Sessions tagged with 'scaling_geek'

Building large data applications can present a unique set of technical challenges because things that often work well in the conventional development environment can become incredibly arduous or expensive when applied on a much bigger scale. This talk will cover some of those challenges and potential solutions for each.

There are many exciting InnoDB performance and Scalability features in MySQL 5.5 and its upcoming release. But how to best use them? What are the caveats? At this session, we will describe those performance and Scalability features in depth. We will also present some benchmark results that explore the performance of those features.

This talk will cover lessons learned in building Urban Airship's large-scale data warehouse in EC2 including PostgreSQL, Kafka, Cassandra, HBase and Hadoop.

Time Series sensors are being ubiquitously integrated in places like cell phones, environmental sensors, and the smart grid. As we scale out this type of data RDBMS systems strain to scale with the high insertion rates and real time query requirements. In this talk we introduce “Lumberyard” which is a scalable indexing and low latency fuzzy pattern searching time series data.

If you've ever had to move from data center to data center or to the cloud, or from old hardware to new hardware, you know that it's even more painful than moving house. In this presentation, survivors will tell you how to stay sane (and how to get it right) with a case study from Mozilla: moving 30TB of crash reports with no downtime in data collection.

We at DeNA (largest social game provider in Japan) handle over 2 billion page views per day with MySQL. We heavily use SSD and tune Linux. We run non-trivial solutions such as non-stop, automated MySQL master failover. We also use MySQL not only as traditional RDBMS but also an extremely high performance NoSQL. I'd like to introduce our MySQL solutions to make our social games scale better.

Solr, an open source enterprise search server, scales very well within an index (vertical scaling). It is when you have multiple indexes (horizontal scaling) that it starts to get hairy, which happens a lot when you are hosting a cloud based solution for multiple users. In this session we will discuss these issue as well as the techniques of how to overcome them in-depth.

The last few years have brought a wealth of new data technologies organized around horizontal scalability. This talk will cover the essential infrastructure areas: real-time stream processing, offline data crunching, large-scale data deployments and live serving. The focus will be on how these ingredients come together to enable innovative data-driven products at LinkedIn.

Between the NoSQL movement and new cloud offerings, it seems there are new storage options popping up every day. How do you select which one is the best for your project? The truth is that it's unlikely one option is best for all your needs. This session walks you through the various options considered by one startup and how it selected five separate storage engines - and has no regret doing so!