123 lines
5.7 KiB
Plaintext
123 lines
5.7 KiB
Plaintext
[role="xpack"]
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[testenv="basic"]
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[[frozen-indices]]
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= Frozen indices
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[partintro]
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--
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{es} indices keep some data structures in memory to allow you to search them
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efficiently and to index into them. If you have a lot of indices then the
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memory required for these data structures can add up to a significant amount.
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For indices that are searched frequently it is better to keep these structures
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in memory because it takes time to rebuild them. However, you might access some
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of your indices so rarely that you would prefer to release the corresponding
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memory and rebuild these data structures on each search.
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For example, if you are using time-based indices to store log messages or time
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series data then it is likely that older indices are searched much less often
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than the more recent ones. Older indices also receive no indexing requests.
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Furthermore, it is usually the case that searches of older indices are for
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performing longer-term analyses for which a slower response is acceptable.
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If you have such indices then they are good candidates for becoming _frozen
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indices_. {es} builds the transient data structures of each shard of a frozen
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index each time that shard is searched, and discards these data structures as
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soon as the search is complete. Because {es} does not maintain these transient
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data structures in memory, frozen indices consume much less heap than normal
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indices. This allows for a much higher disk-to-heap ratio than would otherwise
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be possible.
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You can freeze the index using the <<freeze-index-api, Freeze Index API>>.
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Searches performed on frozen indices use the small, dedicated,
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<<search-throttled,`search_throttled` threadpool>> to control the number of
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concurrent searches that hit frozen shards on each node. This limits the amount
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of extra memory required for the transient data structures corresponding to
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frozen shards, which consequently protects nodes against excessive memory
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consumption.
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Frozen indices are read-only: you cannot index into them.
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Searches on frozen indices are expected to execute slowly. Frozen indices are
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not intended for high search load. It is possible that a search of a frozen
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index may take seconds or minutes to complete, even if the same searches
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completed in milliseconds when the indices were not frozen.
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To make a frozen index writable again, use the <<unfreeze-index-api, Unfreeze Index API>>.
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--
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[role="xpack"]
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[testenv="basic"]
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[[best_practices]]
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== Best practices
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Since frozen indices provide a much higher disk to heap ratio at the expense of search latency, it is advisable to allocate frozen indices to
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dedicated nodes to prevent searches on frozen indices influencing traffic on low latency nodes. There is significant overhead in loading
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data structures on demand which can cause page faults and garbage collections, which further slow down query execution.
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Since indices that are eligible for freezing are unlikely to change in the future, disk space can be optimized as described in <<tune-for-disk-usage>>.
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It's highly recommended to <<indices-forcemerge,`_forcemerge`>> your indices prior to freezing to ensure that each shard has only a single
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segment on disk. This not only provides much better compression but also simplifies the data structures needed to service aggregation
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or sorted search requests.
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[source,js]
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--------------------------------------------------
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POST /twitter/_forcemerge?max_num_segments=1
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--------------------------------------------------
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// CONSOLE
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// TEST[setup:twitter]
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[role="xpack"]
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[testenv="basic"]
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[[searching_a_frozen_index]]
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== Searching a frozen index
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Frozen indices are throttled in order to limit memory consumptions per node. The number of concurrently loaded frozen indices per node is
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limited by the number of threads in the <<search-throttled,search_throttled>> threadpool, which is `1` by default.
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Search requests will not be executed against frozen indices by default, even if a frozen index is named explicitly. This is
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to prevent accidental slowdowns by targeting a frozen index by mistake. To include frozen indices a search request must be executed with
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the query parameter `ignore_throttled=false`.
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[source,js]
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--------------------------------------------------
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GET /twitter/_search?q=user:kimchy&ignore_throttled=false
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--------------------------------------------------
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// CONSOLE
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// TEST[setup:twitter]
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[IMPORTANT]
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================================
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While frozen indices are slow to search, they can be pre-filtered efficiently. The request parameter `pre_filter_shard_size` specifies
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a threshold that, when exceeded, will enforce a round-trip to pre-filter search shards that cannot possibly match.
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This filter phase can limit the number of shards significantly. For instance, if a date range filter is applied, then all indices (frozen or unfrozen) that do not contain documents within the date range can be skipped efficiently.
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The default value for `pre_filter_shard_size` is `128` but it's recommended to set it to `1` when searching frozen indices. There is no
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significant overhead associated with this pre-filter phase.
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================================
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[role="xpack"]
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[testenv="basic"]
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[[monitoring_frozen_indices]]
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== Monitoring frozen indices
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Frozen indices are ordinary indices that use search throttling and a memory efficient shard implementation. For API's like the
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<<cat-indices>> frozen indices may identified by an index's `search.throttled` property (`sth`).
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[source,js]
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--------------------------------------------------
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GET /_cat/indices/twitter?v&h=i,sth
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--------------------------------------------------
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// CONSOLE
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// TEST[s/^/PUT twitter\nPOST twitter\/_freeze\n/]
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The response looks like:
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[source,txt]
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--------------------------------------------------
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i sth
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twitter true
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--------------------------------------------------
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// TESTRESPONSE[_cat]
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