Posts Tagged ‘Apache Cassandra’

Building a Scalable Database on top of Apache Cassandra at SimpleGeo

April 1, 2011 Leave a comment

Mike Malone presents SimpleGeo’s use of Apache Cassandara to scale geospatial data at Cloudstock 2010:

Slides from a various similar presentation delivered by Malone at Strange Loop 2010 can be found here.



Overview of Apache Cassandra – Eben Hewitt at Strange Loop 2010

March 12, 2011 Leave a comment

Nice presentation by Eben Hewitt on Apache Cassandra at Strange Loop 2010.

Slides for the presentation can be found here. Hewitt is also the author of Cassandra: The Definitive Guide, published by O’Reilly in November 2010.


Facebook’s Architectural Stack – designing for Big Data

March 6, 2011 1 comment

This is my fourth of a series of posts exploring the topic of Big Data. The previous posts in this series are:

This post provides two videos in which Facebook’s David Recordon discusses Facebook’s architectural stack as a platform that must scale to massive amounts of data and traffic. The first video is a short video where Recordon discusses Facebook use of the LAMP stack at OSCON 2010:

On Database Technology and NoSQL Databases at Facebook

In the first video, Recordon first addresses how Facebook implements database technology generally, and the topic of NoSQL databases. Says Recordon:

The primary way that we store data – all of our user data that you’re going and accessing when we’re working on the sight, with the exception of some services like newsfeed – is actually stored in MySQL.

So we run thousands of nodes of a MySQL cluster – but we largely don’t care that MySQL is a relational database. We generally don’t use it for joins. We’re not going and running complex queries that are pulling multiple tables together inside a database using views or anything like that.

But the fundamental idea of a relational database from the ’70s hasn’t gone away. You still need those different components.

Recordon says that there are really three different layers Facebook thinks about when working with data, illustrated in the following visual:

Continues Recordon:

You have the database, which is your primary data store. We use MySQL because it’s extremely reliable. [Then you have] Memcache and our web servers.

So we’re going and getting the data from our database. We’re actually using our web server to combine the data and do joins. And this is some of where HipHop becomes so important, because our web server code is fairly CPU-intensive because we’re going and doing all these different sorts of things with data.

And then we use Memcache as our distributed secondary index.

These are all the components that you would traditionally use a relational database for:

Recordon continues:

[These are the same layers that were] talked about 30-40 years ago in terms of database technology, but they’re just happening in different places.

And so whether you’re going and using MySQL, or whether you’re using a NoSQL database, you’re not getting away from the fact that you have to go and combine data together, that you’re needing to have a way to look it up quickly, or any of those things that you would traditionally use a database for.

On the topic of NoSQL databases, Recordon says:

And then when you dig into the NoSQL technology stack, there are a number of different families of NoSQL databases which you can go and use. You have document stores, you have column family stores, you have graph databases, you have key-value pair databases.

And so the first question that you really have is what problem am I trying to solve, and what family of SQL database do I want to go and use.

And then even when you dig into one of these categories – if we just go and look at Cassandra and HBase – there are a number of differences inside of this one category of database. Cassandra and HBase make a number of different tradeoffs from a consistency perspective, from a relationship perspective. And so overall you really go and think about what problem am I trying to solve; how can I pick the best database to do that, and use it.

While we store the majority of our user data inside of SQL, we have about 150 terabytes of data inside Cassandra, which we use for Inbox search on the site. And over 36 petabytes of uncompressed data in Hadoop overall.

On the topic of Big Data


So that leads me into Big Data. We run a Hadoop cluster with a little over 2,200 servers, about 23,000 CPU cores inside of it. And we’ve seen the amount of data which we go and store and process growing rapidly – it’s increased about 70 times over the past 2 years. And by the end of the year, we expect to be storing over 50 petabytes of uncompressed information – which is more than all the works of mankind combined together.

And I think this is really both the combination of the increase in terms of user activity on Facebook … But also just in terms of how important data analysis has become to running large, successful websites.

The diagram below shows Facebook’s Big Data infrastructure:

Says Recordon:

So this is the infrastructure which we use. I’ll take a minute to walk through it.

With all our web servers we use an open source technology we created called Scribe to go and take the data from tens of thousands of web servers, and funnel them into HDFS and into our Hadoop warehouses. The problem that we originally ran into was too many web servers going and trying to send data to one place. And so Scribe really tries to break it out into a series of funnels collecting this data over time.

This data is pushed into our Platinum Hadoop Cluster about every 5-to-15 minutes. And then we’re also going and pulling in data from our MySQL clusters on about a daily basis. Our Platinum Hadoop Cluster is really what is vital to the business. It is the cluster where if it goes down, it directly affects the business. It’s highly maintained, it’s highly monitored. Every query that’s being run across it, a lot of thought has gone into it.

We also then go and replicate this data to a second cluster which we call the Silver Cluster – which is where people can go and run ad-hoc queries. We have about 300 to 400 people which are going running Hadoop and Hive jobs every single month, many of them outside of engineering. We’ve tried to make this sort of data analysis to help people throughout the company make better product decisions really accessible.

And so that’s one of the other technologies which we use, Apache Hive, which gives you an SQL interface on top of Hadoop to go and do data analysis. And all of these components are open source.

So when Facebook thinks about how there stack has evolved over the past few years, it looks something like this:

Where the major new component is the Hadoop technology stack and its related components to manage massive amounts of data, and do data analysis over top of that data.

A deeper look at Scaling challenges at Facebook

The second video is a presentation delivered by David Recordon and Scott MacVicar – both Facebook software engineers – at FOSDEM in February 2010 provides a deeper look into Facebook’s use of open source technology to provide a massively scalable infrastructure:

The question that I am interested in, and isn’t answered in these videos, is how Facebook implements its Open Graph data model in its infrastructure. That would be very interesting to learn. For more specifically about Facebook’s Open Graph technology, please see Facebook’s Open Graph and the Semantic Web – from Facebook F8.

Very interesting stuff.