We used to have a dedicated server (1&1) and very infrequently ran into problems with the server having issues.
Recently, we migrated to a VPS (Wiredtree.com) with similar specs to our old dedicated server, but notice frequent problems running out of memory, mysql having to restart, etc... both when knowingly running intensive scrips and also just randomly during normal use.
Because of this, we're considering migrating to another at VPS - this time at Slicehost to see if it performs better.
My question is two fold...
Are their straightforward ways we could stress test a VPS at Slicehost to see if the same issues occur without having to actually migrate everything over?
Also, is it possible that the issues we're facing aren't just because of the provider (Wiredtree) but just the difference between a dedicated box and VPS (despite having similar specs)?
The best way to stress test an environment is to put it under load. If this VPS is hosting a web application, use one of the many available web server benchmark tools: ab, httperf, Siege or http_load. You don't necessarily care that much about the statistics from the tool itself, but more that it puts a predictable load on the server so that you can tune Apache to handle it, or at least not crash and burn.
The one problem you have with testing against Slicehost is that you are at the mercy of the Internet and your bandwidth to Slicehost. You may not be able to put enough load on the server to reach a meaningful conclusion.
Instead, you might find it just as valuable to run one of the many virtualization products on the market and set up a VM with comparable specs to the VPS plan you're considering. Local testing over your LAN will allow you to put a higher and more predictable load on the server.
In either case, you don't need to migrate everything, but you will need to set up an environment for your application to run in, with representative data in your database.
A VPS with similar specs to a dedicated server should perform approximately the same, but in order to get good performance, you still need to tune Apache, MySQL and any other long-lived server processes. In my experience, the out-of-the-box configuration of Apache in many Linux distributions is not ideal and will allow far too many child processes, overcommitting memory and sending the server into a swap-death spiral.
Related
We want to start working with liferay. But the server is too heavy and the developpers computer don't have enought RAM. We want to centralize the server instance.
In other words, we want to build a development server where all developpers can connect and directly develop in their web browser, compile, view the result and push the code to git repository.
I found some good cloud IDE like eclipse CHE and a good maven archetype for liferay projet. So i can build the projet with maven. But now i want to know if it is possible to configure Liferay like every developpers can work without troubling another. And if possible, How ?
The developpers can share the same database and can use different port. Maybe, the server can generate tempory URL like some online cloud editor.
I found this post Liferay With Multiple Server Instances, but i don't think is the best way because he create one server per project. I think is too heavy.
If necessary, We have kubernetes in our IS.
Liferay's tomcat bundle, by default, is configured to take a maximum of 2.5G for the process, but it can run with far less - the default only recently was bumped up, because many people never change the default and then wonder why production systems run out of memory. For 1 concurrent user (the sole developer) on a machine, I guess that the previous default of 1G heap space is enough. Are you saying that that's too much for your developers' machines?
Having many developers on a shared server poses one problem: Yes, you may deploy different code from different machines, but: How about setting a breakpoint? Can you connect with multiple debuggers? If something fails, how do you know whos recent deployment caused the failure?
Sharing a server is an integration technique, not a development technique. If your developers don't have enough memory available for running their own Liferay server next to their IDE, it's a lot cheaper to upgrade their machines than to slow them down when everybody is accessing the same server and they can't properly debug. You pay the memory once, but your waiting developers by the hour.
Is it possible to share one server? Sure it is.
Is it possible to share one server without troubling each other? I doubt.
When you say: You think it's too heavy: What are you basing that assumption on? What does the actual developer machine look like and what keeps you from investing in the extra memory?
It's trivial to share some infrastructure - i.e. have all of them connect to the same database server (and give everyone their own schema). But just the extra effort and setup might require you to pay the developers by the hour as much as you'd otherwise pay for a couple of memory chips.
And yet another option is: Run Liferay on a remote server, but keep 1 instance per developer. This way you don't need the local memory, but can have the memory in the cloud. Calculate if you pay more for remote cloud machines than for local memory - that decision is up to you.
I'd like to deploy kubernetes on a large physical server (24 cores) and I'm uncertain as to a number of things.
What are the pros and cons of creating virtual machines for the k8s cluster other than running on bare-metal.
I have the following considerations:
Creating vms will allow for work load isolation. New vms for experiments can be created and assigned to devs.
On the other hand, with k8s running on bare metal a new NAMESPACE can be created for each developer for experimentation and they can run their code in it. After all their code should be running in docker containers.
Security:
Having vms would limit the amount of access given to future maintainers, limiting the amount of damage that could be done. While on the other hand the primary task for any future maintainers would be adding/deleting nodes and they would require bare metal access to do that.
Authentication:
At the moment devs would only touch the server when their code runs through the CI pipeline and their running deployments are deployed. But what about viewing logs? Could we setup tiered kubectl authentication to allow devs to only access whatever namespaces have been assigned to them (I believe this should be possible with the k8s namespace authorization plugin).
A number of vms already exist on the server. Would this be an issue?
128 cores and doubts.... That is a lot of cores for a single server.
For kubernetes however this is not relevant:
Kubernetes can use different sized servers and utilize them to the maximum. However if you combine the master server processes and the node/worker processes on a single server, you might create unwanted resource issues. You can manage those with namespaces, as you already mention.
What we do is use continuous integration with namespaces in a single dev/qa kubernetes environment in which changes have their own namespace (So we run many many namespaces) and run full environment deployments in those namespaces. A bunch of shell scripts are used to manage this. This works both with a large server as what you have, as well as it does with smaller (or virtual) boxes. The benefit of virtualization for you could mainly be in splitting the large box in smaller ones so that you can also use it for other purposes then just kubernetes (yes, kubernetes runs except MS Windows, no desktops, no kernel modules for VPN purposes, etc).
I would separate dev and prod in the form of different vms. I once had a webapp inside docker which used too many threads so the docker daemon on the host crashed. It was limited to one host luckily. You can protect this by setting limits, but it's a risk: one mistake in dev could bring down prod as well.
I think the answer is "it depends!" which is not really an answer. Personally, I would split up the machine using VM's and deploy that way. You've got better flexibility as to how much of the server's resources you carve out and you can easily create new environments, then destroy easily.
Even if these vms are really big, I think it's still easier to manage also given that you have existing vm's on the machine.
That said, there's not a technical reason that you can't run a single node server, but you may run into problems with downtime with upgrades (if that's an issue), as well as if that server needs patched or rebooted, then your entire cluster is down.
I would look at your environment needs for HA and uptime, as well as how you are going to deploy VM's (if you go that route), and decide what works the best for you.
I don't actually have any problem, just a bit curious of things.
I make a python web framework based on bottle (http://bottlepy.org/). Today I try to do a bit comparison to compare bottle WSGI server and apache server performance. I work on lubuntu 12.04, using apache 2, python 2.7, bottle development version (0.12) and get this surprising result:
As stated in the bottle documentation, the included WSGI Server is only intended for development purpose. The question is, why the development server is faster than the deployment one (apache)?
As far as I know, development server is usually slower, since it provide some "debugging" features.
Also, I never has any response in less than 100 ms when developing PHP application. But look, it is just 13 ms in bottle.
Can anybody please explain this? This is just doesn't make sense for me. A deployment server should be faster than the development one.
Development servers are not necessarily faster than production grade servers, so such an answer is a bit misleading.
The real reason in this case is likely going to be due to lazy loading of your web application on the first request that hits a process. Especially if you don't configure Apache correctly, you could hit this lazy loading quite a bit if your site doesn't get much traffic.
I would suggest you go watch my PyCon talk which deals with some of these issues.
http://lanyrd.com/2013/pycon/scdyzk/
Especially make sure you aren't using prefork MPM. Use mod_wsgi daemon mode in preference.
A deployment server should be faster than the development one.
True. And it generally is faster... in a "typical" web server environment. To test this, try spinning up 20 concurrent clients and have them make continuous requests to each version of your server. You see, you've only tested 1 request at a time--certainly not a typical web environment. I suspect you'll see different results (we're thinking of both latency AND throughput here) with tens or hundreds of concurrent requests per second.
To put it another way: At 10, 20, 100 requests per second, you might still see ~200ms latency from Apache, but you'd see much worse latency from Bottle's server.
Incidentally, the Bottle docs do refer to concurrency:
The built-in default server is based on wsgiref WSGIServer. This
non-threading HTTP server is perfectly fine for development and early
production, but may become a performance bottleneck when server load
increases.
It's also worth noting that Apache is doing a lot more than the Bottle reference server is (checking .htaccess files, dispatching to child process/thread, robust logging, etc.) and all those features necessarily add to request latency.
Finally, I'd ask whether you tuned the Apache installation. It's possible that you could configure it to be faster than it is now, e.g. by tuning the MPM, simplifying logging, disabling .htaccess checks.
Hope this helps. And if you do run a concurrent benchmark, please do share the results with us.
Is it possible having virtual machines in the cloud, install visual studio there, and making developers using the 'cloud' to do day-to-day programming work? Is the cost going to be too high? Is the speed going to be too slow?
Where can I find statistics or numbers to convince people?
I like using remote virtual machines to run development servers, but I don't like using my IDE on a remote server. The latency is noticeable. If you're without an internet connection you can't work. My happy compromise is to have a dev server available (EC2) and sync it with my laptop via git.
It is completely possible to do this, using a service like Rackspace you can set up a fairly powerful windows server for as little as $60 a month:
http://www.rackspacecloud.com/cloud_hosting_products/servers/pricing
In my experience using Remote Desktop to log into a Rackspace Windows Cloud Server has been snappy and quick (of course a lot of that depends on the strength of your internet connection). The process of standing up the server is lighting fast, backing it up is even easier, and it can be easily resized down the line if you need more storage/bandwidth.
These days I don't understand why a small to mid sized organization would actually waste capital on server hardware.
Evan
Practical uses of virtualization in software development are about as diverse as the techniques to achieve it.
Whether running your favorite editor in a virtual machine, or using a system of containers to host various services, which use cases have proven worth the effort and boosted your productivity, and which ones were a waste of time ?
I'll edit my question to provide a summary of the answers given here.
Also it'd be interesting to read about about the virtualization paradigms employed too, as they have gotten quite numerous over the years.
Edit : I'd be particularly interested in hearing about how people virtualize "services" required during development, over the more obvious system virtualization scenarios mentioned so far, hence the title edit.
Summary of answers :
Development Environment
Allows encapsulation of a particular technology stack, particularly useful for build systems
Testing
Easy switching of OS-specific contexts
Easy mocking of networked workstations in a n-tier application context
We deploy our application into virtual instances at our host (Amazon EC2). It's amazing how easy that makes it to manage our test, QA and production environments.
Version upgrade? Just fire up a few new virtual servers, install the software to be tested/QA'd/used in production, verify the deployment went well, and throw away the old instances.
Need more capacity? Fire up new virtual servers and deploy the software.
Peak usage over? Just dispose of no-longer-needed virtual servers.
Virtualization is used mainly for various server uses where I work:
Web servers - If we create a new non-production environment, the servers for it tend to be virtual ones so there is a virtual dev server, virtual test server, etc.
Version control and QA applications - Quality Center and SVN are run on virtual servers. The SVN box also runs CC.Net for our CI here.
There may be other uses but those seem to be the big ones at the moment.
We're testing the way our application behaves on a new machine after every development iteration, by installing it onto multiple Windows virtual machines and testing the functionality. This way, we can avoid re-installing the operating system and we're able to test more often.
We needed to test the setup of a collaborative network application in which data produced on some of the nodes was shared amongst cooperating nodes on the network in a setup with ~30 machines, which was logistically (and otherwise) prohibitive to deploy and set up. The test runs could be long, up to 48 hours in some cases. It was also tedious to deploy changes based on the results of our tests because we'd have to go around to each workstation and make the appropriate changes, which was a manual and error-prone process involving several tired developers.
One approach we used with some success was to deploy stripped-down virtual machines containing the software to be tested to various people's PCs and run the software in a simulated data-production/sharing mode on those PCs as a background task in the virtual machine. They could continue working on their day-to-day tasks (which largely consisted of producing documentation, writing email, and/or surfing the web, as near as I could tell) while we could make more productive use of the spare CPU cycles without "harming" their PC configuration. Deployment (and re-deployment) of the software was simplified, since we could essentially just update one image and re-use it on all the PCs. This wasn't the entirety of our testing, but it did make that particular aspect a lot easier.
We put the development environments for older versions of the software in virtual machines. This is particularly useful for Delphi development, as not only do we use different units, but different versions of components. Using the VMs makes managing this much easier, and we can be sure that any updated exes or dlls we issue for older versions of our system are built against the right stuff. We don't waste time changing our compiler setups to point at the right shares, or de-installing and re-installing components. That's good for productivity.
It also means we don't have to keep an old dev machine set up and hanging around just-in-case. Dev machines can be re-purposed as test machines, and it's no longer a disaster if a critical old dev machine expires in a cloud of bits.