InfoQ Homepage Scalability Content on InfoQ
-
(un)Common Sense
Mike Solomon shares some of the experiences and lessons learned scaling YouTube over the years.
/presentations/YouTube-Scalability-Lessons/en/mediumimage/MIkebig.jpg)
-
Scaling Pinterest
Yashwanth Nelapati and Marty Weiner share lessons learned growing Pinterest: sharding MySQL, caching, server management, all on Amazon EC2.
/presentations/Pinterest/en/mediumimage/Martybig.jpg)
-
Running the Largest Hadoop DFS Cluster
Hairong Kuang explains how Facebook uses HDFS to store and analyze over 100PB of user log data.
/presentations/Hadoop-HDFS-Facebook/en/mediumimage/Haibig.jpg)
-
Executing Queries on a Sharded Database
Neha Narula provides advice on choosing a data store for a web applications and executing distributed queries.
/presentations/Distributed-Queries/en/mediumimage/Narulabig.jpg)
-
Scaling Scalability: Evolving Twitter Analytics
Dmitriy Ryaboy shares some of the lessons learned scaling Twitter’s analytics infrastructure: Data loves a schema, Make data sources discoverable, and Make costs visible.
/presentations/Twitter-Analytics/en/smallimage/Dimitri.JPG)
-
Go: Code that Grows with Grace
Andrew Gerrand introduces Go, demoing some of its main features through examples: a concurrent echo server, chat, channels, error handling, etc.
/presentations/Google-Go-Introduction/en/smallimage/Andrew.JPG)
-
Scaling Software with Akka
Jonas Bonér explains solving scalability issues, including adaptive automatic load-balancing, cluster rebalancing, replication and partitioning, with Akka 2.
/presentations/Scalability-Akka/en/smallimage/Jonas.JPG)
-
Erlang Scales … Do You?
Erik Happi Stenman discusses 4 scalability basic requirements: the right business model, the right technology, the right people, and the right (amount of) process.
/presentations/Erlang-Scalability/en/smallimage/Erik.JPG)
-
Futures and Promises: Lessons in Concurrency Learned at Tumblr
Blake Matheny discusses the current status of Tumblr, its evolution and lessons learned along the way, 3 types of concurrency -Macro, Mecro and Micro-, and Motherboy –a dashboard system-.
/presentations/Concurrency-Tumblr/en/smallimage/Blake.JPG)
-
Embracing Concurrency at Scale
Justin Sheehy discusses designing reliable distributed systems that can scale in order to deal with concurrency problems and the tradeoffs required by such systems.
/presentations/Concurrency-Scale-Distributed/en/smallimage/Justin.JPG)
-
Event Processing at Massive Scale
Uri Cohen discusses several types of queues with their pros and cons used in financial and trading industries for highly parallelized data processing.
/presentations/Event-Processing-Scale/en/smallimage/Uri.JPG)
/presentations/Tumblr/en/smallimage/Ken.JPG)