Consensus, Raft and Rafter

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& Consensus RAFT RAFTER

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Tom Santero cats I newports distributed systems

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Andrew Stone cats I bread distributed systems

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@tsantero @andew_j_stone

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tsantero andewjstone

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tsantero astone @ basho.com

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tsantero astone @ basho.com (notice Andrew’s contact keeps getting shorter?)

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http://thinkdistributed.io A Chris Meiklejohn Production

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The Usual Suspects

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“Strongly Consistent Datastores” MongoDB Redis MySQL others...

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async {replication disk persistence Failure Detection

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Problem?

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Single Node w/ async disc writes Data is written to fs buﬀer, user is sent acknowledgement, power goes out Data not yet written to disk is LOST System is UNAVAILABLE Single Disk Solutions: fsync, battery backup, prayer Failure Mode 1

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Master/Slave with asynchronous replication Data is written by user and acknowledged Data synced on Primary, but crashes Failure Mode 2

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? Consistent Available

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? Consistent Available

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? Consistent Available Primary Failed. Data not yet written to Secondary Write already ack’d to Client if promote_secondary() == true; { stderr(“data loss”); } else { stderr(“system unavailable”); }

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(›°□°ʣ›ớ ᵲᴸᵲ

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Synchronous Writes FTW?

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PostgreSQL / Oracle Master / Slave Ack when Slave conﬁrms Write

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Problem?

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Problem? Failure Detection Automated Failover “split brain” partitions

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Solution!

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Solution! Consensus protocols! (Paxos, ZAB, Raft) RYOW Consistency Safe Serializability

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What is Consensus?

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“The problem of reaching agreement among remote processes is one of the most fundamental problems in distributed computing and is at the core of many algorithms for distributed data processing, distributed ﬁle management, and fault- tolerant distributed applications.”

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In a distributed system... despite failures. multiple processes agreeing on a value

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host0 host1 host2 Replicated Log

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v0 host0 host1 host2 Replicated Log

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v0 v1 v2 v3 v4 v5 ... v(n-1) host0 host1 host2 Replicated Log

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Consensus {termination agreement validity

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Consensus {termination agreement validity non faulty processes eventually decide on a value

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Consensus {termination agreement validity non faulty processes eventually decide on a value processes that decide do so on the same value

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Consensus {termination agreement validity non faulty processes eventually decide on a value processes that decide do so on the same value value must have been proposed

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Theoretical Real World

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Back to 1985...

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Back to 1985... The FLP Result

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& Safety Liveness

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bad things can’t happen

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good things eventually happen

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Consensus {termination agreement validity non faulty processes eventually decide on a value processes that decide do so on the same value value must have been proposed

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{termination agreement validity non faulty processes eventually decide on a value processes that decide do so on the same value value must have been proposed Safety Liveness

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Safety Liveness {termination agreement validity non faulty processes eventually decide on a value processes that decide do so on the same value value must have been proposed

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Safety Liveness {termination agreement validity non faulty processes eventually decide on a value processes that decide do so on the same value value must have been proposed

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Safety Liveness {termination agreement validity non faulty processes eventually decide on a value processes that decide do so on the same value value must have been proposed non-triviality

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The FLP Result: perfect Safety and Liveness in async consensus is impossible

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Symmetric vs Asymmetric

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Raft

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Motivation: RAMCloud large scale, general purpose, distributed storage all data lives in DRAM strong consistency model https://ramcloud.stanford.edu/

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Motivation: RAMCloud large scale, general purpose, distributed storage all data lives in DRAM strong consistency model 100 byte object reads in 5μs https://ramcloud.stanford.edu/

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John Ousterhout Diego Ongaro In Search of an Understandable Consensus Algorithm https://ramcloud.stanford.edu/raft.pdf

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“Unfortunately, Paxos is quite difﬁcult to understand, in spite of numerous attempts to make it more approachable. Furthermore, its architecture is unsuitable for building practical systems, requiring complex changes to create an efﬁcient and complete solution. As a result, both system builders and students struggle with Paxos.”

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No content

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Design Goals: Understandability & Decomposition Strong Leadership Model Joint Consensus for Membership Changes

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Log SM C Consensus Module Replicated Log State Machine

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Log SM C Log SM C Log SM C Client C

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Log SM C Log SM C Log SM C Client 1. client makes request to Leader C

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Log SM C Log SM C Log SM C Client 2. consensus module manages request C

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Log SM C Log SM C Log SM C Client 3. persist instruction to local log v C

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Log SM C Log SM C Log SM C Client v C

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Log SM C Log SM C Log SM C Client v 4. leader replicates command to other machines C C C

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Log SM C Log SM C Log SM C Client v C C C

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Log SM C Log SM C Log SM C Client v C C v v 5. command recorded to local machines’ log C

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Log SM C Log SM C Log SM C Client v C C v v C

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Log SM C Log SM C Log SM C Client v C C v v C

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Log SM C Log SM C Log SM C Client v C C v v C

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Log SM C Log SM C Log SM C Client v C C v v 7. command forwarded to state machines for processing SM SM SM C

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Log SM C Log SM C Log SM C Client v C C v v 7. command forwarded to state machines for processing SM SM SM C

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Log SM C Log SM C Log SM C Client v C C v v SM C

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Log SM C Log SM C Log SM C Client v C C v v SM 8. SM processes command, ACKs to client C

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Log SM C Log SM C Log SM C Client v C C v v SM C

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Why does that work? job of the consensus module to: C manage replicated logs determine when it’s safe to pass to state machine for execution only requires majority participation

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Why does that work? job of the consensus module to: C manage replicated logs determine when it’s safe to pass to state machine for execution only requires majority participation Safety { Liveness {

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2F + 1

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2F + 1 solve for F

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F + 1 service unavailable

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Fail-Stop Behavior

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What If The Leader DIES?

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Leader Election!

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1. Select 1/N servers to act as Leader 2. Leader ensures Safety and Linearizability 3. Detect crashes + Elect new Leader 4. Maintain consistency after Leadership “coups” 5. Depose old Leaders if they return 6. Manage cluster topology

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Possible Server Roles: Leader Follower Candidate

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Possible Server Roles: Leader Follower Candidate At most only 1 valid Leader at a time Receives commands from clients Commits entries Sends heartbeats

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Possible Server Roles: Leader Follower Candidate Replicate state changes Passive member of cluster during normal operation Vote for Candidates

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Possible Server Roles: Leader Follower Candidate Initiate and coordinate Leader Election Was previously a Follower

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Terms: election normal operation Term 1 Term 2 Term 3 Term 4 no emerging leader

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Leader Follower Candidate

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Leader Follower Candidate times out, starts election

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Leader Follower Candidate

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Leader Follower Candidate times out, new election

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Leader Follower Candidate

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Leader Follower Candidate receives votes from majority of servers

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Leader Follower Candidate

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Leader Follower Candidate discover server with higher term

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Leader Follower Candidate

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Leader Follower Candidate discover current leader or higher term

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Leader Follower Candidate

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Potential Use Cases: Distributed Lock Manager Database Transactions Automated Failover Conﬁguration Management http://coreos.com/blog/distributed-conﬁguration-with-etcd/index.html Service Discovery etc...

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Rafter github.com/andrewjstone/rafter

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•A labor of love, a work in progress •A library for building strongly consistent distributed systems in Erlang •Implements the raft consensus protocol in Erlang •Fundamental abstraction is the replicated log What:

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Replicated Log •API operates on log entries •Log entries contain commands •Commands are transparent to Rafter •Systems build on top of rafter with pluggable state machines that process commands upon log entry commit.

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Erlang

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Erlang: A Concurrent Language •Processes are the fundamental abstraction •Processes can only communicate by sending each other messages •Processes do not share state •Processes are managed by supervisor processes in a hierarchy

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Erlang: A Concurrent Language loop() -‐> receive {From, Msg} -‐> From ! Msg, loop() end. %% Spawn 100,000 echo servers Pids = [spawn(fun loop/0) || _ <-‐ lists:seq(1,100000)] %% Send a message to the first process lists:nth(0, Pids) ! {self(), ayo}.

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Erlang: A Functional Language • Single Assignment Variables • Tail-Recursion • Pattern Matching {op, {set, Key, Val}} = {op, {set, <<“job”>>, <<“developer”>>}} • Bit Syntax Header = <>

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Erlang: A Distributed Language Location Transparency: Processes can send messages to other processes without having to know if the other process is local. %% Send to a local gen_server process gen_server:cast(peer1, do_something) %% Send to a gen_server on another machine gen_server:cast({‘[email protected]’}, do_something) %% wrapped in a function with a variable name for a clean client API do_something(Name) -‐> gen_server:cast(Name, do_something). %% Using the API Result = do_something(peer1).

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Erlang: A Reliable Language •Erlang embraces “Fail-Fast” •Code for the good case. Fail otherwise. •Supervisors relaunch failed processes •Links and Monitors alert other processes of failure •Avoids coding most error paths and helps prevent logic errors from propagating

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OTP • OTP is a set of modules and standards that simpliﬁes the building of reliable, well engineered erlang applications. • The gen_server, gen_fsm and gen_event modules are the most important parts of OTP • They wrap processes as server “behaviors” in order to facilitate building common, standardized distributed applications that integrate well with the Erlang Runtime

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Implementation github.com/andrewjstone/rafter

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Peers •Each peer is made up of two supervised processes •A gen_fsm that implements the raft consensus fsm •A gen_server that wraps the persistent log •An API module hides the implementation

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Rafter API • The entire user api lives in rafter.erl • rafter:start_node(peer1, kv_sm). • rafter:set_conﬁg(peer1, [peer1, peer2, peer3, peer4, peer5]). • rafter:op(peer1, {set, <<“Omar”>>, <<“gonna get got”>>}). • rafter:op(peer1, {get, <<“Omar”>>}).

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Output State Machines •Commands are applied in order to each peer’s state machine as their entries are committed •All peers in a consensus group can only run one type of state machine passed in during start_node/2 •Each State machine must export apply/1

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Hypothetical KV store %% API kv_sm:set(Key, Val) -‐> Peer = get_local_peer(), rafter:op(Peer, {set, Key, Value}). %% State Machine callback kv_sm:apply({set, Key, Value}) -‐> ets:insert({kv_sm_store, {Key, Value}); kv_sm:apply({get, Key}) -‐> ets:lookup(kv_sm_store, Key).

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rafter_consensus_fsm •gen_fsm that implements Raft •3 states - follower, candidate, leader •Messages sent and received between fsm’s according to raft protocol •State handling functions pattern match on messages to simplify and shorten handler clauses.

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rafter_log.erl • Log API used by rafter_consensus_fsm and rafter_conﬁg • Utilizes Binary pattern matching for reading logs • Writes out entries to append only log. • State machine commands encoded with term_to_binary/1

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rafter_config.erl •Rafter handles dynamic reconfiguration of it’s clusters at runtime •Depending upon the configuration of the cluster, different code paths need navigating, such as whether a majority of votes has been received. •Instead of embedding this logic in the consensus fsm, it was abstracted out into a module of pure functions

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rafter_conﬁg.erl API -‐spec quorum_min(peer(), #config{}, dict()) -‐> non_neg_integer(). -‐spec has_vote(peer(), #config{}) -‐> boolean(). -‐spec allow_config(#config{}, list(peer())) -‐> boolean(). -‐spec voters(#config{}) -‐> list(peer()).

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Testing

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Property Based Testing •Use Erlang QuickCheck •Too complex to get into now •Come hear me talk about it at Erlang Factory Lite in Berlin! shameless plug

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Other Raft Implementations https://ramcloud.stanford.edu/wiki/display/logcabin/LogCabin http://coreos.com/blog/distributed-conﬁguration-with-etcd/index.html https://github.com/benbjohnson/go-raft https://github.com/coreos/etcd

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github.com/andrewjstone/rafter

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Plugs Shameless (a few more)

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RICON West http://ricon.io/west.html Études for Erlang http://meetup.com/Erlang-NYC

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Andy Gross - Introducing us to Raft Diego Ongaro - writing Raft, clarifying Tom’s understanding, reviewing slides Chris Meiklejohn - http://thinkdistributed.io - being an inspiration Justin Sheehy - reviewing slides, correcting poor assumptions Reid Draper - helping rubber duck solutions Kelly McLaughlin - helping rubber duck solutions John Daily - for his consistent pedantry concerning Tom’s abuse of English Basho - letting us indulge our intellect on the company’s dime (we’re hiring) Thanks File

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Any and all questions can be sent to /dev/null @tsantero @andrew_j_stone