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Poppen De Email

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Poppen De Email

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Resides in Perryville, MO. Then we have separate machines to serve the site images. There are One of the cool things that Nginx lets us do is to deliver many requests out of Memcached, without the need of hitting the PHP machines to get content that is already cached.

So for example, the users profiles are one of the most CPU intensive pages in the site. Once the profile has been requested we cache the whole content on Memcached.

Then the Nginx server will hit the Memcached and deliver the content from there. There are requests per minute delivered out of the Memcached.

We have 3 Nginx servers that are delivering the images from a local cache. The users upload their pictures to a central file server.

A picture request will then hit one of the 3 Nginx servers. If the picture is not in the local cache filesystem, the Nginx will download the picture from the central server, store in its local cache and serve it.

This lets us load balance the image distribution and alleviate the load in the main storage machine. We use PHP 5. The 5. The code is written using the symfony 1.

On one hand this means extra resource footprint, on the other hand it gives us speed of development and a well know framework that lets us integrate new developers to the team with ease.

Not everything is "Flowers and Roses" here. So while we have a lot of advantages provided by the framework, we had to tweak it a lot to get it up to the task of serving www.

Thanks to the fact that the framework is easy to customize and configure, we were able to cache most of the expensive calculations that were adding extra load to the servers in APC.

This machine is 4 years old. We are planning to replace it by a sharded cluster. We are still working on the design of this system, trying to have a low impact in our data access code.

We want to partition the data by user id, since most of the information on the site is centered on the user itself, like images, videos, messages, etc.

We have 3 machines working in a master-slave-slave configuration for the users' forum. Then there's a cluster of servers that runs as storage for the web site custom message system.

Currently it has more than million messages. We also have an NDB cluster composed by 4 machines for write intensive data, like the statistics of which user visited which other user's profile.

We try to avoid joins like the plague and cache as much as possible. The datastructure is heavily denormalized. For that we have created summary tables, to ease searching.

The problem we are seeing more and more are full table locks. We are moving to the XtraDB storage engine.

We use Memcached heavily. We have 45 GB of cache over 51 nodes. Most of the queries by primary key that we have to the users table are cached in Memcached and then delivered from there.

We have a system that lets automatically invalidate the cache every time one record of that table is modified. With those databases we can update the cache with enough granularity to not need to invalidate it.

Since mid we introduced RabbitMQ into our stack. It's been a solution that was easy to deploy and integrate with our system.

During the last month we have been moving more and more stuff to the queue, meaning that at the moment the 28 PHP frontend machines are publishing around We send logs, email notifications, system messages, image uploads, and much more to the queue.

This allows us to send messages to the queue in an asynchronous fashion. At the same time, all the messages that where hold in an array in memory are then sent to RabbitMQ.

In this way the user doesn't have to wait for this either. We have two machines dedicated to consume those messages, running at the moment 40 PHP processes in total to consume the jobs.

Each PHP process consumes jobs and then dies and respawns again. We do that to avoid any kind of garbage collection problems with PHP.

In the future we may increase the number of jobs consumed per session in order to improve the performance, since respawing a PHP process proved to be quite CPU intensive.

This system lets us improve the resource management. For example during peak time we can even have logins per minute.

This means that we will have concurrent updates to the users table, to store the user last login time. Because now we enqueue those queries, we can run each of them sequentially instead.

If we need more processing speed we can add more consumers to the queue, even joining machines to the cluster, without the need of modifying any configuration or deploying any new code.

To store the logs we run CouchDB in one machine. It proved to be useful to detect where the problem is. Before having CouchDB as a log aggregator, we had to login and tail -f in each of the PHP machines and from there try to find where the problem was.

Now we relay all the logs to the queue, and then a consumer inserts them into CouchDB. In this way we can check for problems at a centralized place.

We use Graphite to collect real time information and statistics from the website. The Graphite server is getting around update operations per minute.

This tool has proven to be really useful to see what's going on in the site. It's simple text protocol and the graphing capabilities make it easy to use and nearly plug and play to any system that we want to monitor.

One cool thing that we did with Graphite was monitoring two versions of the site running at the same time. Last January we deployed our code backed by a new version of the symfony framework.

This meant that we will probably encounter performance regressions. We were able to run one version of the site in half of the servers while the new version was running in the others.

Then in Graphite we created Unix load graphs for each half and then compared them live. Since we found that the Unix load of the new version was higher, we launched the XHProf profiler and compared both versions.

We have a separate server where we send the XHProf profiles and from there we aggregate them and analyze them to find where the problems are.

Our site also serves video to the users. We have two kinds of them. One are videos from the user profiles which are movies produced and uploaded by the users.

Also we have a Video Chat to let our users interact and share their videos. On mid we were streaming 17TB of video per month to our users.

Tsung is a distributed benchmarking tool written in Erlang. We have a tool to record traffic to the main MySQL server and convert that traffic to Tsung benchmarking sessions.

Then we replayed back that traffic and hit the machines in our lab with thousands of concurrent users generated by Tsung. The cool thing is that we could produce test scenarios that look closer to what's happening in the real production environment.

I'd like to thanks Alvaro Videla for this excellent write up. If you would like to share the architecture for your fablous system, please contact me and we'll get started.

Let's do the math. They have 28 PHP boxes with processes each. You need as many PHP processes as you need to be able to handle concurrent requests not per second.

That means either their scripts take 1 second to execute each or they have way to many processes. Either way something is broken.

Quote: This system lets us improve the resource management. No that does not mean you have concurrent updates.

Most of the time a lot less. Also note they have 50 memcached nodes. How many servers do they have to handle this moderate amount of load?

It's insane. Conclusion: not impressive and I have not seen any new insights. I question the efficience of their code a lot. Hi Alvaro, thanks for that interesting insight in your architecture.

Can you provide a link to Graphite? It sounds interesting, and we're beginning to look at those systems, but its such a common word that simple Googles aren't coming up with anything that I think is correct.

Not How many partials components do they show? Is the site information completely dynamic? And the list of questions can go on.

Besides that we keep the load avg on the quite low and we have enough servers for our planned growth. Besides that when you build a website you have to do business decisions.

Is not like you pick your best book about website programming theory. In our case, we use a Framework and an ORM. That let us develop quite fast.

You have to take that into account too. I've learned that is hard to talk about business decisions of other companies without knowing the background behind them.

Regarding the concurrent queries to the database and the login numbers, you are right, I did a mistake on the numbers.

I apology to the readers for giving misleading information. On the other side I hope you and other readers of the site can understand what you can accomplish with a queue server.

If you know that already and you don't need to learn it from me then better for you. I hope this is useful for at least one developer.

We have a Who Is Online server that tracks the online users. It uses a timeout for it to mark them as logged out. We use several Memcached nodes because we have specialized buckets depending on what we want to cache.

For example we have view cache, to cache templates. Function cache, to cache queries to the database. Then One Memcached to specifically cache queries to one table, etc.

In that way the usage of one memcached doesn't affect the others. Hi, Alvaro. I want to introduce you a better streaming server: erlyvideo , it is worthy to test, how many users it will handle in your situation for me it can serve connections from one machine.

I am not very familiar with Mysql but the one on which I work on recommends that we don't create more than partitions. So if they don't even hit PHP then I'm even more correct in that you have either too slow scripts are too many processes.

But that's not really a problem. The sites I am talking about have a lot of dynamic content but very clever caching plus they don't use any framework or ORM wrappers.

Granted you get some advantage in terms of development time but once you reach a certain size, you will whish you didn't go that route.

It's not that hard to code some classes for your objects which use more intelligent queries and caching.

You have 2. And please, don't use "requests per minute", nobody with interest in scale uses this term. It's mostly "requests per second" and suddenly your numbers don't seem so big anymore because it's only one 60th.

He did not say one partition per user, he said partition by user id. That does not suggest anything about partition size. It can be users or 1 million users per partition.

It only tells you what key is used to decide in which partition a value is stored. Also that does not have anything to do with MySQL per se.

The what one you work on? Also partitions? Yea right.. Great post. I think it was interesting to read and see how you solved many of your issues. Also nice tip regarding graphite.

Hi Alvaro. You said you were using memcached to cache view components like user profile. Can you explain more detail on how you invalidate these view cache?

I understand that you wrote your own code to invalidate "data cache" when the data was changed.

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