[role="xpack"]
[[xpack-ccr]]
== {ccr-cap}
With {ccr}, you can replicate indices across clusters to:

* Continue handling search requests in the event of a datacenter outage
* Prevent search volume from impacting indexing throughput
* Reduce search latency by processing search requests in geo-proximity to the
user

{ccr-cap} uses an active-passive model. You index to a _leader_ index, and the
data is replicated to one or more read-only _follower_ indices. Before you can add a follower index to a cluster, you must configure the _remote cluster_ that contains the leader index.

When the leader index receives writes, the follower indices pull changes from
the leader index on the remote cluster. You can manually create follower
indices, or configure auto-follow patterns to automatically create follower
indices for new time series indices.

You configure {ccr} clusters in a uni-directional or bi-directional setup:

* In a uni-directional configuration, one cluster contains only
leader indices, and the other cluster contains only follower indices.
* In a bi-directional configuration, each cluster contains both leader and
follower indices.

In a uni-directional configuration, the cluster containing follower indices
must be running **the same or newer** version of {es} as the remote cluster.
If newer, the versions must also be compatible as outlined in the following matrix.

[%collapsible]
[[ccr-version-compatibility]]
.Version compatibility matrix
====
include::../modules/remote-clusters-shared.asciidoc[tag=remote-cluster-compatibility-matrix]
====

[discrete]
[[ccr-multi-cluster-architectures]]
=== Multi-cluster architectures
Use {ccr} to construct several multi-cluster architectures within the Elastic
Stack:

* <<ccr-disaster-recovery,Disaster recovery>> in case a primary cluster fails,
with a secondary cluster serving as a hot backup
* <<ccr-data-locality,Data locality>> to maintain multiple copies of the
dataset close to the application servers (and users), and reduce costly latency
* <<ccr-centralized-reporting,Centralized reporting>> for minimizing network
traffic and latency in querying multiple geo-distributed {es} clusters, or for
preventing search load from interfering with indexing by offloading search to a
secondary cluster

Watch the
https://www.elastic.co/webinars/replicate-elasticsearch-data-with-cross-cluster-replication-ccr[{ccr} webinar] to learn more about the following use cases.
Then, <<ccr-getting-started-tutorial,set up {ccr}>> on your local machine and work
through the demo from the webinar.

[discrete]
[[ccr-disaster-recovery]]
==== Disaster recovery and high availability
Disaster recovery provides your mission-critical applications with the
tolerance to withstand datacenter or region outages. This use case is the
most common deployment of {ccr}. You can configure clusters in different
architectures to support disaster recovery and high availability:

* <<ccr-single-datacenter-recovery>>
* <<ccr-multiple-datacenter-recovery>>
* <<ccr-chained-replication>>
* <<ccr-bi-directional-replication>>

[discrete]
[[ccr-single-datacenter-recovery]]
===== Single disaster recovery datacenter
In this configuration, data is replicated from the production datacenter to the
disaster recovery datacenter. Because the follower indices replicate the leader
index, your application can use the disaster recovery datacenter if the
production datacenter is unavailable.

image::images/ccr-arch-disaster-recovery.png[Production datacenter that replicates data to a disaster recovery datacenter]

[discrete]
[[ccr-multiple-datacenter-recovery]]
===== Multiple disaster recovery datacenters
You can replicate data from one datacenter to multiple datacenters. This
configuration provides both disaster recovery and high availability, ensuring
that data is replicated in two datacenters if the primary datacenter is down
or unavailable.

In the following diagram, data from Datacenter A is replicated to
Datacenter B and Datacenter C, which both have a read-only copy of the leader
index from Datacenter A.

image::images/ccr-arch-multiple-dcs.png[Production datacenter that replicates data to two other datacenters]

[discrete]
[[ccr-chained-replication]]
===== Chained replication
You can replicate data across multiple datacenters to form a replication
chain. In the following diagram, Datacenter A contains the leader index.
Datacenter B replicates data from Datacenter A, and Datacenter C replicates
from the follower indices in Datacenter B. The connection between these
datacenters forms a chained replication pattern.

image::images/ccr-arch-chain-dcs.png[Three datacenters connected to form a replication chain]

[discrete]
[[ccr-bi-directional-replication]]
===== Bi-directional replication
In a https://www.elastic.co/blog/bi-directional-replication-with-elasticsearch-cross-cluster-replication-ccr[bi-directional replication] setup, all clusters have access to view
all data, and all clusters have an index to write to without manually
implementing failover. Applications can write to the local index within each
datacenter, and read across multiple indices for a global view of all
information.

This configuration requires no manual intervention when a cluster or datacenter
is unavailable. In the following diagram, if Datacenter A is unavailable, you can continue using Datacenter B without manual failover. When Datacenter A
comes online, replication resumes between the clusters.

image::images/ccr-arch-bi-directional.png[Bi-directional configuration where each cluster contains both a leader index and follower indices]

This configuration is particularly useful for index-only workloads, where no updates
to document values occur. In this configuration, documents indexed by {es} are
immutable. Clients are located in each datacenter alongside the {es}
cluster, and do not communicate with clusters in different datacenters.

[discrete]
[[ccr-data-locality]]
==== Data locality
Bringing data closer to your users or application server can reduce latency
and response time. This methodology also applies when replicating data in {es}.
For example, you can replicate a product catalog or reference dataset to 20 or
more datacenters around the world to minimize the distance between the data and
the application server.

In the following diagram, data is replicated from one datacenter to three
additional datacenters, each in their own region. The central datacenter
contains the leader index, and the additional datacenters contain follower
indices that replicate data in that particular region. This configuration
puts data closer to the application accessing it.

image::images/ccr-arch-data-locality.png[A centralized datacenter replicated across three other datacenters, each in their own region]

[discrete]
[[ccr-centralized-reporting]]
==== Centralized reporting
Using a centralized reporting cluster is useful when querying across a large
network is inefficient. In this configuration, you replicate data from many
smaller clusters to the centralized reporting cluster.

For example, a large global bank might have 100 {es} clusters around the world
that are distributed across different regions for each bank branch. Using
{ccr}, the bank can replicate events from all 100 banks to a central cluster to
analyze and aggregate events locally for reporting. Rather than maintaining a
mirrored cluster, the bank can use {ccr} to replicate specific indices.

In the following diagram, data from three datacenters in different regions is
replicated to a centralized reporting cluster. This configuration enables you
to copy data from regional hubs to a central cluster, where you can run all
reports locally.

image::images/ccr-arch-central-reporting.png[Three clusters in different regions sending data to a centralized reporting cluster for analysis]

[discrete]
[[ccr-replication-mechanics]]
=== Replication mechanics
Although you <<ccr-getting-started-tutorial,set up {ccr}>> at the index level, {es}
achieves replication at the shard level. When a follower index is created,
each shard in that index pulls changes from its corresponding shard in the
leader index, which means that a follower index has the same number of
shards as its leader index. All operations on the leader are replicated by the
follower, such as operations to create, update, or delete a document.
These requests can be served from any copy of the leader shard (primary or
replica).

When a follower shard sends a read request, the leader shard responds with
any new operations, limited by the read parameters that you establish when
configuring the follower index. If no new operations are available, the
leader shard waits up to the configured timeout for new operations. If the
timeout elapses, the leader shard responds to the follower shard that there
are no new operations. The follower shard updates shard statistics and
immediately sends another read request to the leader shard. This
communication model ensures that network connections between the remote
cluster and the local cluster are continually in use, avoiding forceful
termination by an external source such as a firewall.

If a read request fails, the cause of the failure is inspected. If the
cause of the failure is deemed to be recoverable (such as a network
failure), the follower shard enters into a retry loop. Otherwise, the
follower shard pauses
<<ccr-pause-replication,until you resume it>>.

[discrete]
[[ccr-update-leader-index]]
==== Processing updates
You can't manually modify a follower index's mappings or aliases. To make
changes, you must update the leader index. Because they are read-only, follower
indices reject writes in all configurations. 

NOTE: Although changes to aliases on the leader index are replicated to follower
indices, write indices are ignored. Follower indices can't accept direct writes,
so if any leader aliases have `is_write_index` set to `true`, that value is
forced to `false`.

For example, you index a document named `doc_1` in Datacenter A, which
replicates to Datacenter B. If a client connects to Datacenter B and attempts
to update `doc_1`, the request fails. To update `doc_1`, the client must
connect to Datacenter A and update the document in the leader index.

When a follower shard receives operations from the leader shard, it places
those operations in a write buffer. The follower shard submits bulk write
requests using operations from the write buffer. If the write buffer exceeds
its configured limits, no additional read requests are sent. This configuration
provides a back-pressure against read requests, allowing the follower shard
to resume sending read requests when the write buffer is no longer full.

To manage how operations are replicated from the leader index, you can
configure settings when
<<ccr-getting-started-follower-index,creating the follower index>>.

The follower index automatically retrieves some updates applied to the leader
index, while other updates are retrieved as needed:

[cols="3"]
|===
h| Update type h| Automatic  h| As needed
| Alias        | {yes-icon} | {no-icon}
| Mapping      | {no-icon}  | {yes-icon}
| Settings     | {no-icon}  | {yes-icon}
|===

For example, changing the number of replicas on the leader index is not
replicated by the follower index, so that setting might not be retrieved.

If you apply a non-dynamic settings change to the leader index that is
needed by the follower index, the follower index closes itself, applies the
settings update, and then re-opens itself. The follower index is unavailable
for reads and cannot replicate writes during this cycle.

[discrete]
[[ccr-remote-recovery]]
=== Initializing followers using remote recovery
When you create a follower index, you cannot use it until it is fully
initialized. The _remote recovery_ process builds a new copy of a shard on a
follower node by copying data from the primary shard in the leader cluster.

{es} uses this remote recovery process to bootstrap a follower index using the
data from the leader index. This process provides the follower with a copy of
the current state of the leader index, even if a complete history of changes
is not available on the leader due to Lucene segment merging.

Remote recovery is a network intensive process that transfers all of the Lucene
segment files from the leader cluster to the follower cluster. The follower
requests that a recovery session be initiated on the primary shard in the
leader cluster. The follower then requests file chunks concurrently from the
leader. By default, the process concurrently requests five 1MB file
chunks. This default behavior is designed to support leader and follower
clusters with high network latency between them.

TIP: You can modify dynamic <<ccr-recovery-settings,remote recovery settings>>
to rate-limit the transmitted data and manage the resources consumed by remote
recoveries.

Use the <<cat-recovery,recovery API>> on the cluster containing the follower
index to obtain information about an in-progress remote recovery. Because {es}
implements remote recoveries using the
<<snapshot-restore,snapshot and restore>> infrastructure, running remote
recoveries are labelled as type `snapshot` in the recovery API.

[discrete]
[[ccr-leader-requirements]]
=== Replicating a leader requires soft deletes
{ccr-cap} works by replaying the history of individual write
operations that were performed on the shards of the leader index. {es} needs to
retain the
<<index-modules-history-retention,history of these operations>> on the leader
shards so that they can be pulled by the follower shard tasks. The underlying
mechanism used to retain these operations is _soft deletes_.

A soft delete occurs whenever an existing document is deleted or updated. By
retaining these soft deletes up to configurable limits, the history of
operations can be retained on the leader shards and made available to the
follower shard tasks as it replays the history of operations.

The <<ccr-index-soft-deletes-retention-period,`index.soft_deletes.retention_lease.period`>>
setting defines the maximum time to retain a shard history retention lease
before it is considered expired. This setting determines how long the cluster
containing your follower index can be offline, which is 12 hours by default. If
a shard copy recovers after its retention lease expires, but the missing
operations are still available on the leader index, then {es} will establish a
new lease and copy the missing operations. However {es} does not guarantee to
retain unleased operations, so it is also possible that some of the missing
operations have been discarded by the leader and are now completely
unavailable. If this happens then the follower cannot recover automatically so
you must <<ccr-recreate-follower-index,recreate it>>.

Soft deletes must be enabled for indices that you want to use as leader
indices. Soft deletes are enabled by default on new indices created on
or after {es} 7.0.0.

// tag::ccr-existing-indices-tag[]
IMPORTANT: {ccr-cap} cannot be used on existing indices created using {es}
7.0.0 or earlier, where soft deletes are disabled. You must
<<docs-reindex,reindex>> your data into a new index with soft deletes
enabled.

// end::ccr-existing-indices-tag[]

[discrete]
[[ccr-learn-more]]
=== Use {ccr}
This following sections provide more information about how to configure
and use {ccr}:

* <<ccr-getting-started-tutorial>>
* <<ccr-managing>>
* <<ccr-auto-follow>>
* <<ccr-upgrading>>

include::getting-started.asciidoc[]
include::managing.asciidoc[]
include::auto-follow.asciidoc[]
include::upgrading.asciidoc[]
