Dynamo

Dynamo: amazon’s highly available key-value store. Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems. This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon’s core services use to provide an ”always-on” experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.


References in zbMATH (referenced in 30 articles )

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  1. Almeida, Paulo Sérgio; Baquero, Carlos: Scalable eventually consistent counters over unreliable networks (2019)
  2. Dubois, Swan; Guerraoui, Rachid; Kuznetsov, Petr; Petit, Franck; Sens, Pierre: The weakest failure detector for eventual consistency (2019)
  3. Zhang, Meng; Qi, Saiyu; Miao, Meixia; Zhang, Fuyou: Enabling compressed encryption for cloud based big data stores (2019)
  4. Cabrita, Gonçalo; Preguiça, Nuno: Non-uniform replication (2018)
  5. Friedman, Roy; Licher, Roni: Hardening Cassandra against Byzantine failures (2018)
  6. Golab, Wojciech; Li, Xiaozhou (Steve); López-Ortiz, Alejandro; Nishimura, Naomi: Computing (k)-atomicity in polynomial time (2018)
  7. Grossi, Roberto; Versari, Luca: Round-hashing for data storage: distributed servers and external-memory tables (2018)
  8. Interlandi, Matteo; Tanca, Letizia: A Datalog-based computational model for coordination-free, data-parallel systems (2018)
  9. Mitzenmacher, Michael; Pagh, Rasmus: Simple multi-party set reconciliation (2018)
  10. Pires, Miguel; Ravi, Srivatsan; Rodrigues, Rodrigo: Generalized Paxos made Byzantine (and less complex) (2018)
  11. Wang, Jiaqi; Talmage, Edward; Lee, Hyunyoung; Welch, Jennifer L.: Improved time bounds for linearizable implementations of abstract data types (2018)
  12. Chan, Carri W.; Armony, Mor; Bambos, Nicholas: Maximum weight matching with hysteresis in overloaded queues with setups (2016)
  13. Chen, Tao; Bahsoon, Rami; Tawil, Abdel-Rahman H.: Scalable service-oriented replication with flexible consistency guarantee in the cloud (2014) ioport
  14. Gandhi, Anshul; Doroudi, Sherwin; Harchol-Balter, Mor; Scheller-Wolf, Alan: Exact analysis of the (\mathrmM/\mathrmM/k/\mathrmsetup) class of Markov chains via recursive renewal reward (2014)
  15. Jiang, Wenbin; Zhang, Lei; Liao, Xiaofei; Jin, Hai; Peng, Yaqiong: A novel clustered MongoDB-based storage system for unstructured data with high availability (2014) ioport
  16. Kuznetsov, S.; Poskonin, A.: NoSQL data management systems (2014) ioport
  17. Liu, Qin; Wang, Guojun; Wu, Jie: Time-based proxy re-encryption scheme for secure data sharing in a cloud environment (2014) ioport
  18. Martalò, M.; Amoretti, M.; Picone, M.; Ferrari, G.: Sporadic decentralized resource maintenance for P2P distributed storage networks (2014) ioport
  19. K. R., Krish; Wang, Guanying; Bhattacharjee, Puranjoy; Butt, Ali R.; Gniady, Chris: On reducing energy management delays in disks (2013) ioport
  20. Matos, Miguel; Schiavoni, Valerio; Felber, Pascal; Oliveira, Rui; Rivière, Etienne: Lightweight, efficient, robust epidemic dissemination (2013) ioport

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