Collaborative Spreadsheet open source
React Typescript CRDT based Collaborative Spreadsheet
This demo app shows a collaborative application developed using a draft implementation of the Concordant API. The application uses the C-Service API which currently supports two eventual consistency backends: revision-based and CRDT-based. This demo shows that with a revision based approach, the user loses updates, either if updates are executed concurrently online, or if multiple users edit the document offline. To have an adequate semantics, the user needs to provide custom, non-trivial, code to merge the updates executed by each user. With the CRDT-based backend, update convergence is available out-of-the-box.
CRDT Markdown Editor open source
React Typescript CRDT based Collaborative Markdown Editor
Collaborative text editing application using two eventual consitency backends: revision-based and CRDT-based. This demo shows that with a revision based approach, the user loses updates, either if updates are executed concurrently online, or if multiple users edit the document offline. To have an adequate semantics, the user needs to provide custom, non-trivial, code to merge the updates executed by each user. With the CRDT-based backend, update convergence is available out-of-the-box.
EdgeAnt: pushing AntidoteDB to the Edge
Pre-implementation: System specification and design
Cloud-scale services improve availability and latency by geo-replicating data in several data centers (DC) across the world. Nevertheless, the closest DC is often still too far away for an optimal user experience. To remain available at all times, client-side applications need to cache data at client machines, caching data at client machines can improve availability and latency for many applications, and also allow for temporary disconnection. This approach is used in many recent cloud services, like Google Drive RT API or Mobius [3, 5, 9, 16], where developers implement caching and buffering at application level, but it doesn’t ensure system-wide consistency guarantees.
Keywords : Geo-replication, Partial Replication, Edge Computing, Causal Consistency, Peer-to-peer.
Geo-Replication and Edge Storage systems
State of the art
Much previous work on data in edge computing focuses on streaming and content delivery.
Examples include sensor systems or propagating database views. We leverage this previous work by propagating shared state as a stream of update events. The distributed sharing of persistent mutable state raises extra challenges, which we address in this state of the art study.
Let's study in detail each of these systems.
Keywords : Geo-replication, Partial Replication, Edge Computing, Causal Consistency, Peer-to-peer.
FR Vers une cohérence causale évolutive sans chaînes de ralentissements
Publié à Compas 2017, Sophia Antipolis le 27 juin 2017
Pour augmenter les performances et la disponibilité, les bases de données sont généralement répliquées sur différents serveurs au sein de centres de données à travers le monde.
Cependant, la réplication implique quelques problèmes de cohérence.
La cohérence causale est un choix intéressant pour construire ce modèle de bases de données.
Elle permet aux clients d'observer un état cohérent par rapport à leurs écritures, en incluant les opérations qu'ils ont précédemment observé, tout en minimisant les anomalies pour le programmeur. Cependant, les mises en œuvre actuelles de la cohérence causale sont susceptibles de connaitre des ralentissements en chaîne à l'échelle de plusieurs centres de données. Ce papier décrit la conception d'un système causalement cohérent, évolutif, et non exposé à l'impact que peut avoir un serveur lent ou défaillant, sur la disponibilité des données.
Mots-clés : Bases de données distribuées, géo-replication, cohérence causale, disponibilité sous partition.