InfoQ Homepage Distributed Systems Content on InfoQ
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Distributed Scheduling with Apache Mesos in the Cloud
Diptanu Choudhury discusses the design of Netflix’ distributed scheduler based on Mesos and Titan, focusing on bin packing algorithms, scaling in and out of clusters, fault tolerance, and redundancy.
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Mini-talks: Deterministic Testing, Typesafe Config, Spreads v Probe, & Real-Time Event-Driven
Small sessions on: Deterministic testing in a non-deterministic world. Hash Spreads and Probe Functions. Typesafe Config on Steroids. Real-Time Distributed Event-Driven Computing at Credit Suisse.
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Building Distributed Systems with Apache Mesos
Benjamin Hindman discusses Apache Mesos, focusing on the Mesos API and how the primitives provided by Mesos can make it easier to build new stateful services and frameworks.
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Five Techniques to Improve How You Debug Servers
Tal Weiss explores five crucial Java techniques for distributed debugging and some of the pitfalls that make bug resolution much harder, and can even lead to downtime.
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Lightning Fast Cluster Computing with Spark and Cassandra
Piotr Kołaczkowski discusses how they integrated Spark with Cassandra, how it was done, how it works in practice and why it is better than using a Hadoop intermediate layer.
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Spring Cloud - A Toolbox for Distributed Systems
Oliver Gierke summarizes the problems Spring Cloud tries to solve and introduces the individual modules through practical code examples.
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Distributed Platform Development with Groovy
Dan Woods discusses the approach to developing a scalable enterprise architecture, and demonstrates implementations based on the variety of technologies available from the Groovy ecosystem.
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Programming and Testing a Distributed Database
Reid Draper shows how real world distributed database work, communicate and are tested, trading RPC for messaging, unit-tests for QuickCheck, and micro-benchmarks for multi-week stress tests.
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Better Together - Using Spark and Redshift to Combine Your Data with Public Datasets
Eugene Mandel discusses challenges of conforming data sources and compares processing stacks: Hadoop+Redshift vs Spark, showing how the technology drives the way the problem is modeled.
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Building a Recommendation Engine with Spring and Hadoop
Michael Minella uses Spring XD and Spring Batch to orchestrate the full lifecycle of Hadoop processing and uses Apache Mahout to provide the audience with the recommendation processing.
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Apps + Data + Cloud: What Does It All Mean?
Matt Stine presents how combine Spring Boot, Spring Data, Spring Reactor, Spring XD, Hadoop and run them in the cloud.
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Consul: Service-oriented at Scale
Armon Dadgar presents Consul, a distributed control plane for the datacenter. Armon demonstrates how Consul can be used to build, configure, monitor, and orchestrate distributed systems.
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