# Gitaly High Availability (HA) Design
Gitaly Cluster is an active-active cluster configuration for resilient git operations. [Refer to our specific requirements](https://gitlab.com/gitlab-org/gitaly/issues/1332).

Refer to [epic &289][epic] for current issues and discussions revolving around
HA MVC development.

## Terminology
The following terminology may be used within the context of the Gitaly Cluster project:

- Shard - partition of the storage for all repos. Each shard will require redundancy in the form of multiple Gitaly nodes (at least 3 when optimal) to maintain HA.
- Praefect - a transparent front end to all Gitaly shards. This reverse proxy ensures that all gRPC calls are forwarded to the correct shard by consulting the coordinator. The reverse proxy also ensures that write actions are performed transactionally when needed.
    - etymology: from Latin praefectus for _a person appointed to any of various positions of command, authority, or superintendence, as a chief magistrate in ancient Rome or the chief administrative official of a department of France or Italy._
    - [pronounced _pree-fect_](https://www.youtube.com/watch?v=MHszCZjPmTQ)
- Node - This is the Gitaly service which performs the actual git read/write operations from/to disk. Has no knowledge of shards/praefects.
- RPC categories (#1496):
    - Accessor - a side effect free (or read-only) RPC; does not modify the git repo (!228)
    - Mutator - an RPC that modifies the data in the git repo (!228)
- Transaction - mechanism used to ensure that a set of voters agree on the same
  modifications.
    - Voter - a node registered in a transaction. Only registered voters may
      cast votes in transactions.
    - Vote - the change a voter intends to commit if the transaction succeeds.
      This is e.g. the hash of all references which are to be updated in their
      old and new state.
    - Quorum - minimum number of voters required to agree in order to commit a
      transaction.
    - Voting strategy - defines how many nodes are required to reach quorum.
        - strong - all nodes need to agree.
        - primary-wins - the transaction always succeeds as long as the primary
          has cast a vote.
        - majority-wins - the transaction succeeds when the primary and at least
          half of the secondaries agree.
    - Subtransactions - ordered list of voting processes of a transaction. For
      each vote cast by a voter, a new subtransaction is created. For a
      transaction to be successful, all subtransactions need to be successful.
      This is done so that Gitaly may perform multiple modifications in a single
      transaction.
    - reference-transaction - Git mechanism to update references. The
      [reference-transaction hook](https://git-scm.com/docs/githooks#_reference_transaction)
      directly hooks into this mechanism whenever a reference is being updated
      via Git.

## Design
The high level design takes a reverse proxy approach to fanning out write requests to the appropriate nodes:

<img src="https://docs.google.com/drawings/d/e/2PACX-1vRl7WS-6RBOWxyLSBbBBAoV9MupmTh5vTqMOw_AX9axlboqkybTbFqGqExLyyYOilqEW7S9euXdBHzX/pub?w=960&amp;h=720">

## Phases
An iterative low risk approach needs to be devised to add functionality and verify assumptions at a sustainable pace while not impeding the existing functionality.

### 1. Simple pass-through proxy - no added functionality
- allows us to set up telemetry for observability of new service
- allows us to evaluate a gRPC proxy library

### 2. Introduce State
The following details need to be persisted in Postgres:
- [x] Primary location for a project
- [ ] Redundant locations for a project
- [ ] Available storage locations (initially can be configuration file)

Initially, the state of the shard nodes will be static and loaded from a configuration file. Eventually, this will be made dynamic via a data store (Postgres).

### Resolving Location
The following existing interaction will remain intact for the first iteration of the HA feature:

```mermaid
sequenceDiagram
    Client->>Rails: Modify repo X
	Rails-->>Datastore: Where is Repo X?
	Datastore-->> Rails: Repo X is at location A
    Rails-->>Gitaly: Modify repo X at location A
	Gitaly-->>Rails: Request succeeded/failed
```

Once the Rails app has resolved the primary location for the project, the request is made to the praefect. The praefect then resolves the redundant locations via the coordinator before applying the changes.

```mermaid
sequenceDiagram
	Rails->>Praefect: Modify repo X at A
	Praefect->>Coordinator: Which locations complement A for X?
	Coordinator->>Praefect: Locations B and C complement A
	Praefect->>Nodes ABC: Modify repo X
	Nodes ABC->>Praefect: Modifications successful!
```

*Note: the above interaction between the praefect and nodes A-B-C is an all-or-nothing transaction. All nodes must complete in success, otherwise a single node failure will cause the entire transaction to fail. This will be improved when replication is introduced.*

### 3. Replication

Praefect relies on replication when a Gitaly RPC doesn't support transactions or
a repository replica needs to be repaired.

For transaction mutator RPCs, Praefect attempts to make the same change to a
quorum of repository replicas in a single transactional write. If a quorum of replicas
successfully applies the RPC, then replication will only be scheduled for any
replicas that were unsuccessful. See the section on [strong consistency
design](#strong-consistency-design) for more details.

```mermaid
sequenceDiagram
	Praefect->>Node A: Modify repo X
	Praefect->>Node B: Modify repo X
	Praefect->>Node C: Modify repo X
	Node A->>Praefect: Success :-)
	Node B->>Praefect: Success :-)
	Node C->>Praefect: FAILURE :'(
	Praefect->>Node C: Replicate From A
	Node C->>Praefect: Success!
```

When Praefect proxies a non-transactional mutator RPC, it will first route the
RPC to the current primary Gitaly for the given repository. Once the RPC
completes, Praefect will schedule replication of these changes from the primary
to all secondaries.

```mermaid
sequenceDiagram
	Praefect->>Node A: Modify repo X
	Node A->>Praefect: Success!
	Praefect->>Node B: Replicate From A
	Praefect->>Node C: Replicate From A
	Node B->>Praefect: Success!
	Node C->>Praefect: Success!
```

#### Replication Process

The actual replication process is still in active development. At the time of
this writing, the replication process looks like this:

1. Instruct the target Gitaly to replicate from the source Gitaly
   1. Does the target repository exist?
      - Yes: continue
      - No:
         1. Snapshot the repository from the source Gitaly
         1. Extract the snapshot to the target Gitaly
         1. Fetch changes from the source Gitaly
         1. Sync misc files (e.g. info attributes)
1. Does the source repository have an object pool?
   - No: continue
   - Yes:
      1. Get source repository object pool information
      1. Manipulate object pool to work for target repo
      1. Link target repo to manipulated object pool

##### Replication Process Concerns

The replication process has been tested in production and works well for small
repositories. For larger repositories, such as `www-gitlab-com` and
`gitlab-org/gitlab`, it starts to show signs of stress.

The snapshot process is very resource intensive for fork operations. When
snapshotting a large repo, you end up with n-1 (n == replica count) copies of
the repository being compressed and extracted to secondary replicas.

Adding to this stress is the constraint of storage limitations for gitlab.com
users. The GitLab handbook (`www-gitlab-com`) is now larger than the storage
quota for free users. Until a secondary replica performs housekeeping, it
will consume the storage quota of the extracted snapshot. If Praefect instead
used fast forking (https://gitlab.com/gitlab-org/gitlab/-/issues/24523), this
would not be an issue since forked copies would only use a small amount of
additional data.

To complicates matter even more, read distribution can contribute to
inconsistent behavior when attempting to determine how much storage a user has
consumed. Since stating a repository's disk space is a read-only operation, it
is load balanced across all up to date replicas of the repository. If any of
those replicas still has the duplicated fork data, this will lead to a much
higher disk usage being reported than a replica that has been deduplicated.

#### Replication Logic

Here are the steps during a Gitaly client GRPC call intercepted by Praefect:

```mermaid
graph TD
  A[Gitaly Client]-->B{Does RPC Mutate a repository?}
  B-->| yes | C[Peek into RPC Stream to determine Repository]
  B-->| no  | G[Forward request to Gitaly]
  C-->D{Scoped for repository?}
	D-->| yes | E[Get target repository from message]
	D-->| no  | G
  E-->F[Schedule Replication]
	F-->G
```

## Stages until v1.0

Rome wasn't built in a day, nor will Praefect be built in one. To enable for an
iterative approach towards a true HA system, some requirements will not be met
until v1.0. Before that milestone is reached, a beta stage will be worked towards.

The beta stage will consist of only a few building blocks required to iterate
towards the envisioned HA system. The first of those building blocks is creating
and maintaining repository replica's. By maintaining a replica, there's no
requirement for the replica to be up to date right after each mutation on the
repository. Detecting that a repository is mutated, and bringing replicas up to
date in a consistent matter is the primary goal. Implicit in this goal is a way
to perform leader election.

When the beta nears completion further stages will be defined.

## Eventual consistency

The beta implementation above describes an eventually consistent system:
when a repository is modified, the secondaries asynchronously fetch the
changes. This is similar to how Geo works today:

1. A new change is detected.
1. An asynchronous process is queued.
1. A `git fetch` is run to synchronize the latest changes.

The main advantages of this approach:

1. Writes are not delayed by a slow replica
1. This can be implemented today without too much refactoring. This is
   less complex than supporting strong consistency, as we will discuss below.

However, the main disadvantage of this approach is that there are no
consistency guarantees: a replica may be out of sync for seconds,
minutes, or even hours. For busy repositories, this makes it difficult
to offload read queries to the replica, which is especially important
for projects that rely on continuous integration to test the latest
changes.

### Failover

Depending on the configured strategy, there are a few ways of handling a failure of a Primary
Gitaly node:

1. Strategies:
    1. Failover disabled
    1. Local elector
    1. SQL elector
1. Read-only mode

#### Failover Disabled

When failover is disabled, nothing will be done about the primary failure. The virtual storage
is inaccessible until the configured primary node is back online.

#### Local Elector

Local election tries to promote another Gitaly node as the new primary. It simply picks the next
node from the list of healthy secondaries of the failed primary. There is no synchronization between
different Praefect nodes thus this strategy is mostly useful for local development. If no healthy secondary
exists to serve as the new primary, the virtual storage will be inaccessible.

#### SQL Elector

SQL elector stores each Praefect's view of the Gitaly nodes' health in Postgres. When a primary fails, SQL
elector promotes a secondary that the majority of Praefect nodes consider healthy as the new primary. When
choosing the new primary, it also prioritizes a secondary that has the least failed replication jobs to minimize
data loss. If there is no eligible candidate for a promotion, the virtual storage will be inaccessible.

#### Read-only Mode

A repository is switched in to read-only mode if its primary storage is outdated. This happen after failing
over to an outdated storage node. A storage's copy of the repository is outdated if it hasn't replicated all of the
writes to the repository.

Switching a repository in to read-only mode prevents the outdated primary from accepting writes that may conflict
with some of the unreplicated writes from the previous primary. This ensures recovery is simply a matter of
replicating the missing data from another storage node.

Praefect's automatic reconciler schedules replication jobs to bring outdated repositories back to speed as long
as there is a healthy storage with a fully up to date copy of the repository available.

When it is not possible to bring a storage node containing the latest copy of the repository back online,
administrator may accept data loss by manually selecting which copy of the repository to use going forward.

## Compared to Geo

Despite the similarities above, there are significant differences
between Gitaly Cluster and Geo:

1. High-availability vs. disaster recovery: These are fundamentally
   different goals. Gitaly Cluster enables GitLab to function even if a
   single Gitaly node goes down by transparently failing over to a
   secondary Gitaly node.

  From operational perspective Geo is a disaster recovery solution. If
  the primary datacenter goes down, any data stored in GitLab will be
  preserved in another location. Some data loss is acceptable, since
  having a significant amount of data--even if it stale--is better than
  having no data at all. In addition, the Geo-replicated instance can
  take over for the primary with some manual coordination. However, Geo
  does not handle failure of a single Gitaly node.

1. Unlike Geo, strong consistency is most likely a requirement for
   Gitaly Cluster. Gitaly Cluster has to be able to fail over to replicas without
   human interaction.

1. Gitaly Cluster only replicates Git repository data and omits other GitLab
   artifacts (e.g. upload attachments, Git LFS files, CI artifacts, Docker
   containers, etc.).

1. Under the hood, the manner in which Geo and Gitaly Cluster detect
   repository changes is subtly different but important. For example,
   when a user pushes to a Geo primary instance, the Git post-receive
   handler emits an event (in the form of a database row in PostgreSQL)
   that tells secondaries that the repository has changed. In Gitaly Cluster,
   Praefect directly handles the RPC that will mutate the repository. This
   architecture makes it possible for Praefect to support strong
   consistency.

## Strong Consistency Design

When doing updates in a Git repository, we want to assure that all Gitaly nodes
in a high-availability setup have the same references afterwards. Historically,
this was accomplished by using NFS as a Gitaly storage backend, but due to
various reasons we have been deprecating this setup in favor of Praefect.

Praefect allows to have multiple Gitaly nodes with individual storage backends.
In order to achieve consistency across these nodes, Praefect inspects incoming
requests and, depending on the request's nature, may decide to replicate these
changes after they have been performed on the primary Gitaly node. This model
guarantees eventual consistency, but there is always at least a brief moment
where the Gitaly nodes will disagree on what the current state is.

To lift this limitation, the next iteration of Praefect's design is to achieve strong
consistency: given a mutating request, all Gitaly nodes should agree to make the
modification before actually persisting it to disk. This document describes the
design for this.

### Reference Updates

While server-side Git repositories can be mutated in a lot of ways, all
user-visible mutations involve updates to one or multiple references. For the
sake of simplicity, we can thus reduce the problem scope to ensure strong
consistency for reference updates, only. There are multiple paths in GitLab that
can trigger such a reference update, including but not limited to:

- Clients execute git-push(1).

- Creation of tags via GitLab's `UserCreateTag` RPC.

- Merges and rebases when accepting merge requests.

Common to all of them is that they perform reference updates using git-core,
and, more importantly, its reference transaction mechanism. An ideal solution
would thus hook into this reference transaction mechanism directly via
githooks(5), which has been implemented in git-core and is going to be part of
release v2.28.0.

Strong consistency is implemented via the reference-transaction hook. This hook
gets executed whenever a Git command updates any reference in a repository.

### Strong Consistency via Reference-Transaction Hook

The following diagram shows the flow of a ReceivePack operation from Praefect
via Gitaly to Git and finally to the reference-transaction hook:

```mermaid
sequenceDiagram
  Praefect->>+Gitaly: ReceivePack
  Gitaly->>+Git: git receive-pack
  Git->>+Hook: update HEAD master
  Hook->>+Praefect: TX: update HEAD master
  Praefect->>+Praefect: TX: collect votes
  Praefect->>+Hook: TX: commit
  Hook->>+Git: exit 0
  Git->>+Gitaly: exit 0
  Gitaly->>+Praefect: success
```

1. Praefect will proxy an incoming `ReceivePack` request to multiple Gitaly
   nodes.
1. Gitaly executes `git receive-pack` and passes incoming data to it.
1. After `git receive-pack` has received all references that are to be updated,
   it executes the reference-transaction hook for each reference which is to be
   updated.
1. The reference-transaction hook reaches out to Praefect and notifies it about
   all reference update it wants to perform.
1. Praefect waits until all Gitaly nodes have notified it about the reference
   update. After it has received all notifications, it verifies that all nodes
   want to perform the same update. If so, it notifies them that they may go
   ahead by sending a "commit" message. Otherwise, it will send an "abort"
   message.
1. When receiving the response, the hook will either return an error in case it
   got an "abort" message, which will instruct Git to not update the references.
   Otherwise, the hook will exit successfully and Git will proceed.
1. Gitaly returns success to Praefect and the transaction is done.

#### Data Channel between Praefect and Hook

While it would be possible to proxy transaction information via Gitaly, this
would require us to update the complete callchain between Praefect and Git hook.
Additionally, it would require us to update all call-sites where a reference
could potentially be updated. Because of this, it was decided to circumvent
Gitaly and have the Git hook talk directly to Praefect.

As Gitaly did not previously know how to connect to Praefect, Git hooks didn't
either. To fix this, Praefect started passing along a gRPC metadata header along
to Gitaly that includes both the listening address as well as the token required
to authenticate. If a request is proxied by Praefect, then Gitaly will know to
extract the connection information and make it available to hooks by exposing it
in an environment variable.

#### Transaction Service

To let Praefect know about reference transactions, the
[transaction service](https://gitlab.com/gitlab-org/gitaly/-/blob/master/proto/transaction.proto)
was implemented. The service has a single RPC `VoteTransaction` to vote on a
transaction created by Praefect.

Each transaction gets the following information at creation time:

- Each transaction is identified by a transaction identifier, which is simply an
  integer that gets randomly generated by the transaction service when `Create`
  is called and is returned to the caller. Both `Start` and `Cleanup` requests
  are required to pass along this identifier. This identifier generated at
  random in contrast to being a sequence counter to avoid collisions.

- Each transaction has a fixed set of voters allowed to take part in the
  transaction. Each voter has a name (which needs to be unique and is typically
  the Gitaly storage name) as well as a number of votes it has. The number of
  votes needs to be in the range of `[0,MAX_UINT32]`. The total number of votes
  must be non-zero.

- Each transaction has a threshold, which indicates how many votes are required
  to be cast for the same item in order to commit the transaction. The threshold
  needs to be in the range of `[ceil((total_votes+1)/2),total_votes)` to
  guarantee that each transaction can have a single outcome, only, and that an
  outcome can be reached.

When casting a vote, each voter supplies the transaction identifier as well as
its own name to uniquely identify itself as well as the transaction.
Furthermore, the voter needs to let the transaction service know which
references it wants to update. Given that a transaction may update thousands of
references at once, it was deemed wasteful to pass the list of all reference
updates to the transaction service. Instead, all queued reference updates are
hashed, where the hash is the item that is then voted on.

The typical lifetime of a given transaction will then look like following:

```mermaid
sequenceDiagram
  Praefect->>+TxService: Create({"A": 1}, {"B": 1})
  TxService-->>-Praefect: id
  Praefect->>Gitaly A: {"A", id}
  Praefect->>Gitaly B: {"B", id}

  par Gitaly A
    Gitaly A->>+TxService: Start("A", id, hash(refsA))
  and Gitaly B
    Gitaly B->>+TxService: Start("B", id, hash(refsB))
  end

  Note over TxService: Count votes

  par Gitaly B
    TxService-->>-Gitaly B: Commit
    Gitaly B->>Praefect: Success
  and Gitaly A
    TxService-->>-Gitaly A: Commit
    Gitaly A->>Praefect: Success
  end

  Praefect->>+TxService: Cleanup(id)
```

#### Voting Strategies

Given the combination of vote counts and threshold, one can implement different
voting strategies, which allows making a tradeoff between performance and
consistency:

- Strong: All voters have a vote count of `1` and the threshold equals the
  number of voters. As a result, all Gitaly nodes will have to agree before
  committing anything.

- Allowed failures: Voters have a vote count of `1` and the threshold is
  decreased to allow for failures of a subset of nodes. E.g. it could be
  `len(nodes)-1` to allow a single Gitaly node to fail.

- Primary wins: The primary gets a vote count of `1` while secondaries get a
  vote count of `0` with a threshold of `1`. As a result, transactions will
  always get committed. This resembles our "Eventual Consistency" implementation
  but with benefit that we need to schedule replication jobs only for the subset
  of nodes that failed for a given transaction.

- Primary mandatory: The primary gets a vote count of `len(secondaries)+1`,
  while each secondary gets a vote count of `1` with a threshold of
  `len(secondaries)+1+n`, where `n` dictates how many nodes need to agree the
  primary. This strategy makes it mandatory that the primary agrees on the
  outcome, but also that at least `n` secondaries agree with the primary.

At the point of writing, Praefect sets up transactions with a "Strong" voting
strategy.

#### Handling failures

In case the voting strategy failed to determine a winner, the transaction needs
to be aborted and no node must commit the updated reference. In this case,
replication jobs should be scheduled to repair nodes and arrive at a consistent
state again, if at all possible. If it is not possible to determine an
up-to-date node that may act as the replication job source, the cluster needs to be
put into read-only-mode.

In case the voting strategy determines a winner but there are failed nodes, a
replication job needs to be scheduled for any of the winning nodes to all of the
failed nodes to repair them.

An edge case is when a transaction was registered, but none of the voters showed
up at all. The root cause for this can be many, e.g. no references were modified
or that the executed command simply wasn't aware of transactions. As it is
impossible to determine the root cause, replication jobs from primary to
secondaries needs to be created unconditionally to replicate any potential
changes.

## Using Strong Consistency

The current implementation uses strong consistency via reference-transaction
hooks.

In order to observe reference transactions, the following metrics can be used:

- `gitaly_praefect_transactions_total`: The number of transactions created.

- `gitaly_praefect_transactions_delay_seconds`: Server-side delay between
  casting a vote and reaching quorum.

- `gitaly_praefect_subtransactions_per_transaction_total`: Number of
  subtransactions created for each transaction.

- `gitaly_praefect_voters_per_transaction_total`: Number of nodes which have
  cast a vote in a given transaction.

**Note:** Required work is only present in Gitaly starting with release
v13.1.0-rc3.

## Notes
* Existing discussions
	* Requirements: https://gitlab.com/gitlab-org/gitaly/issues/1332
	* Design: https://gitlab.com/gitlab-org/gitaly/issues/1335
* Prior art
	* Stemma by Palantir
		* [Announcement](https://medium.com/palantir/stemma-distributed-git-server-70afbca0fc29)
		* Extends jgit (java git implementation)
	* Spokes by GitHub
		* Application layer approach: uses underlying git software to propagate changes to other locations.
	* Bitbucket Data Center (BDC)
		* [BDC FAQ](https://confluence.atlassian.com/enterprise/bitbucket-data-center-faq-776663707.html)
	* Ketch by Google (no longer maintained)
		* [Sid's comment on performance issue](https://news.ycombinator.com/item?id=13934698)
		* Also jgit based
* gRPC proxy considerations
	* [gRPC Proxy library](https://github.com/mwitkow/grpc-proxy)
		* Pros
			* Handles all gRPC requests generically
		* Cons
			* Lack of support
				* [See current importers of project](https://godoc.org/github.com/mwitkow/grpc-proxy/proxy?importers)
			* Low level implementation requires knowledge of gRPC internals
	* Custom code generation
		* Pros
			* Simple and maintainable
			* Allows us to handwrite proxy code and later automate with lessons learned via code generation
		* Cons
			* Process heavy; requires custom tooling
			* Requires a way to tell which methods are read/write
				* [See MR for marking modifying RPCs](https://gitlab.com/gitlab-org/gitaly-proto/merge_requests/228)
	* See also:
		* [nRPC](https://github.com/nats-rpc/nrpc) - gRPC via NATS
		* [grpclb](https://github.com/bsm/grpclb) - gRPC load balancer
* Complications
	* Existing Rails app indicates the Gitaly instance that a request is destined for (e.g. request to modify repo X should be directed to gitaly #1).
		* This means that rails app must be kept in the loop about any changes made to the location of a repo.
		* This may be mitigated by changing the proxy implementation to interpret the destination address as a reference to a shard rather than a specific host. This might open the door to allowing for something like consistent hashing.
    * While Git is distributed in nature, some write operations need to be serialized to avoid race conditions. This includes ref updates.
	* How do we coordinate proxies when applying ref updates? Do we need to?


[epic]: https://gitlab.com/groups/gitlab-org/-/epics/289
