I am using Django for my project and I ll be hosting it on Linode or any other hosting service. Plus if I want to use memcache will I require a new Linode for it? Means just one server will be ok or I ll have to host my site on 2 servers, one for memcache and one for django? And is it the same for Redis? Also will I require a separate server for Mysql?
I don't think you understand that nobody is a fortune telling wizard. Nobody knows how many requests you will receive per second, nor how cpu/memory intensive each request will be. Nobody knows how optimized your code is. Nobody knows if your application is read heavy or write heavy. Your use case is your own, and your probably the only one who estimate it.
My only actual advice to you is to try to estimate your server data and sever load and benchmark your setup on one machine. If you are unsatisfied with the performance then scale up. You can either scale up vertically, by increasing the size of your linode, or scale horizontally by adding more linode instances. In the latter case, you will most likely put your DB on a machine of it's own and have multiple django instances fed by a load balancer. These Django instances could each share the same memcache on a machine, or they can each have their own memcaches on their own machine. Which one is better? I can't tell you. It again depends on your use case.
If I were you, I would set it all up on one linode instance. I would create test data that I assume would be close to real world. Then I would try to test my response times with an estimated number of requests per second. I would measure response times, cache hits, and memory usage. I would then decide based on that if my use case is satisfied with this level of performance or not because I'm really the only one who would know what is satisfactory performance. Additionally, adding more linode resources is not necessarily where I would first try and improve performance.
Some great tips on optimizing and benchmarking can be found here:
https://docs.djangoproject.com/en/1.8/topics/performance/
http://blog.disqus.com/post/62187806135/scaling-django-to-8-billion-page-views
http://scottbarnham.com/blog/2008/04/28/django-performance-testing-a-real-world-example/
Late night reading about scaling up Django can be found in many books, I like this one:
https://highperformancedjango.com/
Sorry if I sound a bit blunt, I just want you to understand that nobody can walk in here and give you an answer with a large degree of confidence. This question doesn't have a straight-forward answer.
TL;DR Start with one instance and scale up only if you've convinced yourself you need to.
You say Memcached or Redis, so I assume Redis would be deployed without persistence, with a purely in-memory configuration.
In such case both Memcached and Redis are unlikely to get saturated even if you run them in one server, since the limiting factor is more likely to be a single Django instance if your requests/second go high.
However you should make sure to have enough memory and to configure an appropriate max memory usage for Memcached / Redis (different ways to accomplish this in the two different services). Note that under memory pressure, the Linux OOM killer may kill your cache otherwise, so if you go for a single instance, which seems to me a sensible first step, make sure your Django memory usage plus the memory you allocate for caching, are not enough to go near the limits of the instance free memory.
CPU is hardly going to be an issue as I said since Memcached / Redis are pretty good at using little CPU, so I can't foresee a setup where Django is ok serving pages but the instance is in trouble since the CPU is burned by the cache.
Related
This question has a conceptual and practical parts.
Conceptually I'd like to know if using the autoscaling functionality is equivalent to simply increasing the compute power by a factor of the number of added instances?
Practically ... how does this work? I have one running instance, its database sitting on an LVM composed of multiple EBS volumes, similarly with all website data. Judging from the load on the instance I either need to upgrade to a more powerful instance or introduce this autoscaling. Is it a copy of the running server? If so, how is the database (etc) kept consistent?
I've read through the AWS documentation, and still haven't got the picture yet - I could set one autoscaling group up which would probably clear my doubts, but I am very leery to do this with a production server.
Any nudges in the right direction would be welcome.
Normally if you have a solution that also uses a database, and several machines in the solution, the database is typically not on any of the machines but is instead hosted seperately with each worker machine pointing to the same database - if you are on AWS platform already, then DynamoDB or RDS are both good solutions for this.
In theory, for some applications, upgrading the size of the single machine will give you the same power as adding several smaller machines, but increasing the size of the single machine, while usually these easiest thing to do at first, should not be considered autoscaling and has its own drawbacks. Here are some things to consider:
Using multiple machines instead of one big one gives you some fault tolerance. One or more machines can go down and if your solution is properly designed new machines will spin up to replace them.
Increasing the size of a single machine solution means you are probably paying too much. If you size that single machine big enough to handle peak workloads, that means at other times (maybe most of the time), you are paying for a bigger machine than you need. If you setup your autoscaling solution properly more machines come on line in response to increasing demand, and then they terminate when that demand decreases - you only pay for the power you need when you need it.
When your solution is designed in this manner, you need to think of all of the worker machines as ephermal - likely to disappear at any time, so you need to build your solution differently. Besides using a hosted database (like on DynamoDB or AWS RDS), you also should not store any data on the machines in your auto-scaling group that doesn't also live somewhere else. For example, if part of your app allows users to upload images, you don't store them on the instances, you store them in S3. Same would apply to any other new data that comes in.
You need to be able to figuratively 'pull the plug' at any instant on any of the machines in your ASG without losing data.
Ultimately a properly setup auto-scaling solution will likely serve you better, but without doubt it is simpler to just 'buy a bigger machine' and the extra money you spend on running that bigger machine may be more than offset by the time and effort you don't have to spend re-architecting your solution to properly run in an autoscaling environment. The unique requirements of your solution will ultimately decide which approach is better.
I have a java web server and am currently using the Guava library to handle my in-memory caching, which I use heavily. I now need to expand to multiple servers (2+) for failover and load balancing. In the process, I switched from a in-process cache to Memcache (external service) instead. However, I'm not terribly impressed with the results, as now for nearly every call, I have to make an external call to another server, which is significantly slower than the in-memory cache.
I'm thinking instead of getting the data from Memcache, I could keep using a local cache on each server, and use RabbitMQ to notify the other servers when their caches need to be updated. So if one server makes a change to the underlying data, it would also broadcast a message to all other servers telling them their cache is now invalid. Every server is both broadcasting and listening for cache invalidation messages.
Does anyone know any potential pitfalls of this approach? I'm a little nervous because I can't find anyone else that is doing this in production. The only problems I see would be that each server needs more memory (in-memory cache), and it might take a little longer for any given server to get the updated data. Anything else?
I am a little bit confused about your problem here, so I am going to restate in a way that makes sense to me, then answer my version of your question. Please feel free to comment if I am not in line with what you are thinking.
You have a web application that uses a process-local memory cache for data. You want to expand to multiple nodes and keep this same structure for your program, rather than rely upon a 3rd party tool (memcached, Couchbase, Redis) with built-in cache replication. So, you are thinking about rolling your own using RabbitMQ to publish the changes out to the various nodes so they can update the local cache accordingly.
My initial reaction is that what you want to do is best done by rolling over to one of the above-mentioned tools. In addition to the obvious development and rigorous testing involved, Couchbase, Memcached, and Redis were all designed to solve the problem that you have.
Also, in theory you would run out of available memory in your application nodes as you scale horizontally, and then you will really have a mess. Once you get to the point when this limitation makes your app infeasible, you will end up using one of the tools anyway at which point all your hard work to design a custom solution will be for naught.
The only exceptions to this I can think of are if your app is heavily compute-intensive and does not use much memory. In this case, I think a RabbitMQ-based solution is easy, but you would need to have some sort of procedure in place to synchronize the cache between the servers on occasion, should messages be missed in RMQ. You would also need a way to handle node startup and shutdown.
Edit
In consideration of your statement in the comments that you are seeing access times in the hundreds of milliseconds, I'm going to advise that you first examine your setup. Typical read times for a single item in the cache from a Memcached (or Couchbase, or Redis, etc.) instance are sub-millisecond (somewhere around .1 milliseconds if I remember correctly), so your "problem child" of a cache server is several orders of magnitude from where it should be in terms of performance. Start there, then see if you still have the same problem.
We're using something similar for data which is read-only and doesn't require updated every time. I'm in doubt, that this is good plan for you. Just imagine you should have one more additional service on each instance, which will monitor queue, and process change to in-memory storage. This is very hard to test.
Are you sure that most of the time is spent on communication between your servers? Maybe you run multiple calls?
I've been runnning a single django application on Amazon EC2 using gunicorn to serve the django portion and nginx for the static files.
I'm going to be starting new project soon, and wondering which of the following options would be better:
A larger amazon EC2 instance (Medium) runnning multiple django applications
Multiple smallers EC2 instances (Small/Micro) all running their own django applications?
Would anybody have any experience with this? What would the relevant performance metrics I could measure to get a good cost to performance ratio?
The answer to this question really depends on your app I'm afraid. You need to benchmark to be sure you are running on the right instance type. Some key metrics to watch are:
CPU
Memory usage
Requests per second, per instance size
App startup time
You will also need to tweak nginx/gunicorn settings to make sure you are running with a configuration that is optimised for your instance size.
If costs are a factor for you, one interesting metric is "cost per ten thousand requests", i.e. how much are you paying per 10000 requests for each instance type?
I agree with Mike Ryan's answer. I would add that you also have to evaluate whether your app needs a separate database. Sometimes it makes sense to isolate large/complex applications with their own database, which makes changes and maintenance easier. (Also reduces your risk in the case that something goes wrong). Not all of your user base would be affected in the case of an outage. You might want to create a separate instance for these applications. Note: Django supports multiple databases in one project but, again, that increases complexity for changes and maintenance.
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 slowly getting into the position where one of my Django sites needs some robustness behind it. I'd currently running on a single VPS on a SQLite database with memcached.. It's about as un-scaled as things can get.
If I bought another VPS account, what would I want to do?
Move to MySQL/PostgreSQL with replication? What's easiest? Does replication protect me from one server exploding? Are there concurrency downsides?
How do I load-balance between the two servers?
I'd put memcached on the new server too. If I put both IPs into the configuration, would that keep a copy of data on both servers? (I'm thinking of what happens to session data - currently stored in memcached)
I'm currently using Cherokee as the httpd - I'm sure this has its own set of issues. If you've any tips, let me know.
Am I going at this the wrong way? Is there an easier way to have faster, more robust django sites?
First step: switch from SQLite to a real production database (I like Postgres). This should happen long before you even think about a second VPS. SQLite essentially does not support concurrency at all. Personally, I wouldn't even consider deploying a live site on SQLite in the first place.
If your site is running on SQLite and is functioning, my guess is you are still quite a long ways from actually outgrowing your single VPS (unless it's already heavily loaded otherwise).
If/when you do need to add a second server, how you configure things depends on where you're actually seeing a bottleneck. Chances are it'll be the database, in which case a good step might be simply moving the database onto its own server (presuming you can guarantee low latency between the two VPSes) and loading the database server with as much RAM as you can afford. In general disk performance suffers most in a VPS, so another step to consider might be putting the DB onto raw metal.
I'd probably look at those steps before I'd think about DB replication or multiple web-tier servers, but it really depends on profiling your actual case (and how you value performance vs reliability).
Watching the Django Deployment Workshop by Jacob Kaplan-Moss should give you a good overview.
MySQL supports Master-Slave and Master-Master setups I don't use PostgreSQL.
You can use nginx as your loadbalancer, HAProxy is an option, too (SO use it).
Memcached distributes the objects over the servers, If one crashes the data is lost.
I don't know Cherokee, but nginx is great.