logic and lattices for distributed programming

Logic and Lattices for Distributed Programming

Neil Conway, William R. Marczak, Peter Alvaro, Joseph M. HellersteinUC Berkeley

David MaierPortland State University

Distributed Programming:Key Challenges

Asynchrony

PartialFailure

Dealing with DisorderEnforce global order– Paxos, Two-Phase Commit, GCS, …– “Strong Consistency”

Tolerate disorder– Programmer must ensure correct behavior

for many possible network orders– “Eventual Consistency”

• Typical goal: replicas converge to same final state

Dealing with DisorderEnforce global order– Paxos, Two-Phase Commit, GCS, …– “Strong Consistency”

Tolerate disorder– Programmer must ensure correct behavior

for many possible network orders– “Eventual Consistency”

• Typical goal: replicas converge to same final state

Goal:Make it easier to write

programs on top ofeventual consistency

This Talk1. Prior Work– Convergent Modules (CRDTs)– Monotonic Logic (CALM)

2. BloomL

3. Case Study

Read: {Alice, Bob}

Write: {Alice, Bob, Dave}

Write: {Alice, Bob, Carol}

Students{Alice, Bob, Dave}

Students{Alice, Bob, Carol}Client0

Client1

Read: {Alice, Bob} Students{Alice, Bob}

How to resolve?

Students{Alice, Bob}

Problem

Replicas perceive different event orders

Goal Same final state at all replicas

Solution

Use commutative operations (“merge functions”)

Students{Alice, Bob, Carol,

Dave}

Students{Alice, Bob, Carol,

Dave}Client0

Client1

Merge = Set Union

Commutative Operations

• Common design pattern• Formalized as CRDTs:

Convergent and Commutative Replicated Data Types– Shapiro et al., INRIA (2009-

2012)– Based on join semilattices

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Lattices

hS,t,?i is a bounded join semilattice iff:– S is a set– t is a binary operator (“least upper

bound”)• Associative, commutative, and idempotent• Induces a partial order on S: x ·S y if x t y = y• Informally, “merge function” for elements of

S– ? is the “least” element in S• 8x 2 S: ? t x = x

Time

Set(LUB = Union)

IncreasingInteger

(LUB = Max)Boolean

(LUB = Or)

Client0

Client1

Students{Alice, Bob, Carol,

Dave}

Students{Alice, Bob, Carol,

Dave}

Teams{<Alice, Bob>}

Teams{<Alice, Bob>}

Read: {Alice, Bob, Carol, Dave}

Read: {<Alice,Bob>}Write: {<Alice,Bob>, <Carol,Dave>}

Teams{<Alice, Bob>,

<Carol, Dave>}

Remove: {Dave} Students{Alice, Bob, Carol}

Replica Synchronization

Students{Alice, Bob, Carol}

Teams{<Alice, Bob>,

<Carol, Dave>}

Teams{<Alice, Bob>,

<Carol, Dave>}

Teams{<Alice, Bob>,

<Carol, Dave>}

Client0

Client1

Students{Alice, Bob, Carol,

Dave}

Students{Alice, Bob, Carol,

Dave}

Teams{<Alice, Bob>}

Read: {Alice, Bob, Carol}

Read: {<Alice,Bob>}Teams

{<Alice, Bob>}

Remove: {Dave} Students{Alice, Bob, Carol}

Replica Synchronization

Students{Alice, Bob, Carol}

Nondeterministic Outcome!

Teams{<Alice, Bob>}

Teams{<Alice, Bob>}

Problem:Composition of CRDTs canresult in non-determinism

Possible Solution:Encapsulate all distributed

state in a single CRDT

Hard to design,verify, and test

Doesn’t scale with application size

Goal:Design a language that allows

safe composition of CRDTs

Solution: … Datalog?• Concurrent work:

distributed programming using Datalog– P2 (2006-2010)– Bloom (2010-2012)

• Monotonic logic: building block for convergent distributed programs

Monotonic Logic• As input set grows,

output set does not shrink– “Retraction-free”

• Order independent• e.g., map, filter, join,

union, intersection

Non-Monotonic Logic• New inputs might

retract previous outputs

• Order sensitive• e.g., aggregation,

negation

Monotonicity and Determinism

Agents learn strictly more knowledge over

time

Different learning order, same final outcome

Result:Program is deterministic!

Consistency

As

Logical

Monotonicity

CALM Analysis

1.All monotone programs are deterministic

2.Simple syntactic test for monotonicity

Result: Whole-program static analysis foreventual consistency

Problem:CALM only applies to

programs over growing sets

Version Numbers Timestamps Threshold Tests

Quorum Vote• A coordinator

accepts votes from agents

• Count # of votes–When Count(Votes) >

k, send “success” message

Quorum Vote• A coordinator

accepts votes from agents

• Count # of votes–When Count(Votes) >

k, send “success” message

Aggregation isnon-monotonic!

CRDTsLimited scope(single object)Flexible types(any lattice)

CALMWhole program analysisLimited types (only sets)

BloomL

Whole program analysisFlexible types (any lattice)

BloomL Constructs

Organization Collection of agentsCommunication

Message passing

State LatticesComputation Functions over

lattices

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Monotone Functions

f : ST is a monotone function iff

8a,b 2 S : a ·S b ) f(a) ·T f(b)

Time

Set(LUB = Union)

IncreasingInteger

(LUB = Max)Boolean

(LUB = Or)

size() >= 5

Monotone function fromset increase-int

Monotone function fromincrease-int boolean

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Quorum Vote in BloomL

QUORUM_SIZE = 5RESULT_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] } endend

Monotone function: set ! maxMonotone function: max ! bool

Threshold test on bool (monotone)

Lattice state declarations

Communication interfaces

Accumulate votesinto set

Annotated Ruby class

Program state

Program logic

Merge function for set lattice

Monotonic CALM

BloomL Features• Generalizes logic programming to

lattices– Integration of relational-style queries

and functions over lattices– Efficient incremental evaluation scheme

• Library of built-in lattices– Booleans, increasing/decreasing

integers, sets, multisets, maps, …• API for defining custom lattices

Case Studies

Key-Value Store– Object versioning

via vector clocks– Quorum replication

Replicated Shopping Cart– Using custom lattice types

to encode domain-specific knowledge

Case Studies

Key-Value Store– Object versioning

via vector clocks– Quorum replication

Replicated Shopping Cart– Using custom lattice types

to encode domain-specific knowledge

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Case Study: Shopping Carts

35

Case Study: Shopping Carts

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Case Study: Shopping Carts

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Case Study: Shopping Carts

Perspectives on Shopping

• CRDTs– Individual server replicas converge

• Bloom– Checkout is non-monotonic requires

distributed coordination• Built-in BloomL lattice types– Checkout is not a monotone function of

any of the built-in lattices

Observation:Once a checkoutoccurs, no more shopping actions

can be performed

Observation:Each client knows

when a checkout can beprocessed “safely”

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Monotone Checkout

OPS = [1]Incomplet

e

OPS = [2]Incomplet

e

OPS = [3]Incomplet

e

OPS = [1,2]

Incomplete

OPS = [2,3]

Incomplete

OPS = [1,2,3]

Complete

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Monotone Checkout

43

Monotone Checkout

44

Monotone Checkout

45

Monotone Checkout

Shopping Takeaways• Checkout summary is a monotone

function of client’s activities• Custom lattice type captures

application-specific notion of “forward progress”– “Unsafe” state hidden behind ADT

interface

Recap1. How to build eventually consistent systems– Write disorderly programs

2. Disorderly state– Lattices

3. Disorderly computation– Monotone functions over lattices

4. BloomL

– Type system for deterministic behavior– Support for custom lattice types

Thank You!http://www.bloom-lang.net

Backup Slides

50

Strong Consistency in Industry

“… there was a single overarching theme within the keynote talks… strong synchronization of the sort provided by a locking service must be avoided like the plague… [the key] challenge is to find ways of transforming services that might seem to need locking into versions that … can operate correctly without locking.”

-- Birman et al.,“Toward a Cloud Computing Research Agenda”

(LADIS, 2009)

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Bloom Operational Model

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QUORUM_SIZE = 5RESULT_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 <~ cnt {|c| [RESULT_ADDR] if c >= QUORUM_SIZE} endend

Quorum Vote in Bloom

Communication

Persistent Storage

Transient StorageAccumulate votes

Send message when quorum reached

Not (set) monotonic!Count votes

Annotated Ruby class

Program state

Program logic

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Built-in LatticesName Description ? a t b Sample Monotone

Functionslbool Threshold test false a ∨ b when_true() ! vlmax Increasing

number1 max(a,

b)gt(n) ! lbool+(n) ! lmax-(n) ! lmax

lmin Decreasing number

−1 min(a,b)

lt(n) ! lbool

lset Set of values ; a [ b intersect(lset) ! lsetproduct(lset) ! lset

contains?(v) ! lboolsize() ! lmax

lpset Non-negative set

; a [ b sum() ! lmax

lbag Multiset of values

; a [ b mult(v) ! lmax+(lbag) ! lbag

lmap Map from keys to lattice values

empty

map

at(v) ! any-latintersect(lmap) ! lmap

Failure HandlingGreat question!

1. Monotone programs handle transient faults very well– Deterministic simple logging– Commutative, idempotent simple recovery

2. Future work: “controlled non-determinism”– Timeout code is fundamentally non-deterministic– But we still want mostly deterministic programs

Handling Non-Monotonicity

… is not the focus of this talk

Basic alternatives:1. Nodes agree on an event order

using distributed coordination (e.g., Paxos)

2. Allow non-deterministic outcomes• If needed, compensate and apologize