Journal ArticleDOI
Cassandra: a decentralized structured storage system
Avinash Lakshman,Prashant Malik +1 more
TLDR
Cassandra is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure.Abstract:
Cassandra is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure. Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different data centers). At this scale, small and large components fail continuously. The way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service. While in many ways Cassandra resembles a database and shares many design and implementation strategies therewith, Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format. Cassandra system was designed to run on cheap commodity hardware and handle high write throughput while not sacrificing read efficiency.read more
Citations
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Proceedings ArticleDOI
Service fabric: a distributed platform for building microservices in the cloud
Gopal Kakivaya,Lu Xun,Richard L. Hasha,Shegufta Bakht Ahsan,Todd F. Pfleiger,Rishi Rakesh Sinha,Anurag Gupta,Mihail Gavril Tarta,Mark W. Fussell,Vipul A. Modi,Mansoor Mohsin,Ray Kong,Anmol Ahuja,Oana Platon,Alex Wun,Matthew Snider,Chacko Daniel,Dan Mastrian,Yang Li,Aprameya Rao,Vaishnav Kidambi,Randy Wang,Abhishek Ram,Sumukh Shivaprakash,Rajeet Nair,Alan M. Warwick,Bharat S. Narasimman,Meng Lin,Jeffrey Chao-Nan Chen,Abhay Balkrishna Mhatre,Preetha Lakshmi Subbarayalu,Mert Coskun,Indranil Gupta +32 more
TL;DR: Service Fabric, Microsoft's distributed platform for building, running, and maintaining microservice applications in the cloud, is described, focusing on modular use and support for strong semantics like fault-tolerance and consistency within each component of SF.
Book ChapterDOI
Performance Evaluation of NoSQL Databases
TL;DR: This paper benchmarks and compares three of the most common NoSQL databases: Cassandra, MongoDB and HBase on the Amazon EC2 cloud platform and shows that the models are able to capture the main performance characteristics of the studied databases and form the basis for a capacity planning tool for service providers and service users.
Proceedings ArticleDOI
JUST: JD Urban Spatio-Temporal Data Engine
Ruiyuan Li,Huajun He,Rubin Wang,Huang Yuchuan,Junwen Liu,Sijie Ruan,Tianfu He,Jie Bao,Yu Zheng +8 more
TL;DR: This paper designs and implements a complete SQL engine, with which all operations can be performed through a SQL-like query language, i.e., JustQL, which can efficiently manage big spatio-temporal data in a convenient way and has a competitive query performance and is much more scalable than other distributed data management systems.
Proceedings ArticleDOI
Salt: combining ACID and BASE in a distributed database
Chao Xie,Chunzhi Su,Manos Kapritsos,Yang Wang,Navid Yaghmazadeh,Lorenzo Alvisi,Prince Mahajan +6 more
TL;DR: SALT as mentioned in this paper is a distributed database that allows developers to improve the performance and scalability of their ACID applications through the incremental adoption of the BASE approach, a new abstraction that encapsulates the workflow of performancecritical transactions.
Journal ArticleDOI
Quantifying eventual consistency with PBS
TL;DR: Probabilistically bounded staleness (PBS) as discussed by the authors is a consistency model which provides expected bounds on data staleness with respect to both versions and wall clock time, and quantitatively demonstrates how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering significant latency benefits.
References
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Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Proceedings ArticleDOI
Chord: A scalable peer-to-peer lookup service for internet applications
TL;DR: Results from theoretical analysis, simulations, and experiments show that Chord is scalable, with communication cost and the state maintained by each node scaling logarithmically with the number of Chord nodes.
Journal ArticleDOI
The Google file system
TL;DR: This paper presents file system interface extensions designed to support distributed applications, discusses many aspects of the design, and reports measurements from both micro-benchmarks and real world use.
Proceedings Article
Bigtable: A Distributed Storage System for Structured Data (Awarded Best Paper!).
Fay W. Chang,Jeffrey Dean,Sanjay Ghemawat,Wilson C. Hsieh,Deborah A. Wallach,Michael Burrows,Tushar Deepak Chandra,Andrew Fikes,Robert Gruber +8 more
TL;DR: Bigtable as mentioned in this paper is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers, including web indexing, Google Earth and Google Finance.