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Emphasize that filesystem-level backups don't work (#33102)
It is not obvious that a filesystem-level backup may capture an inconsistent set of files that may fail on restore, or (worse) succeed having silently discarded some data. This change spells the out, and reorganises the first page or so of the snapshot/restore docs to make this warning fit more nicely.
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docs/reference/modules/snapshots.asciidoc

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[[modules-snapshots]]
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== Snapshot And Restore
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You can store snapshots of individual indices or an entire cluster in
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a remote repository like a shared file system, S3, or HDFS. These snapshots
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are great for backups because they can be restored relatively quickly. However,
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snapshots can only be restored to versions of Elasticsearch that can read the
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indices:
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A snapshot is a backup taken from a running Elasticsearch cluster. You can take
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a snapshot of individual indices or of the entire cluster and store it in a
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repository on a shared filesystem, and there are plugins that support remote
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repositories on S3, HDFS, Azure, Google Cloud Storage and more.
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Snapshots are taken incrementally. This means that when creating a snapshot of
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an index Elasticsearch will avoid copying any data that is already stored in
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the repository as part of an earlier snapshot of the same index. Therefore it
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can be efficient to take snapshots of your cluster quite frequently.
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Snapshots can be restored into a running cluster via the restore API. When
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restoring an index it is possible to alter the name of the restored index as
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well as some of its settings, allowing a great deal of flexibility in how the
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snapshot and restore functionality can be used.
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WARNING: It is not possible to back up an Elasticsearch cluster simply by
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taking a copy of the data directories of all of its nodes. Elasticsearch may be
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making changes to the contents of its data directories while it is running, and
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this means that copying its data directories cannot be expected to capture a
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consistent picture of their contents. Attempts to restore a cluster from such a
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backup may fail, reporting corruption and/or missing files, or may appear to
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have succeeded having silently lost some of its data. The only reliable way to
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back up a cluster is by using the snapshot and restore functionality.
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[float]
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=== Version compatibility
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A snapshot contains a copy of the on-disk data structures that make up an
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index. This means that snapshots can only be restored to versions of
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Elasticsearch that can read the indices:
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* A snapshot of an index created in 5.x can be restored to 6.x.
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* A snapshot of an index created in 2.x can be restored to 5.x.
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* A snapshot of an index created in 1.x can be restored to 2.x.
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Conversely, snapshots of indices created in 1.x **cannot** be restored to
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5.x or 6.x, and snapshots of indices created in 2.x **cannot** be restored
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to 6.x.
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Conversely, snapshots of indices created in 1.x **cannot** be restored to 5.x
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or 6.x, and snapshots of indices created in 2.x **cannot** be restored to 6.x.
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Snapshots are incremental and can contain indices created in various
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versions of Elasticsearch. If any indices in a snapshot were created in an
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Each snapshot can contain indices created in various versions of Elasticsearch,
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and when restoring a snapshot it must be possible to restore all of the indices
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into the target cluster. If any indices in a snapshot were created in an
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incompatible version, you will not be able restore the snapshot.
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IMPORTANT: When backing up your data prior to an upgrade, keep in mind that you
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you can restore it on the latest compatible version and use
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<<reindex-from-remote,reindex-from-remote>> to rebuild the index on the current
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version. Reindexing from remote is only possible if the original index has
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source enabled. Retrieving and reindexing the data can take significantly longer
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than simply restoring a snapshot. If you have a large amount of data, we
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source enabled. Retrieving and reindexing the data can take significantly
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longer than simply restoring a snapshot. If you have a large amount of data, we
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recommend testing the reindex from remote process with a subset of your data to
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understand the time requirements before proceeding.
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