91 lines
5.6 KiB
Markdown
91 lines
5.6 KiB
Markdown
---
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layout: "docs"
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page_title: "Consul Architecture"
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sidebar_current: "docs-internals-architecture"
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description: |-
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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.
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---
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# Consul Architecture
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Consul is a complex system that has many different moving parts. To help
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users and developers of Consul form a mental model of how it works, this
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page documents the system architecture.
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-> Before describing the architecture, we recommend reading the
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[glossary](/docs/glossary.html) of terms to help
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clarify what is being discussed.
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The architecture concepts in this document can be used with the [Reference Architecture guide](https://learn.hashicorp.com/consul/datacenter-deploy/reference-architecture?utm_source=consul.io&utm_medium=docs) when deploying Consul in production.
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## 10,000 foot view
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From a 10,000 foot altitude the architecture of Consul looks like this:
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<div class="center">
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[](/assets/images/consul-arch.png)
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</div>
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Let's break down this image and describe each piece. First of all, we can see
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that there are two datacenters, labeled "one" and "two". Consul has first
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class support for [multiple datacenters](https://learn.hashicorp.com/consul/security-networking/datacenters) and
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expects this to be the common case.
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Within each datacenter, we have a mixture of clients and servers. It is expected
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that there be between three to five servers. This strikes a balance between
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availability in the case of failure and performance, as consensus gets progressively
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slower as more machines are added. However, there is no limit to the number of clients,
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and they can easily scale into the thousands or tens of thousands.
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All the agents that are in a datacenter participate in a [gossip protocol](/docs/internals/gossip.html).
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This means there is a gossip pool that contains all the agents for a given datacenter. This serves
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a few purposes: first, there is no need to configure clients with the addresses of servers;
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discovery is done automatically. Second, the work of detecting agent failures
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is not placed on the servers but is distributed. This makes failure detection much more
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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
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when important events such as leader election take place.
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The servers in each datacenter are all part of a single Raft peer set. This means that
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they work together to elect a single leader, a selected server which has extra duties. The leader
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is responsible for processing all queries and transactions. Transactions must also be replicated to
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all peers as part of the [consensus protocol](/docs/internals/consensus.html). Because of this
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requirement, when a non-leader server receives an RPC request, it forwards it to the cluster leader.
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The server agents also operate as part of a WAN gossip pool. This pool is different from the LAN pool
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as it is optimized for the higher latency of the internet and is expected to contain only
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other Consul server agents. The purpose of this pool is to allow datacenters to discover each
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other in a low-touch manner. Bringing a new datacenter online is as easy as joining the existing
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WAN gossip pool. Because the servers are all operating in this pool, it also enables cross-datacenter
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requests. When a server receives a request for a different datacenter, it forwards it to a random
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server in the correct datacenter. That server may then forward to the local leader.
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This results in a very low coupling between datacenters, but because of failure detection,
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connection caching and multiplexing, cross-datacenter requests are relatively fast and reliable.
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In general, data is not replicated between different Consul datacenters. When a
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request is made for a resource in another datacenter, the local Consul servers forward
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an RPC request to the remote Consul servers for that resource and return the results.
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If the remote datacenter is not available, then those resources will also not be
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available, but that won't otherwise affect the local datacenter. There are some special
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situations where a limited subset of data can be replicated, such as with Consul's built-in
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[ACL replication](https://learn.hashicorp.com/consul/day-2-operations/acl-replication) capability, or
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external tools like [consul-replicate](https://github.com/hashicorp/consul-replicate).
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In some places, client agents may cache data from the servers to make it
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available locally for performance and reliability. Examples include Connect
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certificates and intentions which allow the client agent to make local decisions
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about inbound connection requests without a round trip to the servers. Some API
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endpoints also support optional result caching. This helps reliability because
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the local agent can continue to respond to some queries like service-discovery
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or Connect authorization from cache even if the connection to the servers is
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disrupted or the servers are temporarily unavailable.
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## Getting in depth
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At this point we've covered the high level architecture of Consul, but there are many
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more details for each of the subsystems. The [consensus protocol](/docs/internals/consensus.html) is
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documented in detail as is the [gossip protocol](/docs/internals/gossip.html). The [documentation](/docs/internals/security.html)
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for the security model and protocols used are also available.
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For other details, either consult the code, ask in IRC, or reach out to the mailing list.
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