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Logic and Lattices for Distributed Programming Neil Conway UC Berkeley Joint work with: Peter Alvaro, Peter Bailis, David Maier, Bill Marczak, Joe Hellerstein, Sriram Srinivasan Basho Chats #004 June 27, 2012

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Programming

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Distributed Programming

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Dealing with Disorder Introduce order –Paxos, Zookeeper, Two-Phase Commit, … –“Strong Consistency” Tolerate disorder –Correct behavior in the face of many possible network orders –Typical goal: replicas converge to same final state “Eventual Consistency”

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Eventual Consistency PopularHard to program

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Help developers build reliable programs on top of eventual consistency

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This Talk 1. Theory –CRDTs, Lattices, and CALM 2. Practice –Programming with Lattices –Case Study: KVS

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Read: {Alice, Bob} Write: {Alice, Bob, Dave} Write: {Alice, Bob, Carol} Students {Alice, Bob, Dave} Students {Alice, Bob, Carol} Client 0 Client 1 Read: {Alice, Bob} Students {Alice, Bob} How to resolve? Students {Alice, Bob}

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Proble m Replicas perceive different event orders GoalSame final state at all replicas Solutio n Commutative operations (“merge functions”)

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Students {Alice, Bob, Carol, Dave} Client 0 Client 1 Merge = Set Union

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Commutative Operations Used by Dynamo, Riak, Bayou, etc. Formalized as CRDTs: Convergent and Commutative Replicated Data Types –Shapiro et al., INRIA (2009-2012) –Based on join semilattices –Commutative, associative, idempotent Practical libraries: Statebox, Knockbox

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Time Set (Union) Integer (Max) Boolean (Or) “Growth”: Larger Sets “Growth”: Larger Numbers “Growth”: false true

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Client 0 Client 1 Students {Alice, Bob, Carol, Dave} Teams { } Read: {Alice, Bob, Carol, Dave} Read: { } Write: {, } Teams {, } Remove: {Dave} Students {Alice, Bob, Carol} Replica Synchronization Students {Alice, Bob, Carol} Teams {, }

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Client 0 Client 1 Students {Alice, Bob, Carol, Dave} Teams { } Read: {Alice, Bob, Carol} Read: { } Teams { } Remove: {Dave} Students {Alice, Bob, Carol} Replica Synchronization Students {Alice, Bob, Carol} Nondeterministic Outcome! Teams { }

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Possible Solution: Wrap both replicated values in a single complex CRDT

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Goal: Compose larger application using “safe” mappings between simple lattices

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Time Set (merge = Union) Integer (merge = Max) Boolean (merge = Or) size() >= 5 Monotone function from set max Monotone function from max boolean

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Monotonicity in Practice “The more you know, the more you know” Never retract previous outputs (“mistake-free”) Typical patterns: immutable data accumulate knowledge over time threshold tests (“if” w/o “else”) Typical patterns: immutable data accumulate knowledge over time threshold tests (“if” w/o “else”)

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Monotonicity and Determinism Agents strictly learn more knowledge over time Monotone: different learning order, same final outcome Result: Program is deterministic!

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A program is confluent if it produces the same results regardless of network nondeterminism 20

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A program is confluent if it produces the same results regardless of network nondeterminism 21

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Consistency As Logical Monotonicity CALM Analysis 1.All monotone programs are confluent 2.Simple syntactic test for monotonicity Result: Simple static analysis for eventual consistency

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Handling Non-Monotonicity … is not the focus of this talk Basic choices: 1.Nodes agree on an event order using a coordination protocol (e.g., Paxos) 2.Allow non-deterministic outcomes If needed, compensate and apologize

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Putting It Into Practice What we’d like: Collection of agents No shared state ( message passing) Computation over arbitrary lattices

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Bloom OrganizationCollection of agents CommunicationMessage passing StateRelations (sets) ComputationRelational rules over sets (Datalog, SQL)

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BloomBloom L OrganizationCollection of agents CommunicationMessage passing StateRelations (sets)Lattices ComputationRelational rules over sets (Datalog, SQL) Functions over lattices

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Quorum Vote in Bloom L QUORUM_SIZE = 5 RESULT_ADDR = "example.org" class QuorumVote include Bud state do channel :vote_chn, [:@addr, :voter_id] channel :result_chn, [:@addr] lset :votes lmax :vote_cnt lbool :got_quorum end bloom do votes <= vote_chn {|v| v.voter_id} vote_cnt <= votes.size got_quorum <= vote_cnt.gt_eq(QUORUM_SIZE) result_chn <~ got_quorum.when_true { [RESULT_ADDR] } end Map set ! max Map max ! bool Threshold test on bool Lattice state declarations 27 Communication interfaces Accumulate votes into set Annotated Ruby class Program state Program logic Merge function for set lattice

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Builtin Lattices NameDescription?a t bSample Monotone Functions lboolThreshold testfalse a ∨ b when_true() ! v lmaxIncreasing number 1max(a,b ) gt(n) ! lbool +(n) ! lmax -(n) ! lmax lminDecreasing number −1−1min(a,b)lt(n) ! lbool lsetSet of values;a [ bintersect(lset) ! lset product(lset) ! lset contains?(v) ! lbool size() ! lmax lpsetNon-negative set;a [ bsum() ! lmax lbagMultiset of values;a [ bmult(v) ! lmax +(lbag) ! lbag lmapMap from keys to lattice values empty map at(v) ! any-lat intersect(lmap) ! lmap 28

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Case Study

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Goal: Provably eventually consistent key-value store (KVS) Assumption: Map keys to lattice values (i.e., values do not decrease) Assumption: Map keys to lattice values (i.e., values do not decrease) Solution: Use a map lattice

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Time Replica 1 Replica 2 Nested lattice value

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Time Replica 1 Replica 2 Add new K/V pair

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Time Replica 1 Replica 2 “Grow” value in extant K/V pair

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Time Replica 1 Replica 2 Replica Synchronization

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Goal: Provably eventually consistent KVS that stores arbitrary values Solution: Assign a version to each key-value pair Each replica stores increasing versions, not increasing values

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Object Versions in Dynamo/Riak 1.Each KV pair has a vector clock version 2.Given two versions of a KV pair, prefer the one with the strictly greater version 3.If versions are incomparable, invoke user- defined merge function

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Vector Clock: Map from node IDs logical clocks Logical Clock: Increasing counter Solution: Use a map lattice Solution: Use an increasing-int lattice

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Version-Value Pairs Pair = Pair merge(Pair o) { if self.fst > o.fst: self elsif self.fst < o.fst: o else new Pair(self.fst.merge(o.fst), self.snd.merge(o.snd)) }

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Time Replica 1 Replica 2

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Time Replica 1 Replica 2 Version increase; NOT value increase

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Time Replica 1 Replica 2 R1’s version replaces R2’s version

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Time Replica 1 Replica 2 New version @ R2

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Time Replica 1 Replica 2 Concurrent writes!

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Time Replica 1 Replica 2 Merge VC (automatically), value merge via user’s lattice (as in Dynamo)

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Lattice Composition in KVS

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Conclusion Dealing with EC Many event orders order- independent (disorderly) programs LatticesDisorderly state Monotone Functions Disorderly computation Monotone Bloom Lattices + monotone functions for safe distributed programming

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Questions Welcome Please try Bloom! http://www.bloom-lang.org Or: gem install bud

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Backup Slides

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Lattices hS,t,?i is a bounded join semi-lattice iff: –S is a partially ordered set –t is a binary operator (“least upper bound”) For all x,y 2 S, x t y = z where x · S z, y · S z, and there is no z’ z 2 S such that z’ · S z. Associative, commutative, and idempotent –? is the “least” element in S (8x 2 S: ? t x = x) 49 Example: increasing integers –S = Z, t = max, ? = -∞

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Monotone Functions f : S T is a monotone function iff 8a,b 2 S : a · S b ) f(a) · T f(b) 50 Example: size(Set) ! Increasing-Int size({A, B}) = 2 size({A, B, C}) = 3

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From Datalog ! Lattices Datalog (Bloom)Bloom L StateRelationsLattices Example Values[[“red”, 1], [“green”, 2]]set: [“red”, “green”] map: {“red” => 1, “green” => 2} counter: 5 condition: false ComputationRules over relationsFunctions over lattices Monotone Computation Monotone rulesMonotone functions Program SemanticsFixpoint of rules (stratified semantics) Fixpoint of functions (stratified semantics) 51

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Bloom Operational Model 52

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QUORUM_SIZE = 5 RESULT_ADDR = "example.org" class QuorumVote include Bud state do channel :vote_chn, [:@addr, :voter_id] channel :result_chn, [:@addr] table :votes, [:voter_id] scratch :cnt, [] => [:cnt] end bloom do votes <= vote_chn {|v| [v.voter_id]} cnt <= votes.group(nil, count(:voter_id)) result_chn = QUORUM_SIZE} end Quorum Vote in Bloom Communication Persistent Storage Transient Storage Accumulate votes Send message when quorum reached Not (set) monotonic! 53

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Current Status WriteupsBloom L : UCB Tech ReportUCB Tech Report Bloom/CALM: CIDR’11, websiteCIDR’11website Lattice Runtime Available as a git branch To be merged soon-ish Examples, Case Studies KVS Shopping carts Causal delivery Under development: MDCC, concurrent editingMDCC

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