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Concurrent Caching at Google

Posted by Charles Fry  on  Dec 28, 2011 1

Charles Fry presents MapMaker, an in-memory caching solution on the JVM, discussing its API and implementation evolution along with internal details.

Asynchronous Memcached with a Side of Ketchup and Membase

Posted by Jason Sirota  on  Nov 23, 2011 5

Jason Sirota explains with code samples how to combine caching with asynchronous IO using memcached, Membase and Ketchup in order to maximize the throughput of an application.

Membase NoSQL: Clustered by Erlang

Posted by Sean Lynch and Matt Ingenthron  on  Sep 12, 2011

Sean Lynch and Matt Ingenthron introduce Membase, detailing how they added clustering features in Erlang, what they built and what lessons they leaned along the way.

League of Legends: Scaling to Millions of Ninjas, Yordles, and Wizards

Posted by Scott Delap and Randy Stafford  on  Apr 07, 2011 2

Scott Delap and Randy Stafford explain the architectural decisions made in order to scale, monitor and operate 24/7 the game League of Legends, bringing insight on how they use Oracle Coherence for that.

Yes, SQL!

Posted by Uri Cohen  on  Mar 01, 2011

Uri Cohen presents the key characteristics of SQL and NoSQL databases and how to create a layer on top of distributed data stores in order to use SQL to query for data. For further information please contact Gigaspaces.

Advanced GORM - Performance, Customization and Monitoring

Posted by Burt Beckwith  on  Jan 27, 2011 3

Burt Beckwith discusses potential performance problems using mapped collections and Hibernate 2nd-level cache in GORM, along with strategies for avoiding such performance penalties.

Squid Wrangling

Posted by Sam Newman and Chris Read  on  Jan 14, 2011

Sam Newman and Chris Read describe the architectural change of a large European website by introducing a caching layer based on Squid in order to scale out to handle increased demand, and the cultural change done by breaking down the dev-ops silos.

Scaling Australia's Most Popular Online News Sites with Ehcache

Posted by Ari Zilka and Matthias Matook  on  Dec 06, 2010 4

Matthias Matook and Ari Zilka share the real-world experience of implementing Enterprise Ehcache at Australia's most visited online news sites. The talk will focus on the challenges and technical solutions to deal with massive page hits, high concurrency and how to achieve linear scalability without additional hardware.

Scaling Your Cache & Caching at Scale

Posted by Alex Miller  on  Jun 07, 2010

Alex Miller explains shortly why caching is useful, followed by examples of typical difficulties encountered when setting up a cache, like large datasets, data eviction, stale data, replication, loading, duplication. Miller also discusses available choices for designing a distributed caching architecture, and ways to test a cache for performance.

Scale at Facebook

Posted by Aditya Agarwal  on  May 28, 2010 3

Beside presenting the overall Facebook architecture and scaling solutions used, Aditya Agarwal talks about the iterative process of constantly improving the site, making sure to avoid over-engineering and adapting along the way by dropping solutions that worked in the past but are no longer useful. The last part of the session was dedicated to answering questions from the audience.

Horizontal Scalability via Transient, Shardable, and Share-Nothing Resources

Posted by Adam Wiggins  on  Apr 20, 2010 1

Adam Wiggins believes that now is the time of horizontal scalability achieved by using resources that are transient, shardable and share nothing with other resources. He gives as example several applications and a language: memcached, CouchDB, Hadoop, Redis, Varnish, RabbitMQ, Erlang, detailing how each one applies those principles.

Evolving the Key/Value Programming Model to a Higher Level

Posted by Billy Newport  on  Jan 19, 2010 6

In this presentation from QCon San Francisco 2009, Billy Newport discusses the ways that developers interact with key/value (KV) stores such as memcached and WebSphere eXtreme Scale, entity vs column-oriented approaches, synchronous and asynchronous operations, large data sets, using a DBMS as a column store, collocating closures and data, and features that could be added to increase scalability.

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