean, across all concurrent requests) >>>>> Transfer rate: 10044.25 [Kbytes/sec] received >>>>> >>>>> Connection Times (ms) >>>>> min mean[+/-sd] median max >>>>> Connect: 0 0 0.0 0 0 >>>>> Processing: 1 6 183.4 1 7874 >>>>> Waiting: 1 6 183.4 1 7874 >>>>> Total: 1 6 183.4 1 7874 >>>>> >>>>> Percentage of the requests served within a certain time (ms) >>>>> 50% 1 >>>>> 66% 1 >>>>> 75% 1 >>>>> 80% 1 >>>>> 90% 1 >>>>> 95% 1 >>>>> 98% 1 >>>>> 99% 1 >>>>> 100% 7874 (longest request) >>>>> >>>>> >>>>> That is more than 1K req/s. >>>>> >>>>> In both cases we are talking about sub 1ms req/resp cycles ! >>>>> >>>>> I think all commercial users of Pharo today know what is possible and what needs to be done to achieve their goals. Pure speed might not be the main consideration, ease/speed/joy of development and just being capable of solving complex problems and offering compelling solutions to end users is probably more important. >>>>> >>>>> Sven >>>>> >>>>> >>>>> >>>> >>>> >>> >> >> >> > >

Dec. 16, 2016

Re: [Pharo-users] real world pharo web application set ups

by Sven Van Caekenberghe

> On 16 Dec 2016, at 11:33, Volkert <volkert(a)nivoba.de> wrote: > > come on, i am only interested in what set up Pharo is currently used (as mentioned in my initial question). > This gives me a feeling, if my requirements are nearby the requirement found in current pharo > based systems ... if i am complete out of population of current pharo systems, this is for me a good > indication to bet not on it ... Well, yes. Norbert's conclusion (last two points) was spot on. You can do in the order of 1K req/s on a single image. If you want more, you need to scale (horizontally). Either you don't share state and you can do that easily. Or you do share state and you will have to build something custom (perfectly doable, but you will have to architect/design for that, preferably upfront). Note that the initial versions of all successful web apps that now serve millions of people on thousands of servers all started with very simple, inferior technology stacks. Make something great first, scale later. > On 16.12.2016 10:41, Norbert Hartl wrote: >> I'm still not sure about what we are talking. There are some many opinions regarding totally different things. >> >> These benchmark don't say much. As Sven and you did the benchmark on different machines they are hard to compare in numbers. It is not that important because you can not make many conclusions from a micro benchmark. So what Sven has proven is the fact that there is no limit for pharo per se to handle 1k requests per second and more. >> >> From these 1k req/s to Joachims 5 reqs/s is big difference. You can always assume there is something blocking the vm or a synchron I/O call takes a lot of time. But it is not helpful either because that is an edge case like Svens test with an app that does nothing. I would even state that it is not that easy to produce a situation like Joachim describes. If you have that kind of a problem than I'm pretty sure the reasons are mostly not pharo related. Sure if it comes to blocking I/O then it is pharo's fault because it cannot do async I/O, yet. But a slow database query is not the fault of pharo and you will experience the exact same thing in any other runtime. >> >> Whatever it will be there is no other way then to measure your exact use case and find the bottlenecks that prevent your app from being able to handle 1000 concurrent requests. While I agree with a lot of points mentioned in this thread I cannot share the general notion of saying that you reduce the number of requests per image and "just" use more images and more machines. That is not true. >> The moment you cannot deal with all your requests in a single image you are in trouble. As soon as there is a second image you need to make sure there is no volatile shared state between those images. You need to take caution then. Scaling up using more images and more machines shifts problem to the database because it is a central component that is not easy to scale. But again it is not pharo's fault either. >> >> So I would state two things: >> >> - We are talking about really high numbers of requests/s. The odds you are getting in this kind of scaling trouble are usually close to zero. It means you need to generate an application that has really many users. Most projects we know end up using a single image for everything. >> - Whenever you have performance problems in your application architecture I'm pretty sure pharo is not in the top of the list of bottlenecks. >> >> So yes, you can handle pretty huge numbers using pharo. >> >> Norbert >> >>> Am 16.12.2016 um 09:57 schrieb volkert <volkert(a)nivoba.de>: >>> >>> Sven, >>> >>> compare with an erlang vm (Cowboy) on a standard pc, i5-4570 CPU @ 3.20GHz Ã 4, on linux ... >>> >>> Conncurrent request: 8 >>> >>> $ ab -k -c 8 -n 10240 http://127.0.0.1:8080/ >>> This is ApacheBench, Version 2.3 <$Revision: 1706008 $> >>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>> >>> Benchmarking 127.0.0.1 (be patient) >>> Completed 1024 requests >>> Completed 2048 requests >>> Completed 3072 requests >>> Completed 4096 requests >>> Completed 5120 requests >>> Completed 6144 requests >>> Completed 7168 requests >>> Completed 8192 requests >>> Completed 9216 requests >>> Completed 10240 requests >>> Finished 10240 requests >>> >>> >>> Server Software: >>> Server Hostname: 127.0.0.1 >>> Server Port: 8080 >>> >>> Document Path: / >>> Document Length: 7734 bytes >>> >>> Concurrency Level: 8 >>> Time taken for tests: 0.192 seconds >>> Complete requests: 10240 >>> Failed requests: 0 >>> Keep-Alive requests: 10143 >>> Total transferred: 80658152 bytes >>> HTML transferred: 79196160 bytes >>> Requests per second: 53414.29 [#/sec] (mean) >>> Time per request: 0.150 [ms] (mean) >>> Time per request: 0.019 [ms] (mean, across all concurrent requests) >>> Transfer rate: 410871.30 [Kbytes/sec] received >>> >>> Connection Times (ms) >>> min mean[+/-sd] median max >>> Connect: 0 0 0.0 0 0 >>> Processing: 0 0 0.2 0 3 >>> Waiting: 0 0 0.2 0 3 >>> Total: 0 0 0.2 0 3 >>> >>> Percentage of the requests served within a certain time (ms) >>> 50% 0 >>> 66% 0 >>> 75% 0 >>> 80% 0 >>> 90% 0 >>> 95% 1 >>> 98% 1 >>> 99% 1 >>> 100% 3 (longest request) >>> >>> >>> And here with 1000 concurrent request ... >>> >>> $ab -k -c 1000 -n 10240 http://127.0.0.1:8080/ >>> This is ApacheBench, Version 2.3 <$Revision: 1706008 $> >>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>> >>> Benchmarking 127.0.0.1 (be patient) >>> Completed 1024 requests >>> Completed 2048 requests >>> Completed 3072 requests >>> Completed 4096 requests >>> Completed 5120 requests >>> Completed 6144 requests >>> Completed 7168 requests >>> Completed 8192 requests >>> Completed 9216 requests >>> Completed 10240 requests >>> Finished 10240 requests >>> >>> >>> Server Software: >>> Server Hostname: 127.0.0.1 >>> Server Port: 8080 >>> >>> Document Path: / >>> Document Length: 7734 bytes >>> >>> Concurrency Level: 1000 >>> Time taken for tests: 0.225 seconds >>> Complete requests: 10240 >>> Failed requests: 0 >>> Keep-Alive requests: 10232 >>> Total transferred: 80660288 bytes >>> HTML transferred: 79196160 bytes >>> Requests per second: 45583.23 [#/sec] (mean) >>> Time per request: 21.938 [ms] (mean) >>> Time per request: 0.022 [ms] (mean, across all concurrent requests) >>> Transfer rate: 350642.85 [Kbytes/sec] received >>> >>> Connection Times (ms) >>> min mean[+/-sd] median max >>> Connect: 0 1 3.3 0 23 >>> Processing: 0 6 16.1 0 198 >>> Waiting: 0 6 16.1 0 198 >>> Total: 0 7 18.0 0 211 >>> >>> Percentage of the requests served within a certain time (ms) >>> 50% 0 >>> 66% 2 >>> 75% 6 >>> 80% 10 >>> 90% 21 >>> 95% 32 >>> 98% 47 >>> 99% 108 >>> 100% 211 (longest request) >>> >>> >>> >>> Am 15.12.2016 um 15:00 schrieb Sven Van Caekenberghe: >>>> Joachim, >>>> >>>>> On 15 Dec 2016, at 11:43, jtuchel(a)objektfabrik.de wrote: >>>>> >>>>> Victor, >>>>> >>>>> Am 14.12.16 um 19:23 schrieb Vitor Medina Cruz: >>>>>> If I tell you that my current estimate is that a Smalltalk image with Seaside will not be able to handle more than 20 concurrent users, in many cases even less. >>>>>> >>>>>> Seriously? That is kinda a low number, I would expect more for each image. Certainly it depends much on many things, but it is certainly very low for a rough estimate, why you say that? >>>>> seriously, I think 20 is very optimistic for several reasons. >>>>> >>>>> One, you want to be fast and responsive for every single user, so there is absolutely no point in going too close to any limit. It's easy to lose users by providing bad experience. >>>>> >>>>> Second, in a CRUD Application, you mostly work a lot with DB queries. And you connect to all kinds of stuff and do I/O. Some of these things simply block the VM. Even if that is only for 0.3 seconds, you postpone processing for each "unaffected" user by these 0.3 seconds, so this adds to significant delays in response time. And if you do some heavy db operations, 0.3 seconds is not a terribly bad estimate. Add to that the materialization and stuff within the Smalltalk image. >>>>> >>>>> Seaside adapters usually start off green threads for each request. But there are things that need to be serialized (like in a critical Block). So in reality, users block each other way more often than you'd like. >>>>> >>>>> So if you asked me to give a more realistic estimation, I'd correct myself down to a number between 5 and probably a maximum of 10 users. Everything else means you must use all those fancy tricks and tools people mention in this thread. >>>>> So what you absolutely need to do is start with an estimate of 5 concurrent users per image and look for ways to distribute work among servers/images so that these blocking situations are down to a minimum. If you find your software works much better, congratulate yourself and stack up new machines more slowly than initially estimated. >>>>> >>>>> >>>>> Before you turn around and say: Smalltalk is unsuitable for the web, let's take a brief look at what concurrent users really means. Concurrent users are users that request some processing from the server at they very same time (maybe within an interval of 200-400msec). This is not the same as 5 people being currently logged on to the server and requesting something sometimes. 5 concurrent users can be 20, 50, 100 users who are logged in at the same time. >>>>> >>>>> Then there is this sad "share all vs. share nothing" argument. In Seaside you keep all your objects alive (read from db and materialized) between web requests. IN share nothing, you read everything back from disc/db whenever a request comes in. This also takes time and ressources (and pssibly blocks the server for the blink of an eye or two). You exchange RAM with CPU cycles and I/O. It is extremely hard to predict what works better, and I guess nobody ever made A/B tests. It's all just theoretical bla bla and guesses of what definitely must be better in one's world. >>>>> >>>>> Why do I come up with this share everything stuff? Because it usually means that each user that is logged on holds onto a load of objects on the server side (session storage), like their user account, shopping card, settings, last purchases, account information and whatnot. That's easily a list of a few thousand objects (and be it only Proxies) that take up space and want to be inspected by the garbage collector. So each connected user not only needs CPU cycles whenever they send a request to the server, but also uses RAM. In our case, this can easily be 5-10 MB of objects per user. Add to that the shadow copies that your persistence mechanism needs for undo and stuff, and all the data Seaside needs for Continuations etc, and each logged on users needs 15, 20 or more MB of object space. Connect ten users and you have 150-200 MB. That is not a problem per se, but also means there is some hard limit, especially in a 32 bit world. You don't want your server to slow down because it cannot allocate new memory or can't find contiguous slots for stuff and GCs all the time. >>>>> >>>>> To sum up, I think the number of influencing factors is way too high to really give a good estimate. Our experience (based on our mix of computation and I/O) says that 5 concurrent users per image is doable without negative impact on other users. Some operations take so much time that you really need to move them out of the front-facing image and distribute work to backend servers. More than 5 is probably possible but chances are that there are operations that will affect all users and with every additional user there is a growing chance that you have 2 or more requesting the yery same operation within a very short interval. This will make things worse and worse. >>>>> >>>>> So I trust in you guys having lots of cool tools around and knowing loads of tricks to wrench out much more power of a single Smalltalk image, but you also need to take a look at your productivity and speed in creating new features and fixing bugs. Sometimes throwing hardware at a problem like growth and starting with a clever architecture to scale on multiple layers is just the perfect thing to do. To me, handling 7 instead of 5 concurrent users is not such a big win as long as we are not in a posotion where we have so many users that this really matters. For sites like Amazon, Google, Facebook etc. saving 40% in server cost by optimizing the software (investing a few man years) is significant. I hope we'll soon change our mind about this question ;-) >>>>> >>>>> So load balancing and services outsourced to backend servers are key to scalability. This, btw, is not smalltalk specific (some people seem to think you won't get these problems in Java or Ruby because they are made for the web...). >>>>> >>>>> Joachim >>>> Everything you say, all your considerations, especially the last paragraph is/are correct and I agree. >>>> >>>> But some people will only remember the very low number you seem to be suggesting (which is more of a worse case scenario, with Seaside+blocking/slow connections to back end systems). >>>> >>>> One the other hand, plain HTTP access to a Pharo image can be quite fast. Here is quick & dirty benchmark I just did on one of our modern/big machines (inside an LXD container, light load) using a single stock image on Linux. >>>> >>>> >>>> $ pharo Pharo.image printVersion >>>> [version] 4.0 #40626 >>>> >>>> $ pharo Pharo.image eval 'ZnServer startDefaultOn: 1701. 1 hour wait' & >>>> >>>> $ ab -k -c 8 -n 10240 http://127.0.0.1:1701/bytes/32 >>>> This is ApacheBench, Version 2.3 <$Revision: 1638069 $> >>>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>>> >>>> Benchmarking 127.0.0.1 (be patient) >>>> Completed 1024 requests >>>> Completed 2048 requests >>>> Completed 3072 requests >>>> Completed 4096 requests >>>> Completed 5120 requests >>>> Completed 6144 requests >>>> Completed 7168 requests >>>> Completed 8192 requests >>>> Completed 9216 requests >>>> Completed 10240 requests >>>> Finished 10240 requests >>>> >>>> >>>> Server Software: Zinc >>>> Server Hostname: 127.0.0.1 >>>> Server Port: 1701 >>>> >>>> Document Path: /bytes/32 >>>> Document Length: 32 bytes >>>> >>>> Concurrency Level: 8 >>>> Time taken for tests: 1.945 seconds >>>> Complete requests: 10240 >>>> Failed requests: 0 >>>> Keep-Alive requests: 10240 >>>> Total transferred: 2109440 bytes >>>> HTML transferred: 327680 bytes >>>> Requests per second: 5265.17 [#/sec] (mean) >>>> Time per request: 1.519 [ms] (mean) >>>> Time per request: 0.190 [ms] (mean, across all concurrent requests) >>>> Transfer rate: 1059.20 [Kbytes/sec] received >>>> >>>> Connection Times (ms) >>>> min mean[+/-sd] median max >>>> Connect: 0 0 0.0 0 2 >>>> Processing: 0 2 8.0 2 309 >>>> Waiting: 0 1 8.0 1 309 >>>> Total: 0 2 8.0 2 309 >>>> >>>> Percentage of the requests served within a certain time (ms) >>>> 50% 2 >>>> 66% 2 >>>> 75% 2 >>>> 80% 2 >>>> 90% 2 >>>> 95% 3 >>>> 98% 3 >>>> 99% 3 >>>> 100% 309 (longest request) >>>> >>>> >>>> More than 5K req/s (10K requests, 8 concurrent clients). >>>> >>>> Granted, this is only for just 32 bytes payload and the loopback network interface. But this is the other end of the interval, the maximum speed. >>>> >>>> A more realistic payload (7K HTML) gives the following: >>>> >>>> >>>> $ ab -k -c 8 -n 10240 http://127.0.0.1:1701/dw-bench >>>> This is ApacheBench, Version 2.3 <$Revision: 1638069 $> >>>> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ >>>> Licensed to The Apache Software Foundation, http://www.apache.org/ >>>> >>>> Benchmarking 127.0.0.1 (be patient) >>>> Completed 1024 requests >>>> Completed 2048 requests >>>> Completed 3072 requests >>>> Completed 4096 requests >>>> Completed 5120 requests >>>> Completed 6144 requests >>>> Completed 7168 requests >>>> Completed 8192 requests >>>> Completed 9216 requests >>>> Completed 10240 requests >>>> Finished 10240 requests >>>> >>>> >>>> Server Software: Zinc >>>> Server Hostname: 127.0.0.1 >>>> Server Port: 1701 >>>> >>>> Document Path: /dw-bench >>>> Document Length: 7734 bytes >>>> >>>> Concurrency Level: 8 >>>> Time taken for tests: 7.874 seconds >>>> Complete requests: 10240 >>>> Failed requests: 0 >>>> Keep-Alive requests: 10240 >>>> Total transferred: 80988160 bytes >>>> HTML transferred: 79196160 bytes >>>> Requests per second: 1300.46 [#/sec] (mean) >>>> Time per request: 6.152 [ms] (mean) >>>> Time per request: 0.769 [ms] (mean, across all concurrent requests) >>>> Transfer rate: 10044.25 [Kbytes/sec] received >>>> >>>> Connection Times (ms) >>>> min mean[+/-sd] median max >>>> Connect: 0 0 0.0 0 0 >>>> Processing: 1 6 183.4 1 7874 >>>> Waiting: 1 6 183.4 1 7874 >>>> Total: 1 6 183.4 1 7874 >>>> >>>> Percentage of the requests served within a certain time (ms) >>>> 50% 1 >>>> 66% 1 >>>> 75% 1 >>>> 80% 1 >>>> 90% 1 >>>> 95% 1 >>>> 98% 1 >>>> 99% 1 >>>> 100% 7874 (longest request) >>>> >>>> >>>> That is more than 1K req/s. >>>> >>>> In both cases we are talking about sub 1ms req/resp cycles ! >>>> >>>> I think all commercial users of Pharo today know what is possible and what needs to be done to achieve their goals. Pure speed might not be the main consideration, ease/speed/joy of development and just being capable of solving complex problems and offering compelling solutions to end users is probably more important. >>>> >>>> Sven >>>> >>>> >>>> >>> >>> >> > > >

Dec. 16, 2016