Wildfly 10 tuning - wildfly

I have a JSF application hosted on Wildfly 10. For some reason when web clients are 75+ I see blank page.
Probably there is a default threat pool limit in Wildfly? Can you give some general advice how I can solve the problem?

Have you tried inspecting the memory usage of the server on this load.
You can get very useful stats using VisualVM.
If you see the server is running out of memory, solution could be as simple as
increasing the heap memory, might as well want to tweek MetaspaceSize parameter.
One recommendation would be to use jmeter and gradually increase the load and check stats using profiling tool.
This will help you narrow down if you have some memory leaks in your application it self.

Related

Oracle-SQL Developer consumes lot of system's memory. Has anyone faced issues like this. If so is there is solution to avoid this

I'm using 19.2 version of SQL Developer and I have switched from TOAD.
Offlate when I lock up my system and open after an hour or two, I see my system boots up very slowly.
Most of the system's memory is consumed by Oracle SQL developer. Please see attached screenshot.
Is there any way to avoid the above issues?

Magento 2 website goes down every day and need to restart server

I have one e-commerce website in Magento 2.2.2 and it keeps on going down almost every day. Whenever it goes down, users get site took too long too respond and it never loads. To get web site working again I have to restart the server and then it works.
Total space on the server is 50GB. Out of which the whole website is around 18GB (11GB Media files and then vendor files etc.). Here are things that i cannot figure out why:
a.) The server shows that 33GB has been used although it should show only 18GB has been used. I have checked everywhere and I can't find what is consuming additional 15GB of space. Complete HTML folder is only 18GB.
b.) When I checked log files: it shows the following:
WARNING: Memory size allocated for the temporary table is more than 20% of innodb_buffer_pool_size. Please update innodb_buffer_pool_size or decrease batch size value (which decreases memory usages for the temporary table). Current batch size: 100000; Allocated memory size: 280000000 bytes; InnoDB buffer pool size: 1073741824 bytes.
I have already set innodb_buffer_pool_size to 2GB. But still, this problem keeps coming.
The server is an Amazon EC2 server and Magento is in production mode. Can allocating 100GB instead of 50GB will solve the problem?
Increased innodb buffer pool size to 10GB and logs do not show error anymore but server still goes down every day. Since RAM is only 4GB on our server, can that be the main cause? Because everyone is suggesting at least 8GB RAM?
Try the things below.
Magento2 has big log files and caching system. There may be increase your files in var folder.
But still you have to check whether your site belongs to more than 3000 products with high size images for products and you are storing all these in your server itself.
The suggestions what I can give, If your site have more products which I already mentioned better you have to use CDN for better performance. So the entire image will be process from the third party.
Next is You have to setup cloud flare to avoid the down time errors or customer side effect. You can make your index page to load while the server is down. And obviously you have to write script to restart the site automatically while its down.
In your server side check the memory size for php, you can better to give to 2G.
In Mysql side : Check the whether its making sleep query or not. If its making through your custom extension area ask your developer to optimize the code.
for eg : May be the code passing 'collection' for a single item fetch.
You can use the tool like nurelic
If everything is fine from your developer area please try to optimize the site with making memory limit mysql killing etc.. with your server side.
In the mean while magento is a big platform for e-commerce sector, so it has more area to cover by default. Better to avoid the unwanted modules from your active site, like disable the core modules which you are not using yet.
For an average site Use 16gb RAM,
A restart your mysql to make it effect ?
Also you need to set that buffer up to 20971520000, thats around 20GB.
Magento uses a lot of sessions and cache.

What are the limitations of the flask built-in web server

I'm a newbie in web server administration. I've read multiple times that flask built-in web server is not designed for "production", and must be used only for tests and debug...
But what if my app touchs only a thousand users who occasionnaly send data to the server ?
If it works, when will I have to bother with the configuration of a more sophisticated web server ? (I am looking for approximative metrics).
In a nutshell, I would love to find what the builtin web server can do (with approx thresholds) and what it cannot.
Thanks a lot !
There isn't one right answer to this question, but here are some things to keep in mind:
With the right amount of horizontal scaling, it is quite possible you could keep scaling out use of the debug server forever. When exactly you would need to start scaling (or switch to using a "real" web server) would also depend on the environment you are hosting in, the expectations of the users, etc.
The main issue you would probably run into is that the server is single-threaded. This means that it will handle each request one at a time, serially. This means that if you are trying to serve more than one request (including favicons, static items like images, CSS and Javascript files, etc.) the requests will take longer. If any given requests happens to take a long time (say, 20 seconds) then your entire application is unresponsive for that time (20 seconds). This is only the default, of course: you could bump the thread counts (or have requests be handled in other processes), which might alleviate some issues. But once again, it can still be slow under a "high" load. What is considered a "high" load will be dependent on your application and the expectations of a maximum acceptable response time.
Another issue is security: if you are concerned at ALL about security (and not just the security of the data in the application itself, but the security of the box that will be running it as well) then you should not use the development server. It is not ready to withstand any sort of attack.
Finally, the development server could just fail outright. It is not designed to be used as a long-running process (days, weeks, months), and so it has not been well tested to work in this capacity.
So, yes, it has limitations. Yes, you could still conceivably use it in production. And yes, I would still recommend using a "real" web server. If you don't like the idea of needing to install something like Apache or Nginx, you can still go with a solution that is still as easy as "run a python script" by using some of the WSGI Standalone servers, which can run a server that is designed to be in production with something just as simple as running python run_app.py in the command line. You typically just need to create a 4-5 line python script to import and create the server object, point it to your Flask app, and run it.
gunicorn could be run with only the following on the command line, no extra script needed:
gunicorn myproject:app
...where "myproject" is the Python package that contains the app Flask object. Keep in mind that one of developers of gunicorn would probably recommend against this approach. See https://serverfault.com/questions/331256/why-do-i-need-nginx-and-something-like-gunicorn.
The OP has long-since moved on, but for those who encounter this question in the future I would just add that setting up an Apache server, even on a laptop, is free and pretty easy. It can be readily configured for as few or as many features as you want just by uncomment in or commenting out lines in the config file. There might be an even easier GUI method for doing that nowdays, but just editing the configs is simple.

How should I benchmark a system to determine the overall best architecture choice?

This is a bit of an open ended question, but I'm looking for an open ended answer. I'm looking for a resource that can help explain how to benchmark different systems, but more importantly how to analyze the data and make intelligent choices based on the results.
In my specific case, I have a 4 server setup that includes mongo that serves as the backend for an iOS game. All servers are running Ubuntu 11.10. I've read numerous articles that make suggestions like "if CPU utilization is high, make this change." As a new-comer to backend architecture, I have no concept of what "high CPU utilization" is.
I am using Mongo's monitoring service (MMS), and I am gathering some information about it, but I don't know how to make choices or identify bottlenecks. Other servers serve requests from the game client to mongo and back, but I'm not quite sure how I should be benchmarking or logging important information from them. I'm also using Amazon's EC2 to host all of my instances, which also provides some information.
So, some questions:
What statistics are important to log on a backend setup? (CPU, RAM, etc)
What is a good way to monitor those statistics?
How do I analyze the statistics? (RAM usage is high/read requests are low, etc)
What tips should I know before trying to create a stress-test or benchmarking script for my architecture?
Again, if there is a resource that answers many of these questions, I don't need an explanation here, I was just unable to find one on my own.
If more details regarding my setup are helpful, I can provide those as well.
Thanks!
I like to think of performance testing as a mini-project that is undertaken because there is a real-world need. Start with the problem to be solved: is the concern that users will have a poor gaming experience if the response time is too slow? Or is the concern that too much money will be spent on unnecessary server hardware?
In short, what is driving the need for the performance testing? This exercise is sometimes called "establishing the problem to be solved." It is about the goal to be achieved-- because if there is not goal, why go through all the work of testing the performance? Establishing the problem to be solved will eventually drive what to measure and how to measure it.
After the problem is established, a next set is to write down what questions have to be answered to know when the goal is met. For example, if the goal is to ensure the response times are low enough to provide a good gaming experience, some questions that come to mind are:
What is the maximum response time before the gaming experience becomes unacceptably bad?
What is the maximum response time that is indistinguishable from zero? That is, if 200 ms response time feels the same to a user as a 1 ms response time, then the lower bound for response time is 200 ms.
What client hardware must be considered? For example, if the game only runs on iOS 5 devices, then testing an original iPhone is not necessary because the original iPhone cannot run iOS 5.
These are just a few question I came up with as examples. A full, thoughtful list might look a lot different.
After writing down the questions, the next step is decide what metrics will provide answers to the questions. You have probably comes across a lot metrics already: response time, transaction per second, RAM usage, CPU utilization, and so on.
After choosing some appropriate metrics, write some test scenarios. These are the plain English descriptions of the tests. For example, a test scenario might involve simulating a certain number of games simultaneously with specific devices or specific versions of iOS for a particular combination of game settings on a particular level of the game.
Once the scenarios are written, consider writing the test scripts for whatever tool is simulating the server work loads. Then run the scripts to establish a baseline for the selected metrics.
After a baseline is established, change parameters and chart the results. For example, if one of the selected metrics is CPU utilization versus the number of of TCP packets entering the server second, make a graph to find out how utilization changes as packets/second goes from 0 to 10,000.
In general, observe what happens to performance as the independent variables of the experiment are adjusted. Use this hard data to answer the questions created earlier in the process.
I did a Google search on "software performance testing methodology" and found a couple of good links:
Check out this white paper Performance Testing Methodology by Johann du Plessis
Have a look at the Methodology section of this Wikipedia article.

How to determine the minimum JRE version and system requirements for my Java application

I have written an application in Java using Eclipse IDE and I now need to know the minimum JRE version that is required to run the application! I know that certain methods are only available under later JREs, but I was wondering what the easiest way to find out the highest requirement of my application would be, so any suggestions would be appreciated...
Also whilst I am on the topic of requirements, I would appreciate any advice or methods for determining the minimum system requirements for my software in general - i.e minimum amount of RAM...
Thanks in advance
Method 1: For minimum JRE version, that's going to be tough. The easiest way is to simply require the same version that you're building against, or later, e.g. JRE 6.x.x or higher.
Method 2: Install multiple JDK's, making them available in Eclipse, and just change the version you're building against, running your app's test suite each time, and making sure they all pass. The earliest version of the JDK that allows all your tests to pass is the lowest JRE it can run against. Simply having your app successfully compile isn't enough, because previous versions of the JRE/JDK might have bugs that allow for successful compilation, but don't allow for proper program execution.
Method 3: Always require the latest on the client side, because Oracle is constantly patching security holes, and ultimately, it may be best to require the latest versions, if you have that kind of control, on the client side.
As far as RAM, that's easy. When the JVM starts it sets a 'maximum' amount of RAM (I believe the default may be 128MB), and that's a hard limit that your application cannot exceed without crashing. Profile your app over time, tweaking the memory settings on the JVM, and find out what the minimum amount of RAM is that you'll need for your app to run both (a) with acceptable performance, and (b) without throwing an OutOfMemoryError, and you're done.
Ref: How to configure JVM options and memory?
For other requirements such as CPU req., things get a little fuzzier. There are a lot of CPUs out there, and the throughput that a given system produces can vary not just based on CPU speed, but the speed of the hard drive, the amount of RAM installed in the system, the speed of the network interface (if you're writing a network app), and other things. For requirements such as that, you'll want to just test it on a variety of systems and sort of draw a line somewhere, and say, "You can expect acceptable performance if you have hardware that is at least as powerful as X, Y, Z".
The other thing you could do is build in a benchmark, or some kind of performance logging, and have that performance data sent back to you. Lots of apps do this. You know that "May we send anonymous usage data back to the mothership?" question you get when installing some software? Well, common among that data are system-specific details such as RAM, CPU, hard drive model, and other hardware details (whatever data you determine is relevant to your app), along with performance logging data. By taking that kind of approach, what you get is a lot of performance data from lots of different system configurations without needing to have a huge number of differently configured machines in-house.
You can do the same thing for program crashes and bugs - have the stack traces, system info, and other relevant data dumped to a log file that is sent back to you - but of course, only if your users have said it's okay to send that data back to you.