CRDTs with Akka Distributed Data
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Akka Distributed Data is useful when building distributed systems that focus on high availability rather than strong consistency. The module uses Conflict-Free Replicated Data Types (CRDTs) to replicate data across nodes without conflicts. This talk will give an introduction to distributed systems, data replication and how CRDTs are working. It will serve as a basis to explain Akka Distributed Data and typical uses cases in which it should be used.
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CRDTs with Akka Distributed Data
- 1. CRDTs with Akka Distributed Data Markus Jura @markusjura
- 3. Consistency on a single node Bob
- 4. Consistency on a single node Meat Bob
- 5. Consistency on a single node Meat Meat Bob
- 6. Alice Consistency on a single node Meat Meat Bob
- 7. Alice Consistency on a single node Meat Meat Fruit Bob
- 8. Alice Consistency on a single node Meat Meat Fruit Meat Fruit Bob
- 9. Consistency in a distributed system Bob
- 10. Consistency in a distributed system Meat Bob
- 11. Consistency in a distributed system Meat Meat Bob
- 12. Alice Consistency in a distributed system Meat Meat Bob
- 13. Alice Consistency in a distributed system Meat Meat Fruit Bob
- 14. Alice Consistency in a distributed system Meat Meat Fruit Fruit Bob
- 15. Alice Consistency in a distributed system Meat Meat Fruit Fruit ? Bob
- 16. CAP theorem Consistency Clients have the same view of the same data Availability Clients can always read and write Partition tolerance System continues to function despite physical network partitions
- 17. Strongly consistent Write Bob Leader Follower FollowerAlice
- 18. Strongly consistent Write Bob Leader Follower Follower Meat write Alice
- 19. Meat Strongly consistent Write Bob Leader Follower Follower Meat write Alice
- 20. Meat Strongly consistent Write Bob Leader Follower Follower Meat write replicate Alice
- 21. Meat Meat Meat Strongly consistent Write Bob Leader Follower Follower Meat write replicate Alice
- 22. Meat Meat Meat Strongly consistent Write Bob Leader Follower Follower Meat write replicate ACK Alice
- 23. Meat Meat Meat Strongly consistent Write Bob Leader Follower Follower Meat write replicate ACK Commit Alice
- 24. Meat Meat Meat Strongly consistent Write Bob Leader Follower Follower Meat write Alice
- 25. Meat Meat Meat Strongly consistent Write Bob Leader Follower Follower Meat write Alice Fruit write
- 26. Meat Meat Fruit Meat Meat Strongly consistent Write Bob Leader Follower Follower Meat write Alice Fruit write
- 27. Meat Meat Fruit Meat Meat Meat Fruit Meat Fruit Strongly consistent Write Bob Leader Follower Follower Meat write Alice Fruit write replicate ACK Commit
- 28. Meat Meat Fruit Meat Meat Meat Fruit Meat Fruit Strongly consistent Read Bob Leader Follower FollowerAlice
- 29. Meat Meat Fruit Meat Meat Meat Fruit Meat Fruit Strongly consistent Read Bob Leader Follower FollowerAlice Meat Fruit read
- 30. Meat Meat Fruit Meat Meat Meat Fruit Meat Fruit Strongly consistent Read Bob Leader Follower FollowerAlice Meat Fruit read read Meat Fruit
- 31. Strong consistency • Needs consensus • Tolerate N failures out of N*2+1 (Quorum) • Common protocols • Paxos (Mesos, Neo4J) • Raft (Consul, Etcd) • ZAB (ZooKeeper)
- 36. Meat Meat Meat Fruit Meat Eventually consistent Write Bob Alice Meat write replicate
- 37. Meat Meat Meat Fruit Meat Eventually consistent Write Bob Alice Meat write
- 38. Meat Meat Meat Fruit Meat Eventually consistent Write Bob Alice Meat write Fruit write
- 39. Meat Meat Meat Fruit Meat Fruit Meat Eventually consistent Write Bob Alice Meat write Fruit write
- 40. Meat Meat Meat Fruit Meat Fruit Meat Eventually consistent Write Bob Alice Meat write Fruit write replicate
- 41. Meat Meat Meat Fruit Meat Fruit Meat Meat Fruit Meat Fruit Eventually consistent Write Bob Alice Meat write Fruit write replicate
- 42. Fruit Meat Meat Fruit Meat Eventually consistent Read Bob Alice networkpartition
- 43. Fruit Meat Meat Fruit Meat Eventually consistent Read Bob Alice read networkpartition
- 44. Fruit Meat Meat Fruit Meat Eventually consistent Read Bob Alice read Meat networkpartition
- 45. Fruit Meat Meat Fruit Meat Eventually consistent Read Bob Alice read read Meat networkpartition
- 46. Fruit Meat Meat Fruit Meat Eventually consistent Read Bob Alice read read Meat Fruit networkpartition
- 47. Eventual consistency • Tolerates N node failures out of N+1 nodes • Only 1 node needs to be available • Updates are observed eventually
- 48. Strong vs eventual consistency • Strong consistency • Consistent state at any time • Operations require Quorum • Higher latency • Eventual consistency • Possible stale state at a given time • High availability • Lower latency
- 49. Strong eventual consistency (SEC) • Type of eventual consistency • Guarantees that N nodes that received same (unordered) updates will be in the same state • Usage • Cassandra, DynamoDB, Riak, CouchDB, Voldemort • Needs non conflicting merge algorithm
- 55. CRDT
- 56. What is a CRDT? • Operation-based CRDTs (CmRDTs) • Broadcasts update operations • Not idempotent (require causal delivery of order) • State-based CRDTs (CvRDTs) • Broadcasts full or delta state • Merge must be commutative, associative, idempotent Conflict-free replicated data type
- 57. State-based G-Counter (grow only) 14 [3,5,6] Node A 17 [3,7,7] Node B 16 [1,6,9] Node C
- 58. State-based G-Counter (grow only) 14 [3,5,6] Node A 17 [3,7,7] Node B 16 [1,6,9] Node C 17 [3,7,7] 19 [3,7,9] 18 [3,6,9]
- 59. State-based G-Counter (grow only) 14 [3,5,6] Node A 17 [3,7,7] Node B 16 [1,6,9] Node C 17 [3,7,7] 19 [3,7,9] 18 [3,6,9] 19 [3,7,9] 19 [3,7,9] 19 [3,7,9]
- 61. What is Akka Distributed Data? • Uses CvRDT to replicate data across cluster • Replication via gossip protocol • Data kept in memory • Every node has all data
- 63. Replicator • Actor to interact with CRDT data • Get replicator via Akka Extension val replicator = DistributedData(context.system).replicator
- 64. Update • Pure modify function to update the CRDT • Choose consistency level replicator ! Update(key, consistency, request)(modify)
- 65. Get def receive = { case GetFromCache(key) => replicator ! Get(key, consistency, request) case g @ GetSuccess(key, request) => // CRDT by key found val currentValue = g.get(key).value case GetFailure(key, request) => // Failed to retrieve data based on consistency level case NotFound(key, request) => // Key not found }
- 66. Consistency • Specify how many nodes must respond successfully to write or read data • Request-based • Consistency levels • ReadLocal, WriteLocal • ReadMajority, WriteMajority • ReadFrom, WriteFrom • ReadAll, WriteAll
- 67. Subscribe Receive changed notifications with updated data replicator ! Subscribe(key, actorRef) def receive: Receive = { case c @ Changed(key) => val currentValue = c.get(key).value }
- 68. Data types • Counters: GCounter, PNCounter • Registers: LWWRegister, Flag • Sets: GSet, ORSet • Maps: ORMap, ORMultiMap, LWWMap, PNCounterMap • Values in data types must be serializable
- 69. Custom data type • Extend from ReplicatedData trait • Implement function • Must be serializable def merge(that: T: T)
- 70. Code
- 71. Delta CRDTs • Sending only the delta of the state • Occasionally full state is replicated • When node joins • After network partition • Support for causal consistency • Since Akka 2.5.0
- 72. Delta CRDTs • Supported built-in data types • GCounter, PNCounter • GSet, ORSet • Ensures causal consistency (if required) • Custom data types • Implement methods of trait DeltaReplicatedData • Use trait RequiresCausalDeliveryOfDeltas to ensure causal consistency
- 73. Durable storage • Configuration to store data on disk akka.cluster.distributed-data.durable.keys = [“key1", "durable*"] • Update flushed to disk before UpdateSuccess • Replicator sends WriteFailure if write to disk failed
- 74. Limitations • Eventual consistent • Not suitable for Big Data • Data kept in memory on every node • Maximum 100000 top level entries • Replicating entire state to a new node can take several seconds
- 75. Use Cases • Key value store • Service discovery • Shopping cart • Distributing state across Akka cluster
- 76. Learn more • Akka Distributed Data Samples • The Final Causal Frontier talk by Sean Cribbs • Eventually Consistent Data Structures talk by Sean Cribbs • Strong Eventual Consistency and Conflict-free Replicated Data Types talk by Mark Shapiro • A comprehensive study of Convergent and Communitative Replicated Data Types paper by Mark Shapiro et. al. • Delta State Replicated Data Types paper by Paulo Sergio Almeida et. al. • Please stop calling databased CP or AP blog post by Martin Kleppmann
- 77. Questions