I need some guidance in the realm of server architecture for Django.
My current Django-based web app stats (reached in two weeks - run on one VPS w/ Apache, mod_wsgi, mysql):
10,000 users total
20 avg requests/user/day
200,000 requests/day
8,000 users access site daily
Where the app could reach (where I'd be panicking - this assumes approx linear growth):
200,000 users total
20 avg requests/user/day
4,000,000 requests/day
160,000 users access site daily
The issue here is really just handling page requests. I only store short strings of text-based data, so DB size shouldn't be an issue.
What sort of server architecture should I be setting up from a hardware and software perspective? I need to think about caching, load balancing, multiple processing servers, multiple DB servers, etc, but don't know where to start.
You're projected growth of ~45 / requests per second really isn't that intensive. I think using a standard nginx load balancer in front of your web servers will handle everything. If your DB access isn't very intense you will probably do fine with just 1 DB machine.
I really think the most important thing is not to do any premature optimization. Deal with issues as they come, or else you may end up wasting a lot of time.
There are tons of caching, multiple server configurations, and load balancing tutorials.
Google is a good place to start.
Growing traffic is a standard problem, there are no lack of tutorials on these things.
Related
Any recommendation on how to make superset faster?
Cache seems to load full data from the cache, I thought it load only old data from the cache, and real-time data from the database, isn't it like this?
What about some parallel processing?
This answer is valid as of Superset 0.37.0.
At the moment, dashboard performance is affected by a few different factors. I'll enumerate them below along with methods to improve performance:
Database concurrency limits can have an impact on dashboard performance. Dashboards load their information in parallel via concurrent web requests. Make sure that the database user provided allows enough concurrency that queries aren't being queued at the database layer.
Cache performance your caching layer should be able to return multiple results, if not in parallel, extremely quickly. We've had success leveraging S3 for our cache.
Cache hit percentage Superset will hit the cache only for queries that exactly match one that has been run recently. Otherwise the full query will fall through to the underlying analytical DB (Druid in this case). You can reduce the query load on Druid by using a less granular resolution on your dashboard - if it's possible to have it update less frequently, say a couple of times a day rather than in real-time, this can hit cache for all requests other than the first request in the new period under consideration.
Python Web Process Concurrency Limits make sure that your web application server can handle enough parallel requests. The browser will request multiple charts' data at the same time, and the system will need to be able to handle these requests in parallel.
Chart Query Performance As data is frequently requested, especially for real-time data from a database like Druid, optimizing the queries run by the charts can be very useful. I'd take a look at any virtual datasources that are being leveraged to see if they can be materialized or made more efficient.
Web browser concurrent request limits By default most web browsers limit the number of concurrent requests that can be made to the same FQDN. If you have more than 6 charts on the same dashboard, it can be helpful to balance requests across multiple FQDNs running Superset to get around this browser limitation. There's more information on the approach to that in the issue history on Github, but Superset does support this type of configuration.
The community is very interested in improving performance over time, and as such there have been recommendations to move all analytical queries to Celery as well as making other architectural changes to improve performance. I hope this description helps and that something in here will help you track down the issue!
We need our Sitecore web application to process 60-80 web requests per second. We are using Sitecore 7.0. We have tried a 1 Webserver + 1 Database server deployment, but it only processes 20-25 requests per second. Web server queues up all the other requests in the Memory. As we increase the load, memory fills up.(We did all Sitecore performance enhancements recommended). We need 4X performance to reach the goal :).
Will it be possible to achieve this goal by upgrading the existing server, or do we have to add more web servers in production environment.
Note: We are using Lucene indexing as well.
Here are some things you can consider without changing overall architecture of your deployment
CDN to offload media and static asset requests
This leaves your content delivery server available to handle important content queries and display logic.
Example www.cloudflare.com
Configure and use Sitecore's built-in caching
This is from the guide:
Investigation and configuration of the Sitecore Caches is broken down
into multiple tasks. This way each task is more focused and
simplified. The focus is on configuration and tuning of the Sitecore
Database Caches (prefetch, data, and item caches.)
For configuration
of the output rendering caching properties, the customer should be
made aware of both the Sitecore Cache Configuration Reference and the
Sitecore Presentation Component Reference as to how properly enable
and the properties to expire these caches.
Check out the Sitecore Tuning Guide
Find Slow Queries or Controls
It sounds like your application follows Sitecore best practices, but I leave this note in for anyone that might find this answer. Use Sitecore's built-in Debug mode to identify the slowest running controls and sublayouts. Additionally, if you have Analytics set up there is a "Slow Pages" report that might give you some information on where your application is slowing down.
Those things being said, if you're prepared to provision additional servers and set up a load-balanced environment then read on.
Separate Content Delivery and Content Management
To me the first logical step before load-balancing content delivery servers is to separate the content management from the equation. This is pretty easy and the Scaling Guide walks you through getting the HistoryEngine set up to keep those Lucene indexes up to date.
Set up Load Balancer with 2 or more Content Delivery servers
Once you've done the first step this can be as easy as cloning your content delivery server and adding it to your load balancer "pool". There are a couple of things to consider here like: Does your web application allow users to log in? So you'll need to worry about sticky sessions or machine keys. Does your web application use file media instead of blob media? I haven't had to deal with this, but I understand that's another consideration.
Scale your SQL solution
I've seen applications with up to four load balanced content delivery servers and the SQL Server did not have a problem - I think this will be unique to each case depending on a lot of factors: horsepower and tuning of SQL Server, content model of your application, complexity of your queries, caching configuration on content delivery servers, etc. Again, the Scaling Guide covers SQL Mirroring and Failover, so that is going to be your first stop on getting that going.
Finally, I would say contact Sitecore. These guys have probably seen more of what's gone right and what's gone wrong with installations and could get you on the right path. Good luck!
This answer written from a Sitecore developer perspective:
Bottom line: You need to figure out exactly where your performance bottleneck is. That is going to take some digging, but will be very worthwhile. You should definitely be able to serve 60-80 requests/s without any trouble... but of course that makes a lot of assumptions about the nature of your site and the requests.
For my site, I found Sitecore's caching implementation to be sub-par... I created some very simple and aggressive application-specific caches in my app and this made all the difference in the world. For instance, we have 900+ "Partner" items where our sites' advertisements live... and simply putting all these objects in an array in the Application object sped up page requests significantly. Finding an object in a Hashtable indexed by its Item.Name or ID is going to be a lot faster than Sitecore.Context.Database.GetItem("/itempath") or a SelectItems() call (at least, that's my experience). If your architecture and data set will allow this strategy, we've had good experience with it.
Another thing to watch out for is XSLT renderings. Personally, I avoid them completely in favor of ASP.NET UserControls. The XSLT rendering is just slow. As much as 10x slower than a native UserControl rendering the same HTML. So if you have a few of these... replace with some custom code and you'll see a world of difference.
I have a django web app that uses postgres db.It allows users to login and make some posts which get saved to db and later on the user can list how many posts he made on a particular day etc and list the posts belonging to a particular category etc.While this worked without any delay in my machine,it is taking a lot of time to load each page when hosted on a free host.
How do you find out why this is happening?which part of the app should I look first?Is there any meaning in using a profiler since this app used to run with no delays in my local machine?
I would like to find out how to approach this problem in general.I was able to access other apps hosted on the same free host without much delays ..so this may be a problem specific to my app
I would like some advice regarding this..If anyone can help..
thank you
p.s:(I intentionally left out the host's name because ,since that was a free service ,there was no point in complaining and also other apps on the same host works well)
The here is the free host bit, when on a free host you could be sharing a box with hundreds of other sites (that can equate to a very small amount of ram or CPU). Pay a little money, ($30 dollars / £22 a year) and get your self a better host.
You will find the performance and reliability so much better.
Failing that I would firstly find out what the latency between you and the server is, on a local machine there is no / little network traffic so your pages will appear to load a lot faster.
Next i would look at the actual download speeds you are getting. It could be that your site is limited to 20-30k, which means even a small site will take over a second to load.
Are you hosting many images? If this is the case are you serving them through django or is the webserver doing this. If it is django then make the webserver take this load.
Finally check the processing speed of the pages. Analise the queries which are being run and find out what is taking the time. Make sure that postgres is correctly configured and has enough resources. You can analyses the query speed using the django debug toolbar.
I was contracted to make a groupon-clone website for my client. It was done in PHP with MYSQL and I plan to host it on an Amazon EC2 server. My client warned me that he will be email blasting to about 10k customers so my site needs to be able to handle that surge of clicks from those emails. I have two questions:
1) Which Amazon server instance should I choose? Right now I am on a Small instance, I wonder if I should upgrade it to a Large instance for the week of the email blast?
2) What are the configurations that need to be set for a LAMP server. For example, does Amazon server, Apache, PHP, or MySQL have a maximum-connections limit that I should adjust?
Thanks
Technically, putting the static pages, the PHP and the DB on the same instance isn't the best route to take if you want a highly scalable system. That said, if the budget is low and high availablity isn't a problem then you may get away with it in practise.
One option, as you say, is to re-launch the server on a larger instance size for the period you expect heavy traffic. Often this works well enough. You problem is that you don't know the exact model of the traffic that will come. You will get a certain percentage who are at their computers when it arrives and they go straight to the site. The rest will trickle in over time. Having your client send the email whilst the majority of the users are in bed, would help you somewhat, if that's possible, by avoiding the surge.
If we take the case of, say, 2,000 users hitting your site in 10 minutes, I doubt a site that hasn't been optimised would cope, there's very likely to be a silly bottleneck in there. The DB is often the problem, a good sized in-memory cache often helps.
This all said, there are a number of architectural design and features provided by the likes of Amazon and GAE, that enable you, with a correctly designed back-end, to have to worry very little about scalability, it is handled for you on the most part.
If you split the database away from the web server, you would be able to put the web server instances behind an elastic load balancer and have that scale instances by demand. There also exist standard patterns for scaling databases, though there isn't any particular feature to help you with that, apart from database instances.
You might want to try Amazon mechanical turk, which basically lots of people who'll perform often trivial tasks (like navigate to a web page click on this, etc) for a usually very small fee. It's not a bad way to simulate real traffic.
That said, you'd probably have to repeat this several times, so you're better off with a load testing tool. And remember, you can't load testing a time-slicing instance with another time-slicing instance...
I'm planing to deploy a django powered site. But I feel confused about the choice of web servers, which includes apache, lighttpd, nginx and others.
I've read some articles about the performance of each of these choice. But it seems no one agrees. So I'm wondering why not test the performance by myself?
I can't find information about the best approach to performance testing web servers. So my questions are:
Is there any easy approach to test the performance without the production site?
Or can I have a method to simulate the heavy traffic to have a fair test?
How can I keep my test fair and close to production situation?
After the test, I want to figure out:
Why some ones say nginx has a better performance when serving static files.
The cpu and memory needs of each web server.
My best choice.
Tools like ab are commonly used towards testing how much load you can take from a battering of requests at once, alongside cacti/munin/your system monitoring tool or choice you can generate data on system load & requests/sec. The problem with this is many people benchmarking don't realise that they need to request a lot of different requests, as different parts of your code executes it will take varying amounts of time. Profiling and benchmarking code and not requests is also important, to which plenty of folk have already done so for django, benchrun is also not a bad tool either.
The other issue, is how many HTTP requests each page view takes. The less amount of requests, and the quicker they can be processed is the key to having websites that can sustain a high amount of traffic, as the quicker you can finish and close connections, the quicker you allocate resources for new ones.
In terms of general speed of web servers, it goes without saying that a proxy server (running reverse at your end) will always perform faster than a webserver with static content. As for Apache vs nginx in regards to your django app, it seems that mod_python is indeed faster than nginx/lighty + FastCGI but that's no surprise because CGI, regardless of any speed ups is still slow. Executing and caching code at the webserver and letting it manage it is always faster (mod_perl vs use CGI, mod_php vs CGI, etc) if you do it right.
Apache JMeter is an excellent tool for stress-testing web applications. It can be used with any web server, not just Apache.
You need to set up the web server + website of your choice on a machine somewhere, preferably a physical machine with similar hardware specs to the one you will eventually be deploying to.
You then need to use a load testing framework, for example The Grinder (free), to simulate many users using your site at the same time.
The load testing framework should be on separate machine(s) and you should monitor the network and CPU usage of those machines as well to make sure that the limiting factor of your testing is in fact the web server and not your load injectors.
Other than that its just about altering the content and monitoring response times, throughput, memory and CPU use etc... to see how they change depending on what web server you use and what sort of content you are hosting.