Just-Right Consistency - Closing the CAP Gap

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Just-Right Consistency Closing the CAP Gap Christopher S. Meiklejohn (@cmeik) J on the Beach 2017, Málaga, Spain

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Outline: Closing the CAP Gap • Just-Right Consistency  Available as possible, and consistent when necessary 2

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Outline: Closing the CAP Gap • Just-Right Consistency  Available as possible, and consistent when necessary • AntidoteDB  The ﬁrst database that provides transactions with strong semantics, targeted at the JRC approach 2

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Motivation Cloud Databases 3

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[Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A Centralized database. [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A Clients read and write against the primary copy. [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Geo-replicated for both fault-tolerance and high-availability. [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Clients read and write locally for low-latency. [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C What happens if C can’t communicate with other replicas? [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Choice 1: Consistent-Under-Partition (CP) • Synchronize each operation  Maintains “single system image” [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Choice 1: Consistent-Under-Partition (CP) • Synchronize each operation  Maintains “single system image” • Spanner/F1, serializability model  Coordination is expensive; Spanner typically has to wait 100ms to commit an update transaction [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Choice 1: Consistent-Under-Partition (CP) • Synchronize each operation  Maintains “single system image” • Spanner/F1, serializability model  Coordination is expensive; Spanner typically has to wait 100ms to commit an update transaction Over-conservative, but easy to program! [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Choice 2: Available-Under-Partition (AP) • Riak, Cassandra, Dynamo  Operations issued against local copy, and across the cluster in parallel [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Choice 2: Available-Under-Partition (AP) • Riak, Cassandra, Dynamo  Operations issued against local copy, and across the cluster in parallel • Local operation only, asynchronous propagation  Stale reads and write conﬂicts will occur without synchronization [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C Choice 2: Available-Under-Partition (AP) • Riak, Cassandra, Dynamo  Operations issued against local copy, and across the cluster in parallel • Local operation only, asynchronous propagation  Stale reads and write conﬂicts will occur without synchronization Available, but difﬁcult to program! [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem CP AP [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem High cost CP AP [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem High cost Low availability CP AP [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem High cost Low availability Synchronization CP AP [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem High cost Low availability Synchronization Low cost CP AP [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem High cost Low availability Synchronization Low cost High availability CP AP [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem High cost Low availability Synchronization Low cost High availability Anomalies CP AP [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452]

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A B C CAP Theorem High cost Low availability Synchronization Low cost High availability Anomalies CP AP False dichotomy! [Photo: http://vignette3.wikia.nocookie.net/the-titans-rp-and-information/images/f/f5/Blank-World-map2.gif/revision/latest/scale-to-width-down/1280?cb=20141016203452] • No “one-size-ﬁts-all” consistency model  Choosing either model will either be over-conservative or risk anomalies

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Just Right Consistency • Preserve sequential patterns  Applications written sequentially that are correct should maintain correctness under concurrency 13

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Just Right Consistency • Preserve sequential patterns  Applications written sequentially that are correct should maintain correctness under concurrency • AP-compatible invariants  Strongest AP model; invariants that only require “one way” communications • CAP-sensitive invariants  Transactions that require coordination; “two way” communication invariants 13

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Example Fælles Medicinkort 14

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Fælles Medicinkort • FMK [production] / FMKe [synthetic workload]  Danish National Joint Medicine Card; operating 24x7 since 2013 for 6 million Danish citizens 15

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Fælles Medicinkort • FMK [production] / FMKe [synthetic workload]  Danish National Joint Medicine Card; operating 24x7 since 2013 for 6 million Danish citizens • Lifecycle management for prescriptions  Involves patient, pharmacy, and doctor management around active prescriptions in Denmark • Assumed correct in isolation  “Correct-Individually”, C in ACID, each operation ensures application-level invariants 15 • create-prescription  Create prescription for patient, doctor, pharmacy • update-prescription-medication  Add or increase medication to prescription • process-prescription  Deliver a medication by a pharmacy • get-*-prescriptions  Query functions to return information about prescriptions

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FMKe Invariants • Relative order [secondary indexes]  Create a prescription and reference it by a patient 16

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FMKe Invariants • Relative order [secondary indexes]  Create a prescription and reference it by a patient • Joint update [atomicity]  Create prescription, then update doctor, patient, and pharmacy 16

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Invariants AP-compatible 17

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AP-compatible • No synchronization  Updates occur locally without blocking, no synchronization in the critical path 18

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AP-compatible • No synchronization  Updates occur locally without blocking, no synchronization in the critical path • Asynchronous operation  Updates are fast, available, and exploit concurrency • Compatible invariants  Relative order and joint update invariants can be preserved 18

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AP-compatible Data Model 19

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RA RB

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RA RB 1 set(1)

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RA RB 1 set(1) 3 2 set(2) set(3)

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RA RB 1 set(1) 3 2 set(2) set(3) 2 3 Concurrent assignments don’t commute!

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RA RB 1 set(1) 3 2 set(2) set(3) 2 3 Concurrent assignments don’t commute! Assignment requires CP.

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24 Can we ﬁnd a suitable data model for AP systems?

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Can we make non-commutative updates commutative? 24 Can we ﬁnd a suitable data model for AP systems?

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RA RB 1 set(1) 3 2 set(2) set(3) ? ? How do we deterministically pick a value to keep?

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RA RB 1 set(1) 3 2 set(2) set(3) ? ? How do we deterministically pick a value to keep? Do we use a timestamp? (like Cassandra, and drop a value?)

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RA RB 1 set(1) 3 2 set(2) set(3) ? ? How do we deterministically pick a value to keep? Do we use a timestamp? (like Cassandra, and drop a value?) Timestamps make concurrent operations commute but fail to capture intent.

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Can we be smarter about the merge function? 26

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RA RB 1 set(1) 3 2 set(2) set(3) 3 3 max(2,3) max(2,3) Deterministic conﬂict resolution function.

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RA RB 1 set(1) 3 2 set(2) set(3) 3 3 max(2,3) max(2,3) Deterministic conﬂict resolution function. CRDTs generalize this framework.

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Conﬂict-Free   Replicated Data Types • Replicated abstract data types  Extension of sequential data type that encapsulates deterministic merge function 28

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Conﬂict-Free   Replicated Data Types • Replicated abstract data types  Extension of sequential data type that encapsulates deterministic merge function • Many existing designs  Sets, counters, registers, ﬂags, maps 28

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AP-compatible Relative Order 29

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RA RB

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RA RB Maintain program order implication invariant.

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RA RB Maintain program order implication invariant. For instance, P => Q.

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RA RB Q true(Q) Make Q true.

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RA RB Q true(Q) P true(P) Make P true.

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RA RB Q true(Q) P true(P) Program order implies ordering relationship.

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RA RB Q true(Q) P true(P) Ordering is respected at other replicas.

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RA RB Q true(Q) P true(P) Out of order propagation violates invariant!

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RA RB Q true(Q) P true(P) P is true, Q is NOT true!

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Let’s look at a concrete example. 37

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RA RB

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RA RB Q true(Q) Change default administrator password.

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RA RB Q true(Q) P true(P) Enable administrator login.

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RA RB Q true(Q) P true(P) Replica A is secure.

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RA RB Q true(Q) P true(P) Replica B is secure.

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RA RB Q true(Q) P true(P) Reordering allows default password to be used to login!

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Causal Consistency • Respect causality  Ensure updates are delivered in the causal order 44

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Causal Consistency • Respect causality  Ensure updates are delivered in the causal order • Strongest available AP model  Always able to return some compatible version for an object 44

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Causal Consistency • Respect causality  Ensure updates are delivered in the causal order • Strongest available AP model  Always able to return some compatible version for an object • Secondary indexing  Causal consistency is sufﬁcient for providing secondary indexing in an AP database 44

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…relative order invariants are preserved transparently! 45 Causal consistency means…

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AP-compatibe Joint Update 46

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RA RB C1 Client performing reads.

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RA RB C1 Rx create Rx Create prescription.

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RA RB C1 Rx create Rx Dr update Dr(Rx) Add reference in doctor record.

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RA RB C1 Rx create Rx Dr update Dr(Rx) Pt update Pt(Rx) Add reference in patient record.

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RA RB C1 Rx create Rx Dr update Dr(Rx) Pt update Pt(Rx) Ph update Ph(Rx) Add reference in pharmacy record.

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RA RB C1 Rx create Rx Dr update Dr(Rx) Pt update Pt(Rx) Ph update Ph(Rx) Updates are causally consistent.

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RA RB C1 Rx create Rx Dr update Dr(Rx) Pt update Pt(Rx) Ph update Ph(Rx) Client can read inconsistent state.

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RA RB C1 Rx create Rx Dr update Dr(Rx) Pt update Pt(Rx) Ph update Ph(Rx) Client is missing update to pharmacy.

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Can we ensure updates are All-or-Nothing? 55

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RA RB C1 T1 create Rx update Dr(Rx) update Pt(Rx) update Ph(Rx) Group updates into an atomic transaction.

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RA RB C1 T1 create Rx update Dr(Rx) update Pt(Rx) update Ph(Rx) Updates reﬂect “All-Or-Nothing” property through snapshots.

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RA RB C1 T1 create Rx update Dr(Rx) update Pt(Rx) update Ph(Rx) T2 Transactions are delivered in causal order.

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RA RB C1 T1 create Rx update Dr(Rx) update Pt(Rx) update Ph(Rx) T2 Therefore, snapshots are causally consistent.

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AP-compatible transactions provide the “A” in ACID 60

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Transactional Causal Consistency 61 Strongest model that is available (AP)

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Invariants CAP-sensitive 62

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What about preventing over delivery of prescriptions? 63

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RA(2) RB(2) ? ? RC(2) ? Three replicas each with two available medications.

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RA(2) RB(2) 1 1 1 pp(1) RC(2) 1 Replica A checks precondition and delivers medication.

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RA(2) RB(2) 1 1 1 pp(1) RC(2) 1 Correct outcome where one medication remains.

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Is this safe with concurrent operations? 67

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RA(2) RB(2) ? ? RC(2) ? Three replicas each with two available medications.

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RA(2) RB(2) 4 4 1 pp(1) RC(2) 4 4 add(3) Replica A checks precondition and delivers medication.

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RA(2) RB(2) 4 4 1 pp(1) RC(2) 4 4 add(3) Replica C adds three medications to the prescription.

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RA(2) RB(2) 4 4 1 pp(1) RC(2) 4 4 add(3) Correct outcome with four remaining medications.

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RA(2) RB(2) 4 4 1 pp(1) RC(2) 4 4 add(3) Correct outcome with four remaining medications. Precondition is stable under concurrent addition.

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Is this safe with concurrent deliveries? 72

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RA(2) RB(2) ? ? RC(2) ? Three replicas each with two available medications.

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RA(2) RB(2) -1 -1 1 pp(1) RC(2) -1 0 pp(2) Replica A checks precondition and delivers medication.

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RA(2) RB(2) -1 -1 1 pp(1) RC(2) -1 0 pp(2) Replica C concurrently checks precondition and delivers two medications.

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RA(2) RB(2) -1 -1 1 pp(1) RC(2) -1 0 pp(2) Incorrect outcome violating non-negative invariant.

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RA(2) RB(2) -1 -1 1 pp(1) RC(2) -1 0 pp(2) Incorrect outcome violating non-negative invariant. Precondition is NOT stable under concurrent fulﬁllment.

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RA(2) RB(2) -1 -1 1 pp(1) RC(2) -1 0 pp(2) Incorrect outcome violating non-negative invariant. Precondition is NOT stable under concurrent fulﬁllment. • Forbid concurrency  Prevent operations from proceeding without synchronization to enforce invariant • Allow concurrency and remove invariant  Allow operation to proceed, knowing that the invariant may be violated under concurrent operations

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How do we know when it’s safe? 77

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CISE Analysis 78

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RA RB I? I? ? Upre? RC I? ? Vpre? Analyze possible pairs of concurrent operations…

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RA RB I? I? ? Upre? RC I? ? Vpre? …to identify operations where the invariant can be violated.

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CISE Analysis • Individually correct  Individual operations never violate the invariant 81

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CISE Analysis • Individually correct  Individual operations never violate the invariant • Convergence  Concurrent effects commute 81

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CISE Analysis • Individually correct  Individual operations never violate the invariant • Convergence  Concurrent effects commute • Precondition stability  Preconditions are stable under every pair of concurrent operations 81

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Database AntidoteDB 82

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AntidoteDB • Open-source Erlang database  Developed in Erlang, on top of the Riak Core distributed systems framework 83

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AntidoteDB • Open-source Erlang database  Developed in Erlang, on top of the Riak Core distributed systems framework • Transactional Causal Consistency  Only industrial-grade database providing both causal consistency and all-or-nothing transactions • Alpha release available  Currently under development, but an alpha release of the product is available on GitHub 83

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A B N1 N2 TxnMgr Materializer Log InterDC-Repl Each data center…

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A B N1 N2 TxnMgr Materializer Log InterDC-Repl …contains multiple nodes…

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A B N1 N2 TxnMgr Materializer Log InterDC-Repl …each operating a transaction manager, materializers, log.

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A B N1 N2 TxnMgr Materializer Log InterDC-Repl Strong consistency inside of the data center…

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A B N1 N2 TxnMgr Materializer Log InterDC-Repl …with a causal consistency protocol running in the wide area.

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Data Model 89 Register • Last-Writer Wins • Multi-Value Set • Grow-Only • Add-Wins • Remove-Wins Map Counter • Unlimited • Restricted ≥ 0 Graph • Directed • Monotonic DAG • Edit graph Sequence

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Object API 90 User1 = {michel, antidote_crdt_mvreg, user_bucket}, {ok, Time2} = antidote:update_objects(ignore, [], [{User1, assign, {["Michel", “[email protected]”], ClientIdentifier}}]), {ok, Result, Time2} = antidote:read_objects( ignore, [], [User1]).

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Object API 91 User1 = {michel, antidote_crdt_mvreg, user_bucket}, {ok, Time2} = antidote:update_objects(ignore, [], [{User1, assign, {["Michel", “[email protected]”], ClientIdentifier}}]), {ok, Result, Time2} = antidote:read_objects( ignore, [], [User1]). Identify an object by object identiﬁer.

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Object API 92 User1 = {michel, antidote_crdt_mvreg, user_bucket}, {ok, Time2} = antidote:update_objects(ignore, [], [{User1, assign, {["Michel", “[email protected]”], ClientIdentifier}}]), {ok, Result, Time2} = antidote:read_objects( ignore, [], [User1]). Use the update API to assign a value to this register.

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Object API 93 User1 = {michel, antidote_crdt_mvreg, user_bucket}, {ok, Time2} = antidote:update_objects(ignore, [], [{User1, assign, {["Michel", “[email protected]”], ClientIdentifier}}]), {ok, Result, Time2} = antidote:read_objects( ignore, [], [User1]). Read the object, providing a minimum snapshot time.

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Transaction API 94 {ok, TxId} = antidote:start_transaction(Timestamp, []), {ok, _} = antidote:read_objects([Set], TxId), ok = antidote:update_objects([{Set, add, "Java"}], TxId), {ok, _} = antidote:commit_transaction(TxId).

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Transaction API 95 Start a transaction with the transaction API, with a given snapshot time and return a transaction identiﬁer. {ok, TxId} = antidote:start_transaction(Timestamp, []), {ok, _} = antidote:read_objects([Set], TxId), ok = antidote:update_objects([{Set, add, "Java"}], TxId), {ok, _} = antidote:commit_transaction(TxId).

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{ok, TxId} = antidote:start_transaction(Timestamp, []), {ok, _} = antidote:read_objects([Set], TxId), ok = antidote:update_objects([{Set, add, "Java"}], TxId), {ok, _} = antidote:commit_transaction(TxId). Transaction API 96 Read objects using the interactive transaction API.

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{ok, TxId} = antidote:start_transaction(Timestamp, []), {ok, _} = antidote:read_objects([Set], TxId), ok = antidote:update_objects([{Set, add, "Java"}], TxId), {ok, _} = antidote:commit_transaction(TxId). Transaction API 97 Update objects using the interactive transaction API.

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{ok, TxId} = antidote:start_transaction(Timestamp, []), {ok, _} = antidote:read_objects([Set], TxId), ok = antidote:update_objects([{Set, add, "Java"}], TxId), {ok, _} = antidote:commit_transaction(TxId). Transaction API 98 Once ﬁnished updating, commit the transaction. Transactions read causally consistent snapshots and updates are applied atomically.

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Scalability 99 Kops / s 100 200 300 400 500 600 700 800 1 x 5 1 x 10 1 x 25 2 x 25 3 x 25 1 x 5 1 x 10 1 x 25 2 x 25 3 x 25 1 x 5 1 x 10 1 x 25 2 x 25 3 x 25 1 x 5 1 x 10 1 x 25 2 x 25 3 x 25 99(1) 90(10) 75(25) 50(50) read(update) ratio DCs × Servers LWW registers 100k keys/partition power law distribution

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Cure vs. SOA 100 Kops / s 0 100 200 300 400 500 600 700 800 900 1000 1100 Eiger GR Cure EC Eiger GR Cure EC Eiger GR Cure EC Eiger GR Cure EC 99(1) 90(10) 75(25) 50(50) read(update) ratio 3 DCs × 25 Servers LWW registers

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Cure vs. EC 101 Kops / s 100 200 300 400 500 600 700 800 900 1000 1100 1200 Cure, 1KB EC, 1KB Cure, 10KB EC, 10KB Cure, 1KB EC, 1KB Cure, 10KB EC, 10KB Cure, 1KB EC, 1KB Cure, 10KB EC, 10KB Cure, 1KB EC, 1KB Cure, 10KB EC, 10KB 99(1) 90(10) 75(25) 50(50) read(update) ratio 3 DCs x 25 Servers CRDT sets

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Future Features • Intra-DC replication  Antidote provides no replication within the datacenter and assumes only geo- replication at the moment 102

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Future Features • Intra-DC replication  Antidote provides no replication within the datacenter and assumes only geo- replication at the moment • ACID transactions  For Antidote to provide all of JRC, it needs ACID transaction support: no research needed, only implementation 102

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Moving Forward • Research prototype  Originally a research prototype to build a database requiring reduced synchronization (SyncFree FP7) with Basho, Rovio, and Trifork 103

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Moving Forward • Research prototype  Originally a research prototype to build a database requiring reduced synchronization (SyncFree FP7) with Basho, Rovio, and Trifork • Research ahead  LightKone (H2020) will investigate moving AntidoteDB close to the edge to provide DDN services 103

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Resources • https://github.com/SyncFree/antidote  AntidoteDB 104

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Resources • https://github.com/SyncFree/antidote  AntidoteDB • http://syncfree.github.io/antidote/  Documentation for AntidoteDB 104

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Resources • https://github.com/SyncFree/antidote  AntidoteDB • http://syncfree.github.io/antidote/  Documentation for AntidoteDB • www.antidotedb.com  Website 104

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Resources • https://github.com/SyncFree/antidote  AntidoteDB • http://syncfree.github.io/antidote/  Documentation for AntidoteDB • www.antidotedb.com  Website • docker pull antidotedb/antidote  Try out Antidote! 104

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Thanks! 105 Questions?