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e81804cfa4
Today if we search across a large amount of shards we hit every shard. Yet, it's quite common to search across an index pattern for time based indices but filtering will exclude all results outside a certain time range ie. `now-3d`. While the search can potentially hit hundreds of shards the majority of the shards might yield 0 results since there is not document that is within this date range. Kibana for instance does this regularly but used `_field_stats` to optimize the indexes they need to query. Now with the deprecation of `_field_stats` and it's upcoming removal a single dashboard in kibana can potentially turn into searches hitting hundreds or thousands of shards and that can easily cause search rejections even though the most of the requests are very likely super cheap and only need a query rewriting to early terminate with 0 results. This change adds a pre-filter phase for searches that can, if the number of shards are higher than a the `pre_filter_shard_size` threshold (defaults to 128 shards), fan out to the shards and check if the query can potentially match any documents at all. While false positives are possible, a negative response means that no matches are possible. These requests are not subject to rejection and can greatly reduce the number of shards a request needs to hit. The approach here is preferable to the kibana approach with field stats since it correctly handles aliases and uses the correct threadpools to execute these requests. Further it's completely transparent to the user and improves scalability of elasticsearch in general on large clusters.
321 lines
6.7 KiB
Plaintext
321 lines
6.7 KiB
Plaintext
[[analysis-edgengram-tokenizer]]
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=== Edge NGram Tokenizer
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The `edge_ngram` tokenizer first breaks text down into words whenever it
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encounters one of a list of specified characters, then it emits
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https://en.wikipedia.org/wiki/N-gram[N-grams] of each word where the start of
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the N-gram is anchored to the beginning of the word.
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Edge N-Grams are useful for _search-as-you-type_ queries.
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TIP: When you need _search-as-you-type_ for text which has a widely known
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order, such as movie or song titles, the
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<<search-suggesters-completion,completion suggester>> is a much more efficient
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choice than edge N-grams. Edge N-grams have the advantage when trying to
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autocomplete words that can appear in any order.
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[float]
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=== Example output
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With the default settings, the `edge_ngram` tokenizer treats the initial text as a
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single token and produces N-grams with minimum length `1` and maximum length
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`2`:
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[source,js]
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---------------------------
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POST _analyze
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{
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"tokenizer": "edge_ngram",
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"text": "Quick Fox"
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}
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---------------------------
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// CONSOLE
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/////////////////////
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[source,js]
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----------------------------
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{
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"tokens": [
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{
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"token": "Q",
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"start_offset": 0,
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"end_offset": 1,
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"type": "word",
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"position": 0
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},
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{
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"token": "Qu",
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"start_offset": 0,
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"end_offset": 2,
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"type": "word",
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"position": 1
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}
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]
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}
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----------------------------
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// TESTRESPONSE
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/////////////////////
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The above sentence would produce the following terms:
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[source,text]
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---------------------------
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[ Q, Qu ]
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---------------------------
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NOTE: These default gram lengths are almost entirely useless. You need to
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configure the `edge_ngram` before using it.
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[float]
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=== Configuration
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The `edge_ngram` tokenizer accepts the following parameters:
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[horizontal]
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`min_gram`::
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Minimum length of characters in a gram. Defaults to `1`.
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`max_gram`::
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Maximum length of characters in a gram. Defaults to `2`.
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`token_chars`::
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Character classes that should be included in a token. Elasticsearch
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will split on characters that don't belong to the classes specified.
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Defaults to `[]` (keep all characters).
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+
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Character classes may be any of the following:
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+
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* `letter` -- for example `a`, `b`, `ï` or `京`
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* `digit` -- for example `3` or `7`
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* `whitespace` -- for example `" "` or `"\n"`
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* `punctuation` -- for example `!` or `"`
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* `symbol` -- for example `$` or `√`
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[float]
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=== Example configuration
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In this example, we configure the `edge_ngram` tokenizer to treat letters and
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digits as tokens, and to produce grams with minimum length `2` and maximum
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length `10`:
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[source,js]
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----------------------------
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PUT my_index
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"my_analyzer": {
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"tokenizer": "my_tokenizer"
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}
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},
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"tokenizer": {
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"my_tokenizer": {
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"type": "edge_ngram",
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"min_gram": 2,
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"max_gram": 10,
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"token_chars": [
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"letter",
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"digit"
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]
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}
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}
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}
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}
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}
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POST my_index/_analyze
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{
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"analyzer": "my_analyzer",
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"text": "2 Quick Foxes."
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}
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----------------------------
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// CONSOLE
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/////////////////////
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[source,js]
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----------------------------
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{
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"tokens": [
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{
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"token": "Qu",
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"start_offset": 2,
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"end_offset": 4,
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"type": "word",
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"position": 0
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},
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{
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"token": "Qui",
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"start_offset": 2,
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"end_offset": 5,
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"type": "word",
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"position": 1
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},
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{
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"token": "Quic",
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"start_offset": 2,
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"end_offset": 6,
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"type": "word",
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"position": 2
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},
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{
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"token": "Quick",
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"start_offset": 2,
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"end_offset": 7,
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"type": "word",
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"position": 3
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},
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{
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"token": "Fo",
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"start_offset": 8,
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"end_offset": 10,
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"type": "word",
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"position": 4
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},
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{
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"token": "Fox",
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"start_offset": 8,
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"end_offset": 11,
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"type": "word",
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"position": 5
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},
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{
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"token": "Foxe",
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"start_offset": 8,
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"end_offset": 12,
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"type": "word",
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"position": 6
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},
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{
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"token": "Foxes",
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"start_offset": 8,
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"end_offset": 13,
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"type": "word",
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"position": 7
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}
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]
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}
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----------------------------
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// TESTRESPONSE
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/////////////////////
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The above example produces the following terms:
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[source,text]
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---------------------------
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[ Qu, Qui, Quic, Quick, Fo, Fox, Foxe, Foxes ]
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---------------------------
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Usually we recommend using the same `analyzer` at index time and at search
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time. In the case of the `edge_ngram` tokenizer, the advice is different. It
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only makes sense to use the `edge_ngram` tokenizer at index time, to ensure
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that partial words are available for matching in the index. At search time,
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just search for the terms the user has typed in, for instance: `Quick Fo`.
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Below is an example of how to set up a field for _search-as-you-type_:
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[source,js]
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-----------------------------------
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PUT my_index
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"autocomplete": {
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"tokenizer": "autocomplete",
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"filter": [
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"lowercase"
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]
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},
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"autocomplete_search": {
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"tokenizer": "lowercase"
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}
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},
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"tokenizer": {
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"autocomplete": {
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"type": "edge_ngram",
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"min_gram": 2,
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"max_gram": 10,
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"token_chars": [
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"letter"
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]
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}
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}
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}
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},
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"mappings": {
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"doc": {
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"properties": {
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"title": {
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"type": "text",
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"analyzer": "autocomplete",
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"search_analyzer": "autocomplete_search"
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}
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}
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}
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}
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}
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PUT my_index/doc/1
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{
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"title": "Quick Foxes" <1>
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}
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POST my_index/_refresh
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GET my_index/_search
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{
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"query": {
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"match": {
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"title": {
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"query": "Quick Fo", <2>
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"operator": "and"
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}
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}
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}
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}
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-----------------------------------
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// CONSOLE
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<1> The `autocomplete` analyzer indexes the terms `[qu, qui, quic, quick, fo, fox, foxe, foxes]`.
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<2> The `autocomplete_search` analyzer searches for the terms `[quick, fo]`, both of which appear in the index.
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/////////////////////
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[source,js]
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----------------------------
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{
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"took": $body.took,
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"timed_out": false,
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"_shards": {
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"total": 5,
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"successful": 5,
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"skipped" : 0,
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"failed": 0
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},
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"hits": {
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"total": 1,
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"max_score": 0.5753642,
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"hits": [
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{
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"_index": "my_index",
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"_type": "doc",
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"_id": "1",
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"_score": 0.5753642,
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"_source": {
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"title": "Quick Foxes"
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}
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}
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]
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}
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}
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----------------------------
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// TESTRESPONSE[s/"took".*/"took": "$body.took",/]
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/////////////////////
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