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Preserve Libby persona state and database preverify flow
2026-06-15 19:43:29 +01:00
<EFBFBD><EFBFBD>cultcache.store.v1<76><31><EFBFBD>!epiphany.repo_personality_profile<6C>!epiphany.repo_personality_profile<6C>$epiphany.repo_personality_profile.v1<76>!epiphany.repo_personality_profile<6C>v{"schemaName":"epiphany.repo_personality_profile","schemaVersion":"epiphany.repo_personality_profile.v1","members":[]}<7D><>!epiphany.repo_personality_profile<6C><65><EFBFBD>epiphany.repo_terrain_report<72>epiphany.repo_terrain_report<72>epiphany.repo_terrain_report.v1<76>epiphany.repo_terrain_report<72>l{"schemaName":"epiphany.repo_terrain_report","schemaVersion":"epiphany.repo_terrain_report.v1","members":[]}<7D><>epiphany.repo_terrain_report<72><74><EFBFBD>epiphany.repo_trajectory_report<72>epiphany.repo_trajectory_report<72>"epiphany.repo_trajectory_report.v1<76>epiphany.repo_trajectory_report<72>r{"schemaName":"epiphany.repo_trajectory_report","schemaVersion":"epiphany.repo_trajectory_report.v1","members":[]}<7D><>epiphany.repo_trajectory_report<72><74><EFBFBD>$epiphany.role_personality_projection<6F>$epiphany.role_personality_projection<6F>'epiphany.role_personality_projection.v1<76>$epiphany.role_personality_projection<6F>|{"schemaName":"epiphany.role_personality_projection","schemaVersion":"epiphany.role_personality_projection.v1","members":[]}<7D><>$epiphany.role_personality_projection<6F><6E><EFBFBD><EFBFBD>cultlib<69>!epiphany.repo_personality_profile<6C>2026-06-14T18:16:10Z<30>)<29><><EFBFBD>$epiphany.repo_personality_profile.v0<76>cultlib<69>CultLib projects as cult_protocol_storage + gamecult_web_lore_ops + unity_runtime_body with dominant pressures: contract_strictness:1.00, interface_orientation:1.00, runtime_proximity:1.00, content_canon_bias:0.90, sensory_salience:0.88, protocol_intolerance:0.85.<2E><>cult_protocol_storage<67>?<3F>O<EFBFBD>;dZ<1D>gamecult_web_lore_ops<70>?<3F>O<EFBFBD>;dZ<1D>unity_runtime_body<64>?<3F>O<EFBFBD>;dZ<1D><1B>actuation_risk<73>?<3F>9XbMӲaesthetic_appetite<74>?<3F><><EFBFBD>S<EFBFBD><53>ٱboundary_severity<74>?<3F>
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<EFBFBD><EFBFBD>consolidation<01>content<01>evidence<02>production <0B>protocol!<21><>'Make SoA cache-managed document storage<67>Add CultCache SoA happy path<74>,Keep Huginn runtime out of CultCache package<67>!Add C# CultMesh streaming surface<63>#Add CultMesh streaming mode surface<63>"Page CultCache MessagePack records<64>)Add Kotlin Eve media observation contract<63> Add Kotlin Eve surface contracts<74>$Add Kotlin CultMesh client substrate<74>$Document CultLib language workspaces<65>-Bring language packages into CultLib monorepo<70>$Verify Rust geometry payload interop<6F><1B>actuation_risk<73>?<3F>9XbMӲaesthetic_appetite<74>?<3F><><EFBFBD>S<EFBFBD><53>ٱboundary_severity<74>?<3F>
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## Repository Guidance
- Refrain from commenting on current changes in README files unless the user explicitly asks for that commentary.
- Treat clean API surface as a core project priority.
- Favor developer ergonomics in public APIs, examples, naming, defaults, and documentation.
- Prefer interfaces and behaviors that are predictable, easy to discover, and easy to integrate correctly.
- Keep public abstractions small and coherent; avoid exposing internal workflow complexity through the API when it can be encapsulated.
- When adding documentation or examples, optimize for practical usage by downstream developers.
- When proposing or implementing changes, consider how they affect usability, readability, and maintenance for library consumers.
<EFBFBD><EFBFBD>README.md<6D>readme<6D>2<EFBFBD><32><EFBFBD>#(# CultLib
CultLib is a set of reusable C# libraries for game backends, game-adjacent services, and Unity-integrated tooling, including a declarative runtime UI composition framework for Unity.
The libraries cover three main areas:
- logging primitives and implementations
- a typed in-memory cache with pluggable persistence
- LiteNetLib-based networking with encrypted credential exchange and signed session tokens
- CultMesh distributed realtime database and simulation-consensus primitives
- typed geometry domain/chunk documents for distributed CSG and LOD streaming
- declarative Unity UI composition and reflective runtime inspector tooling
## Which Package Do I Want?
If the job is shared game state, start here before inventing a fourth drawer
and pretending the label makes it furniture.
| Job | Start With | Owns | Use When | Do Not Use It For |
| --- | --- | --- | --- | --- |
| Local typed state | `GameCult.Caching` / CultCache | Document identity, schema compatibility, record keys, indexes, globals, and local persistence | You need a typed cache, file-compatible save data, local reactive reads, or a stable domain document model | Peer discovery, transport security, shard routing, or mesh consensus |
| Procedural geometry state | `GameCult.Geometry` | CultCache-native domain trees, LOD build requests, selected-cut diagnostics, and chunk artifact payloads | Rust, Unity, or remote workers need to share CSG/LOD geometry and graph metadata as typed state | Transport policy, peer discovery, or gameplay authority |
| Network transport and database plumbing | `GameCult.Networking` / CultNet | LiteNetLib transport, authentication, schema-v0 wire contracts, shard authority, raw document replication, snapshots, and subscriptions | You need a client/server pipe, login/session flow, schema discovery, or a low-level distributed CultCache lane | Gameplay-facing mesh ergonomics, Verse policy, mod branches, or simulation consensus composition |
| Distributed realtime gameplay state | `GameCult.Mesh` / CultMesh | Public mesh entrypoints, Verse discovery, peer exchange, shard replication defaults, authority leases, client prediction, and witness consensus | You want the game to treat clients and servers as one reactive database for persistent state, input state, and simulation facts | A tiny local-only tool, a bare transport client, or a storage format contract |
| Realtime media/frame streams | `GameCult.Mesh` / CultMesh streaming mode | Stream identity, authority, clock metadata, body transport negotiation, frame cursors, and backpressure state | Audio/video/tensor frames need to move between runtimes through shared memory, GPU handles, platform buffers, or CultCache page refs | Durable document mutation, mesh consensus facts, or pretending inline bytes are zero-copy |
Quick rule:
- Choose CultCache when the problem is "how do I model and persist typed state?"
- Choose GameCult.Geometry when the problem is "how do geometry workers share
domain trees, LOD build requests, and mesh chunks as typed state?"
- Choose CultNet when the problem is "how do peers exchange authenticated,
schema-aware database messages?"
- Choose CultMesh when the problem is "how does a game join a Verse and share
realtime state across a mesh?"
- Choose CultMesh streaming mode when the problem is "how do these runtimes move
audio/video frames while the mesh owns identity, clocks, cursors, and pressure?"
CultMesh sits on CultNet, and CultNet distributes CultCache documents. That is
the stack. If a design needs another peer-to-peer category, first check whether
it is really Verse policy, peer exchange, or shard authority wearing a fake
mustache.
If you want the smallest durable "open node -> put typed doc -> get typed doc"
path, start with
[`src/GameCult.Mesh/docs/durable-node-quickstart.md`](src/GameCult.Mesh/docs/durable-node-quickstart.md).
If you want the lower-level "typed local document -> raw wire document ->
mesh/runtime watch surface" handoff, follow it with
[`src/GameCult.Mesh/docs/typed-document-path.md`](src/GameCult.Mesh/docs/typed-document-path.md).
## Repository Scope
The solution includes:
- `GameCult.Logging`: common logging abstraction plus console and file implementations
- `GameCult.Caching`: `DatabaseEntry`-based cache, indexes, global entries, and backing-store abstractions
- `GameCult.Caching.MessagePack`: MessagePack-backed persistence for the cache
- `GameCult.Caching.NewtonsoftJson`: Newtonsoft.Json-backed persistence for the cache
- `GameCult.Caching.MessagePack.Generator`: source generator for MessagePack formatters for cache models
- `GameCult.Caching.MessagePack.Analyzers`: packaging project that delivers the generator to consuming projects
- `GameCult.Geometry`: CultCache-native geometry domain, selected-cut, and chunk artifact documents for VibeGeometry/Fensalir-style pipelines
- `GameCult.Networking`: encrypted login/register/verify flows and message dispatch over LiteNetLib
- `GameCult.Mesh`: CultMesh package home for distributed realtime database, shard replication, client prediction, Verse discovery, and mesh witness consensus
- `GameCult.Caching.Tests`: NUnit tests for cache and backing-store behavior
- `GameCult.Networking.Tests`: NUnit tests for networking behavior
- `GameCult.Unity`: CultUI, a Unity runtime UI composition framework with reflective inspector generation, prefab-backed field resolvers, reusable controls, and a demo project packaged for UPM-style consumption
- `packages/cultcache-ts`: TypeScript CultCache with MessagePack persistence and inspector tooling
- `packages/cultnet-ts`: TypeScript CultNet schema-v0 contracts, framing, discovery, raw document replication, and interop tests
- `packages/cultmesh-ts`: TypeScript CultMesh local node and mesh catalog surface for local runtimes such as VoidBot
- `packages/cultcache-rs`: Rust CultCache and derive macro
- `packages/cultnet-rs`: Rust CultNet contracts, framing, discovery, and interop peer
## Repository Layout
```text
package.json
src/
GameCult.Logging/
GameCult.Caching/
GameCult.Caching.MessagePack/
GameCult.Caching.NewtonsoftJson/
GameCult.Caching.MessagePack.Generator/
GameCult.Caching.MessagePack.Analyzers/
GameCult.Geometry/
GameCult.Networking/
GameCult.Mesh/
GameCult.Unity/
tests/
GameCult.Caching.Tests/
GameCult.Geometry.Tests/
GameCult.Networking.Tests/
packages/
cultcache-ts/
cultnet-ts/
cultmesh-ts/
cultcache-rs/
cultnet-rs/
```
## Build
```powershell
dotnet build CultLib.sln
```
## Test
```powershell
dotnet test CultLib.sln
```
TypeScript package tests:
```powershell
npm test --workspace packages/cultcache-ts
npm test --workspace packages/cultnet-ts
npm run test:interop --workspace packages/cultnet-ts
npm test --workspace packages/cultmesh-ts
```
Rust package tests:
```powershell
cargo test --manifest-path packages/cultcache-rs/Cargo.toml
cargo test --manifest-path packages/cultnet-rs/Cargo.toml
```
## Common Concepts
### `DatabaseEntry`
The cache-centric libraries revolve around `DatabaseEntry`. Every entry has a stable `Guid` identifier and can optionally:
- expose a human-readable name through `INamedEntry`
- participate in generic indexes registered at runtime
- be treated as a global singleton entry through `GlobalSettingsAttribute`
Typical entry shape:
```csharp
using GameCult.Caching;
public class ItemData : DatabaseEntry, INamedEntry
{
public string Name = string.Empty;
public int Value;
public string EntryName
{
get => Name;
set => Name = value;
}
}
```
### `CultCache` and Backing Stores
`CultCache` is an in-memory index over `DatabaseEntry` objects. It can operate entirely in memory, or it can be attached to one or more backing stores for persistence and synchronization.
- the cache is the query surface
- backing stores are persistence adapters
- indexes and name maps are maintained inside the cache, not inside the store
### Important: Multiple Backing Stores
When multiple backing stores are added, behavior depends on how they are registered.
If a store is added with domain types:
```csharp
cache.AddBackingStore(playerStore, typeof(PlayerData));
cache.AddBackingStore(settingsStore, typeof(AppSettings));
```
then that store becomes the direct persistence target for those types.
If a store is added without domain types:
```csharp
cache.AddBackingStore(primaryStore);
cache.AddBackingStore(mirrorStore);
```
then the first generic store acts as the primary writable store for non-domain-specific entries. Additional generic stores subscribe to the existing stores and mirror their change events.
Implications:
- order matters for generic stores
- `AddAsync` writes to the type-specific store when one exists
- otherwise `AddAsync` writes to the first generic store
- later generic stores do not become co-primaries; they mirror earlier s<><73>notes/operator-interventions.md<6D>documentation<6F><01><><EFBFBD><01># Operator Interventions
## 2026-05-08
- External Codex session from `E:\Projects\EpiphanyAgent` added
`src/GameCult.Caching/Contracts/cultcache-persistence-format.md`.
- Intent: write down the next canonical CultCache persistence-format design in
the C# source-of-truth repo before Rust/TS implementations harden the wrong
shape.
- Existing unrelated worktree changes in `src/GameCult.Networking/PlayerData.cs`
and `src/GameCult.Unity/Assets/UI/Default Resolver.asset` were left alone.
<EFBFBD><EFBFBD>.research/cultnet-distributed-database/swim.pdf<64>research<63><07><><EFBFBD>&<26>%PDF-1.2 %<25><><EFBFBD><EFBFBD>
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Purpose: ground CultNet's distributed realtime database design in prior art
before adding more machinery. The goal is a coherent local-first mesh database
over CultCache, not a hand-rolled distributed-systems costume party.
## Sources Stored Here
- `hashgraph-swirlds-tr-2016-01.pdf`
- Source: <https://www.swirlds.com/downloads/SWIRLDS-TR-2016-01.pdf>
- Topic: gossip-about-gossip, virtual voting, fair ordering, asynchronous BFT.
- `raft-extended.html`
- Source: <https://yygcode.com/papers/consensus-raft-extended-version.html>
- Topic: understandable consensus, leader election, log replication, safety.
- `raft-extended.pdf`
- Source: <https://web.stanford.edu/~ouster/cgi-bin/papers/raft-extended.pdf>
- Topic: canonical Raft paper copy.
- `dynamo-amazon-science.html`
- Source: <https://www.amazon.science/publications/dynamo-amazons-highly-available-key-value-store>
- Topic: Dynamo publication page and abstract.
- `dynamo-sosp2007.pdf`
- Source: <https://web.stanford.edu/class/cs244/papers/amazon-dynamo-sosp2007.pdf>
- Topic: highly available key-value store, consistent hashing, versioning,
quorums, hinted handoff, anti-entropy.
- `swim.pdf`
- Source: <https://www.cs.cornell.edu/projects/quicksilver/public_pdfs/SWIM.pdf>
- Topic: scalable weakly consistent process-group membership.
- `rethinkdb-changefeeds.html`
- Source: <https://rethinkdb.com/docs/changefeeds/java/>
- Topic: realtime query/changefeed ergonomics.
- `firebase-realtime-offline.html`
- Source: <https://firebase.google.com/docs/database/web/offline-capabilities>
- Topic: offline behavior, presence, server-side disconnect operations.
- `crdt-arxiv-1805.06358.html`
- Source: <https://arxiv.org/abs/1805.06358>
- Topic: conflict-free replicated data types and deterministic convergence.
## Design Takeaways
### RethinkDB
Keep: changefeeds as the product feel. Subscribers should receive document
changes continuously, with enough old/new context to render, reconcile, or
debug. Point subscriptions and filtered subscriptions are both first-class.
CultNet implication: database subscriptions should be explicit schema-v0
messages. The server should stream raw document changes that clients can apply
through the same CultCache reconciliation path as snapshots.
### Firebase Realtime Database
Keep: realtime sync and local/offline ergonomics. Presence and disconnect
behavior are database features, not application afterthoughts.
Defer: full offline writes. CultNet should not pretend arbitrary offline
multi-writer changes merge safely. Offline/local-first behavior needs declared
per-document conflict policy.
CultNet implication: add presence/disconnect records later as ordinary
CultCache documents with server authority, not as hidden transport state.
### Raft
Keep: the understandable authority model. One leader/primary owns ordered
writes for a shard. Decompose the problem into ownership, log/mutation
replication, and safety.
Defer: full automatic leader election and replicated logs until shard catalogs
and explicit epochs exist.
CultNet implication: the current primary-shard policy is the correct first
foundation. Every write should either hit the primary, be forwarded to the
primary, or fail with routing information.
### Dynamo
Keep: partitioning, replication metadata, vector-ish causality, hinted recovery,
anti-entropy, and application-visible conflicts.
Reject for now: "always writeable" semantics. That is attractive, expensive,
and easy to lie about.
CultNet implication: shard descriptors should grow into a shard catalog with
owner runtime id, epoch, schema/key ranges, and later replica/preference-list
metadata. Conflicts must surface as data, not disappear into last-writer-wins
unless a document explicitly chose that policy.
### SWIM
Keep: gossip-shaped membership once the mesh grows beyond a small static
cluster. SWIM separates failure detection from membership dissemination and
keeps per-node message load stable.
Defer: membership implementation until there is a shard catalog to disseminate.
CultNet implication: do not build peer discovery as a side-channel registry.
When it arrives, it should update membership and shard-catalog state together.
### CRDTs
Keep: CRDTs for documents whose merge law is explicit and deterministic.
Reject: generic automatic merge for arbitrary domain objects. That is a
language cop with a nicer hat.
CultNet implication: CRDT support belongs in schema metadata or document
contract metadata. A document type can opt into a known merge strategy; the
default distributed write policy remains primary authority.
### Hashgraph
Keep: gossip-about-gossip as an idea for compact event provenance and possible
fair ordering research. Virtual voting is interesting when every member sees
the same gossip history.
Reject for now: adopting hashgraph consensus as CultNet's core. The public
ledger/crypto smell is not the main issue; the issue is that CultNet does not
currently need asynchronous Byzantine total ordering to become a good mesh
database. Dragging that in now would make the machine harder to explain.
CultNet implication: if we later need decentralized event ordering, use a
dedicated design pass. Do not smuggle hashgraph metadata into ordinary document
replication because it sounds powerful.
## Current CultNet Direction
The coherent path is:
1. Primary-shard authority.
2. Explicit schema-v0 snapshot, put, delete, subscribe, unsubscribe, and change
messages.
3. Shard catalog exchange with owner runtime id and epoch.
4. Optional forwarding from non-owner nodes to owners.
5. Membership/failure detection, likely SWIM-shaped.
6. Replication and failover, likely Raft-shaped per shard.
7. Optional CRDT policies for document types that deserve offline multi-writer
semantics.
8. Optional gossip-history research if fair decentralized ordering becomes a
real requirement.
## Live Invariants
- CultCache owns document identity, schema compatibility, local indexes, and
reconciliation.
- CultNet owns transport, shard authority, subscriptions, and remote mutation
delivery.
- Raw wire records must pass through `CultNetDatabase` before mutating local
cache state.
- A write without authority is rejected or explicitly forwarded. It is never
silently applied.
- Realtime change streams publish domain changes, not storage implementation
details.
- Conflict policy must be declared before conflicting writes are accepted.
## Current Cut
Shard catalog exchange now exists:
- `cultnet.shard_catalog_request.v0`
- `cultnet.shard_catalog_response.v0`
- per-shard id, owner runtime id, epoch, schema ids, key prefix/range
- stale epoch rejection on writes
- routing error that tells clients where the primary lives when known
- injectable non-primary write forwarding policy
- concrete schema-v0 write forwarder for `cultnet://host:port` primary
endpoints
- in-memory per-shard mutation logs and catch-up by last seen sequence
- wire-level shard mutation log catch-up with raw put/delete entries
- replica-side application of shard log responses with epoch checks, gap
rejection, and idempotent replay
- background shard log replicator plus schema-v0 fetcher for primary endpoints
- restart-safe replica cursor store with a local MessagePack file
implementation
- client authority scopes for predicted local input documents
- predicted/reconciled change events for client-side prediction
- simulation witness observations and deterministic consensus candidates
- schema-v0 simulation observation and candidate messages
- reactive observation hub for witness gossip and consensus candidates
- server-side observation bridge for observation messages and candidate replies
## Next Cut
Build durable authoritative mutation logs:
- persist per-shard primary logs
- define the snapshot fallback boundary for compacted history
- return explicit resync-required responses when requested history is gone
- then add simulation-frame rollback and resimulation helpers on top of
predicted input streams
- add peer-to-peer fanout for observation gossip and candidate propagation
That is the next foundation needed before membership, Raft-style failover, or
CRDT policy work can be honest.
<EFBFBD><EFBFBD>7research/cultnet-distributed-database/raft-extended.pdf<64>research<63><08>Z<EFBFBD><5A>:4%PDF-1.4 %<25><><EFBFBD><EFBFBD>
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<h1 class="title">In Search of an Understandable Consensus Algorithm (Extended Version)</h1>
<div id="table-of-contents">
<h2>Table of Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a href="#orgf960eb7">DECLARATION</a></li>
<li><a href="#orgd05d3d0">Abstract</a></li>
<li><a href="#orge9839bb">1. Introduction</a></li>
<li><a href="#orgae8bb96">2. Replicated state machines</a></li>
<li><a href="#org1fe8017">3. What's wrong with Paxos?</a></li>
<li><a href="#orgfc9c6b4">4. Designing for understandability</a></li>
<li><a href="#orge3d8f10">5. The Raft consensus algorithm</a>
<ul>
<li><a href="#org752f98e">5.1. Raft basics</a></li>
<li><a href="#org291797f">5.2. Leader election</a></li>
<li><a href="#org44bb0a6">5.3. Log replication</a></li>
</ul>
</li>
</ul>
</div>
</div>
<div id="outline-container-orgf960eb7" class="outline-2">
<h2 id="orgf960eb7">DECLARATION</h2>
<div class="outline-text-2" id="text-orgf960eb7">
<p>
This page is the In Search of an Understandable Consensus Algorithm
(Extended Version) paper. Original Paper Link
is: <a href="https://raft.github.io/raft.pdf">https://raft.github.io/raft.pdf</a>
</p>
<p>
In Search of an Understandable Consensus Algorithm<br />
(Extended Version)<br />
Diego Ongaro and John Ousterhout<br />
Stanford University
</p>
</div>
</div>
<div id="outline-container-orgd05d3d0" class="outline-2">
<h2 id="orgd05d3d0">Abstract</h2>
<div class="outline-text-2" id="text-orgd05d3d0">
<p>
Raft is a consensus algorithm for managing a replicated log. It produces a
result equivalent to (multi-)Paxos, and it is as efficient as Paxos, but its
structure is different from Paxos; this makes Raft more understandable than
Paxos and also provides a better foundation for building practical systems.
In order to enhance understandability, Raft separates the key elements of
consensus, such as leader election, log replication, and safety, and it enforces
a stronger degree of coherency to reduce the number of states that must be
considered. Results from a user study demonstrate that Raft is easier for
students to learn than Paxos. Raft also includes a new mechanism for changing
the cluster membership, which uses overlapping majorities to guarantee safety.
</p>
</div>
</div>
<div id="outline-container-orge9839bb" class="outline-2">
<h2 id="orge9839bb"><span class="section-number-2">1</span> Introduction</h2>
<div class="outline-text-2" id="text-1">
<p>
Consensus algorithms allow a collection of machines to work as a coherent group
that can survive the failures of some of its members. Because of this, they play
a key role in building reliable large-scale software systems. Paxos [15, 16] has
dominated the discussion of consensus algorithms over the last decade: most
implementations of consensus are based on Paxos or influenced by it, and Paxos
has become the primary vehicle used to teach students about consensus.
</p>
<p>
Unfortunately, Paxos is quite difficult to understand, in spite of numerous
attempts to make it more approachable. Furthermore, its architecture requires
complex changes to support practical systems. As a result, both system builders
and students struggle with Paxos.
</p>
<p>
After struggling with Paxos ourselves, we set out to find a new consensus
algorithm that could provide a better foundation for system building and
education. Our approach was unusual in that our primary goal was
understandability: could we define a consensus algorithm for practical systems
and describe it in a way that is significantly easier to learn than Paxos?
Furthermore, we wanted the algorithm to facilitate the development of intuitions
that are essential for system builders. It was important not just for the
algorithm to work, but for it to be obvious why it works.
</p>
<p>
The result of this work is a consensus algorithm called Raft. In designing Raft
we applied specific techniques to improve understandability,including
decomposition (Raft separates leader election, log replication, and safety) and
This tech report is an extended version of [32]; additional material is noted
with a gray bar in the margin. Published May 20, 2014. state space reduction
(relative to Paxos, Raft reduces the degree of nondeterminism and the ways
servers can be inconsistent with each other). A user study with 43 students
at two universities shows that Raft is significantly easier to understand than
Paxos: after learning both algorithms, 33 of these students were able to answer
questions about Raft better than questions about Paxos.
</p>
<p>
Raft is similar in many ways to existing consensus algorithms (most notably, Oki
and Liskov's Viewstamped Replication [29, 22]), but it has several novel
features:
</p>
<p>
1/. Strong leader: Raft uses a stronger form of leadership than other consensus
algorithms. For example, log entries only flow from the leader to other servers.
This simplifies the management of the replicated log and makes Raft easier to
understand.
</p>
<p>
2/. Leader election: Raft uses randomized timers to elect leaders. This adds
only a small amount of mechanism to the heartbeats already required for any
consensus algorithm, while resolving conflicts simply and rapidly.
</p>
<p>
3/. Membership changes: Raft's mechanism for changing the set of servers in the
cluster uses a new joint consensus approach where the majorities of two
different configurations overlap during transitions. This allows the cluster to
continue operating normally during configuration changes.
</p>
<p>
We believe that Raft is superior to Paxos and other consensus algorithms, both
for educational purposes and as a foundation for implementation. It is simpler
and more understandable than other algorithms; it is described completely enough
to meet the needs of a practical system; it has several open-source
implementations and is used by several companies; its safety properties have
been formally specified and proven; and its efficiency is comparable to other
algorithms.
</p>
<p>
The remainder of the paper introduces the replicated state machine problem
(Section 2), discusses the strengths and weaknesses of Paxos (Section 3),
describes our general approach to understandability (Section 4), presents the
Raft consensus algorithm (Sections 5–8), evaluates Raft (Section 9), and
discusses related work (Section 10).
</p>
</div>
</div>
<div id="outline-container-orgae8bb96" class="outline-2">
<h2 id="orgae8bb96"><span class="section-number-2">2</span> Replicated state machines</h2>
<div class="outline-text-2" id="text-2">
<p>
Consensus algorithms typically arise in the context of replicated state machines
[37]. In this approach, state machines on a collection of servers compute
identical copies of the same state and can continue operating even if some of
the servers are down. Replicated state machines are used to solve a variety of
fault tolerance problems in distributed systems. For example, large-scale
systems that have a single cluster leader, such as GFS [8], HDFS [38], and
RAMCloud [33], typically use a separate replicated state machine to manage
leader election and store configuration information that must survive leader
crashes. Examples of replicated state machines include Chubby [2] and
ZooKeeper [11].
</p>
<div id="org1bdf0c5" class="figure">
<p><img src="img/raft-figure1.jpg" alt="raft-figure1.jpg" />
</p>
<p><span class="figure-number">Figure 1: </span>Replicated state machine architecture. The consensus algorithm manages a replicated log containing state machine commands from clients. The state machines process identical sequences of commands from the logs, so they produce the same outputs.</p>
</div>
<p>
Replicated state machines are typically implemented using a replicated log, as
shown in Figure 1. Each server s<><73>9research/cultnet-distributed-database/dynamo-sosp2007.pdf<64>research<63> <0A>t<EFBFBD><74>-%PDF-1.3 %<25><><EFBFBD><EFBFBD>
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<head> <title>[1805.06358] Conflict-free Replicated Data Types (CRDTs)</title>
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<meta name="description" content="Abstract page for arXiv paper 1805.06358: Conflict-free Replicated Data Types (CRDTs)"><meta property="og:type" content="website" />
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<meta property="og:title" content="Conflict-free Replicated Data Types (CRDTs)" />
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<meta property="og:description" content="A conflict-free replicated data type (CRDT) is an abstract data type, with a well defined interface, designed to be replicated at multiple processes and exhibiting the following properties: (1) any replica can be modified without coordinating with another replicas; (2) when any two replicas have received the same set of updates, they reach the same state, deterministically, by adopting mathematically sound rules to guarantee state convergence."/>
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