本文由 简悦 SimpRead 转码， 原文地址 blog.csdn.net
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：
1. PUT movies
2. {
3. "settings": {
4. "analysis": {
5. "analyzer": {
6. "en_analyzer": {
7. "tokenizer": "standard",
8. "filter": [
9. "lowercase",
10. "stop"
11. ]
12. },
13. "shingle_analyzer": {
14. "type": "custom",
15. "tokenizer": "standard",
16. "filter": [
17. "lowercase",
18. "shingle_filter"
19. ]
20. }
21. },
22. "filter": {
23. "shingle_filter": {
24. "type": "shingle",
25. "min_shingle_size": 2,
26. "max_shingle_size": 3
27. }
28. }
29. }
30. },
31. "mappings": {
32. "properties": {
33. "title": {
34. "type": "text",
35. "analyzer": "en_analyzer",
36. "fields": {
37. "suggest": {
38. "type": "text",
39. "analyzer": "shingle_analyzer"
40. }
41. }
42. },
43. "actors": {
44. "type": "text",
45. "analyzer": "en_analyzer",
46. "fields": {
47. "keyword": {
48. "type": "keyword",
49. "ignore_above": 256
50. }
51. }
52. },
53. "description": {
54. "type": "text",
55. "analyzer": "en_analyzer",
56. "fields": {
57. "keyword": {
58. "type": "keyword",
59. "ignore_above": 256
60. }
61. }
62. },
63. "director": {
64. "type": "text",
65. "fields": {
66. "keyword": {
67. "type": "keyword",
68. "ignore_above": 256
69. }
70. }
71. },
72. "genre": {
73. "type": "text",
74. "fields": {
75. "keyword": {
76. "type": "keyword",
77. "ignore_above": 256
78. }
79. }
80. },
81. "metascore": {
82. "type": "long"
83. },
84. "rating": {
85. "type": "float"
86. },
87. "revenue": {
88. "type": "float"
89. },
90. "runtime": {
91. "type": "long"
92. },
93. "votes": {
94. "type": "long"
95. },
96. "year": {
97. "type": "long"
98. },
99. "title_suggest": {
100. "type": "completion",
101. "analyzer": "simple",
102. "preserve_separators": true,
103. "preserve_position_increments": true,
104. "max_input_length": 50
105. }
106. }
107. }
108. }
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：
1. POST movies/_bulk
2. {"index": {}}
3. {"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}
4. {"index": {}}
5. {"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}
7. ....
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：
1. GET movies/_search?filter_path=**.hits
2. {
3. "_source": false,
4. "fields": [
5. "actors"
6. ],
7. "query": {
8. "prefix": {
9. "actors.keyword": {
10. "value": "Mar"
11. }
12. }
13. }
14. }
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：
1. {
2. "hits": {
3. "hits": [
4. {
5. "_index": "movies",
6. "_id": "RgJfWIYBfOmyc7Qq5geX",
7. "_score": 1,
8. "fields": {
9. "actors": [
10. "Mark Wahlberg, Michelle Monaghan, J.K. Simmons, John Goodman"
11. ]
12. }
13. },
14. {
15. "_index": "movies",
16. "_id": "SQJfWIYBfOmyc7Qq5geX",
17. "_score": 1,
18. "fields": {
19. "actors": [
20. "Mark Wahlberg, Kurt Russell, Douglas M. Griffin, James DuMont"
21. ]
22. }
23. },
24. {
25. "_index": "movies",
26. "_id": "awJfWIYBfOmyc7Qq5geX",
27. "_score": 1,
28. "fields": {
29. "actors": [
30. "Mario Casas, Ana Wagener, José Coronado, Bárbara Lennie"
31. ]
32. }
33. },
34. {
35. "_index": "movies",
36. "_id": "ggJfWIYBfOmyc7Qq5geX",
37. "_score": 1,
38. "fields": {
39. "actors": [
40. "Mark Wahlberg, Nicola Peltz, Jack Reynor, Stanley Tucci"
41. ]
42. }
43. },
44. {
45. "_index": "movies",
46. "_id": "mgJfWIYBfOmyc7Qq5geX",
47. "_score": 1,
48. "fields": {
49. "actors": [
50. "Mark Rylance, Ruby Barnhill, Penelope Wilton,Jemaine Clement"
51. ]
52. }
53. },
54. {
55. "_index": "movies",
56. "_id": "xAJfWIYBfOmyc7Qq5geX",
57. "_score": 1,
58. "fields": {
59. "actors": [
60. "Mark Ruffalo, Michael Keaton, Rachel McAdams, Liev Schreiber"
61. ]
62. }
63. },
64. {
65. "_index": "movies",
66. "_id": "3gJfWIYBfOmyc7Qq5geX",
67. "_score": 1,
68. "fields": {
69. "actors": [
70. "Mark Huberman, Susan Loughnane, Steve Oram,Catherine Walker"
71. ]
72. }
73. },
74. {
75. "_index": "movies",
76. "_id": "EwJfWIYBfOmyc7Qq5giX",
77. "_score": 1,
78. "fields": {
79. "actors": [
80. "Martin Freeman, Ian McKellen, Richard Armitage,Andy Serkis"
81. ]
82. }
83. },
84. {
85. "_index": "movies",
86. "_id": "MQJfWIYBfOmyc7Qq5giX",
87. "_score": 1,
88. "fields": {
89. "actors": [
90. "Mark Wahlberg, Taylor Kitsch, Emile Hirsch, Ben Foster"
91. ]
92. }
93. },
94. {
95. "_index": "movies",
96. "_id": "tgJfWIYBfOmyc7Qq5giY",
97. "_score": 1,
98. "fields": {
99. "actors": [
100. "Marilyn Manson, Mark Boone Junior, Sam Quartin, Niko Nicotera"
101. ]
102. }
103. }
104. ]
105. }
106. }
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：
1. GET movies/_search?filter_path=**.hits
2. {
3. "_source": false,
4. "fields": [
5. "actors"
6. ],
7. "query": {
8. "prefix": {
9. "actors.keyword": "Mar"
10. }
11. }
12. }
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。
1. GET movies/_search?filter_path=**.hits
2. {
3. "_source": false,
4. "query": {
5. "prefix": {
6. "actors.keyword": "Mar"
7. }
8. },
9. "highlight": {
10. "fields": {
11. "actors.keyword": {}
12. }
13. }
14. }
上面的搜索结果显示：
1. {
2. "hits": {
3. "hits": [
4. {
5. "_index": "movies",
6. "_id": "RgJfWIYBfOmyc7Qq5geX",
7. "_score": 1,
8. "highlight": {
9. "actors.keyword": [
10. "<em>Mark Wahlberg, Michelle Monaghan, J.K. Simmons, John Goodman</em>"
11. ]
12. }
13. },
14. {
15. "_index": "movies",
16. "_id": "SQJfWIYBfOmyc7Qq5geX",
17. "_score": 1,
18. "highlight": {
19. "actors.keyword": [
20. "<em>Mark Wahlberg, Kurt Russell, Douglas M. Griffin, James DuMont</em>"
21. ]
22. }
23. },
24. {
25. "_index": "movies",
26. "_id": "awJfWIYBfOmyc7Qq5geX",
27. "_score": 1,
28. "highlight": {
29. "actors.keyword": [
30. "<em>Mario Casas, Ana Wagener, José Coronado, Bárbara Lennie</em>"
31. ]
32. }
33. },
34. {
35. "_index": "movies",
36. "_id": "ggJfWIYBfOmyc7Qq5geX",
37. "_score": 1,
38. "highlight": {
39. "actors.keyword": [
40. "<em>Mark Wahlberg, Nicola Peltz, Jack Reynor, Stanley Tucci</em>"
41. ]
42. }
43. },
44. {
45. "_index": "movies",
46. "_id": "mgJfWIYBfOmyc7Qq5geX",
47. "_score": 1,
48. "highlight": {
49. "actors.keyword": [
50. "<em>Mark Rylance, Ruby Barnhill, Penelope Wilton,Jemaine Clement</em>"
51. ]
52. }
53. },
54. {
55. "_index": "movies",
56. "_id": "xAJfWIYBfOmyc7Qq5geX",
57. "_score": 1,
58. "highlight": {
59. "actors.keyword": [
60. "<em>Mark Ruffalo, Michael Keaton, Rachel McAdams, Liev Schreiber</em>"
61. ]
62. }
63. },
64. {
65. "_index": "movies",
66. "_id": "3gJfWIYBfOmyc7Qq5geX",
67. "_score": 1,
68. "highlight": {
69. "actors.keyword": [
70. "<em>Mark Huberman, Susan Loughnane, Steve Oram,Catherine Walker</em>"
71. ]
72. }
73. },
74. {
75. "_index": "movies",
76. "_id": "EwJfWIYBfOmyc7Qq5giX",
77. "_score": 1,
78. "highlight": {
79. "actors.keyword": [
80. "<em>Martin Freeman, Ian McKellen, Richard Armitage,Andy Serkis</em>"
81. ]
82. }
83. },
84. {
85. "_index": "movies",
86. "_id": "MQJfWIYBfOmyc7Qq5giX",
87. "_score": 1,
88. "highlight": {
89. "actors.keyword": [
90. "<em>Mark Wahlberg, Taylor Kitsch, Emile Hirsch, Ben Foster</em>"
91. ]
92. }
93. },
94. {
95. "_index": "movies",
96. "_id": "tgJfWIYBfOmyc7Qq5giY",
97. "_score": 1,
98. "highlight": {
99. "actors.keyword": [
100. "<em>Marilyn Manson, Mark Boone Junior, Sam Quartin, Niko Nicotera</em>"
101. ]
102. }
103. }
104. ]
105. }
106. }
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：
1. PUT new_movies
2. {
3. "settings": {
4. "analysis": {
5. "analyzer": {
6. "en_analyzer": {
7. "tokenizer": "standard",
8. "filter": [
9. "lowercase",
10. "stop"
11. ]
12. },
13. "shingle_analyzer": {
14. "type": "custom",
15. "tokenizer": "standard",
16. "filter": [
17. "lowercase",
18. "shingle_filter"
19. ]
20. }
21. },
22. "filter": {
23. "shingle_filter": {
24. "type": "shingle",
25. "min_shingle_size": 2,
26. "max_shingle_size": 3
27. }
28. }
29. }
30. },
31. "mappings": {
32. "properties": {
33. "title": {
34. "type": "text",
35. "index_prefixes": {}
36. },
37. "actors": {
38. "type": "text",
39. "analyzer": "en_analyzer",
40. "fields": {
41. "keyword": {
42. "type": "keyword",
43. "ignore_above": 256
44. }
45. }
46. },
47. "description": {
48. "type": "text",
49. "analyzer": "en_analyzer",
50. "fields": {
51. "keyword": {
52. "type": "keyword",
53. "ignore_above": 256
54. }
55. }
56. },
57. "director": {
58. "type": "text",
59. "fields": {
60. "keyword": {
61. "type": "keyword",
62. "ignore_above": 256
63. }
64. }
65. },
66. "genre": {
67. "type": "text",
68. "fields": {
69. "keyword": {
70. "type": "keyword",
71. "ignore_above": 256
72. }
73. }
74. },
75. "metascore": {
76. "type": "long"
77. },
78. "rating": {
79. "type": "float"
80. },
81. "revenue": {
82. "type": "float"
83. },
84. "runtime": {
85. "type": "long"
86. },
87. "votes": {
88. "type": "long"
89. },
90. "year": {
91. "type": "long"
92. },
93. "title_suggest": {
94. "type": "completion",
95. "analyzer": "simple",
96. "preserve_separators": true,
97. "preserve_position_increments": true,
98. "max_input_length": 50
99. }
100. }
101. }
102. }
在上面，我们为 new_movies 添加了如下的 index_prefixes 相：
1. "title": {
2. "type": "text",
3. "index_prefixes": {},
4. "analyzer": "en_analyzer",
5. "fields": {
6. "suggest": {
7. "type": "text",
8. "analyzer": "shingle_analyzer"
9. }
10. }
11. }
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：
1. POST _reindex
2. {
3. "source": {
4. "index": "movies"
5. },
6. "dest": {
7. "index": "new_movies"
8. }
9. }
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。
1. PUT my-index-000001
2. {
3. "mappings": {
4. "properties": {
5. "full_name": {
6. "type": "text",
7. "index_prefixes": {
8. "min_chars" : 1,
9. "max_chars" : 10
10. }
11. }
12. }
13. }
14. }
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：
1. GET new_movies/_search?filter_path=**.hits
2. {
3. "_source": false,
4. "fields": [
5. "title"
6. ],
7. "query": {
8. "prefix": {
9. "title": {
10. "value": "ga"
11. }
12. }
13. }
14. }
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：
1. {
2. "hits": {
3. "hits": [
4. {
5. "_index": "new_movies",
6. "_id": "BAJfWIYBfOmyc7Qq5geX",
7. "_score": 1,
8. "fields": {
9. "title": [
10. "Guardians of the Galaxy"
11. ]
12. }
13. },
14. {
15. "_index": "new_movies",
16. "_id": "jQJfWIYBfOmyc7Qq5geX",
17. "_score": 1,
18. "fields": {
19. "title": [
20. "The Great Gatsby"
21. ]
22. }
23. },
24. {
25. "_index": "new_movies",
26. "_id": "lQJfWIYBfOmyc7Qq5geX",
27. "_score": 1,
28. "fields": {
29. "title": [
30. "Ah-ga-ssi"
31. ]
32. }
33. },
34. {
35. "_index": "new_movies",
36. "_id": "mwJfWIYBfOmyc7Qq5geX",
37. "_score": 1,
38. "fields": {
39. "title": [
40. "The Hunger Games"
41. ]
42. }
43. },
44. {
45. "_index": "new_movies",
46. "_id": "sAJfWIYBfOmyc7Qq5geX",
47. "_score": 1,
48. "fields": {
49. "title": [
50. "Beyond the Gates"
51. ]
52. }
53. },
54. {
55. "_index": "new_movies",
56. "_id": "ygJfWIYBfOmyc7Qq5geX",
57. "_score": 1,
58. "fields": {
59. "title": [
60. "The Imitation Game"
61. ]
62. }
63. },
64. {
65. "_index": "new_movies",
66. "_id": "jQJfWIYBfOmyc7Qq5giY",
67. "_score": 1,
68. "fields": {
69. "title": [
70. "Whisky Galore"
71. ]
72. }
73. },
74. {
75. "_index": "new_movies",
76. "_id": "nAJfWIYBfOmyc7Qq5giY",
77. "_score": 1,
78. "fields": {
79. "title": [
80. "The Hunger Games: Mockingjay - Part 2"
81. ]
82. }
83. },
84. {
85. "_index": "new_movies",
86. "_id": "1QJfWIYBfOmyc7Qq5giY",
87. "_score": 1,
88. "fields": {
89. "title": [
90. "Sherlock Holmes: A Game of Shadows"
91. ]
92. }
93. },
94. {
95. "_index": "new_movies",
96. "_id": "2gJfWIYBfOmyc7Qq5giY",
97. "_score": 1,
98. "fields": {
99. "title": [
100. "American Gangster"
101. ]
102. }
103. }
104. ]
105. }
106. }
很显然，返回的结果里都含有 "ga" 为开头的单词。
