88 lines
5.5 KiB
Plaintext
88 lines
5.5 KiB
Plaintext
---
|
|
layout: docs
|
|
page_title: Consul Internals Overview
|
|
description: >-
|
|
Consul datacenters consist of clusters of server agents (control plane) and client agents deployed alongside service instances (dataplane). Learn how these components and their different communication methods make Consul possible.
|
|
---
|
|
|
|
# Consul Internals Overview
|
|
|
|
Consul is a complex system that has many different moving parts. To help
|
|
users and developers of Consul form a mental model of how it works, this
|
|
page documents the system architecture.
|
|
|
|
-> Before describing the architecture, we recommend reading the
|
|
[glossary](/docs/install/glossary) of terms to help
|
|
clarify what is being discussed.
|
|
|
|
The architecture concepts in this document can be used with the [Reference Architecture guide](https://learn.hashicorp.com/tutorials/consul/reference-architecture?in=consul/production-deploy#deployment-system-requirements) when deploying Consul in production.
|
|
|
|
## 10,000 foot view
|
|
|
|
From a 10,000 foot altitude the architecture of Consul looks like this:
|
|
|
|
[](/img/consul-arch.png)
|
|
|
|
Let's break down this image and describe each piece. First of all, we can see
|
|
that there are two datacenters, labeled "one" and "two". Consul has first
|
|
class support for [multiple datacenters](https://learn.hashicorp.com/consul/security-networking/datacenters) and
|
|
expects this to be the common case.
|
|
|
|
Within each datacenter, we have a mixture of clients and servers. It is expected
|
|
that there will be between three to five servers. This strikes a balance between
|
|
availability in the case of failure and performance, as consensus gets progressively
|
|
slower as more machines are added. However, there is no limit to the number of clients,
|
|
and they can easily scale into the thousands or tens of thousands.
|
|
|
|
All the agents that are in a datacenter participate in a [gossip protocol](/docs/architecture/gossip).
|
|
This means there is a gossip pool that contains all the agents for a given datacenter. This serves
|
|
a few purposes: first, there is no need to configure clients with the addresses of servers;
|
|
discovery is done automatically. Second, the work of detecting agent failures
|
|
is not placed on the servers but is distributed. This makes failure detection much more
|
|
scalable than naive heartbeating schemes. It also provides failure detection for the nodes; if the agent is not reachable, then the node may have experienced a failure. Thirdly, it is used as a messaging layer to notify
|
|
when important events such as leader election take place.
|
|
|
|
The servers in each datacenter are all part of a single Raft peer set. This means that
|
|
they work together to elect a single leader, a selected server which has extra duties. The leader
|
|
is responsible for processing all queries and transactions. Transactions must also be replicated to
|
|
all peers as part of the [consensus protocol](/docs/architecture/consensus). Because of this
|
|
requirement, when a non-leader server receives an RPC request, it forwards it to the cluster leader.
|
|
|
|
The server agents also operate as part of a WAN gossip pool. This pool is different from the LAN pool
|
|
as it is optimized for the higher latency of the internet and is expected to contain only
|
|
other Consul server agents. The purpose of this pool is to allow datacenters to discover each
|
|
other in a low-touch manner. Bringing a new datacenter online is as easy as joining the existing
|
|
WAN gossip pool. Because the servers are all operating in this pool, it also enables cross-datacenter
|
|
requests. When a server receives a request for a different datacenter, it forwards it to a random
|
|
server in the correct datacenter. That server may then forward to the local leader.
|
|
|
|
This results in a very low coupling between datacenters, but because of failure detection,
|
|
connection caching and multiplexing, cross-datacenter requests are relatively fast and reliable.
|
|
|
|
In general, data is not replicated between different Consul datacenters. When a
|
|
request is made for a resource in another datacenter, the local Consul servers forward
|
|
an RPC request to the remote Consul servers for that resource and return the results.
|
|
If the remote datacenter is not available, then those resources will also not be
|
|
available, but that won't otherwise affect the local datacenter. There are some special
|
|
situations where a limited subset of data can be replicated, such as with Consul's built-in
|
|
[ACL replication](https://learn.hashicorp.com/tutorials/consul/access-control-replication-multiple-datacenters) capability, or
|
|
external tools like [consul-replicate](https://github.com/hashicorp/consul-replicate).
|
|
|
|
In some places, client agents may cache data from the servers to make it
|
|
available locally for performance and reliability. Examples include Connect
|
|
certificates and intentions which allow the client agent to make local decisions
|
|
about inbound connection requests without a round trip to the servers. Some API
|
|
endpoints also support optional result caching. This helps reliability because
|
|
the local agent can continue to respond to some queries like service-discovery
|
|
or Connect authorization from cache even if the connection to the servers is
|
|
disrupted or the servers are temporarily unavailable.
|
|
|
|
## Getting in depth
|
|
|
|
At this point we've covered the high level architecture of Consul, but there are many
|
|
more details for each of the subsystems. The [consensus protocol](/docs/architecture/consensus) is
|
|
documented in detail as is the [gossip protocol](/docs/architecture/gossip). The [documentation](/docs/security)
|
|
for the security model and protocols used are also available.
|
|
|
|
For other details, either consult the code, ask in IRC, or reach out to the mailing list.
|