InfoQ Homepage Distributed Systems Content on InfoQ
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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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Leveraging Your Hadoop Cluster Better - Running Performant Code at Scale
Michael Kopp explains how to run performance code at scale with Hadoop and how to analyze and optimize Hadoop jobs.
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Lessons Learned Building Storm
Nathan Marz shares lessons learned building Storm, an open-source, distributed, real-time computation system.
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