InfoQ Homepage Distributed Systems Content on InfoQ
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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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Scaling out with Akka Actors
Joshua Suereth designs a scalable distributed search service with Akka and Scala using actors, and covering practical aspects of how to scale out with Akka’s clustering API.
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High Speed Smart Data Ingest into Hadoop
Oleg Zhurakousky discusses architectural tradeoffs and alternative implementations of real-time high speed data ingest into Hadoop.
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The Free Lunch Is Over, Again
Andy Gross discusses the challenges introduced by distributed systems and the need for developing new skills and tools for dealing with them.
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A Guide to Python Frameworks for Hadoop
Uri Laserson reviews the different available Python frameworks for Hadoop, including a comparison of performance, ease of use/installation, differences in implementation, and other features.
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