A Raft Implementation for Erlang and Elixir

Ra is a Raft implementation by Team RabbitMQ. It is not tied to RabbitMQ and can be used in any Erlang or Elixir project. It is, however, heavily inspired by and geared towards RabbitMQ needs.

Ra (by virtue of being a Raft implementation) is a library that allows users to implement persistent, fault-tolerant and replicated state machines.

Project Maturity

This library has been extensively tested and is suitable for production use. This means the primary APIs (ra, ra_machine modules) and on disk formats will be backwards-compatible going forwards in line with Semantic Versioning. Care has been taken to version all on-disk data formats to enable frictionless future upgrades.

Status

The following Raft features are implemented:

• Leader election
• Log replication
• Cluster membership changes: one server (member) at a time
• Log compaction (with limitations and RabbitMQ-specific extensions)
• Snapshot installation

Build Status

Supported Erlang/OTP Versions

Ra supports the following Erlang/OTP versions:

• 24.x
• 23.x

Modern Erlang releases provide distribution traffic fragmentation which algorithms such as Raft significantly benefit from.

Design Goals

• Low footprint: use as few resources as possible, avoid process tree explosion
• Able to run thousands of ra clusters within an Erlang node
• Provide adequate performance for use as a basis for a distributed data service

Use Cases

This library is primarily developed as the foundation for replication layer for replicated queues in a future version of RabbitMQ. The design it aims to replace uses a variant of Chain Based Replication which has two major shortcomings:

• Replication algorithm is linear
• Failure recovery procedure requires expensive topology changes

Smallest Possible Usage Example

The example below assumes a few things:

• You are familiar with the basics of distributed Erlang
• Three Erlang nodes are started on the local machine or reachable resolvable hosts. Their names are [email protected], [email protected], and [email protected] in the example below but your actual hostname will be different. Therefore the naming scheme is ra{N}@{hostname}. This is not a Ra requirement so you are welcome to use different node names and update the code accordingly.

Erlang nodes can be started using rebar3 shell --name {node name}. They will have Ra modules on code path:

# replace hostname.local with your actual hostname
rebar3 shell --name [email protected]

# replace hostname.local with your actual hostname
rebar3 shell --name [email protected]

# replace hostname.local with your actual hostname
rebar3 shell --name [email protected]

After Ra nodes form a cluster, state machine commands can be performed.

Here's what a small example looks like:

%% All servers in a Ra cluster are named processes on Erlang nodes.
%% The Erlang nodes must have distribution enabled and be able to
%% communicate with each other.
%% See https://learnyousomeerlang.com/distribunomicon if you are new to Erlang/OTP.

%% These Erlang nodes will host Ra nodes. They are the "seed" and assumed to
%% be running or come online shortly after Ra cluster formation is started with ra:start_cluster/4.
ErlangNodes = ['[email protected]', '[email protected]', '[email protected]'],

%% This will check for Erlang distribution connectivity. If Erlang nodes
%% cannot communicate with each other, Ra nodes would not be able to cluster or communicate
%% either.
[io:format("Attempting to communicate with node ~s, response: ~s~n", [N, net_adm:ping(N)]) || N <- ErlangNodes],

%% The Ra application has to be started on all nodes before it can be used.
[rpc:call(N, ra, start, []) || N <- ErlangNodes],

%% Create some Ra server IDs to pass to the configuration. These IDs will be
%% used to address Ra nodes in Ra API functions.
ServerIds = [{quick_start, N} || N <- ErlangNodes],

ClusterName = quick_start,
%% State machine that implements the logic
Machine = {simple, fun erlang:'+'/2, 0},

%% Start a Ra cluster  with an addition state machine that has an initial state of 0.
%% It's sufficient to invoke this function only on one Erlang node. For example, this
%% can be a "designated seed" node or the node that was first to start and did not discover
%% any peers after a few retries.
%%
%% Repeated startup attempts will fail even if the cluster is formed, has elected a leader
%% and is fully functional.
{ok, ServersStarted, _ServersNotStarted} = ra:start_cluster(default, ClusterName, Machine, ServerIds),

%% Add a number to the state machine.
%% Simple state machines always return the full state after each operation.
{ok, StateMachineResult, LeaderId} = ra:process_command(hd(ServersStarted), 5),

%% Use the leader id from the last command result for the next one
{ok, 12, LeaderId1} = ra:process_command(LeaderId, 7).

Dynamically Changing Cluster Membership

Nodes can be added to or removed from a Ra cluster dynamically. Only one cluster membership change at a time is allowed: concurrent changes will be rejected by design.

In this example, instead of starting a "pre-formed" cluster, a local server is started and then members are added by calling ra:add_member/2.

Start 3 Erlang nodes:

# replace hostname.local with your actual hostname
rebar3 shell --name [email protected]

# replace hostname.local with your actual hostname
rebar3 shell --name [email protected]

# replace hostname.local with your actual hostname
rebar3 shell --name [email protected]

Start the ra application:

([email protected])1> ra:start().
% => ok

([email protected])1> ra:start().
% => ok

([email protected])1> ra:start().
% => ok

A single node cluster can be started from any node.

For the purpose of this example, [email protected] is used as the starting member:

ClusterName = dyn_members,
Machine = {simple, fun erlang:'+'/2, 0},

% Start a cluster
{ok, _, _} =  ra:start_cluster(default, ClusterName, Machine, [{dyn_members, '[email protected]'}]).

After the cluster is formed, members can be added.

Add [email protected] to the cluster:

% Add member
{ok, _, _} = ra:add_member({dyn_members, '[email protected]'}, {dyn_members, '[email protected]'}),

% Start the server
ok = ra:start_server(default, ClusterName, {dyn_members, '[email protected]'}, Machine, [{dyn_members, '[email protected]'}]).

Add [email protected] to the cluster:

% Add member
{ok, _, _} = ra:add_member({dyn_members, '[email protected]'}, {dyn_members, '[email protected]'}),

% Start the server
ok = ra:start_server(default, ClusterName, {dyn_members, '[email protected]'}, Machine, [{dyn_members, '[email protected]'}]).

Check the members from any node:

([email protected])2> ra:members({dyn_members, node()}).
% => {ok,[{dyn_members,'[email protected]'},
% =>      {dyn_members,'[email protected]'},
% =>      {dyn_members,'[email protected]'}],
% =>      {dyn_members,'[email protected]'}}

Other examples

See Ra state machine tutorial for how to write more sophisiticated state machines by implementing the ra_machine behaviour.

A Ra-based key/value store example is available in a separate repository.

Documentation

• API reference
• How to write a Ra state machine: Ra state machine tutorial
• Design and implementation details: Ra internals guide

Examples

• Ra-based key/value store

Configuration Reference

Key	Description	Data Type
data_dir	A directory name where Ra node will store its data	Local directory path
wal_data_dir	A directory name where Ra will store it's WAL (Write Ahead Log) data. If unspecified, `data_dir` is used.	Local directory path
wal_max_size_bytes	The maximum size of the WAL in bytes. Default: 512 MB	Positive integer
wal_max_entries	The maximum number of entries per WAL file. Default: undefined	Positive integer
wal_compute_checksums	Indicate whether the wal should compute and validate checksums. Default: `true`	Boolean
wal_write_strategy	default: used by default. write(2) system calls are delayed until a buffer is due to be flushed. Then it writes all the data in a single call then fsyncs. Fastest option but incurs some additional memory use. o_sync: Like default but will try to open the file with O_SYNC and thus wont need the additional fsync(2) system call. If it fails to open the file with this flag this mode falls back to default.	Enumeration: default | o_sync
wal_sync_method	datasync: used by default. Uses the fdatasync(2) system call after each batch. This avoids flushing file meta-data after each write batch and thus may be slightly faster than sync on some system. When datasync is configured the wal will try to pre-allocate the entire WAL file. Not all systems support fdatasync. Please consult system documentation and configure it to use sync instead if it is not supported. sync: uses the fsync system call after each batch.	Enumeration: datasync | sync
logger_module	Allows the configuration of a custom logger module. The default is logger. The module must implement a function of the same signature as logger:log/4 (the variant that takes a format not the variant that takes a function).	Atom
wal_max_batch_size	Controls the internal max batch size that the WAL will accept. Higher numbers may result in higher memory use. Default: 32768.	Positive integer
wal_hibernate_after	Enables hibernation after a timeout of inactivity for the WAL process.	Milliseconds
metrics_key	Metrics key. The key used to write metrics into the ra_metrics table.	Atom
low_priority_commands_flush_size	When commands are pipelined using the low priority mode Ra tries to hold them back in favour of normal priority commands. This setting determines the number of low priority commands that are added to the log each flush cycle. Default: 25	Positive integer

Logging

Ra will use default OTP logger by default, unless logger_module configuration key is used to override.

To change log level to debug for all applications, use

logger:set_primary_config(level, debug).

Copyright and License

(c) 2017-2021, VMware Inc or its affiliates.

Dual licensed under the Apache License Version 2.0 and Mozilla Public License Version 2.0.

This means that the user can consider the library to be licensed under any of the licenses from the list above. For example, you may choose the Apache Public License 2.0 and include this library into a commercial product.

See LICENSE for details.