I was just wondering how many db queries might facebook be issuing to render a user's home page. Does anybody have some idea on how the facebook DB is designed. I've heard it runs MySql and there are thousands of replica plus more memcache server than DB Servers.
Is the facebook data shard-ed?
If it is does it go to every shard and search for the latest update of my friend. In worst case if I've 100 friends and suppose facebook has 101 shards, there is a possibility that each of my friend is in a different shard. How might facebook be handling this?
I'll be highly grateful if somebody can provide me seom hints or pointers towards something like "How to Design a DB for Social Networking Website". I'm just curious!
Facebook is using LAMP structure. Facebook’s backend services are written in a variety of different programming languages including C++, Java, Python, and Erlang and they are used according to requirement. With LAMP Facebook uses some technologies ,to support large number of requests, like
Memcache - It is a memory caching system that is used to speed up dynamic database-driven websites (like Facebook) by caching data and objects in RAM to reduce reading time. Memcache is Facebook’s primary form of caching and helps alleviate the database load. Having a caching system allows Facebook to be as fast as it is at recalling your data.
Thrift (protocol) - It is a lightweight remote procedure call framework for scalable cross-language services development. Thrift supports C++, PHP, Python, Perl, Java, Ruby, Erlang, and others.
Cassandra (database) - It is a database management system designed to handle large amounts of data spread out across many servers.
HipHop for PHP - It is a source code transformer for PHP script code and was created to save server resources. HipHop transforms PHP source code into optimized C++. After doing this, it uses g++ to compile it to machine code.
If we go into more detail, then answer to this question go longer. We can understand more from following posts:
How Does Facebook Work?
Data Management, Facebook-style
Facebook database design?
Facebook wall's database structure
Facebook "like" data structure
At this website you find lots of details about those big internet companies and their technical structures:
http://highscalability.com/
Adding to #Somnath Muluk's answer - Facebook uses few other technologies like Hadoop etc.
Refer to the following links for more details:
http://www.quora.com/Facebook-Engineering/What-is-Facebooks-architecture
Facebook Architecture
Hope it helps.
Zero. On average, that is. A highly interconnected network with a large number of users such as Facebook can only run effectively if it runs fully out of ram for the pages that are shown often. Nearly all data should already be in the memcache.
Related
I have a web design issue regarding to performance to ask advice. On a web site, there are many personalized information, for example the friends of a user of facebook. Personalized I mean different users have different friend list.
Suppose friend list is stored in database like Oracle or Mysql, each time the user clicks Home of his/her facebook page or login, we need to read database again. Each time the user add/remove friend, the database needs some update operations.
My question is, I think the performance capability (e.g. concurrency of transactions of read/write) of database is limited, and if facebook is using database to store friend list, it is hard to implement the good performance. But if not using database (e.g. MySql or Oracle), how did Facebook implement such personalization function?
This is a pretty good article about the technology behind facebook.
As Justin said, it looks like a combination of Memcached and Cassandra.
Facebook and other large sites typically use a caching layer to store that kind of data so that you don't have to make a round trip to the database each time you need to fetch it.
One of the most popular is Memcached (which, last I remember reading, is used by Facebook).
You could also check out how some sites are using NoSQL databases as their caching layer. I actually just read an article yesterday about how StackOverflow utilizes Redis to handle their caching.
From what I can gather they use a MySQL cluster and memcached and lots of custom written software. They open source plenty of it: http://developers.facebook.com/opensource/
the solution is to use a super-fast NoSQL-style database. Start with Simon Willison's excellent tutorial on redis, and it will all begin to become clear :)
I am looking into creating a web application that will make heavy use of a database to store data about the different users. Problem is, I don't really know that much about databases, especially in an environment that will (hopefully) have lots of traffic.
I have used MySQL before for some basic websites (college clubs, blog, low traffic stuff), but have heard this isn't a good choice for a real application.
Where can I learn more about the different databases out there? I have tried Google and Wikipedia, but there is just so much information I don't know where to start...
Write down your application's functional and non-functional requirements. Translate these into the conditions that the database must satisfy. Then match those with the readily available lists of features.
MySQL is used as a backend database on some large web sites.
(My preference is SQL Server, but it's not free like MySQL...)
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[An Updated List 21st Aug 09]
Help me Compile a List of all the Advantages & Disadvantages of Building an Application on the Google App Engine
Pros:
No need to buy servers or server space (no maintenance).
Makes solving the problem of scaling easier.
Free up to a certain level of consumed resources.
Cons:
Locked into Google App Engine ?
Developers have read-only access to the filesystem on App Engine.
App Engine can only execute code called from an HTTP request (except for scheduled background tasks).
Users may upload arbitrary Python modules, but only if they are pure-Python; C and Pyrex modules are not supported.
App Engine limits the maximum rows returned from an entity get to 1000 rows per Datastore call. (Update - App Engine now supports cursors for accessing larger queries)
Java applications may only use a subset (The JRE Class White List) of the classes from the JRE standard edition.
Java applications cannot create new threads.
Known Issues!! : http://code.google.com/p/googleappengine/issues/list
Hard limits
Apps per developer - 10
Time per request - 30 sec
Files per app - 3,000
HTTP response size - 10 MB
Datastore item size - 1 MB
Application code size - 150 MB
Update Blob store now allows storage of files up to 50MB
Pro or Con?
App Engine's infrastructure removes many of the system administration and development challenges of building applications to scale to millions of hits. Google handles deploying code to a cluster, monitoring, failover, and launching application instances as necessary.
While other services let users install and configure nearly any *NIX compatible software, App Engine requires developers to use Python or Java as the programming language and a limited set of APIs. Current APIs allow storing and retrieving data from a BigTable non-relational database; making HTTP requests; sending e-mail; manipulating images; and caching. Most existing Web applications can't run on App Engine without modification, because they require a relational database.
Pros:
Scalable
Easy and cheaper (in short term).
Nice option for start-ups/individuals.
Suitable for apps that just store and retrieve data.
Cons:
Not suitable for CPU intensive calculations. They are slower and expensive.
Scalability doesn't matter much cuz if an app works at Google scale then probably it makes enough money to run on its own servers.
They have lots of limitations thrown here and there, as a result deep data analysis is difficult. Like you cannot produce a social graph using GAE.
I would say its not meant for serious businesses and expensive in long run.
(A huge new) PRO: GAE now supports MySQL :
https://developers.google.com/cloud-sql/
Pros:
built-in ui for unified logs
built-in web interface for task queues
built-in indexes on list of primary objects.
Cons:
loose logs very fast
VERY expensive
VERY expensive
VERY expensive
Un-hackable. Scales because you're obligated to code in a way that scales.
Longer development cycles. Sometimes you just want to hack something together and throw it away after 5 hors. With appengine you have to proper code it and write a lot of stuff to make it sure it scales. You can't just do a "find . | grep .avi | xargs ffmpeg -compress ...." :)
You will loose hours trying to do the simplest tasks like sending push notifications to APNS (iPhone). Although it's fine if you only want to support android in the future.
Terrible to make cleanups on the database. It's a HUGE pain in the ass to fix rows in the database, mainly because terribly slow, but it also requires a lot of code to loop properly within it's time constraints.
It was a pain to port Lucene to work on it's "filesystem".
Slow for what you pay.
Even MORE expensive if your app has spikes of traffic. My app has those spikes if a user that has many followers makes an action and we have to push notifications to his followers. Because of that I have to keep 10 inactive servers always on ($$$$$) to handle spikes.
Appengine isn't too bad due to the fact that I have the option to burn $$$$ instead of being concerned about scalability and fixing bottlenecks to reduce server usage. Sometimes it worth it.
My advice to people starting new products is to go with hetzner.de which is where I host my other products servers. It's cheap and extremely hackable. I have one server at hetzner that is handling 3x more traffic than the product that I have on appengine. The difference in price is $100 a month versions $2700 a month!
I have system admin experience, so the bottom line is that I would never choose appengine over having my own ROOT server. Don't be that bored software engineer wanting to experiment new things instead of building great products!
Pro: Unlimited scalabity to your application and scales with demand.
Con: Not available in some countries (Argentina).
Edit
Available worldwide, but only through Google Groups for App Engine.
When assessing pros and cons, I think it is important to clarify the market for which one is representing. Developers looking for a cost-effective solution to help them with the steep part of their planned hockey-stick growth curve will weigh heavily the cons already listed. For a small business owner, however, GAE is a God-send. These folks most often are looking to "the cloud" as a means to more effectively run their business (i.e. sell physical product and services). For the SMB, GAE the pros already listed can be much more valuable compared to the hockey-stick seeking dev, whilst the cons weight in at a fraction of the devs' measure. I don't see the GAE team doing anything related to SMB positioning, so I guess answers like this are me just pulling on Superman's cape, and spitting into the wind. Really GAE should be absolutely ruling the SMB space now. If not (I have no insights re: user base), then its is a greatly lamentable failure.
I believe , GAE is yet to mature in terms of providing the basic features for serious business such as Datastore with complex primary key, java.awt.* support, these are just a few I'm naming.
Other than the free space and to build some "Hobby" websites, I strongly feel GAE is NOT the place java guys should looking into.
I'm having applications built on the JSP/Servlets and MySQL, thinking about migrating to GAE, but I find I will be spending more "value time" on the migration than just buying a space from some java hosting provider such as EATJ, etc (Sorry not marketing, just an experience).
Another big issue I've got is migration of my existing mySQL data into GAE, bulkupload is really pathetic and has no client support.
No support for Local Db to Server DB upload.
Once the GAE is ready with "all the Cons" mentioned by above, then I'll think we can look in to this migration.
You are force to own a cell phone line, and your country+carrier must be able to receive international SMSs.
(I hate cell phones, and my mom's or co-workers won't get the SMSs)
Con: No Other RDBMS or NoSQL databases are not possible ....
Con: All your base are belong to us
... On a serious note:
Con: You don't control the environment your application runs in. The same cons as with outsourcing any component. Fun for toys, not for business (yet) IMHO.
Various things like API for Google proprietary backends such as their database system and other 'lockdowns' and frameworks that mean your code is tied, in some loose sense to their system can create cost issues later if you want to migrate from GAE. Of course, you could abstract these.
I like GAE, AppJet and others. They are cool. But everything has its place. If you want freedom and the ability to control your language's modules, API, syntax/stdlib versions and whatnot ... don't relinquish control to a service provider.
The lack of standards for environments and specifications for what your app can expect worries me in the cloud arena.
common sense stuff really.
Con: Limited to Java and Python
Ideally, where would an application like Facebook store its "Friends" data?
In a database table? in an xml file?
From Facebooks engineering page:
"Already, we are the second most-trafficked PHP site in the world (Yahoo is #1), and one of the largest MySQL installations anywhere, running thousands of databases."
and
"We've built a lightweight but powerful multi-language RPC framework that allows us to seamlessly and easily tie together subsystems written in any language, running on any platform. Facebook is built in PHP, C++, Perl, Python, Erlang, Java, and even a little bit of ML—and it all works together.
* We are the largest user in the world of memcached, an open-source caching system. Originally developed by LiveJournal, we've since made so many scalability improvements and performance upgrades that we will be the primary contributor of features in the next major release.
* We've created a custom-built search engine serving millions of queries a day, completely distributed and entirely in-memory, with real-time updates."
Relational databases?
check out this blog: http://highscalability.com/ many real-world examples of systems architecures to learn from
"Friends" data is well-described in a graph database. Neo4j is an example, though I know it's not the way Facebook stores this information.
Facebook uses a number of database technologies that may be involved:
a patched version of MySQL
Cassandra
Hadoop
... others
Most probably it should contain some other mechanism. As an example a search engine does not keep its index as a database or XML file. To obtain a maximum performance generally they keep some tree (Binary search tree or more complicated one) and store them on disk in performance effective manner. So I guess such mechanism.
Certainly not in a XML file.
Yes, in a database, in one or several tables. And for the precise exemple of facebook, on several server.
With the rising of non-sql database usage in high traffic website, I'm interested to use it for my project. Now I've heard several names like Voldermort, MongoDB and CouchDB. But which are among these NonSQL database that is production ready? I've seen the download pages and it seems that none of them is production ready because is not version 1.0 yet. Is there any other names other than these 3 that is recommendable to be used in production?
What do you mean by production ready? As far as I know, all of them are being used on live systems.
You should make your choice based on how the features they provide fit your needs.
You can also add Tokyo Cabinet to the list as well as the mnesia database provided by the Erlang VM.
I think you need to start out from your project requirements to see what kind of database you really need. There are many non-relational DBMS:s out there and they differ a lot in what kind of problems they are good at solving. I think the article Should you go Beyond Relational Databases? by Martin Kleppmann is a good starting point for finding out what you need. There's also a lot of stackoverflow threads on similar topics, these are my favorites:
The Next-gen Databases
Non-Relational Database Design
When shouldn’t you use a relational
database?
Good reasons NOT to use a relational
database?
When you have narrowed down what you actually need you can take a deeper look into the alternatives to see which DBMS are production ready for your use case. Production readiness isn't a yes/no thing: people may successfully deploy some solution that for example lacks in tool support - in another project this could be a no-go.
As for version numbers different projects have a different take on this, so you can't just compare the version numbers. I'm involved in the graph database project Neo4j and even if it has been in production use for 5+ years by now we still haven't released a version 1.0 final yet.
I'm tempted to answer "use SIRA_PRISE".
It's definitely non-SQL.
And its current version is 1.2, meaning that someone like you must definitely assume it's "production-ready".
But perhaps I shouldn't be answering at all.
Nice article comparing rdbms with 'next gen' and listing some providers:
Is the Relational Database Doomed?
http://readwrite.com/2009/02/12/is-the-relational-database-doomed
I will suggest you to use Arangodb.
ArangoDB is a multi-model mostly-memory database with a flexible data model for documents and graphs. It is designed as a “general purpose database”, offering all the features you typically need for modern web applications.
ArangoDB is supposed to grow with the application—the project may start as a simple single-server prototype, nothing you couldn’t do with a relational database equally well. After some time, some geo-location features are needed and a shopping cart requires transactions. ArangoDB’s graph data model is useful for the recommendation system. The smartphone app needs a lean API to the back-end—this is where Foxx, ArangoDB’s integrated Javascript application framework, comes into play.
Another unique feature is ArangoDB’s query language AQL — it makes querying powerful and convenient. AQL enables you to describe complex filter conditions and joins in a readable format, much in the same way as SQL.
You can model your data in several ways:
in key/value pairs
as collections of documents
as graphs with nodes, edges, and properties for both
You can access data in ArangoDB:
using the general HTTP REST API via curl/wget, or your browser
via the ArangoDB shell (“arangosh”)
using a programming language specific client library
Server requirements for ArangoDB:
ArangoDB runs on Linux, OS X and Microsoft Windows.
It runs on 32bit and 64bit systems, though using a 32bit system will limit you to using only approximately 2 to 3 GB of data with ArangoDB.