Raft is a distributed consensus algorithm that is designed to be easy to understand and is equivalent to Paxos in fault tolerance and performance. Verdi Raft is a verified implementation of Raft in Coq, constructed using the Verdi framework. Included is a verified fault-tolerant key-value store using Raft.
- Author(s):
- Justin Adsuara
- Steve Anton
- Ryan Doenges
- Karl Palmskog
- Pavel Panchekha
- Zachary Tatlock
- James R. Wilcox
- Doug Woos
- License: BSD 2-Clause "Simplified" license
- Compatible Coq versions: 8.14 or later
- Additional dependencies:
- Coq namespace:
VerdiRaft - Related publication(s):
Executable vard key-value store:
OCaml(4.02.3 or later)OCamlbuildverdi-runtimecheerios-runtime
Client for vard:
Integration testing of vard:
Unit testing of unverified vard code:
OUnit(2.0.0 or later)
We recommend installing the dependencies of Verdi Raft via opam:
opam repo add coq-extra-dev https://coq.inria.fr/opam/extra-dev
opam install coq-struct-tact coq-cheerios coq-verdiThen, run make in the root directory. This will compile the Raft
implementation and proof interfaces, and check all the proofs.
To speed up proof checking on multi-core machines, use make -jX,
where X is at least the number of cores on your machine.
To build the vard key-value store program in extraction/vard,
you first need to install its requirements. Then, run make vard
in the root directory. If the Coq implementation has been compiled
as above, this simply compiles the extracted OCaml code to a native
executable; otherwise, the implementation is extracted to OCaml and
compiled without checking any proofs.
The Raft and RaftProofs subdirectories of theories contain the implementation
and verification of Raft. For each proof interface file in Raft, there is a
corresponding proof file in RaftProofs. The files in the Raft
subdirectory include:
Raft.v: an implementation of Raft in VerdiRaftRefinementInterface.v: an application of the ghost-variable transformer to Raft which tracks several ghost variables used in the verification of RaftCommonTheorems.v: several useful theorems about functions used by the Raft implementationOneLeaderPerTermInterface: a statement of Raft's election safety property. See also the corresponding proof file inRaftProofs.CandidatesVoteForSelvesInterface.v,VotesCorrectInterface.v, andCroniesCorrectInterface.v: statements of properties used by the proofOneLeaderPerTermProof.v
LogMatchingInterface.v: a statement of Raft's log matching property. See alsoLogMatchingProof.vinRaftProofsLeaderSublogInterface.v,SortedInterface.v, andUniqueIndicesInterface.v: statements of properties used byLogMatchingProof.v
The file EndToEndLinearizability.v in RaftProofs uses the proofs of
all proof interfaces to show Raft's linearizability property.
vard is a simple key-value store implemented using
Verdi. vard is specified and verified against Verdi's state-machine
semantics in the VarD.v example system distributed with Verdi. When the Raft transformer
is applied, vard can be run as a strongly-consistent, fault-tolerant key-value store
along the lines of etcd.
After running make vard in the root directory, OCaml code for vard
is extracted, compiled, and linked against a Verdi shim and some vard-specific
serialization/debugging code, to produce a vard.native binary in extraction/vard.
Running make bench-vard in extraction/vard will produce some
benchmark numbers, which are largely meaningless on
localhost (multiple processes writing and fsync-ing to the same disk
and communicating over loopback doesn't accurately model real-world
use cases). Running make debug will get you a tmux session where
you can play around with a vard cluster in debug mode; look in
bench/vard.py for a simple Python vard client.
As the name suggests, vard is designed to be comparable to the etcd
key-value store (although it currently supports many fewer
features). To that end, we include a very simple etcd "client" which
can be used for benchmarking. Running make bench-etcd will run the
vard benchmarks against etcd (although see above for why these results
are not particularly meaningful). See below for instructions to run
both stores on a cluster in order to get a more useful performance
comparison.
vard accepts the following command-line options:
-me NAME name for this node
-port PORT port for client commands
-dbpath DIRECTORY directory for storing database files
-node NAME,IP:PORT node in the cluster
-debug run in debug mode
Note that vard node names are integers starting from 0.
For example, to run vard on a cluster with IP addresses
192.168.0.1, 192.168.0.2, 192.168.0.3, client (input) port 8000,
and port 9000 for inter-node communication, use the following:
# on 192.168.0.1
$ ./vard.native -dbpath /tmp/vard-8000 -port 8000 -me 0 -node 0,192.168.0.1:9000 \
-node 1,192.168.0.2:9000 -node 2,192.168.0.3:9000
# on 192.168.0.2
$ ./vard.native -dbpath /tmp/vard-8000 -port 8000 -me 1 -node 0,192.168.0.1:9000 \
-node 1,192.168.0.2:9000 -node 2,192.168.0.3:9000
# on 192.168.0.3
$ ./vard.native -dbpath /tmp/vard-8000 -port 8000 -me 2 -node 0,192.168.0.1:9000 \
-node 1,192.168.0.2:9000 -node 2,192.168.0.3:9000
When the cluster is set up, a benchmark can be run as follows:
# on the client machine
$ python2 bench/setup.py --service vard --keys 50 \
--cluster "192.168.0.1:8000,192.168.0.2:8000,192.168.0.3:8000"
$ python2 bench/bench.py --service vard --keys 50 \
--cluster "192.168.0.1:8000,192.168.0.2:8000,192.168.0.3:8000" \
--threads 8 --requests 100
We can compare numbers for vard and etcd running on the same cluster as
follows:
# on 192.168.0.1
$ etcd --name=one \
--listen-client-urls http://192.168.0.1:8000 \
--advertise-client-urls http://192.168.0.1:8000 \
--initial-advertise-peer-urls http://192.168.0.1:9000 \
--listen-peer-urls http://192.168.0.1:9000 \
--data-dir=/tmp/etcd \
--initial-cluster "one=http://192.168.0.1:9000,two=http://192.168.0.2:9000,three=http://192.168.0.3:9000"
# on 192.168.0.2
$ etcd --name=two \
--listen-client-urls http://192.168.0.2:8000 \
--advertise-client-urls http://192.168.0.2:8000 \
--initial-advertise-peer-urls http://192.168.0.2:9000 \
--listen-peer-urls http://192.168.0.2:9000 \
--data-dir=/tmp/etcd \
--initial-cluster "one=http://192.168.0.1:9000,two=http://192.168.0.2:9000,three=http://192.168.0.3:9000"
# on 192.168.0.3
$ etcd --name=three \
--listen-client-urls http://192.168.0.3:8000 \
--advertise-client-urls http://192.168.0.3:8000 \
--initial-advertise-peer-urls http://192.168.0.3:9000 \
--listen-peer-urls http://192.168.0.3:9000 \
--data-dir=/tmp/etcd \
--initial-cluster "one=http://192.168.0.1:9000,two=http://192.168.0.2:9000,three=http://192.168.0.3:9000"
# on the client machine
$ python2 bench/setup.py --service etcd --keys 50 \
--cluster "192.168.0.1:8000,192.168.0.2:8000,192.168.0.3:8000"
$ python2 bench/bench.py --service etcd --keys 50 \
--cluster "192.168.0.1:8000,192.168.0.2:8000,192.168.0.3:8000" \
--threads 8 --requests 100
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