Practical Data Synchronization with CRDTs (StrangeLoop, 2016)

In a connected world, synchronising mutable information between different devices with different clock precision can be a difficult problem. A piece of data may have many out-of-sync replicas but all of those should eventually be in a consistent state. For example, TomTom users, having personal navigation devices, smartphones, MyDrive website accounts, expect their navigation information be synchronised properly even in the occasional absence of network connection. Conflict-free Replicated Data Types (CRDTs) provide robust data structures to achieve proper synchronisation in an unreliable network of devices. They enable the conflict resolution being done locally at the data type level while guaranteeing the eventual consistency between replicas.

In addition to an introduction to common CRDT types, the main focus is on the special subtype of CRDT-Set called OUR-Set (Observed, Updated, Removed), which we created to extend known CRDT sets with update functionality.

I will demonstrate basic implementations of various CRDTs in Scala and enumerate subtle considerations which should be taken into account. I will also explain the advantages of these data structures to solve many synchronisation problems as well as their limitations.

Transcript

1. Prac%cal Data Synchroniza%on
   with
   CRDTs
   Dmitry Ivanov @idajan0s
   St. Louis, 2016
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2. Disclaimer
   I'm NOT:
   • Distributed systems expert.
   • Hardcore academia guy.
   Just a curious engineer hacking on real
   world problems.
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3. NavCloud
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4. Who We Are
   "Fool" stack developers hacking on:
   • Backend services
   • Mobile || SDKs
   • Infrastructure && AWS && DevOps
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5. Server Development Stack
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6. Client Libraries
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7. NavCloud Nature
   • Unstable connec,ons
   • Limited bandwidth
   • Seamless edit/view in offline mode
   • Concurrent changes with poten7al
   conflicts
   • No guarantee on updates order
   • No data loss
   • Data convergence to expected value
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8. How to Deal with this Nature?
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9. Bad programmers worry about the
   code. Good programmers worry
   about data structures
   — Linus Torvalds
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10. CRDT
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11. CRDT
    DT: Data Type
    CRDT is a data type with its own algebra
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12. CRDT
    R: Replicated
    CRDT is a family of data structures which
    has been designed to be distributed
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13. CRDT
    C: Conflict Free
    Resolving conflicts is done automa2cally
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14. How?
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15. Merge
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16. What is Merge?
    • A binary opera-on on two CRDTs
    • Commuta've: x • y = y • x
    • Associa've: ( x • y ) • z = x • ( y • z )
    • Idempotent: x • x = x
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17. How Does it Help?
    In Distributed Systems:
    • Order is not guaranteed:
    • No Problem: Merge is Commuta-ve and Associa-ve
    • Events can be delivered more than once:
    • No problem: Merge is Idempotent
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18. What Does it Bring in Prac1ce?
    • Local updates
    • Local merge of receiving data
    • All local merges converge
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19. Examples
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20. G-Counter
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21. G-Counter
    Merge: Max of corresponding elements: A:6 B:3 C:9
    TotalValue: Sum of all elements: 6 + 3 + 9 = 18
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22. Max Func)on
    • A binary opera-on on two CRDTs
    • Commuta've: x max y = y max x
    • Associa've: ( x max y ) max z = x max ( y max z )
    • Idempotent: x max x = x
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23. G-Set
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24. G-Set
    Merge: Union of sets: { x, y, z, a, b, c }
    Total Value: The same as the merge result
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25. Union Func)on
    • A binary opera-on on two CRDTs
    • Commuta've: x ∪ y = y ∪ x
    • Associa've: ( x ∪ y ) ∪ z = x ∪ ( y ∪ z )
    • Idempotent: x ∪ x = x
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26. CRDT in NavCloud
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27. Favorite Loca,ons
    Synchroniza,on
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28. Naive Approach?
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29. Last Write Wins
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30. Problems
    • Unstable connec-ons
    • Actual update -me < Sent -me
    • Network latency
    • Sent -me < Received -me
    • Unreliable clocks
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31. Stale update may win!
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32. So What?
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33. CRDT
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34. NavCloud Nature vs CRDT
    • Unstable connec,ons ✔
    • Limited bandwidth ✔
    • Seamless edit/view in offline mode ✔
    • Concurrent changes with poten7al
    conflicts ✔
    • No guarantee on updates order ✔
    • No data loss ✔
    • Data convergence to expected value ✔
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35. Same Data Model Everywhere
    • Server
    • Clients
    • Data store
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36. Merging Conflicts in Riak
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37. Implemen'ng a CRDT Set
    What do we want?
    • Support for addi-on and removal opera-ons.
    • Op-mized for element muta-ons.
    • Footprint as compact as possible.
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38. 2-Phase-Set
    Supports addi,ons and removals.
    • G-Set for added elements
    • G-Set for removed elements aka Tombstones
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39. 2-Phase-Set
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40. 2-Phase-Set
    Merge: [ Add { "cat", "dog", "ape" }; Rem { "ape" } ]
    Lookup: { "cat", "dog" }
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41. 2-Phase-Set
    Lookup
    def lookup: Set[E] = addSet.diff(removeSet).lookup
    Merge
    def merge(anotherSet: TwoPSet[E]): TwoPSet[E] =
    new TwoPSet( union(addset, anotherSet.addSet ),
    union(removeSet, anotherSet.removeSet ))
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42. 2-Phase-Set
    Doesn't work for us:
    • Removed element can't be added again
    • Immutable elements: no updates possible
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43. LWW-Element-Set
    Supports addi,ons and removals, with !mestamps.
    • G-Set for added elements
    • G-Set for removed elements aka Tombstones
    • Each element has a 3mestamp
    • Supports re-adding removed element using a higher 3mestamp
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44. LWW-Element-Set
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45. LWW-Element-Set
    Merge
    Add { (1, "cat"), (5, "cat"), (1, "dog"), (1, "ape") }
    Rem { (1, "cat"), (3, "cat") }
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46. LWW-Element-Set
    Merge
    Add { (1, "cat"), (5, "cat"), (1, "dog"), (1, "ape") }
    Rem { (1, "cat"), (3, "cat") }
    Lookup
    { "cat", "dog", "ape" }
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47. LWW-Element-Set
    Lookup
    def lookup: Set[E] = addSet.lookup.filter { addElem =>
    !removeSet.exists { removeElem =>
    removeElem.value == addElem.value && removeElem.timestamp > addElem.timestamp
    }
    }.map(_.value)
    Merge
    def merge(LWWSet anotherSet): LWWSet =
    new LWWSet( union(addset, anotherSet.addSet ),
    union(removeSet, anotherSet.removeSet ))
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48. LWW-Element-Set
    Doesn't work for us:
    • Immutable elements: no updates possible.
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49. OR-Set
    OR - Observed / Removed
    Supports addi,ons and removals, with tags.
    • G-Set for added elements
    • G-Set for removed elements aka Tombstones
    • Unique tag is associated with each element
    • Supports re-adding removed elements
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50. OR-Set
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51. OR-Set
    Merge
    Add { (#a, "cat"), (#c, "cat"), (#b, "dog"), (#d, "ape") }
    Rem { (#a, "cat") }
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52. OR-Set
    Merge
    Add { (#a, "cat"), (#c, "cat"), (#b, "dog"), (#d, "ape") }
    Rem { (#a, "cat") }
    Lookup
    { "cat", "dog", "ape" }
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53. OR-Set
    Lookup
    E exists iff it has in AddSet a tag that is not in the RemoveSet.
    def lookup(): Set =
    addSet.filter { addElem =>
    !removeSet.exists { remElem =>
    addElem.value == remElem.value
    && remElem.tag.equals(addElem.tag) }
    }
    .map(_.value);
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54. OR-Set
    Merge
    def merge(anotherSet: ORSet[E]): ORSet[E] =
    new ORSet( union(addset, anotherSet.addSet ),
    union(removeSet, anotherSet.removeSet))
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55. OR-Set
    Doesn't work for us:
    • Immutable elements: no updates possible.
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56. OUR-Set
    Our take on Observed-Updated-Removed Set
    • Each element has a unique iden%fier
    • Element can be changed if iden4fier remains the same
    • Each element has a %mestamp
    • Timestamp is updated on each element muta4on
    Iden%ty (immutable unique id) vs Value (mutable)
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57. OUR-Set
    Contains a single underlying set of elements with metadata:
    • Each element has a unique id field (e.g. a UUID)
    • Each element has a "removed" boolean flag
    • Each element has a )mestamp
    • Set can only contain one element with a par'cular id
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58. OUR-Set
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59. OUR-Set
    Merge
    { (id1, 5, "*ger"), (id2, 2, "dog", removed), (id3, 1, "ape") }
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60. OUR-Set
    Merge:
    { (id1, 5, "*ger"), (id2, 2, "dog", removed), (id3, 1, "ape") }
    Lookup
    { "$ger", "ape" }
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61. OUR-Set
    Merge
    def merge(anotherSet: OURSet[E]]): OURSet[E] =
    OURSet[E]( elements ++ anotherSet.elements)
    .groupBy (_.id)
    .map (group => group._2.maxBy(_.timestamp))
    .toSet)
    Lookup
    def lookup(ourSet: OURSet[E]): Set[E] =
    ourSet.filter (!_.removed)
    .map (_.value)
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62. Implementa)on
    NavCloud CRDT Model: Favorites
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63. CRDT Model: Favorites
    FavoriteState element:
    • ID (to uniquely iden.fy a favorite)
    • Timestamp (to indicate the last change .me)
    • Removed flag (to indicate if favorite has been removed)
    • Favorite data: ( Name, Loca2on, ... )
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64. Convergence in case of equal !mestamps
    Compare func-on checks all the fields in order of priority:
    • Timestamp
    • Removed flag (Add or Delete bias)
    • .. rest a6ributes ..
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65. Using CRDT everywhere
    • Use the same algorithm everywhere
    As simple as calling the merge func8on
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66. Using CRDT everywhere
    Client <-> Server <-> Database
    def update(fromClient: OURSet[E]): OURSet[E] = {
    val fromDatabase = database.fetch(...)
    val newSet = fromDatabase.merge(fromClient)
    database.store(..., newSet)
    newSet
    }
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68. Considera*ons & Limita*ons
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69. "What about garbage?"
    • CRDTs tend to grow because of tombstones.
    • Deleted Element in the Set == Tombstone.
    • A poten?ally unbounded growth.
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70. Prune deleted elements
    But when?
    Requirement:
    All nodes holding a CRDT Set replica should have seen a deleted
    element before it can be pruned.
    Otherwise deleted elements can be resurrected.
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71. Time-To-Live for tombstones
    Prune tombstones once TTL exceeded.
    if ((DateTime.now() - tombstone.timestamp) > TimeToLive) {
    crdtSet.remove(tombstone)
    }
    Requirement: all nodes holding a CRDT set should apply the same
    TTL rule independently.
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72. Send and reply with a Diff
    Client modifies and sends only updated elements (Diff).
    Before: Server responds with a full merge result.
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73. Send and reply with a Diff
    We introduced a 'Scoped Diff':
    Server responds only with the elements which have won against
    those sent by the client.
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74. Server -> Client Diff
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75. Trouble With Time
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76. There is no such thing as reliable (me*.
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77. Tracking *me is actually
    tracking causality.
    — Jonas Bonér, "Life Beyond the Illusion of Present"
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78. Causality & Ordering of events.
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79. Time can be just good enough.
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80. Ordering updates within a single node
    Timestamp field as a logical clock.
    Absolute value is not important,
    but it should always grow monotonically.
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81. Ordering updates within a single node
    "+1 Strategy" (aka ensure monotonicity):
    Long resolveNewTimestamp(ElementState state) {
    return Math.max( retrieveTimestamp(),
    state.lastModified() + 1 );
    }
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82. Ordering updates from different nodes
    If GPS clock is available -> use it (mainly Naviga&on Devices case).
    Prefer the server &me to a client's local 0me.
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83. Edge case
    Mul$ple Clients modify the same element
    (concurrently || without a reliable clock).
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84. One "merge" to rule them all
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85. Clients & Server MUST have same 'merge'
    behaviour.
    ==
    Given the same input, their 'merge' func/ons
    emit the same results.
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86. Divergence may lead to endless synchroniza1on loops!
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87. Lazy (data) loading
    OURSet Element
    • Metadata: UUID, $mestamp, "removed" flag
    • Data:
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88. Lazy (data) loading
    New OURSet Element
    • Metadata: UUID, $mestamp, "removed" flag, + tag / hash
    • (Op(onal) Data:
    Flexible synchroniza1on strategy
    Eager || Lazy Fetch
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89. What have we learned?
    • Academia is not as scary as it some-mes seems to pragma,c devs.
    • We need be2er and simpler abstrac-ons to develop
    Offline-friendly apps.
    • CRDTs give a great value, but there are some caveats.
    • Things like Lasp (lasp-lang.org) also could be the answer (?).
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90. Show me the code
    github.com/ajan/s/{scala | java}-crdt
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91. Want to know more? (Part 1)
    • CRDTs for fun and eventual profit, - Noel Welsh, 2013.
    • Readings in conflict-free replicated data types, - Christopher
    Meiklejohn, 2015.
    • A comprehensive study of Convergent and CommutaJve
    Replicated Data Types, - Marc Shapiro, Nuno Preguiça, Carlos
    Baquero, Marek Zawirski, 2011.
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92. Want to know more? (Part 2)
    • Lasp: A language for distributed, coordina7on-free programming,
    - Meiklejohn & Van Roy, 2015.
    • Swarm.js+React — real-7me, offline-ready Holy Grail web apps, -
    Victor Grishchenko.
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93. Thanks!
    Dmitry Ivanov @idajan0s
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