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The reference manual includes docs on version compatibility in various places, but it's not clear that these docs only apply to released versions and that the rules for pre-release versions are stricter than folks expect. This commit adds some words to the docs for unreleased versions which explains this subtlety.
317 lines
15 KiB
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317 lines
15 KiB
Text
[role="xpack"]
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[testenv="platinum"]
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[[xpack-ccr]]
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== {ccr-cap}
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With {ccr}, you can replicate indices across clusters to:
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* Continue handling search requests in the event of a datacenter outage
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* Prevent search volume from impacting indexing throughput
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* Reduce search latency by processing search requests in geo-proximity to the
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user
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{ccr-cap} uses an active-passive model. You index to a _leader_ index, and the
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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.
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When the leader index receives writes, the follower indices pull changes from
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the leader index on the remote cluster. You can manually create follower
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indices, or configure auto-follow patterns to automatically create follower
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indices for new time series indices.
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You configure {ccr} clusters in a uni-directional or bi-directional setup:
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* In a uni-directional configuration, one cluster contains only
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leader indices, and the other cluster contains only follower indices.
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* In a bi-directional configuration, each cluster contains both leader and
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follower indices.
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In a uni-directional configuration, the cluster containing follower indices
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must be running **the same or newer** version of {es} as the remote cluster.
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If newer, the versions must also be compatible as outlined in the following matrix.
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[%collapsible]
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[[ccr-version-compatibility]]
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.Version compatibility matrix
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====
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include::../modules/remote-clusters-shared.asciidoc[tag=remote-cluster-compatibility-matrix]
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====
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[discrete]
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[[ccr-multi-cluster-architectures]]
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=== Multi-cluster architectures
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Use {ccr} to construct several multi-cluster architectures within the Elastic
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Stack:
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* <<ccr-disaster-recovery,Disaster recovery>> in case a primary cluster fails,
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with a secondary cluster serving as a hot backup
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* <<ccr-data-locality,Data locality>> to maintain multiple copies of the
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dataset close to the application servers (and users), and reduce costly latency
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* <<ccr-centralized-reporting,Centralized reporting>> for minimizing network
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traffic and latency in querying multiple geo-distributed {es} clusters, or for
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preventing search load from interfering with indexing by offloading search to a
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secondary cluster
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Watch the
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https://www.elastic.co/webinars/replicate-elasticsearch-data-with-cross-cluster-replication-ccr[{ccr} webinar] to learn more about the following use cases.
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Then, <<ccr-getting-started-tutorial,set up {ccr}>> on your local machine and work
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through the demo from the webinar.
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[discrete]
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[[ccr-disaster-recovery]]
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==== Disaster recovery and high availability
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Disaster recovery provides your mission-critical applications with the
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tolerance to withstand datacenter or region outages. This use case is the
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most common deployment of {ccr}. You can configure clusters in different
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architectures to support disaster recovery and high availability:
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* <<ccr-single-datacenter-recovery>>
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* <<ccr-multiple-datacenter-recovery>>
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* <<ccr-chained-replication>>
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* <<ccr-bi-directional-replication>>
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[discrete]
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[[ccr-single-datacenter-recovery]]
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===== Single disaster recovery datacenter
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In this configuration, data is replicated from the production datacenter to the
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disaster recovery datacenter. Because the follower indices replicate the leader
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index, your application can use the disaster recovery datacenter if the
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production datacenter is unavailable.
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image::images/ccr-arch-disaster-recovery.png[Production datacenter that replicates data to a disaster recovery datacenter]
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[discrete]
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[[ccr-multiple-datacenter-recovery]]
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===== Multiple disaster recovery datacenters
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You can replicate data from one datacenter to multiple datacenters. This
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configuration provides both disaster recovery and high availability, ensuring
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that data is replicated in two datacenters if the primary datacenter is down
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or unavailable.
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In the following diagram, data from Datacenter A is replicated to
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Datacenter B and Datacenter C, which both have a read-only copy of the leader
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index from Datacenter A.
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image::images/ccr-arch-multiple-dcs.png[Production datacenter that replicates data to two other datacenters]
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[discrete]
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[[ccr-chained-replication]]
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===== Chained replication
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You can replicate data across multiple datacenters to form a replication
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chain. In the following diagram, Datacenter A contains the leader index.
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Datacenter B replicates data from Datacenter A, and Datacenter C replicates
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from the follower indices in Datacenter B. The connection between these
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datacenters forms a chained replication pattern.
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image::images/ccr-arch-chain-dcs.png[Three datacenters connected to form a replication chain]
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[discrete]
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[[ccr-bi-directional-replication]]
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===== Bi-directional replication
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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
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all data, and all clusters have an index to write to without manually
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implementing failover. Applications can write to the local index within each
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datacenter, and read across multiple indices for a global view of all
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information.
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This configuration requires no manual intervention when a cluster or datacenter
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is unavailable. In the following diagram, if Datacenter A is unavailable, you can continue using Datacenter B without manual failover. When Datacenter A
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comes online, replication resumes between the clusters.
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image::images/ccr-arch-bi-directional.png[Bi-directional configuration where each cluster contains both a leader index and follower indices]
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This configuration is particularly useful for index-only workloads, where no updates
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to document values occur. In this configuration, documents indexed by {es} are
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immutable. Clients are located in each datacenter alongside the {es}
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cluster, and do not communicate with clusters in different datacenters.
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[discrete]
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[[ccr-data-locality]]
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==== Data locality
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Bringing data closer to your users or application server can reduce latency
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and response time. This methodology also applies when replicating data in {es}.
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For example, you can replicate a product catalog or reference dataset to 20 or
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more datacenters around the world to minimize the distance between the data and
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the application server.
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In the following diagram, data is replicated from one datacenter to three
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additional datacenters, each in their own region. The central datacenter
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contains the leader index, and the additional datacenters contain follower
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indices that replicate data in that particular region. This configuration
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puts data closer to the application accessing it.
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image::images/ccr-arch-data-locality.png[A centralized datacenter replicated across three other datacenters, each in their own region]
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[discrete]
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[[ccr-centralized-reporting]]
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==== Centralized reporting
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Using a centralized reporting cluster is useful when querying across a large
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network is inefficient. In this configuration, you replicate data from many
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smaller clusters to the centralized reporting cluster.
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For example, a large global bank might have 100 {es} clusters around the world
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that are distributed across different regions for each bank branch. Using
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{ccr}, the bank can replicate events from all 100 banks to a central cluster to
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analyze and aggregate events locally for reporting. Rather than maintaining a
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mirrored cluster, the bank can use {ccr} to replicate specific indices.
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In the following diagram, data from three datacenters in different regions is
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replicated to a centralized reporting cluster. This configuration enables you
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to copy data from regional hubs to a central cluster, where you can run all
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reports locally.
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image::images/ccr-arch-central-reporting.png[Three clusters in different regions sending data to a centralized reporting cluster for analysis]
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[discrete]
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[[ccr-replication-mechanics]]
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=== Replication mechanics
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Although you <<ccr-getting-started-tutorial,set up {ccr}>> at the index level, {es}
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achieves replication at the shard level. When a follower index is created,
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each shard in that index pulls changes from its corresponding shard in the
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leader index, which means that a follower index has the same number of
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shards as its leader index. All operations on the leader are replicated by the
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follower, such as operations to create, update, or delete a document.
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These requests can be served from any copy of the leader shard (primary or
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replica).
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When a follower shard sends a read request, the leader shard responds with
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any new operations, limited by the read parameters that you establish when
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configuring the follower index. If no new operations are available, the
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leader shard waits up to the configured timeout for new operations. If the
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timeout elapses, the leader shard responds to the follower shard that there
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are no new operations. The follower shard updates shard statistics and
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immediately sends another read request to the leader shard. This
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communication model ensures that network connections between the remote
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cluster and the local cluster are continually in use, avoiding forceful
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termination by an external source such as a firewall.
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If a read request fails, the cause of the failure is inspected. If the
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cause of the failure is deemed to be recoverable (such as a network
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failure), the follower shard enters into a retry loop. Otherwise, the
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follower shard pauses
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<<ccr-pause-replication,until you resume it>>.
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[discrete]
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[[ccr-update-leader-index]]
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==== Processing updates
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You can't manually modify a follower index's mappings or aliases. To make
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changes, you must update the leader index. Because they are read-only, follower
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indices reject writes in all configurations.
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For example, you index a document named `doc_1` in Datacenter A, which
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replicates to Datacenter B. If a client connects to Datacenter B and attempts
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to update `doc_1`, the request fails. To update `doc_1`, the client must
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connect to Datacenter A and update the document in the leader index.
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When a follower shard receives operations from the leader shard, it places
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those operations in a write buffer. The follower shard submits bulk write
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requests using operations from the write buffer. If the write buffer exceeds
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its configured limits, no additional read requests are sent. This configuration
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provides a back-pressure against read requests, allowing the follower shard
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to resume sending read requests when the write buffer is no longer full.
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To manage how operations are replicated from the leader index, you can
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configure settings when
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<<ccr-getting-started-follower-index,creating the follower index>>.
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The follower index automatically retrieves some updates applied to the leader
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index, while other updates are retrieved as needed:
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[cols="3"]
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|===
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h| Update type h| Automatic h| As needed
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| Alias | {yes-icon} | {no-icon}
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| Mapping | {no-icon} | {yes-icon}
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| Settings | {no-icon} | {yes-icon}
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|===
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For example, changing the number of replicas on the leader index is not
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replicated by the follower index, so that setting might not be retrieved.
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If you apply a non-dynamic settings change to the leader index that is
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needed by the follower index, the follower index closes itself, applies the
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settings update, and then re-opens itself. The follower index is unavailable
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for reads and cannot replicate writes during this cycle.
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[discrete]
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[[ccr-remote-recovery]]
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=== Initializing followers using remote recovery
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When you create a follower index, you cannot use it until it is fully
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initialized. The _remote recovery_ process builds a new copy of a shard on a
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follower node by copying data from the primary shard in the leader cluster.
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{es} uses this remote recovery process to bootstrap a follower index using the
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data from the leader index. This process provides the follower with a copy of
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the current state of the leader index, even if a complete history of changes
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is not available on the leader due to Lucene segment merging.
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Remote recovery is a network intensive process that transfers all of the Lucene
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segment files from the leader cluster to the follower cluster. The follower
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requests that a recovery session be initiated on the primary shard in the
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leader cluster. The follower then requests file chunks concurrently from the
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leader. By default, the process concurrently requests five 1MB file
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chunks. This default behavior is designed to support leader and follower
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clusters with high network latency between them.
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TIP: You can modify dynamic <<ccr-recovery-settings,remote recovery settings>>
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to rate-limit the transmitted data and manage the resources consumed by remote
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recoveries.
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Use the <<cat-recovery,recovery API>> on the cluster containing the follower
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index to obtain information about an in-progress remote recovery. Because {es}
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implements remote recoveries using the
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<<snapshot-restore,snapshot and restore>> infrastructure, running remote
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recoveries are labelled as type `snapshot` in the recovery API.
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[discrete]
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[[ccr-leader-requirements]]
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=== Replicating a leader requires soft deletes
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{ccr-cap} works by replaying the history of individual write
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operations that were performed on the shards of the leader index. {es} needs to
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retain the
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<<index-modules-history-retention,history of these operations>> on the leader
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shards so that they can be pulled by the follower shard tasks. The underlying
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mechanism used to retain these operations is _soft deletes_.
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A soft delete occurs whenever an existing document is deleted or updated. By
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retaining these soft deletes up to configurable limits, the history of
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operations can be retained on the leader shards and made available to the
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follower shard tasks as it replays the history of operations.
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The <<ccr-index-soft-deletes-retention-period,`index.soft_deletes.retention_lease.period`>>
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setting defines the maximum time to retain a shard history retention lease
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before it is considered expired. This setting determines how long the cluster
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containing your follower index can be offline, which is 12 hours by default. If
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a shard copy recovers after its retention lease expires, but the missing
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operations are still available on the leader index, then {es} will establish a
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new lease and copy the missing operations. However {es} does not guarantee to
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retain unleased operations, so it is also possible that some of the missing
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operations have been discarded by the leader and are now completely
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unavailable. If this happens then the follower cannot recover automatically so
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you must <<ccr-recreate-follower-index,recreate it>>.
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Soft deletes must be enabled for indices that you want to use as leader
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indices. Soft deletes are enabled by default on new indices created on
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or after {es} 7.0.0.
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// tag::ccr-existing-indices-tag[]
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IMPORTANT: {ccr-cap} cannot be used on existing indices created using {es}
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7.0.0 or earlier, where soft deletes are disabled. You must
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<<docs-reindex,reindex>> your data into a new index with soft deletes
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enabled.
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// end::ccr-existing-indices-tag[]
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[discrete]
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[[ccr-learn-more]]
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=== Use {ccr}
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This following sections provide more information about how to configure
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and use {ccr}:
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* <<ccr-getting-started-tutorial>>
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* <<ccr-managing>>
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* <<ccr-auto-follow>>
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* <<ccr-upgrading>>
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include::getting-started.asciidoc[]
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include::managing.asciidoc[]
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include::auto-follow.asciidoc[]
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include::upgrading.asciidoc[]
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