Weblogic capacity sizing - oracle

Please can anyone suggest.We are trying to estimate how manay managed server instances are required in a weblogic domain and how many servers would we need as well.
Our estimate is that on there will be 10,000 concurrent users(doesn't include logged in users) for the application
and about 400 transactions per second and each http session will carry around 40kB data..
How many managed servers would we
ideally need ?
How many windows vm's & cpu's would we need ideally.Each windows vm has memory of 4gb
We are planning to allocate atleast 1 Gb memory to each managed server.
the weblogic environment will be on virtualisation farm with each vm having around 5GB memory..
Many thanks

In my experience, it is impossible to size an architecture armed with just numbers like this. I realize that's all you got right now, but I would recommend you set up one of your servers, place a servlet there that simulates your transaction time and use a tool like JMeter to hit it to see where it breaks.
BTW, there can be many other bottlenecks - is your database up to handling 400 transactions/sec?

Related

10k Concurrent connections with jmeter

I have a 32GB, i7 core processor running on windows 10 and I am trying to generate 10kVU concurrent load via jmeter. For some reason I am unable to go beyond 1k concurrent and I start getting BindException error or Socket connection error. Can someone help me with the settings to achieve that kind of load? Also if someone is up for freelancing I am happy to consider that as well. Any help would be great as I am nearing production and am unable to load test this use case. If you guys have any other tools that I can use effectively, that would also help.
You reach the limit of 1 computer, thus you must execute in distributed environment of multiple computers.
You can setup JMeter's distributed testing on your own environment or use blazemeter or other cloud based load testing tool
we can use BlazeMeter, which provides us with an easy way to handle our load tests. All we need to do is to upload our JMX file to BlazeMeter. We can also upload a consolidated CSV file with all the necessary data and BlazeMeter will take care of splitting it, depending on the amount of engines we have set.
On BlazeMeter we can set the amount of users or the combination of engines (slave systems) and threads that we want to apply to our tests. We can also configure additional values like multi locations.
1k concurrent sounds low enough that it's something else ... it's also the default amount of open file descriptor limits on a lot of Linux distributions so maybe try to raise the limit.
ulimit -Sn
will show you your current limit and
ulimit -Hn
will show you the hard limit you can go before you have to touch configuration files. Editing /etc/security/limits.conf as root and setting something like
yourusername soft nofile 50000
yourusername hard nofile 50000
yourusername - will have to be the username of the user which with you run jmeter.
After this you will probably have to restart in order for the changes to take effect. If not on Linux I don't know how to actually do this you will have to google :D
Recommendation:
As a k6 developer I can propose it as an alternative tool, but running 10k VUs on a single machine will be hard with it as well. Every VU will take some memory - like at least 1-3mb and this will go up the larger your script is. But with 32gb you could still run upto 1-2kVUs and use http.batch to make concurrent requests which might simulate the 10k VUs depending on what your actual workflow is like.
I managed to run the stages sample with 300VUs on a single 3770 i7 CPU and 4gb of ram in a virtual machine and got 6.5k+ rps to another virtual machine on a neighboring physical machine (the latency is very low) so maybe 1.5-2kVUs with a a somewhat more interesting script and some higher latency as this will give time to the golang to actually run GC while waiting for tcp packets. I highly recommend using discardResponseBodies if you don't need them and even if you need some to actually get the response for them. This helps a lot with the memory consumption a each VU

How many users should a EC2 Micro Instance be able to handle only with a nginx server?

I have a iOS Social App.
This app talks to my server to do updates & retrieval fairly often. Mostly small text as JSON. Sometimes users will upload pictures that my web-server will then upload to a S3 Bucket. No pictures or any other type of file will be retrieved from the web-server
The EC2 Micro Ubuntu 13.04 Instance runs PHP 5.5, PHP-FPM and NGINX. Cache is handled by Elastic Cache using Redis and the database connects to a separate m1.large MongoDB server. The content can be fairly dynamic as newsfeed can be dynamic.
I am a total newbie in regards to configuring NGINX for performance and I am trying to see whether I've configured my server properly or not.
I am using Siege to test my server load but I can't find any type of statistics on how many concurrent users / page loads should my system be able to handle so that I know that I've done something right or something wrong.
What amount of concurrent users / page load should my server be able to handle?
I guess if I cant get hold on statistic from experience what should be easy, medium, and extreme for my micro instance?
I am aware that there are several other questions asking similar things. But none provide any sort of estimates for a similar system, which is what I am looking for.
I haven't tried nginx on microinstance for the reasons Jonathan pointed out. If you consume cpu burst you will be throttled very hard and your app will become unusable.
IF you want to follow that path I would recommend:
Try to cap cpu usage for nginx and php5-fpm to make sure you do not go over the thereshold of cpu penalities. I have no ideia what that thereshold is. I believe the main problem with micro instance is to maintain a consistent cpu availability. If you go over the cap you are screwed.
Try to use fastcgi_cache, if possible. You want to hit php5-fpm only if really needed.
Keep in mind that gzipping on the fly will eat alot of cpu. I mean alot of cpu (for a instance that has almost none cpu power). If you can use gzip_static, do it. But I believe you cannot.
As for statistics, you will need to do that yourself. I have statistics for m1.small but none for micro. Start by making nginx serve a static html file with very few kb. Do a siege benchmark mode with 10 concurrent users for 10 minutes and measure. Make sure you are sieging from a stronger machine.
siege -b -c10 -t600s 'http:// private-ip /test.html'
You will probably see the effects of cpu throttle by just doing that! What you want to keep an eye on is the transactions per second and how much throughput can the nginx serve. Keep in mind that m1small max is 35mb/s so m1.micro will be even less.
Then, move to a json response. Try gzipping. See how much concurrent requests per second you can get.
And dont forget to come back here and report your numbers.
Best regards.
Micro instances are unique in that they use a burstable profile. While you may get up two 2 ECU's in terms of performance for a short period of time, after it uses its burstable allotment it will be limited to around 0.1 or 0.2 ECU. Eventually the allotment resets and you can get 2 ECU's again.
Much of this is going to come down to how CPU/Memory heavy your application is. It sounds like you have it pretty well optimized already.

How to reduce memory usage on windows azure shared website?

I have a site hosted on Windows Azure shared websites. It just got suspended for going over memory usage limit of 512MB/hour.
I do use .net caching rather heavily (to prevent multiple calls to database/external APIs, etc...).
Is that caching a no-no in shared websites on Windows Azure?
Do you use System.Runtime.Cache? You should be able to limit the amount of caching e.g. the memorycache object uses. See http://msdn.microsoft.com/en-us/library/dd941874.aspx for more information.
Even if you will stop using Cache it still can be used by framework/libs. I also have same problem (interesting, that in free mode memory limit is 1024MB, but shared one is lowered to 512).
As I see, memory amount that Azure shows on portal seems very close to System.Diagnostics.Process.GetCurrentProcess().PrivateMemorySize value.
At this moment I'm experimenting with caching settings to set maximum memory:
<system.web>
<caching>
<cache privateBytesLimit="250000000" privateBytesPollTime="00:00:15"/>
</caching>
</system.web>
Several days ago I set 300MB but several minutes ago got suspended again :(, so lowering to 250MB.
But anyway, this is very unclear, strange and "wrong" solution imho.
UPDATE
Got suspended again this morning. Temporarily converted to standard mode with small instance (1.7 GB RAM).
My WorkingSet counter now is about 200 megs now (with PeakWorkingSet 330 megs). BUT! GC's CollectionCount is increased approx 8 times (Gen0 is 1800 times instead of 250 for less that a day).
My current theory is that in "shared" mode websites are running inside "big" VM with a lot of memory and Garbage Collector just not have a need to run often, leading to longer "garbage life" and more memory consumption.
Have no access to my developer computer right now for some verification, but planing to convert site to web role in cloud service ASAP - with extra small instance (cost is comparable to shared web site cost)...
Might be worth checking a profile using perfmon on your local machine to see if what if its hitting the limits normally first, then look at maybe configuring the logging on Azure and again digging through it.
Also ensuring everything is precompiled and that your not loading and modules etc you don't need can really effect performance etc on Azure.
I think what you might want to try here is scale our instead of up. If you add a second instance that will double your resource limit.

Memory footprint for large systems in Vaadin

I'm working in financial sector and we are about to select Vaadin 7 for development of large heavy load system.
But I'm a bit worried about Vaadin memory footprint for large systems since Vaadin keeps all state in session. It means that for every new user all application state will be stored in memory, won't it?
We cannot aford to build monolithic system - system must be scalable and agile instead. Since we have huge client base it must be easy to customize and ready to grow.
Could anyone please share the experience and possible workarounds how to minimize or eliminate those problems in Vaadin?
During the development of our products we faced the problem of large memory footprint using the default Vaadin architecture.
The Vaadin architecture is based on components driven by events. Using components is fairly simple to create a tightly coupled application. The reason is that components are structured into a hierarchy. It's like a pyramid. The larger application is built; the larger pyramid is stored in the session for each user.
In order to significantly reduce the memory allocation we've created a page based approach for the application with a comprehensive event model on the background using the old school state management. It is based on the Statechart notation in XML format.
As the result the session keeps only visited pages during the user workflow, described by the Statechart configuration. When the user finishes the workflow, all the pages are released to be collected by garbage collector.
To see the difference we have done some tests to compare memory allocated for the user working with the application.
The applications developed:
with tightly coupled approach consume from 5 to 15MB of heap per user
with loose-coupled approach - up to 2 MB
We are quite happy with results since it let us scale the large system using 4GB RAM up to 1000-1500 concurrent users per server.
Almost forgot. We used Lexaden Web Flow library. It is with Apache license.
I think you should have a look here: https://vaadin.com/blog/-/blogs/vaadin-scalability-study-quicktickets
Plus, I have found the following info by people who run Vaadin in production.
Balázs Hódossy:
We have a back office system with more than 10 000 users. The daily
user number is about 3000 but half of them use the system 8 hours
without logout. We use Liferay 6.0.5 Tomcat bundle and Vaadin as
portlet. Our two servers have 48 GB RAM and we give Tomcat 24 GB heap.
DB got 18 GB and the system the rest. Measure the heap to the session
size, concurrent users, and the activity. More memory cause more
rarely but longer full GC. We plan to increase the number of Tomcat
workers and reduce the heap. When you measure your server, try to add
a little bit more memory. If the cost is so important than decrease
the processor cost and buy more RAM. Most of the time it is valuable
with a little tuning.
Pierre-Emmanuel Gros:
For 1000 dayly user heavyly used , a pure vaadin application: Server
3 gb 2 core Jetty with ulimit to 50000 Postgresql 9 with 50
concurent users ( a connection pool is used). As software part, I used also ehcache to cache DTO objects,and pure JDBC.

how does windows azure platform scale for my app?

Just a question about Azure.
Yes, I know roughly about Azure and cloud computing. I will put it in this way:
say, in normal way, I build a program listening to a TCP port. I run this server program in a server. I also build a client program, which connects to the server through specified port. Once a client is connected, my server program will compute some thing and return to the client.
Above is the normal model, or say my program's model.
Now I want to use Azure. I want to use because my clients are too many, let's say 1 million a day. I don't want to rent 1000 servers and maintain them. ( just a assumption for the number of clients)
I have looked at the Azure pricing plan. It say about CPU and talks about small, median, large instances.
I don't know what they mean. for e.g., in my above assumed case, how many instances do I need? or at most I can get from azure for extra large (8 small instances?)
How does Azure scale for my program? If I choose small instance (my server program is very little, just compute some data and return to clients), will Azure scale for me? or Azure just gives me one virture server and let it overload?
Please consider the CPU only, not storage or network traffic.
You choose two things: what size of VM to run (small, medium, large) and how many of those VMs to run. That means you could choose a small VM (single processor) and run 100 "instances" of it (100 VMs), or you could choose a large VM (eight processors on the same server) and run 10 instances of it (10 VMs).
Today, Windows Azure doesn't automatically adjust your scale, so it's up to you to use the web portal or the Service Management API to increase the number of instances as your need increases.
One factor to consider is if your app can take advantage of multi-core environments - multi-thread, shared memory, etc. to improve its scale. If it can, it may be better to use 5 2x core (i.e. medium) VMs than 10 1x core (small) VMs. You may find in some cases that 2 4x core VMs perform better than 5 2core.
If your app is not parallel/multi-core, then you could just do some 'x' number of small VMs. The charges are linear anyway - i.e. a 2core VM is twice the cost of a single core.
Other factors would include the scratch disk size & memory available in the VM.
One other suggestion - you may want to look into leveraging the Azure queues (i.e. have the client post to the queue and the workers pull from there). This would allow you to transparently (to the client) increase/decrease the workers w/out worrying about connections, etc. Also, if a processing step failed and crashed your instance the message would persist and be picked up by one of the others.
I suggest you also monitor, evaluate, and perfect the results of your Azure configuration.
For "Monitoring Applications in Windows Azure" (and performance) please reference
http://channel9.msdn.com/learn/courses/Azure/Deployment/DeployingApplicationsinWindowsAzure/Exercise-3-Monitoring-Applications-in-Windows-Azure/
There is also a good blog entry titled "Visualizing Windows Azure diagnostic data"
Check out http://www.paraleap.com - simple service for automatically adjusting number of instances that you have according to demand.

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