What’s the difference between Reliability, Durability, and Availability for data storage system?

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Some important concepts in distributed system like Hadoop distributed file system, Google file system and so on. Answer from http://www.quora.com/Whats-the-difference-between-Reliability-Durability-and-Availability-for-data-storage-system The difference between durability and availability is fairly simple. Durability is about what happens when all power goes out everywhere. Has all data been written to stable storage that doesn’t require power (e.g. disk/flash), in
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SQL layers on NoSQL databases

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What are the SQL layer solution over NoSQL databases such as key/value stores? Phoenix: A SQL layer on HBase: https://github.com/forcedotcom/phoenix They also show some performance results: https://github.com/forcedotcom/phoenix/wiki/Performance F1 – The Fault-Tolerant Distributed RDBMS Supporting Google’s Ad Business: http://research.google.com/pubs/pub38125.html With F1, we have built a novel hybrid system that combines the scalability, fault tolerance, transparent sharding,
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Hadoop Installation Tutorial (Hadoop 2.x)

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Hadoop 2 or YARN is the new version of Hadoop. It adds the yarn resource manager in addition to the HDFS and MapReduce components. Hadoop MapReduce is a programming model and software framework for writing applications, which is an open-source variant of MapReduce designed and implemented by Google initially for processing and generating large data
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Data Consistency Models of Public Cloud Storage Services: Amazon S3, Google Cloud Storage and Windows Azure Storage

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The public cloud storage services like Amazon S3, Google Cloud Storage and Windows Azure Storage replicate the data to ensure high availability. On the other hand, with data being replicated, the storage services exhibits certain data consistency models. Different cloud service providers employ different data consistency models nowadays. In this post, we survey the data
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Storage Architecture and Challenges by Andrew Fikes at Google Faculty Summit 2010

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Storage Architecture and Challenges in Faculty Summit, July 29, 2010, by Andrew Fikes, Principal Engineer. Download PDF (from archive.org). This slides introduces some of Google’s storage systems with insights and discussion of problems.

Large-scale Data Storage and Processing System in Datacenters

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Research on Cloud Computing has made big progresses and many excellent large-scale systems have been designed in recent years. I compiled a list of some large-scale data storage and processing systems in datacenters as follows. Storage systems Google File System (GFS): http://research.google.com/archive/gfs.html HDFS implementation: https://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html Colossus (GFS2): Colossus: Successor to the Google File System (GFS)
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Colossus: Successor to the Google File System (GFS)

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Colossus is the successor to the Google File System (GFS) as mentioned in the paper on Spanner at OSDI 2012. Colossus is also used by spanner to store its tablets. The information about Colossus is slim compared with GFS which is published in the paper at SOSP 2003. There is still some information about Colossus
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