Prefix queries 被用于在查询时返回在提供的字段中包含特定前缀的文档。有时我们可能想使用前缀查询单词，例如 Leonardo 的 Leo 或 Marlon Brando、Mark Hamill 或 Martin Balsam 的 Mar。 Elasticsearch 提供了一个前缀查询，用于获取匹配单词开头部分（前缀）的记录。
准备数据
示例
我们先准备数据。我们想创建如下的一个 movies 的索引：
-
PUT movies
-
{
-
"settings": {
-
"analysis": {
-
"analyzer": {
-
"en_analyzer": {
-
"tokenizer":
"standard",
-
"filter": [
-
"lowercase",
-
"stop"
-
]
-
},
-
"shingle_analyzer": {
-
"type":
"custom",
-
"tokenizer":
"standard",
-
"filter": [
-
"lowercase",
-
"shingle_filter"
-
]
-
}
-
},
-
"filter": {
-
"shingle_filter": {
-
"type":
"shingle",
-
"min_shingle_size":
2,
-
"max_shingle_size":
3
-
}
-
}
-
}
-
},
-
"mappings": {
-
"properties": {
-
"title": {
-
"type":
"text",
-
"analyzer":
"en_analyzer",
-
"fields": {
-
"suggest": {
-
"type":
"text",
-
"analyzer":
"shingle_analyzer"
-
}
-
}
-
},
-
"actors": {
-
"type":
"text",
-
"analyzer":
"en_analyzer",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"description": {
-
"type":
"text",
-
"analyzer":
"en_analyzer",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"director": {
-
"type":
"text",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"genre": {
-
"type":
"text",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"metascore": {
-
"type":
"long"
-
},
-
"rating": {
-
"type":
"float"
-
},
-
"revenue": {
-
"type":
"float"
-
},
-
"runtime": {
-
"type":
"long"
-
},
-
"votes": {
-
"type":
"long"
-
},
-
"year": {
-
"type":
"long"
-
},
-
"title_suggest": {
-
"type":
"completion",
-
"analyzer":
"simple",
-
"preserve_separators":
true,
-
"preserve_position_increments":
true,
-
"max_input_length":
50
-
}
-
}
-
}
-
}
我们接下来使用 _bulk 命令来写入一些文档到这个索引中去。我们使用这个链接中的内容。我们使用如下的方法：
-
POST movies
/_bulk
-
{
"index": {}}
-
{
"title":
"Guardians of the Galaxy",
"genre":
"Action,Adventure,Sci-Fi",
"director":
"James Gunn",
"actors":
"Chris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana",
"description":
"A group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe.",
"year":
2014,
"runtime":
121,
"rating":
8.1,
"votes":
757074,
"revenue":
333.13,
"metascore":
76}
-
{
"index": {}}
-
{
"title":
"Prometheus",
"genre":
"Adventure,Mystery,Sci-Fi",
"director":
"Ridley Scott",
"actors":
"Noomi Rapace, Logan Marshall-Green, Michael Fassbender, Charlize Theron",
"description":
"Following clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone.",
"year":
2012,
"runtime":
124,
"rating":
7,
"votes":
485820,
"revenue":
126.46,
"metascore":
65}
-
-
....
在上面，为了说明的方便，我省去了其它的文档。你需要把整个 movies.txt 的文件拷贝过来，并全部写入到 Elasticsearch 中。它共有1000 个文档。
Prefix 查询
我们使用如下的例子来进行查询：
-
GET movies
/_
search?filter_path
=
**.hits
-
{
-
"_source":
false,
-
"fields": [
-
"actors"
-
],
-
"query": {
-
"prefix": {
-
"actors.keyword": {
-
"value":
"Mar"
-
}
-
}
-
}
-
}
当我们搜索前缀 Mar 时，上面的查询获取了演员以 Mar 开头的电影。请注意，我们正在 actors.keyword 字段上运行前缀查询。它是一个 keyword 字段。返回的结果为：
-
{
-
"hits": {
-
"hits": [
-
{
-
"_index":
"movies",
-
"_id":
"RgJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mark Wahlberg, Michelle Monaghan, J.K. Simmons, John Goodman"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"SQJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mark Wahlberg, Kurt Russell, Douglas M. Griffin, James DuMont"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"awJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mario Casas, Ana Wagener, José Coronado, Bárbara Lennie"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"ggJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mark Wahlberg, Nicola Peltz, Jack Reynor, Stanley Tucci"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"mgJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mark Rylance, Ruby Barnhill, Penelope Wilton,Jemaine Clement"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"xAJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mark Ruffalo, Michael Keaton, Rachel McAdams, Liev Schreiber"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"3gJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mark Huberman, Susan Loughnane, Steve Oram,Catherine Walker"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"EwJfWIYBfOmyc7Qq5giX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Martin Freeman, Ian McKellen, Richard Armitage,Andy Serkis"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"MQJfWIYBfOmyc7Qq5giX",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Mark Wahlberg, Taylor Kitsch, Emile Hirsch, Ben Foster"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"tgJfWIYBfOmyc7Qq5giY",
-
"_score":
1,
-
"fields": {
-
"actors": [
-
"Marilyn Manson, Mark Boone Junior, Sam Quartin, Niko Nicotera"
-
]
-
}
-
}
-
]
-
}
-
}
很显然，actors 的列表中都是以 Mar 为开头的列表。
注意：前缀查询是一个昂贵的查询 - 有时会破坏集群的稳定性。
我们不需要在字段块级别添加由 value 组成的对象。 相反，你可以创建一个缩短的版本，如下所示，为简洁起见：
-
GET movies
/_
search?filter_path
=
**.hits
-
{
-
"_source":
false,
-
"fields": [
-
"actors"
-
],
-
"query": {
-
"prefix": {
-
"actors.keyword":
"Mar"
-
}
-
}
-
}
由于我们希望在结果中找出匹配的字段，因此我们将通过在查询中添加高亮来突出显示结果。 我们向前缀查询添加一个 highlight 显示块。 这会突出一个或多个匹配的字段，如下面的清单所示。
-
GET movies
/_
search?filter_path
=
**.hits
-
{
-
"_source":
false,
-
"query": {
-
"prefix": {
-
"actors.keyword":
"Mar"
-
}
-
},
-
"highlight": {
-
"fields": {
-
"actors.keyword": {}
-
}
-
}
-
}
上面的搜索结果显示：
-
{
-
"hits": {
-
"hits": [
-
{
-
"_index":
"movies",
-
"_id":
"RgJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mark Wahlberg, Michelle Monaghan, J.K. Simmons, John Goodman</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"SQJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mark Wahlberg, Kurt Russell, Douglas M. Griffin, James DuMont</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"awJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mario Casas, Ana Wagener, José Coronado, Bárbara Lennie</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"ggJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mark Wahlberg, Nicola Peltz, Jack Reynor, Stanley Tucci</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"mgJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mark Rylance, Ruby Barnhill, Penelope Wilton,Jemaine Clement</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"xAJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mark Ruffalo, Michael Keaton, Rachel McAdams, Liev Schreiber</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"3gJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mark Huberman, Susan Loughnane, Steve Oram,Catherine Walker</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"EwJfWIYBfOmyc7Qq5giX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Martin Freeman, Ian McKellen, Richard Armitage,Andy Serkis</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"MQJfWIYBfOmyc7Qq5giX",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Mark Wahlberg, Taylor Kitsch, Emile Hirsch, Ben Foster</em>"
-
]
-
}
-
},
-
{
-
"_index":
"movies",
-
"_id":
"tgJfWIYBfOmyc7Qq5giY",
-
"_score":
1,
-
"highlight": {
-
"actors.keyword": [
-
"<em>Marilyn Manson, Mark Boone Junior, Sam Quartin, Niko Nicotera</em>"
-
]
-
}
-
}
-
]
-
}
-
}
我们之前讨论过，前缀查询在运行查询时会施加额外的计算压力。 幸运的是，有一种方法可以加快这种煞费苦心的性能不佳的前缀查询 —— 将在下一节中讨论。
加速前缀查询
这是因为引擎必须根据前缀（任何带字母的单词）得出结果。 因此，前缀查询运行起来很慢，但有一种机制可以加快它们的速度：在字段上使用 index_prefixes 参数。
我们可以在开发映射模式时在字段上设置 index_prefixes 参数。 例如，下面清单中的映射定义在我们为本练习创建的新索引 new_movies 上使用附加参数 index_prefixes 设置 title 字段（请记住，title 字段是 text 数据类型）。我们按照如下的命令来创建这个新索引：
-
PUT new_movies
-
{
-
"settings": {
-
"analysis": {
-
"analyzer": {
-
"en_analyzer": {
-
"tokenizer":
"standard",
-
"filter": [
-
"lowercase",
-
"stop"
-
]
-
},
-
"shingle_analyzer": {
-
"type":
"custom",
-
"tokenizer":
"standard",
-
"filter": [
-
"lowercase",
-
"shingle_filter"
-
]
-
}
-
},
-
"filter": {
-
"shingle_filter": {
-
"type":
"shingle",
-
"min_shingle_size":
2,
-
"max_shingle_size":
3
-
}
-
}
-
}
-
},
-
"mappings": {
-
"properties": {
-
"title": {
-
"type":
"text",
-
"index_prefixes": {}
-
},
-
"actors": {
-
"type":
"text",
-
"analyzer":
"en_analyzer",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"description": {
-
"type":
"text",
-
"analyzer":
"en_analyzer",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"director": {
-
"type":
"text",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"genre": {
-
"type":
"text",
-
"fields": {
-
"keyword": {
-
"type":
"keyword",
-
"ignore_above":
256
-
}
-
}
-
},
-
"metascore": {
-
"type":
"long"
-
},
-
"rating": {
-
"type":
"float"
-
},
-
"revenue": {
-
"type":
"float"
-
},
-
"runtime": {
-
"type":
"long"
-
},
-
"votes": {
-
"type":
"long"
-
},
-
"year": {
-
"type":
"long"
-
},
-
"title_suggest": {
-
"type":
"completion",
-
"analyzer":
"simple",
-
"preserve_separators":
true,
-
"preserve_position_increments":
true,
-
"max_input_length":
50
-
}
-
}
-
}
-
}
在上面，我们为 new_movies 添加了如下的 index_prefixes 相：
-
"title": {
-
"type":
"text",
-
"index_prefixes": {},
-
"analyzer":
"en_analyzer",
-
"fields": {
-
"suggest": {
-
"type":
"text",
-
"analyzer":
"shingle_analyzer"
-
}
-
}
-
}
从清单中的代码可以看出，title 属性包含一个附加属性 index_prefixes。 这向引擎表明，在索引过程中，它应该创建带有预置前缀的字段并存储这些值。 我们使用如下的代码来写入数据到这个索引中：
-
POST _reindex
-
{
-
"source": {
-
"index":
"movies"
-
},
-
"dest": {
-
"index":
"new_movies"
-
}
-
}
我们使用 reindex 把之前的 movies 里的文档写入到 new_movies 索引中去。
因为我们在上面显示的列表中的 title 字段上设置了 index_prefixes，所以 Elasticsearch 默认为最小字符大小 2 和最大字符大小 5 索引前缀。 这样，当我们运行前缀查询时，就不需要计算前缀了。 相反，它从存储中获取它们。
当然，我们可以更改 Elasticsearch 在索引期间尝试为我们创建的前缀的默认最小和最大大小。 这是通过调整 index_prefixes 对象的大小来完成的，如下面的清单所示。
-
PUT my-
index-
000001
-
{
-
"mappings": {
-
"properties": {
-
"full_name": {
-
"type":
"text",
-
"index_prefixes": {
-
"min_chars" :
1,
-
"max_chars" :
10
-
}
-
}
-
}
-
}
-
}
在清单中，我们要求引擎预先创建最小和最大字符长度分别为 4 个和 10 个字母的前缀。 注意，min_chars 必须大于 0，max_chars 应小于 20 个字符。 这样，我们就可以在索引过程中自定义 Elasticsearch 应该预先创建的前缀。
我们接着可以对 title 字段做类似下面的搜索：
-
GET new_movies
/_
search?filter_path
=
**.hits
-
{
-
"_source":
false,
-
"fields": [
-
"title"
-
],
-
"query": {
-
"prefix": {
-
"title": {
-
"value":
"ga"
-
}
-
}
-
}
-
}
在上面的搜索中，我们查询 titile 字段里 含有 ga 为开头的文档。上述搜索返回如下的结果：
-
{
-
"hits": {
-
"hits": [
-
{
-
"_index":
"new_movies",
-
"_id":
"BAJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"Guardians of the Galaxy"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"jQJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"The Great Gatsby"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"lQJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"Ah-ga-ssi"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"mwJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"The Hunger Games"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"sAJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"Beyond the Gates"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"ygJfWIYBfOmyc7Qq5geX",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"The Imitation Game"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"jQJfWIYBfOmyc7Qq5giY",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"Whisky Galore"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"nAJfWIYBfOmyc7Qq5giY",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"The Hunger Games: Mockingjay - Part 2"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"1QJfWIYBfOmyc7Qq5giY",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"Sherlock Holmes: A Game of Shadows"
-
]
-
}
-
},
-
{
-
"_index":
"new_movies",
-
"_id":
"2gJfWIYBfOmyc7Qq5giY",
-
"_score":
1,
-
"fields": {
-
"title": [
-
"American Gangster"
-
]
-
}
-
}
-
]
-
}
-
}
很显然，返回的结果里都含有 "ga" 为开头的单词。
转载：https://blog.csdn.net/UbuntuTouch/article/details/129084061
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