InfoQ Homepage Distributed Systems Content on InfoQ
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Pickles & Spores: Improving Distributed Prog. in Scala
Heather Miller presents attempts at better supporting distributed programming in Scala, including a new fast pickling framework, as well as Spores - composable pieces of mobile functional behaviour.
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From The Lab To The Factory: Building A Production Machine Learning Infrastructure
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
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Data & Infrastructure at Airbnb
Brenden Matthews describes the infrastructure built at Airbnb using Mesos in order to support Hadoop and Storm.
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Graph Computing at Scale
Matthias Broecheler discusses graph computing, introducing the Aurelius graph cluster enabling graph computing at scale by building on distributed systems like Cassandra, HBase, and Hadoop.
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REEF: Retainable Evaluator Execution Framework
Rusty Sears introduces REEF along with examples of computational frameworks, including interactive sessions, iterative graph processing, bulk synchronous computations, Hive queries, and MapReduce.
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Apache Tez: Accelerating Hadoop Query Processing
Bikas Saha and Arun Murthy detail the design of Tez, highlighting some of its features and sharing some of the initial results obtained by Hive on Tez.
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Raft - The Understandable Distributed Protocol
Ben Johnson discusses the Raft protocol and how it works. Raft is a consensus distributed protocol.
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Grails SOA: Building Distributed Scalable Services with Grails and RabbitMQ
Steve Pember discusses creating Grails applications integrating message broker technologies, especially RabbitMQ, and applying SOA principles.
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Spanner - Google's Distributed Database
Sebastian Kanthak details how Spanner relies on GPS and atomic clocks to provide two of its innovative features: Lock-free strong reads and global snapshots consistent with external events.
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Add ALL the Things: Abstract Algebra Meets Analytics
Avi Bryant discusses how the laws of group theory provide a useful codification of the practical lessons of building efficient distributed and real-time aggregation systems.
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Partitions for Everyone!
Kyle Kingsbury discusses some of the limitations found in distributed systems and the way some of them behave under partitioning.
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Big Data Platform as a Service at Netflix
Jeff Magnusson details some of Netflix' key services: Franklin, Sting and Lipstick.
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