Automated conflict resolution - enabling masterless data distribution (Rune Skou Larsen, Trifork)
•
3 j'aime•2,497 vues
This talk was held at the third meeting of the Swiss Big Data User Group on September 17 at ETH Zürich.
Recommandé
Recommandé
Contenu connexe
Similaire à Automated conflict resolution - enabling masterless data distribution (Rune Skou Larsen, Trifork)
Similaire à Automated conflict resolution - enabling masterless data distribution (Rune Skou Larsen, Trifork) (20)
Plus de Swiss Big Data User Group
Plus de Swiss Big Data User Group (20)
Dernier
Dernier (20)
Automated conflict resolution - enabling masterless data distribution (Rune Skou Larsen, Trifork)
- 1. Automated conflict resolution - enabling masterless data distribution GOTO Aarhus 2012 Premier software development conference created by developers for developers. Conference: Oct. 1-3 // Training: Sept. 30, Oct. 4-5 Se mere på www.gotocon.com/aarhus-2012 1
- 2. About the speaker ■ Working with databases since '97 ■ NoSQL since 2008 ■ Danish Shared Medication Record ■ Migrating data from MySQL to Riak ■ Devel Riak clients ■ NoSQL architect on various international projects ■ Seoul, Korea ■ Toulouse, France ■ Leeds, UK RuneSkouLarsen ■ Copenhagen, Denmark 2
- 3. Agenda ■Polyglot persistence landscape ■Distribute those data! ■Types of Consistency ■Moving towards consistency ■Introduction to CRDTs ■Consistency models of OLTP databases 3
- 4. Polyglot persistence landscape In-memory OLTP Neo4j Riak VoltDB Cassandra Redis Voldemort CouchBase EasyDB Analytics MongoDB Hadoop CouchDB 4
- 5. Why distributed databases? ■ Redundancy ■ Availability ■ Scaling ■ Getting closer to your users 5
- 6. Types of Concistency ■ Consistency: All nodes see the same data at the same time ■ Eventual consistency → Autonomous consistency ■ Sequential consistency → Bureaucratic consistency 6
- 7. When to be Consistent with what ■ Eventual consistency Support disconnected operations – Better to read a stale value than nothing – Better to save writes somewhere than nothing Potentially anomalous application behavior – Stale reads and conflicting writes… ■ Sequential consistency Requires highly available connections Not suitable for certain scenarios: – Disconnected clients (e.g. your phone) – Apps might prefer potential inconsistency to loss of availability 7
- 8. Conflicting updates User A User B A B A B Asynchronous Synchronization 8
- 9. Last Write Wins (LWW) User A User B A B t=t0 t=t1 A B t=t0 t=t1 Asynchronous Synchronization ■ Assign timestamp to all objects ■ Simple but fragile – depends on precise synchronization of timers ■ Data is lost 9
- 10. Detecting conflicts using Vector Clocks (1) User A User B A B vclock=a:1 vclock=a:1,b:1 A A B vclock=a:1 vclock=a:1 Asynchronous vclock=a:1,b:1 Synchronization ■ Assign vector clock to objects ■ Spawn siblings when causality chain is broken ■ Data is never lost 10
- 11. Detecting conflicts using Vector Clocks (2) User A User B A B vclock=a:1 vclock=b:1 A B vclock=a:1 vclock=b:1 Asynchronous Synchronization ■ Assign vector clock to objects ■ Spawn siblings when causality chain is broken ■ Data is never lost 11
- 12. Conflict-free Replicated DataTypes User A User B A B AB A AB B Asynchronous Synchronization ■ Datastructure intrinsically merges objects ■ No data loss ■ Limited applicability 12
- 13. Semantic resolution User A User B C A B A B Asynchronous Synchronization ■ Keep both values as siblings ■ User does the merging ■ Only solution for complex, important data 13
- 14. Methods for resolving conflicts ■ Last Write Wins ■ Easy ■ Data is lost ■ Depends on timestamps ■ Conflict-free Data Types ■ Data structure has built-in convergence ■ Limited ability to model real-world problems ■ Semantic resolution ■ Requires application/user involvement ■ Generic solution 14
- 15. Conflict-free Replicated Data Types ■ Convergent (CvRDT) ■ State is replicated ■ Moves towards one value ■ Commutative (CmRDT) ■ Operations to the state are replicated ■ The order of operations is insignificant a*b = b*a ■ CvRDT and CmRDT can emulate eachother 15
- 16. CvRDT examples: G-set and 2P-Set Tombstone RIP 16
- 17. CRDT References ■ CRDTs: Consistency without concurrency control 2009 INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE ■ A comprehensive study of Convergent and Commutative Replicated Data Types 2009 INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE ■ Sean Cribbs - Eventually Consistent Data Structures http://vimeo.com/43903960 17
- 18. Consistency models of OLTP databases ■ Hinted handoff with sloppy quorums ■ Last write wins (highest write-availability) Riak Riak CouchDB/CouchBase Cassandra Cassandra ■ Strong consistency (read you own writes + strict quorums) ■ User resolvable conflicts Riak Riak Voldemort Voldemort Cassandra CouchDB/CouchBase (but unreliable) CouchBase ■ Active anti-entropy MongoDB Riak (Soon) Traditional SQL databases (Oracle, MySQL, etc.) 18
- 19. Thank you Rune Skou Larsen rsl@trifork.com Twitter: RuneSkouLarsen 19