- published: 17 Jul 2015
- views: 3047
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Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common?
Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...]
Download slides and read the full abstract here:
https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910
https://twitter.com/gotober
https://www.facebook.com/GOTOConference
http://gotocon.com
- published: 21 Dec 2016
- views: 3034
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move.
This talk was given at Cassandra Day Silicon Valley 2014.
- published: 08 May 2014
- views: 2322
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency.
This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing.
Attendees of this session will:
* Get a solid grounding in the CAP theorem and BASE
* Understand the special data requirements Cloud Computing presents when architecting applications
* Think differently about basic concepts like transactionality and consistency
- published: 14 May 2013
- views: 1879
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE semantics, in contrast to traditional ACID guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee and does not make safety guarantees: an eventually consistent system can return any value before it converges.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
- published: 21 Dec 2015
- views: 517
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE (Basically Available, Soft state, Eventual consistency) semantics, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee (reads eventually return the same value) and does not make safety guarantees: an eventually consistent system can return any value before it converges.
In order to ensure replica convergence, a system must reconcile differences between multiple copies of distributed data. This consists of two parts:
exchanging versions or updates of data between servers (often known as anti-entropy); and
choosing an appropriate final state when concurrent updates have occurred, called reconciliation.
The most appropriate approach to reconciliation depends on the application. A widespread approach is "last writer wins". Another is to invoke a user-specified conflict handler. Timestamps and vector clocks are often used to detect concurrency between updates.
Reconciliation of concurrent writes must occur sometime before the next read, and can be scheduled at different instants:
Read repair: The correction is done when a read finds an inconsistency. This slows down the read operation.
Write repair: The correction takes place during a write operation, if an inconsistency has been found, slowing down the write operation.
Asynchronous repair: The correction is not part of a read or write operation.
Whereas eventual consistency is only a liveness guarantee (updates will be observed eventually), strong eventual consistency (SEC) adds the safety guarantee that any two nodes that have received the same (unordered) set of updates will be in the same state. If, furthermore, the system is monotonic, the application will never suffer rollbacks. Conflict-free replicated data types are a common approach to ensuring SEC.
- published: 23 May 2017
- views: 50
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
- published: 28 Apr 2014
- views: 1707
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell
Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test.
I will show how QuickCheck helped us to model Riak's behavior, improving understanding and revealing the occasional bug.
- published: 20 Mar 2013
- views: 741
Eventual consistency
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE (Basically Available, Soft state, Eventual consistency) semantics, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee (reads eventually return the same value) and does not make safety guarantees: an eventually consistent system can return any value before it converges.
This page contains text from Wikipedia, the Free Encyclopedia -
https://wn.com/Eventual_consistency
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
Conflict resolution
Conflict resolution, otherwise known as reconciliation, is conceptualized as the methods and processes involved in facilitating the peaceful ending of conflict and retribution. Committed group members attempt to resolve group conflicts by actively communicating information about their conflicting motives or ideologies to the rest of the group (e.g., intentions; reasons for holding certain beliefs), and by engaging in collective negotiation. Dimensions of resolution typically parallel the dimensions of conflict in the way the conflict is processed. Cognitive resolution is the way disputants understand and view the conflict, with beliefs and perspectives and understandings and attitudes. Emotional resolution is in the way disputants feel about a conflict, the emotional energy. Behavioral resolution is how one thinks the disputants act, their behavior. Ultimately, a wide range of methods and procedures for addressing conflict exist, including but not limited to negotiation, mediation, diplomacy, and creative peacebuilding.
This page contains text from Wikipedia, the Free Encyclopedia -
https://wn.com/Conflict_resolution
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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8:02Eventual Consistency
Eventual ConsistencyEventual Consistency
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale. -
51:36GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin KleppmannGOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
This presentation was recorded at GOTO Berlin 2016 http://gotober.com Martin Kleppmann - Researcher at University of Cambridge ABSTRACT What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common? Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...] Download slides and read the full abstract here: https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910 https://twitter.com/gotober https://www.facebook.com/GOTOConference http://gotocon.com -
26:04Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency ModelNetflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move. This talk was given at Cassandra Day Silicon Valley 2014. -
36:05Dropping ACID: Architecting with Eventual Consistency in the Cloud
Dropping ACID: Architecting with Eventual Consistency in the CloudDropping ACID: Architecting with Eventual Consistency in the Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency. This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing. Attendees of this session will: * Get a solid grounding in the CAP theorem and BASE * Understand the special data requirements Cloud Computing presents when architecting applications * Think differently about basic concepts like transactionality and consistency -
2:56Eventual consistency
Eventual consistencyEventual consistency
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints. Eventually consistent services are often classified as providing BASE semantics, in contrast to traditional ACID guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee and does not make safety guarantees: an eventually consistent system can return any value before it converges. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video -
50:22Martin Kleppmann - Conflict Resolution for Eventual Consistency
Martin Kleppmann - Conflict Resolution for Eventual ConsistencyMartin Kleppmann - Conflict Resolution for Eventual Consistency
-
1:07:14Handling Eventual Consistency in JVM Microservices with Event Sourcing
Handling Eventual Consistency in JVM Microservices with Event SourcingHandling Eventual Consistency in JVM Microservices with Event Sourcing
-
3:38What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaningWhat is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints. Eventually consistent services are often classified as providing BASE (Basically Available, Soft state, Eventual consistency) semantics, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee (reads eventually return the same value) and does not make safety guarantees: an eventually consistent system can return any value before it converges. In order to ensure replica convergence, a system must reconcile differences between multiple copies of distributed data. This consists of two parts: exchanging versions or updates of data between servers (often known as anti-entropy); and choosing an appropriate final state when concurrent updates have occurred, called reconciliation. The most appropriate approach to reconciliation depends on the application. A widespread approach is "last writer wins". Another is to invoke a user-specified conflict handler. Timestamps and vector clocks are often used to detect concurrency between updates. Reconciliation of concurrent writes must occur sometime before the next read, and can be scheduled at different instants: Read repair: The correction is done when a read finds an inconsistency. This slows down the read operation. Write repair: The correction takes place during a write operation, if an inconsistency has been found, slowing down the write operation. Asynchronous repair: The correction is not part of a read or write operation. Whereas eventual consistency is only a liveness guarantee (updates will be observed eventually), strong eventual consistency (SEC) adds the safety guarantee that any two nodes that have received the same (unordered) set of updates will be in the same state. If, furthermore, the system is monotonic, the application will never suffer rollbacks. Conflict-free replicated data types are a common approach to ensuring SEC. -
56:13Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful ConsistencyEventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency Speaker: Christos Kalantzis, Engineering Manager, Netflix This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model. -
35:07Testing Eventual Consistency in Riak
Testing Eventual Consistency in RiakTesting Eventual Consistency in Riak
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test. I will show how QuickCheck helped us to model Riak's behavior, improving understanding and revealing the occasional bug.
-
Eventual Consistency
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
published: 17 Jul 2015 -
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
This presentation was recorded at GOTO Berlin 2016 http://gotober.com Martin Kleppmann - Researcher at University of Cambridge ABSTRACT What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common? Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...] Download slides and read the full abstract here: https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910 https://twitter.com/gotober https://www.facebook.com/GOTOConference http://gotocon.com
published: 21 Dec 2016 -
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move. This talk was given at Cassandra Day Silicon Valley 2014.
published: 08 May 2014 -
Dropping ACID: Architecting with Eventual Consistency in the Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency. This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore ...
published: 14 May 2013 -
Eventual consistency
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints. Eventually consistent services are o...
published: 21 Dec 2015 -
Martin Kleppmann - Conflict Resolution for Eventual Consistency
published: 22 Nov 2016 -
Handling Eventual Consistency in JVM Microservices with Event Sourcing
published: 22 Sep 2016 -
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica conv...
published: 23 May 2017 -
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency Speaker: Christos Kalantzis, Engineering Manager, Netflix This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
published: 28 Apr 2014 -
Testing Eventual Consistency in Riak
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test. I will show how QuickCheck helped us to ...
published: 20 Mar 2013
Eventual Consistency
- Order: Reorder
- Duration: 8:02
- Updated: 17 Jul 2015
- views: 3047
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
https://wn.com/Eventual_Consistency
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
- published: 17 Jul 2015
- views: 3047
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
- Order: Reorder
- Duration: 51:36
- Updated: 21 Dec 2016
- views: 3034
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative ...
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common?
Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...]
Download slides and read the full abstract here:
https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910
https://twitter.com/gotober
https://www.facebook.com/GOTOConference
http://gotocon.com
https://wn.com/Goto_2016_•_Conflict_Resolution_For_Eventual_Consistency_•_Martin_Kleppmann
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common?
Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...]
Download slides and read the full abstract here:
https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910
https://twitter.com/gotober
https://www.facebook.com/GOTOConference
http://gotocon.com
- published: 21 Dec 2016
- views: 3034
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
- Order: Reorder
- Duration: 26:04
- Updated: 08 May 2014
- views: 2322
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Pers...
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move.
This talk was given at Cassandra Day Silicon Valley 2014.
https://wn.com/Netflix's_Distributed_Computing_Strategies_Optimistic_Design_For_The_Eventual_Consistency_Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move.
This talk was given at Cassandra Day Silicon Valley 2014.
- published: 08 May 2014
- views: 2322
Dropping ACID: Architecting with Eventual Consistency in the Cloud
- Order: Reorder
- Duration: 36:05
- Updated: 14 May 2013
- views: 1879
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DB...
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency.
This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing.
Attendees of this session will:
* Get a solid grounding in the CAP theorem and BASE
* Understand the special data requirements Cloud Computing presents when architecting applications
* Think differently about basic concepts like transactionality and consistency
https://wn.com/Dropping_Acid_Architecting_With_Eventual_Consistency_In_The_Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency.
This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing.
Attendees of this session will:
* Get a solid grounding in the CAP theorem and BASE
* Understand the special data requirements Cloud Computing presents when architecting applications
* Think differently about basic concepts like transactionality and consistency
- published: 14 May 2013
- views: 1879
Eventual consistency
- Order: Reorder
- Duration: 2:56
- Updated: 21 Dec 2015
- views: 517
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are ma...
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE semantics, in contrast to traditional ACID guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee and does not make safety guarantees: an eventually consistent system can return any value before it converges.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
https://wn.com/Eventual_Consistency
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE semantics, in contrast to traditional ACID guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee and does not make safety guarantees: an eventually consistent system can return any value before it converges.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
- published: 21 Dec 2015
- views: 517
Martin Kleppmann - Conflict Resolution for Eventual Consistency
- Order: Reorder
- Duration: 50:22
- Updated: 22 Nov 2016
- views: 1599
- published: 22 Nov 2016
- views: 1599
Handling Eventual Consistency in JVM Microservices with Event Sourcing
- Order: Reorder
- Duration: 1:07:14
- Updated: 22 Sep 2016
- views: 3115
- published: 22 Sep 2016
- views: 3115
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
- Order: Reorder
- Duration: 3:38
- Updated: 23 May 2017
- views: 50
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explana...
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE (Basically Available, Soft state, Eventual consistency) semantics, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee (reads eventually return the same value) and does not make safety guarantees: an eventually consistent system can return any value before it converges.
In order to ensure replica convergence, a system must reconcile differences between multiple copies of distributed data. This consists of two parts:
exchanging versions or updates of data between servers (often known as anti-entropy); and
choosing an appropriate final state when concurrent updates have occurred, called reconciliation.
The most appropriate approach to reconciliation depends on the application. A widespread approach is "last writer wins". Another is to invoke a user-specified conflict handler. Timestamps and vector clocks are often used to detect concurrency between updates.
Reconciliation of concurrent writes must occur sometime before the next read, and can be scheduled at different instants:
Read repair: The correction is done when a read finds an inconsistency. This slows down the read operation.
Write repair: The correction takes place during a write operation, if an inconsistency has been found, slowing down the write operation.
Asynchronous repair: The correction is not part of a read or write operation.
Whereas eventual consistency is only a liveness guarantee (updates will be observed eventually), strong eventual consistency (SEC) adds the safety guarantee that any two nodes that have received the same (unordered) set of updates will be in the same state. If, furthermore, the system is monotonic, the application will never suffer rollbacks. Conflict-free replicated data types are a common approach to ensuring SEC.
https://wn.com/What_Is_Eventual_Consistency_What_Does_Eventual_Consistency_Mean_Eventual_Consistency_Meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE (Basically Available, Soft state, Eventual consistency) semantics, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee (reads eventually return the same value) and does not make safety guarantees: an eventually consistent system can return any value before it converges.
In order to ensure replica convergence, a system must reconcile differences between multiple copies of distributed data. This consists of two parts:
exchanging versions or updates of data between servers (often known as anti-entropy); and
choosing an appropriate final state when concurrent updates have occurred, called reconciliation.
The most appropriate approach to reconciliation depends on the application. A widespread approach is "last writer wins". Another is to invoke a user-specified conflict handler. Timestamps and vector clocks are often used to detect concurrency between updates.
Reconciliation of concurrent writes must occur sometime before the next read, and can be scheduled at different instants:
Read repair: The correction is done when a read finds an inconsistency. This slows down the read operation.
Write repair: The correction takes place during a write operation, if an inconsistency has been found, slowing down the write operation.
Asynchronous repair: The correction is not part of a read or write operation.
Whereas eventual consistency is only a liveness guarantee (updates will be observed eventually), strong eventual consistency (SEC) adds the safety guarantee that any two nodes that have received the same (unordered) set of updates will be in the same state. If, furthermore, the system is monotonic, the application will never suffer rollbacks. Conflict-free replicated data types are a common approach to ensuring SEC.
- published: 23 May 2017
- views: 50
Eventual Consistency != Hopeful Consistency
- Order: Reorder
- Duration: 56:13
- Updated: 28 Apr 2014
- views: 1707
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency...
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
https://wn.com/Eventual_Consistency_Hopeful_Consistency
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
- published: 28 Apr 2014
- views: 1707
Testing Eventual Consistency in Riak
- Order: Reorder
- Duration: 35:07
- Updated: 20 Mar 2013
- views: 741
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/Er...
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell
Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test.
I will show how QuickCheck helped us to model Riak's behavior, improving understanding and revealing the occasional bug.
https://wn.com/Testing_Eventual_Consistency_In_Riak
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell
Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test.
I will show how QuickCheck helped us to model Riak's behavior, improving understanding and revealing the occasional bug.
- published: 20 Mar 2013
- views: 741
-
Eventual Consistency
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
published: 17 Jul 2015 -
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
This presentation was recorded at GOTO Berlin 2016 http://gotober.com Martin Kleppmann - Researcher at University of Cambridge ABSTRACT What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common? Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...] Download slides and read the full abstract here: https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910 https://twitter.com/gotober https://www.facebook.com/GOTOConference http://gotocon.com
published: 21 Dec 2016 -
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move. This talk was given at Cassandra Day Silicon Valley 2014.
published: 08 May 2014 -
Dropping ACID: Architecting with Eventual Consistency in the Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency. This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore ...
published: 14 May 2013 -
Eventual consistency
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints. Eventually consistent services are o...
published: 21 Dec 2015 -
Martin Kleppmann - Conflict Resolution for Eventual Consistency
published: 22 Nov 2016 -
Handling Eventual Consistency in JVM Microservices with Event Sourcing
published: 22 Sep 2016 -
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica conv...
published: 23 May 2017 -
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency Speaker: Christos Kalantzis, Engineering Manager, Netflix This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
published: 28 Apr 2014 -
Testing Eventual Consistency in Riak
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test. I will show how QuickCheck helped us to ...
published: 20 Mar 2013
Eventual Consistency
- Order: Reorder
- Duration: 8:02
- Updated: 17 Jul 2015
- views: 3047
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
https://wn.com/Eventual_Consistency
Changing your applications from a primary data store, to multiple data stores; and utilizing eventual consistency. This topic is an enabler of scale.
- published: 17 Jul 2015
- views: 3047
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
- Order: Reorder
- Duration: 51:36
- Updated: 21 Dec 2016
- views: 3034
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative ...
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common?
Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...]
Download slides and read the full abstract here:
https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910
https://twitter.com/gotober
https://www.facebook.com/GOTOConference
http://gotocon.com
https://wn.com/Goto_2016_•_Conflict_Resolution_For_Eventual_Consistency_•_Martin_Kleppmann
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common?
Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...]
Download slides and read the full abstract here:
https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910
https://twitter.com/gotober
https://www.facebook.com/GOTOConference
http://gotocon.com
- published: 21 Dec 2016
- views: 3034
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
- Order: Reorder
- Duration: 26:04
- Updated: 08 May 2014
- views: 2322
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Pers...
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move.
This talk was given at Cassandra Day Silicon Valley 2014.
https://wn.com/Netflix's_Distributed_Computing_Strategies_Optimistic_Design_For_The_Eventual_Consistency_Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move.
This talk was given at Cassandra Day Silicon Valley 2014.
- published: 08 May 2014
- views: 2322
Dropping ACID: Architecting with Eventual Consistency in the Cloud
- Order: Reorder
- Duration: 36:05
- Updated: 14 May 2013
- views: 1879
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DB...
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency.
This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing.
Attendees of this session will:
* Get a solid grounding in the CAP theorem and BASE
* Understand the special data requirements Cloud Computing presents when architecting applications
* Think differently about basic concepts like transactionality and consistency
https://wn.com/Dropping_Acid_Architecting_With_Eventual_Consistency_In_The_Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency.
This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing.
Attendees of this session will:
* Get a solid grounding in the CAP theorem and BASE
* Understand the special data requirements Cloud Computing presents when architecting applications
* Think differently about basic concepts like transactionality and consistency
- published: 14 May 2013
- views: 1879
Eventual consistency
- Order: Reorder
- Duration: 2:56
- Updated: 21 Dec 2015
- views: 517
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are ma...
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE semantics, in contrast to traditional ACID guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee and does not make safety guarantees: an eventually consistent system can return any value before it converges.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
https://wn.com/Eventual_Consistency
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent doesn't usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE semantics, in contrast to traditional ACID guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee and does not make safety guarantees: an eventually consistent system can return any value before it converges.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
- published: 21 Dec 2015
- views: 517
Martin Kleppmann - Conflict Resolution for Eventual Consistency
- Order: Reorder
- Duration: 50:22
- Updated: 22 Nov 2016
- views: 1599
- published: 22 Nov 2016
- views: 1599
Handling Eventual Consistency in JVM Microservices with Event Sourcing
- Order: Reorder
- Duration: 1:07:14
- Updated: 22 Sep 2016
- views: 3115
- published: 22 Sep 2016
- views: 3115
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
- Order: Reorder
- Duration: 3:38
- Updated: 23 May 2017
- views: 50
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explana...
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE (Basically Available, Soft state, Eventual consistency) semantics, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee (reads eventually return the same value) and does not make safety guarantees: an eventually consistent system can return any value before it converges.
In order to ensure replica convergence, a system must reconcile differences between multiple copies of distributed data. This consists of two parts:
exchanging versions or updates of data between servers (often known as anti-entropy); and
choosing an appropriate final state when concurrent updates have occurred, called reconciliation.
The most appropriate approach to reconciliation depends on the application. A widespread approach is "last writer wins". Another is to invoke a user-specified conflict handler. Timestamps and vector clocks are often used to detect concurrency between updates.
Reconciliation of concurrent writes must occur sometime before the next read, and can be scheduled at different instants:
Read repair: The correction is done when a read finds an inconsistency. This slows down the read operation.
Write repair: The correction takes place during a write operation, if an inconsistency has been found, slowing down the write operation.
Asynchronous repair: The correction is not part of a read or write operation.
Whereas eventual consistency is only a liveness guarantee (updates will be observed eventually), strong eventual consistency (SEC) adds the safety guarantee that any two nodes that have received the same (unordered) set of updates will be in the same state. If, furthermore, the system is monotonic, the application will never suffer rollbacks. Conflict-free replicated data types are a common approach to ensuring SEC.
https://wn.com/What_Is_Eventual_Consistency_What_Does_Eventual_Consistency_Mean_Eventual_Consistency_Meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning - EVENTUAL CONSISTENCY definition - EVENTUAL CONSISTENCY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency is widely deployed in distributed systems, often under the moniker of optimistic replication, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints.
Eventually consistent services are often classified as providing BASE (Basically Available, Soft state, Eventual consistency) semantics, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee (reads eventually return the same value) and does not make safety guarantees: an eventually consistent system can return any value before it converges.
In order to ensure replica convergence, a system must reconcile differences between multiple copies of distributed data. This consists of two parts:
exchanging versions or updates of data between servers (often known as anti-entropy); and
choosing an appropriate final state when concurrent updates have occurred, called reconciliation.
The most appropriate approach to reconciliation depends on the application. A widespread approach is "last writer wins". Another is to invoke a user-specified conflict handler. Timestamps and vector clocks are often used to detect concurrency between updates.
Reconciliation of concurrent writes must occur sometime before the next read, and can be scheduled at different instants:
Read repair: The correction is done when a read finds an inconsistency. This slows down the read operation.
Write repair: The correction takes place during a write operation, if an inconsistency has been found, slowing down the write operation.
Asynchronous repair: The correction is not part of a read or write operation.
Whereas eventual consistency is only a liveness guarantee (updates will be observed eventually), strong eventual consistency (SEC) adds the safety guarantee that any two nodes that have received the same (unordered) set of updates will be in the same state. If, furthermore, the system is monotonic, the application will never suffer rollbacks. Conflict-free replicated data types are a common approach to ensuring SEC.
- published: 23 May 2017
- views: 50
Eventual Consistency != Hopeful Consistency
- Order: Reorder
- Duration: 56:13
- Updated: 28 Apr 2014
- views: 1707
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency...
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
https://wn.com/Eventual_Consistency_Hopeful_Consistency
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
- published: 28 Apr 2014
- views: 1707
Testing Eventual Consistency in Riak
- Order: Reorder
- Duration: 35:07
- Updated: 20 Mar 2013
- views: 741
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/Er...
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell
Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test.
I will show how QuickCheck helped us to model Riak's behavior, improving understanding and revealing the occasional bug.
https://wn.com/Testing_Eventual_Consistency_In_Riak
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info and slides, please go here: http://www.erlang-factory.com/conference/ErlangUserConference2012/speakers/UlfNorell
Riak is one of the new breed of no-SQL database management systems, which has begun to replace relational databases for some applications. Riak is a distributed key-value store, inspired by Amazon's Dynamo, designed for applications where scalability, low latency and high availability are critical. Riak uses replication to provide fast access to data, even when multiple nodes or parts of the network fail. It supports concurrent access to the same data by multiple clients, even when the network is partitioned. All of this makes it very hard to test.
I will show how QuickCheck helped us to model Riak's behavior, improving understanding and revealing the occasional bug.
- published: 20 Mar 2013
- views: 741
-
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
This presentation was recorded at GOTO Berlin 2016 http://gotober.com Martin Kleppmann - Researcher at University of Cambridge ABSTRACT What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common? Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...] Download slides and read the full abstract here: https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910 https://twitter.com/gotober https://www.facebook.com/GOTOConference http://gotocon.com
published: 21 Dec 2016 -
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move. This talk was given at Cassandra Day Silicon Valley 2014.
published: 08 May 2014 -
Dropping ACID: Architecting with Eventual Consistency in the Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency. This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore ...
published: 14 May 2013 -
Martin Kleppmann - Conflict Resolution for Eventual Consistency
published: 22 Nov 2016 -
Handling Eventual Consistency in JVM Microservices with Event Sourcing
published: 22 Sep 2016 -
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency Speaker: Christos Kalantzis, Engineering Manager, Netflix This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
published: 28 Apr 2014 -
"Consistency without consensus in production systems" by Peter Bourgon
published: 20 Sep 2014 -
"Distributed, Eventually Consistent Computations" by Christopher Meiklejohn
Consistency is hard and coordination is expensive. As we move into the world of connected 'Internet of Things' style applications, or large-scale mobile applications, devices have less power, periods of limited connectivity, and operate over unreliable asynchronous networks. This poses a problem with shared state: how do we handle concurrent operations over shared state, while clients are offline, and ensure that values converge to a desirable result without making the system unavailable? We look at a new programming model, called Lasp. This programming model combines distributed convergent data structures with a dataflow execution model designed for distribution over large-scale applications. This model supports arbitrary placement of processing node: this enables the user to author appl...
published: 27 Sep 2015 -
GOTO 2013 • The Smallest Distributed System • Mathias Meyer
This presentation was recorded at GOTO Aarhus 2013 http://gotocon.com Mathias Meyer - Infrastructure Mad Man ABSTRACT Even in its smallest incarnation, a distributed system is bound to fall prey to network partitions, data arriving out of order and eventual consistency. The more data pushed through the system, the more painful it gets to ignore these pitfalls. At Travis CI we had to learn the hard way that anything can fail at any time and that our chance of fixing it is to accept that and to rework our system to be more resilient to failure, going back to solutions that turn out to much simpler than our initial approaches but that required rethinking all parts of the application, from the code that runs tests up to the user interface that tails build logs as it's streamed from the buil...
published: 27 Oct 2015 -
Eventual Consistent Systems a.k.a mostly inconsistent systems vs. Strongly Consistent Systems
This talk explores the actual behavior of eventual consistent systems and argues that they should, in fact, be called mostly inconsistent systems. It presents a paxos algorithm based strongly consistent system as an alternative. Amazon’s eventually consistent storage system S3 is explored. The presentation highlights the various fixes that Amazon had to make to S3 in order to make it work with Hadoop workflows. Also, it delves into the various inconsistent alternatives offered by Cassandra. As an alternative to such inconsistent systems, paxos-based strongly consistent solutions for Hadoop Storage and HBase are presented.
published: 24 Jun 2016
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
- Order: Reorder
- Duration: 51:36
- Updated: 21 Dec 2016
- views: 3034
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative ...
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common?
Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...]
Download slides and read the full abstract here:
https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910
https://twitter.com/gotober
https://www.facebook.com/GOTOConference
http://gotocon.com
https://wn.com/Goto_2016_•_Conflict_Resolution_For_Eventual_Consistency_•_Martin_Kleppmann
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - Researcher at University of Cambridge
ABSTRACT
What do collaborative editors like Google Docs, the calendar app on your phone, and multi-datacenter database clusters have in common?
Answer: They all need to cope with network interruptions, and still work offline. They all allow state to be updated [...]
Download slides and read the full abstract here:
https://gotocon.com/berlin-2016/presentations/show_talk.jsp?oid=7910
https://twitter.com/gotober
https://www.facebook.com/GOTOConference
http://gotocon.com
- published: 21 Dec 2016
- views: 3034
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
- Order: Reorder
- Duration: 26:04
- Updated: 08 May 2014
- views: 2322
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Pers...
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move.
This talk was given at Cassandra Day Silicon Valley 2014.
https://wn.com/Netflix's_Distributed_Computing_Strategies_Optimistic_Design_For_The_Eventual_Consistency_Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-eventual-consistency-model
In this talk, Christos Kalantzis (Cloud Persistence Engineering Manager, Netflix), explains why he believes companies should adopt an Optimistic Software Design model that is similar to what is used at Netflix. This is a practical talk with strategies for implementing the design model, testing for eventual consistency, and convincing your organization that it's the right move.
This talk was given at Cassandra Day Silicon Valley 2014.
- published: 08 May 2014
- views: 2322
Dropping ACID: Architecting with Eventual Consistency in the Cloud
- Order: Reorder
- Duration: 36:05
- Updated: 14 May 2013
- views: 1879
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DB...
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency.
This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing.
Attendees of this session will:
* Get a solid grounding in the CAP theorem and BASE
* Understand the special data requirements Cloud Computing presents when architecting applications
* Think differently about basic concepts like transactionality and consistency
https://wn.com/Dropping_Acid_Architecting_With_Eventual_Consistency_In_The_Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate consistency, availability, and partition tolerance. Most enterprise DBMSs are inherently partition intolerant, thus allowing for ACID transactionality. However, Cloud Computing environments must be partition tolerant in order to achieve the elasticity benefit so important to the Cloud. Highly available Cloud applications must therefore forego immediate data consistency, and with it, ACID. What's left? BASE: Basic availability, soft state, and eventual consistency.
This session will explore how eventual data consistency affects architecting -- and rearchitecting -- applications for a Cloud environment. We'll discard the common misconception that the Cloud is a "virtual server in the sky," and explore how architects must think differently about architecting for Cloud environments vs. traditional on-premise computing.
Attendees of this session will:
* Get a solid grounding in the CAP theorem and BASE
* Understand the special data requirements Cloud Computing presents when architecting applications
* Think differently about basic concepts like transactionality and consistency
- published: 14 May 2013
- views: 1879
Martin Kleppmann - Conflict Resolution for Eventual Consistency
- Order: Reorder
- Duration: 50:22
- Updated: 22 Nov 2016
- views: 1599
- published: 22 Nov 2016
- views: 1599
Handling Eventual Consistency in JVM Microservices with Event Sourcing
- Order: Reorder
- Duration: 1:07:14
- Updated: 22 Sep 2016
- views: 3115
- published: 22 Sep 2016
- views: 3115
Eventual Consistency != Hopeful Consistency
- Order: Reorder
- Duration: 56:13
- Updated: 28 Apr 2014
- views: 1707
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency...
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
https://wn.com/Eventual_Consistency_Hopeful_Consistency
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manager, Netflix
This session will address Cassandra's tunable consistency model and cover how developers and companies should adopt a more Optimistic Software Design model.
- published: 28 Apr 2014
- views: 1707
"Consistency without consensus in production systems" by Peter Bourgon
- Order: Reorder
- Duration: 41:10
- Updated: 20 Sep 2014
- views: 6163
- published: 20 Sep 2014
- views: 6163
"Distributed, Eventually Consistent Computations" by Christopher Meiklejohn
- Order: Reorder
- Duration: 40:05
- Updated: 27 Sep 2015
- views: 6261
Consistency is hard and coordination is expensive. As we move into the world of connected 'Internet of Things' style applications, or large-scale mobile applica...
Consistency is hard and coordination is expensive. As we move into the world of connected 'Internet of Things' style applications, or large-scale mobile applications, devices have less power, periods of limited connectivity, and operate over unreliable asynchronous networks. This poses a problem with shared state: how do we handle concurrent operations over shared state, while clients are offline, and ensure that values converge to a desirable result without making the system unavailable?
We look at a new programming model, called Lasp. This programming model combines distributed convergent data structures with a dataflow execution model designed for distribution over large-scale applications. This model supports arbitrary placement of processing node: this enables the user to author applications that can be distributed across data centers and pushed to the edge. In this talk, we will focus on the design of the language and show a series of sample applications.
Christopher Meiklejohn
MACHINE ZONE, INC.
@cmeik
Christopher Meiklejohn is a Senior Software Engineer with Machine Zone, Inc. working on distributed systems. Previously, Christopher worked at Basho Technologies, Inc. on the distributed key-value store, Riak. In his spare time, Christopher develops a programming language for distributed computation, called Lasp. Christopher is starting his Ph.D. studies at the Université catholique de Louvain in Belgium in 2016.
https://wn.com/Distributed,_Eventually_Consistent_Computations_By_Christopher_Meiklejohn
Consistency is hard and coordination is expensive. As we move into the world of connected 'Internet of Things' style applications, or large-scale mobile applications, devices have less power, periods of limited connectivity, and operate over unreliable asynchronous networks. This poses a problem with shared state: how do we handle concurrent operations over shared state, while clients are offline, and ensure that values converge to a desirable result without making the system unavailable?
We look at a new programming model, called Lasp. This programming model combines distributed convergent data structures with a dataflow execution model designed for distribution over large-scale applications. This model supports arbitrary placement of processing node: this enables the user to author applications that can be distributed across data centers and pushed to the edge. In this talk, we will focus on the design of the language and show a series of sample applications.
Christopher Meiklejohn
MACHINE ZONE, INC.
@cmeik
Christopher Meiklejohn is a Senior Software Engineer with Machine Zone, Inc. working on distributed systems. Previously, Christopher worked at Basho Technologies, Inc. on the distributed key-value store, Riak. In his spare time, Christopher develops a programming language for distributed computation, called Lasp. Christopher is starting his Ph.D. studies at the Université catholique de Louvain in Belgium in 2016.
- published: 27 Sep 2015
- views: 6261
GOTO 2013 • The Smallest Distributed System • Mathias Meyer
- Order: Reorder
- Duration: 48:22
- Updated: 27 Oct 2015
- views: 3642
This presentation was recorded at GOTO Aarhus 2013
http://gotocon.com
Mathias Meyer - Infrastructure Mad Man
ABSTRACT
Even in its smallest incarnation, a dist...
This presentation was recorded at GOTO Aarhus 2013
http://gotocon.com
Mathias Meyer - Infrastructure Mad Man
ABSTRACT
Even in its smallest incarnation, a distributed system is bound to fall prey to network partitions, data arriving out of order and eventual consistency. The more data pushed through the system, the more painful it gets to ignore these pitfalls.
At Travis CI we had to learn the hard way that anything can fail at any time and that our chance of fixing it is to accept that and to rework our system to be more resilient to failure, going back to solutions that turn out to much simpler than our initial approaches but that required rethinking all parts of the application, from the code that runs tests up to the user interface that tails build logs as it's streamed from the build process.
The lessons learned are surprisingly simple and they turned Travis CI into a system that we can grow with a lot more confidence than initially. We took it from a a single, monolithic application, to a distributed system.
This is the story of our failures and what we learned from them.
https://twitter.com/gotocon
https://www.facebook.com/GOTOConference
http://gotocon.com
https://wn.com/Goto_2013_•_The_Smallest_Distributed_System_•_Mathias_Meyer
This presentation was recorded at GOTO Aarhus 2013
http://gotocon.com
Mathias Meyer - Infrastructure Mad Man
ABSTRACT
Even in its smallest incarnation, a distributed system is bound to fall prey to network partitions, data arriving out of order and eventual consistency. The more data pushed through the system, the more painful it gets to ignore these pitfalls.
At Travis CI we had to learn the hard way that anything can fail at any time and that our chance of fixing it is to accept that and to rework our system to be more resilient to failure, going back to solutions that turn out to much simpler than our initial approaches but that required rethinking all parts of the application, from the code that runs tests up to the user interface that tails build logs as it's streamed from the build process.
The lessons learned are surprisingly simple and they turned Travis CI into a system that we can grow with a lot more confidence than initially. We took it from a a single, monolithic application, to a distributed system.
This is the story of our failures and what we learned from them.
https://twitter.com/gotocon
https://www.facebook.com/GOTOConference
http://gotocon.com
- published: 27 Oct 2015
- views: 3642
Eventual Consistent Systems a.k.a mostly inconsistent systems vs. Strongly Consistent Systems
- Order: Reorder
- Duration: 42:27
- Updated: 24 Jun 2016
- views: 46
This talk explores the actual behavior of eventual consistent systems and argues that they should, in fact, be called mostly inconsistent systems. It presents a...
This talk explores the actual behavior of eventual consistent systems and argues that they should, in fact, be called mostly inconsistent systems. It presents a paxos algorithm based strongly consistent system as an alternative. Amazon’s eventually consistent storage system S3 is explored. The presentation highlights the various fixes that Amazon had to make to S3 in order to make it work with Hadoop workflows. Also, it delves into the various inconsistent alternatives offered by Cassandra. As an alternative to such inconsistent systems, paxos-based strongly consistent solutions for Hadoop Storage and HBase are presented.
https://wn.com/Eventual_Consistent_Systems_A.K.A_Mostly_Inconsistent_Systems_Vs._Strongly_Consistent_Systems
This talk explores the actual behavior of eventual consistent systems and argues that they should, in fact, be called mostly inconsistent systems. It presents a paxos algorithm based strongly consistent system as an alternative. Amazon’s eventually consistent storage system S3 is explored. The presentation highlights the various fixes that Amazon had to make to S3 in order to make it work with Hadoop workflows. Also, it delves into the various inconsistent alternatives offered by Cassandra. As an alternative to such inconsistent systems, paxos-based strongly consistent solutions for Hadoop Storage and HBase are presented.
- published: 24 Jun 2016
- views: 46
- Playlist
- Chat
- Playlist
- Chat
8:02
Eventual Consistency
Changing your applications from a primary data store, to multiple data stores; and utilizi...
published: 17 Jul 2015
Eventual Consistency
Eventual Consistency
- Report rights infringement
- published: 17 Jul 2015
- views: 3047
51:36
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - ...
published: 21 Dec 2016
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
- Report rights infringement
- published: 21 Dec 2016
- views: 3034
26:04
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-ev...
published: 08 May 2014
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
- Report rights infringement
- published: 08 May 2014
- views: 2322
36:05
Dropping ACID: Architecting with Eventual Consistency in the Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate ...
published: 14 May 2013
Dropping ACID: Architecting with Eventual Consistency in the Cloud
Dropping ACID: Architecting with Eventual Consistency in the Cloud
- Report rights infringement
- published: 14 May 2013
- views: 1879
2:56
Eventual consistency
Eventual consistency is a consistency model used in distributed computing to achieve high ...
published: 21 Dec 2015
Eventual consistency
Eventual consistency
- Report rights infringement
- published: 21 Dec 2015
- views: 517
50:22
Martin Kleppmann - Conflict Resolution for Eventual Consistency
published: 22 Nov 2016
Martin Kleppmann - Conflict Resolution for Eventual Consistency
Martin Kleppmann - Conflict Resolution for Eventual Consistency
- Report rights infringement
- published: 22 Nov 2016
- views: 1599
1:07:14
Handling Eventual Consistency in JVM Microservices with Event Sourcing
published: 22 Sep 2016
Handling Eventual Consistency in JVM Microservices with Event Sourcing
Handling Eventual Consistency in JVM Microservices with Event Sourcing
- Report rights infringement
- published: 22 Sep 2016
- views: 3115
3:38
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY me...
published: 23 May 2017
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
- Report rights infringement
- published: 23 May 2017
- views: 50
56:13
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manag...
published: 28 Apr 2014
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency
- Report rights infringement
- published: 28 Apr 2014
- views: 1707
35:07
Testing Eventual Consistency in Riak
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info a...
published: 20 Mar 2013
Testing Eventual Consistency in Riak
Testing Eventual Consistency in Riak
- Report rights infringement
- published: 20 Mar 2013
- views: 741
- Playlist
- Chat
8:02
Eventual Consistency
Changing your applications from a primary data store, to multiple data stores; and utilizi...
published: 17 Jul 2015
Eventual Consistency
Eventual Consistency
- Report rights infringement
- published: 17 Jul 2015
- views: 3047
51:36
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - ...
published: 21 Dec 2016
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
- Report rights infringement
- published: 21 Dec 2016
- views: 3034
26:04
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-ev...
published: 08 May 2014
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
- Report rights infringement
- published: 08 May 2014
- views: 2322
36:05
Dropping ACID: Architecting with Eventual Consistency in the Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate ...
published: 14 May 2013
Dropping ACID: Architecting with Eventual Consistency in the Cloud
Dropping ACID: Architecting with Eventual Consistency in the Cloud
- Report rights infringement
- published: 14 May 2013
- views: 1879
2:56
Eventual consistency
Eventual consistency is a consistency model used in distributed computing to achieve high ...
published: 21 Dec 2015
Eventual consistency
Eventual consistency
- Report rights infringement
- published: 21 Dec 2015
- views: 517
50:22
Martin Kleppmann - Conflict Resolution for Eventual Consistency
published: 22 Nov 2016
Martin Kleppmann - Conflict Resolution for Eventual Consistency
Martin Kleppmann - Conflict Resolution for Eventual Consistency
- Report rights infringement
- published: 22 Nov 2016
- views: 1599
1:07:14
Handling Eventual Consistency in JVM Microservices with Event Sourcing
published: 22 Sep 2016
Handling Eventual Consistency in JVM Microservices with Event Sourcing
Handling Eventual Consistency in JVM Microservices with Event Sourcing
- Report rights infringement
- published: 22 Sep 2016
- views: 3115
3:38
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY me...
published: 23 May 2017
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
What is EVENTUAL CONSISTENCY? What does EVENTUAL CONSISTENCY mean? EVENTUAL CONSISTENCY meaning
- Report rights infringement
- published: 23 May 2017
- views: 50
56:13
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manag...
published: 28 Apr 2014
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency
- Report rights infringement
- published: 28 Apr 2014
- views: 1707
35:07
Testing Eventual Consistency in Riak
This is a video of Ulf Norell speaking at the Erlang User Conference 2012. For more info a...
published: 20 Mar 2013
Testing Eventual Consistency in Riak
Testing Eventual Consistency in Riak
- Report rights infringement
- published: 20 Mar 2013
- views: 741
- Playlist
- Chat
51:36
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
This presentation was recorded at GOTO Berlin 2016
http://gotober.com
Martin Kleppmann - ...
published: 21 Dec 2016
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
GOTO 2016 • Conflict Resolution for Eventual Consistency • Martin Kleppmann
- Report rights infringement
- published: 21 Dec 2016
- views: 3034
26:04
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
See the full post here: http://www.hakkalabs.co/articles/netflixs-optimistic-design-for-ev...
published: 08 May 2014
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
Netflix's Distributed Computing Strategies: Optimistic Design for the Eventual Consistency Model
- Report rights infringement
- published: 08 May 2014
- views: 2322
36:05
Dropping ACID: Architecting with Eventual Consistency in the Cloud
It's impossible for any distributed computer system to simultaneously guarantee immediate ...
published: 14 May 2013
Dropping ACID: Architecting with Eventual Consistency in the Cloud
Dropping ACID: Architecting with Eventual Consistency in the Cloud
- Report rights infringement
- published: 14 May 2013
- views: 1879
50:22
Martin Kleppmann - Conflict Resolution for Eventual Consistency
published: 22 Nov 2016
Martin Kleppmann - Conflict Resolution for Eventual Consistency
Martin Kleppmann - Conflict Resolution for Eventual Consistency
- Report rights infringement
- published: 22 Nov 2016
- views: 1599
1:07:14
Handling Eventual Consistency in JVM Microservices with Event Sourcing
published: 22 Sep 2016
Handling Eventual Consistency in JVM Microservices with Event Sourcing
Handling Eventual Consistency in JVM Microservices with Event Sourcing
- Report rights infringement
- published: 22 Sep 2016
- views: 3115
56:13
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency
Speaker: Christos Kalantzis, Engineering Manag...
published: 28 Apr 2014
Eventual Consistency != Hopeful Consistency
Eventual Consistency != Hopeful Consistency
- Report rights infringement
- published: 28 Apr 2014
- views: 1707
41:10
"Consistency without consensus in production systems" by Peter Bourgon
published: 20 Sep 2014
"Consistency without consensus in production systems" by Peter Bourgon
"Consistency without consensus in production systems" by Peter Bourgon
- Report rights infringement
- published: 20 Sep 2014
- views: 6163
40:05
"Distributed, Eventually Consistent Computations" by Christopher Meiklejohn
Consistency is hard and coordination is expensive. As we move into the world of connected ...
published: 27 Sep 2015
"Distributed, Eventually Consistent Computations" by Christopher Meiklejohn
"Distributed, Eventually Consistent Computations" by Christopher Meiklejohn
- Report rights infringement
- published: 27 Sep 2015
- views: 6261
48:22
GOTO 2013 • The Smallest Distributed System • Mathias Meyer
This presentation was recorded at GOTO Aarhus 2013
http://gotocon.com
Mathias Meyer - Inf...
published: 27 Oct 2015
GOTO 2013 • The Smallest Distributed System • Mathias Meyer
GOTO 2013 • The Smallest Distributed System • Mathias Meyer
- Report rights infringement
- published: 27 Oct 2015
- views: 3642
42:27
Eventual Consistent Systems a.k.a mostly inconsistent systems vs. Strongly Consistent Systems
This talk explores the actual behavior of eventual consistent systems and argues that they...
published: 24 Jun 2016
Eventual Consistent Systems a.k.a mostly inconsistent systems vs. Strongly Consistent Systems
Eventual Consistent Systems a.k.a mostly inconsistent systems vs. Strongly Consistent Systems
- Report rights infringement
- published: 24 Jun 2016
- views: 46
GOTO 2016 • Conflict Resolution for Eventual Consi...
Netflix's Distributed Computing Strategies: Optimi...
Dropping ACID: Architecting with Eventual Consiste...
Martin Kleppmann - Conflict Resolution for Eventua...
Handling Eventual Consistency in JVM Microservices...
Eventual Consistency != Hopeful Consistency...
"Consistency without consensus in production syste...
"Distributed, Eventually Consistent Computations" ...
GOTO 2013 • The Smallest Distributed System • Math...
Eventual Consistent Systems a.k.a mostly inconsist...
See more photos of eventual consistency
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Mystery Woman With Putin Raises Eyebrows Across Russia
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Russian President Vladimir Putin took a trip to the northwestern countryside of Russia for a local feast day but many were talking about a brief clip that showed an anonymous pair of animated hands in the back of Putin's black Mercedes, according to Newsweek ... But the new footage showing Putin extending his arm to a mystery backseat passenger before quickly shutting the door to his car has made headline news ... -WN.com, Maureen Foody....
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Edit WorldNews.com 14 Jul 2017
A day after a 20-year-old man confessed to his role in killing four Pennsylvania men whose disappearances set off an intensive search, prosecutors on Friday charged him and a second man with homicide in the case, The Washington Post reported.Authorities said the men were all shot after meeting with Cosmo Dinardo, the Solebury, Pa., man at the center of the investigation ... In exchange for that confession, Mr ... WN.com, Jim Berrie ....
Russian pair enjoy Wimbledon final whitewash
Edit Yahoo Daily News 16 Jul 2017
... the roof on Centre Court, after a four-hour, 40-minute epic men's doubles final eventually won by Marcelo Melo and Lukasz Kubot....
Bush showing improvement as setup reliever
Edit MLB 16 Jul 2017
KANSAS CITY -- Right-hander Matt Bush still maintains a crucial role in the Rangers' bullpen. His scoreless eighth inning as a setup reliever was a big part of the 5-3 victory over the Royals on Friday night ... "Every closer is going to blow games eventually, every pitcher is going to struggle eventually, but it's all about bouncing back and having something to lean on like having a good routine and things to continue to improve on ... T.R ... ....
MY STARTUP IDEA: Pokkt and Baby Essentials founders on their eureka moments
Edit The Times of India 16 Jul 2017
In this column, we bring you the stories behind how startup founders got their business idea, and the things, events, and actions that eventually led them to their big eureka moment ... 'I wasn't keen to continue my corporate life'. Rohit Sharma, Pokkt. The idea. Mobile video advertising platform ... Early days ... The couple did research for three years and eventually developed a fabric that had all the properties of cloth but did not have lint....
Military cyber operations headed for revamp after long delay
Edit Sacramento Bee 16 Jul 2017
officials. Under the plans, U.S. Cyber Command would eventually be split off from the intelligence-focused National Security Agency. Details are still being worked out, but officials say they expect a decision and announcement in the coming weeks ... The goal, they said, is to give U.S ... ....
From the Observer archive: this week in 1950
Edit The Guardian 16 Jul 2017
Britain wavers over joining Korean war. World news. From the Observer archive. From the Observer archive. this week in 1950. Britain wavers over joining Korean war ... Shall a local act of aggression be left to take its course, or shall it be regarded as a challenge to that rule of law which, if it is not defended against minor infractions, will quite certainly be broken eventually on a very much larger and more dangerous scale? ... Key quote ... ....
Women's World Cup, Ind vs NZ: Mithali Raj's brilliant ton helps India to seal semi-final berth
Edit Yahoo Daily News 16 Jul 2017
Women's World Cup, Ind vs NZ. Mithali Raj's brilliant ton helps India to seal semi-final berth ... Chasing a stiff target of 266, the White Ferns couldn't stand against a ruthless Indian bowling line up and were eventually bowled out for a mere 79 runs in just 25.3 overs at the County Cricket Ground on Saturday ... -ANi ... ... ....
Scotland likely to face Samoa at 2019 Rugby World Cup
Edit Scotsman 16 Jul 2017
Tonga qualified for the 2019 Rugby World Cup after Fiji beat Samoa 38-16 in the final match of the Pacific Nations Cup in Apia yesterday, a result which is likely to have ramifications for Scotland ... The Scots and the Samoans had a titanic battle at the last World Cup in 2015, with Scotland eventually winning 36-33 at St James' Park in Newcastle ... Seniloli scored his second when winger Vereniki Goneva made a break down the right flank ... ....
England vs South Africa 2017: 2nd Test day 2, 5 talking points
Edit Yahoo Daily News 16 Jul 2017
. South Africa's best day with the ball since landing in England helped them take a decisive lead on Day 2 of the second Investec Test series at Trent Bridge ... Brief Scores. South Africa 335 & 75/1 (Elgar 38, Amla 23) ... NOTTINGHAM, ENGLAND - JULY 15 ... More. ... Philander consistently seamed the ball in causing problems for Cook. The former skipper eventually nicked one onto his pads and the ball reared up into de Kock's gloves ... ....
Countryside in crisis: why rural Britain needs a new deal
Edit The Guardian 16 Jul 2017
As the Rural Coalition fights for the future of the countryside, a community in North Yorkshire is showing the way. Rural affairs. The Observer. Countryside in crisis. why rural Britain needs a new deal ... In a pointed letter to the Times, the coalition declared that ... Twitter ... “Buy the pub?” Eventually though, with no sign of a buyer and the prospect of the pub closing for good, they made an offer and hastily worked out a finance model ... ....
Jose Mourinho denies he fell out with new Manchester United signing Romelu Lukaku at Chelsea
Edit The Independent 15 Jul 2017Man beats side chick in oral sex feud
Edit Jamaica Star 15 Jul 2017
romantic relationship ... However, the prosecution said the complainant visited Nelson's shop to get her phone repaired, and the two began communicating until a romantic relationship eventually developed. The complainant reported that Nelson wanted her to perform oral sex on him, but she did not want to engage in the act, so the relationship eventually deteriorated ... ....
Why the Uber-Yandex merger in Eastern Europe won’t cover Ukraine
Edit Venture Beat 15 Jul 2017
As the dust settles from the momentous merger news that will eventually see Uber and Yandex form a new ride-sharing company covering six Eastern European markets, it’s worth taking a moment to dig down into the finer nuances of the deal....
Zimbabwe fight back to restrict Sri Lanka at 184/3 at tea, Day 2 of Colombo Test
Edit Cricket Country 15 Jul 2017
Zimbabwe got 3 wickets in the second session © AFP ... As Dinesh Chandimal hit the ball straight past Tiripano, the bowler got his fingers on it and it eventually dislodged the bails at the non-striker’s end, getting Tharanga out for 71 who was standing outside the crease ... Chandimal paced up the scoring rate eventually once he found support from Mathews at the other end and got to 42 at tea, with Mathews himself batting 14 ... Brief scores. ... ....
Horoscopes by Holiday
Edit Arkansas Online 15 Jul 2017
Happy birthday. The world will have ideas, but ultimately you will decide who you want to be and you'll do it bigger than ever ... ARIES (March 21-April 19). Don't be swayed by immature and petty demands, or you'll train the world to pester you until you eventually cave to its requests ... TAURUS (April 20-May 20) ... It doesn't feel like it in the moment, but eventually the kind of work you're doing today will make you stronger and more complete....
Texas Gov. Abbott begins re-election bid with no rivals yet
Edit The Providence Journal 15 Jul 2017
Greg Abbott launched his re-election bid Friday with no clear rival in sight, the money to likely outspend any eventual challenger and a new immigration crackdown that could test in 2018 support among Hispanic voters he aggressively courted four years ago.Supporters cheered as Abbott lauded a new Texas ban on "sanctuary cities" while kicking off ......
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