seo策略是什么_福田区罗湖区最新通告_seo排名如何_南宁seo推广外包

零、文章目录

ElasticSearch04-高级操作

1、文档添加

（1）生成文档ID

不指定 ID，即自动生成ID，ID 是一行数据的唯一键。
语法：POST /index/_doc

# 创建索引
PUT testid# 默认情况下自动生成ID
POST /testid/_doc
{"test_field": "test"
}# 返回
{"_index" : "testid","_type" : "_doc","_id" : "pyxXs5MBW--orQEFRZhH","_version" : 1,"result" : "created","_shards" : {"total" : 2,"successful" : 1,"failed" : 0},"_seq_no" : 0,"_primary_term" : 1
}

场景：如果导入 ES 的数据想使用外部定义的 ID，需要在添加文档数据的时候指定 ID。
语法：POST /index/_doc/id

# 指定ID
POST /testid/_doc/1
{"test_field": "testid"
}# 返回 1是指定的ID
{"_index" : "testid","_type" : "_doc","_id" : "1","_version" : 1,"result" : "created","_shards" : {"total" : 2,"successful" : 1,"failed" : 0},"_seq_no" : 1,"_primary_term" : 1
}

2、文档条件查询

（1）准备数据

# 创建索引
PUT teacherPOST /teacher/_doc/001
{"name":"zhangsan","nickname":"zhangsan","sex":"男","age":30
}POST /teacher/_doc/002
{"name":"lisi","nickname":"lisi","sex":"男","age":20
}POST /teacher/_doc/003
{"name":"wangwu1","nickname":"wangwu2","sex":"女","age":40
}POST /teacher/_doc/004
{"name":"zhaoliu1","nickname":"zhaoliu2","sex":"女","age":50
}POST /teacher/_doc/005
{"name":"zhangsan2","nickname":"zhangsan2","sex":"女","age":30
}POST /teacher/_doc/006
{"name":"zhangsan222","nickname":"zhangsan222","sex":"女","age":30
}

（2）查询全部(match_all)

ES中提供了一种强大的检索数据方式，这种检索方式称之为Query DSL,Query DSL是利用Rest API传递JSON格式的请求体与ES进行交互，这种方式的丰富查询语法让ES检索变得更强大，更简洁。
使用_search 表示查询

# 请求
Get /teacher/_search
{"query": {"match_all": {}}
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "002","_score" : 1.0,"_source" : {"name" : "lisi","nickname" : "lisi","sex" : "男","age" : 20}},{"_index" : "teacher","_type" : "_doc","_id" : "003","_score" : 1.0,"_source" : {"name" : "wangwu1","nickname" : "wangwu2","sex" : "女","age" : 40}},{"_index" : "teacher","_type" : "_doc","_id" : "004","_score" : 1.0,"_source" : {"name" : "zhaoliu1","nickname" : "zhaoliu2","sex" : "女","age" : 50}},{"_index" : "teacher","_type" : "_doc","_id" : "005","_score" : 1.0,"_source" : {"name" : "zhangsan2","nickname" : "zhangsan2","sex" : "女","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "006","_score" : 1.0,"_source" : {"name" : "zhangsan222","nickname" : "zhangsan222","sex" : "女","age" : 30}}]}
}

返回的结果说明：
- took：这个字段表示查询执行所花费的毫秒数。在这个例子中，查询执行花费了0毫秒。
- timed_out：这个字段表示查询是否超时。false表示查询没有超时。
- _shards：这个字段包含了关于分片的信息。
  - total：查询被分发到的分片总数。
  - successful：成功处理的分片数。
  - skipped：被跳过的分片数。
  - failed：处理失败的分片数。
- hits：这个字段包含了查询结果的相关信息。
  - total：匹配的文档总数。value表示总数，relation表示总数的类型，这里是eq，表示精确匹配。
  - max_score：所有匹配文档中最高的相关性得分，这里是1.0。
  - hits：实际匹配的文档列表，每个文档包含以下信息：
    - _index：文档所在的索引名称。
    - _type：文档的类型（在Elasticsearch 7.x之后，类型被标记为废弃）。
    - _id：文档的唯一标识符。
    - _score：文档的相关性得分。
    - _source：文档的原始数据，包含了文档的所有字段和值。
- 在hits数组中，每个元素都是一个文档，包含了文档的元数据（如索引名、类型、ID、得分）和文档的内容（在_source字段中）。这个例子中返回了6个文档，每个文档都有name、nickname、sex和age字段。

（3）分词查询(match)

match查询用于在单个字段上执行全文检索。它会使用指定字段的分词器（analyzer）来处理查询文本，然后搜索与这些词元匹配的文档。
只有 text 类型的字段才会进行分词查询。

# 请求
GET /teacher/_search
{"query": {"match": {"name": "zhangsan"}}
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.540445,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.540445,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}}]}
}

（4）精确查询(term)

term查询用于精确匹配，不进行分词处理，适用于关键字（keyword）、数字、日期等精确值字段。

# 请求
GET /teacher/_search
{"query": {"term": {"name": {"value": "zhangsan"}}}
}# 返回
{"took" : 1,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.540445,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.540445,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}}]}
}

（5）范围查询(range)

# 请求
GET /teacher/_search
{"query": {"range": {"age": {"gte": 30,"lte": 30}}}
}# 返回
{"took" : 1,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 3,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "005","_score" : 1.0,"_source" : {"name" : "zhangsan2","nickname" : "zhangsan2","sex" : "女","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "006","_score" : 1.0,"_source" : {"name" : "zhangsan222","nickname" : "zhangsan222","sex" : "女","age" : 30}}]}
}

（6）前缀查询(prefix)

# 请求
GET /teacher/_search
{"query": {"prefix": {"name": {"value": "zhangsan"}}}
}# 返回
{"took" : 7,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 3,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "005","_score" : 1.0,"_source" : {"name" : "zhangsan2","nickname" : "zhangsan2","sex" : "女","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "006","_score" : 1.0,"_source" : {"name" : "zhangsan222","nickname" : "zhangsan222","sex" : "女","age" : 30}}]}
}

（7）通配符查询(wildcard)

?：匹配任意一个字符
*：匹配任意多个字符

# 请求
GET /teacher/_search
{"query": {"wildcard": {"name": {"value": "*san*"}}}
}# 返回
{"took" : 6,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 3,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "005","_score" : 1.0,"_source" : {"name" : "zhangsan2","nickname" : "zhangsan2","sex" : "女","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "006","_score" : 1.0,"_source" : {"name" : "zhangsan222","nickname" : "zhangsan222","sex" : "女","age" : 30}}]}
}

（8）多id查询(ids)

# 请求
GET /teacher/_search
{"query": {"ids": {"values": ["001","003","005"]}}
}#返回
{"took" : 1,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 3,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "003","_score" : 1.0,"_source" : {"name" : "wangwu1","nickname" : "wangwu2","sex" : "女","age" : 40}},{"_index" : "teacher","_type" : "_doc","_id" : "005","_score" : 1.0,"_source" : {"name" : "zhangsan2","nickname" : "zhangsan2","sex" : "女","age" : 30}}]}
}

（9）模糊查询(fuzzy)

Elasticsearch中的fuzzy查询允许用户搜索包含与指定查询词相似的文档，即使它们不完全相同。这种查询特别适用于处理近似或不精确的搜索词，例如用户可能打错字或输入同一词的不同变体的情况。
以下是fuzzy查询的一些关键点：
- 编辑距离（Edit Distance）：fuzzy查询使用Levenshtein编辑距离来衡量搜索词之间的相似度。编辑距离是指将一个词转换成另一个词所需的单字符更改次数，包括改变一个字符、删除一个字符、插入一个字符或交换两个相邻字符。
- 创建变体（Creating Variations）：fuzzy查询在指定的编辑距离内创建搜索词的所有可能变体，然后返回每个变体的精确匹配。
- 参数配置：
  - fuzziness：允许的最大编辑距离，默认为AUTO。可以设置为AUTO或一个整数，表示允许的编辑次数。
  - max_expansions：创建的变体的最大数量，默认为50。高值可能会导致性能问题，因为会检查大量的变体。
  - prefix_length：在创建变体时保持不变的开始字符数，默认为0。
  - transpositions：是否包括两个相邻字符的置换作为编辑，默认为true。
  - rewrite：用于重写查询的方法，如果fuzziness参数不为0，默认使用top_terms_blended_freqs_${max_expansions}。
- 性能考虑：由于fuzzy查询可能会生成大量的变体，特别是当max_expansions参数值较高且prefix_length参数值为0时，可能会影响查询性能。因此，合理配置这些参数对于保持Elasticsearch查询性能至关重要。
- 使用场景：fuzzy查询是term查询的模糊等价物，通常不会直接使用，但理解其工作原理有助于在更高级的match查询中使用模糊匹配。
- 不进行分析：与match查询不同，fuzzy查询是一个term级别的查询，不会对查询词进行分析，它直接在词典中查找与指定fuzziness范围内的所有项。

# 请求
GET /teacher/_search
{"query": {"fuzzy": {"name": "zhangsan"}}
}# 返回
{"took" : 2,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 2,"relation" : "eq"},"max_score" : 1.540445,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.540445,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "005","_score" : 1.3478894,"_source" : {"name" : "zhangsan2","nickname" : "zhangsan2","sex" : "女","age" : 30}}]}
}

（10）组合查询(bool)

must: 相当于&& 同时成立
should: 相当于|| 成立一个就行
must_not: 相当于! 不能满足任何一个

# 请求
GET /teacher/_search
{"query": {"bool": {"must": [{"match": {"name": "zhangsan"}}],"must_not": [{"match": {"age": "40"}}],"should": [{"match": {"sex": "男"}}]}}
}# 返回
{"took" : 3,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 2.5700645,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 2.5700645,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}}]}
}

（11）多字段查询(multi_match)

multi_match查询允许你在多个字段上执行全文检索。与match查询类似，它也会进行分词，但是可以在多个字段上搜索给定的文本

# 请求
GET /teacher/_search
{"query": {"multi_match": {"query": "zhangsan","fields": ["name","nickname"]}}
}# 返回
{"took" : 6,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.540445,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.540445,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}}]}
}

（12）多值查询(terms)

terms 查询和 term 查询一样，但它允许你指定多值进行匹配。类似 SQL 中的 in。

# 请求
GET /teacher/_search
{"query": {"terms": {"name": ["zhangsan","lisi"]}}
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 2,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "002","_score" : 1.0,"_source" : {"name" : "lisi","nickname" : "lisi","sex" : "男","age" : 20}}]}
}

（13）复杂查询(query_string)

Elasticsearch中的query_string查询是一个强大的全文搜索查询，它允许用户使用一个查询字符串来构造复杂的查询。这个查询字符串可以包含多个字段、逻辑操作符（如AND, OR, NOT）以及通配符等。query_string查询非常灵活，可以模拟其他查询类型，如match、bool等。
以下是query_string查询的一些关键点：
- 查询语法：
  - 支持布尔操作符：AND, OR, NOT。
  - 支持通配符查询：*（匹配任意字符）。
  - 支持短语搜索：使用双引号""包围短语。
  - 支持前缀搜索：使用问号?表示单个字符，使用星号*表示任意长度的字符。
  - 支持正则表达式：使用正斜杠/包围正则表达式。
- 字段选择：可以在查询中指定一个或多个字段进行搜索。
- 分析器：默认使用字段的搜索分析器（search analyzer），但也可以使用查询分析器（query analyzer）。
- 默认操作：默认的布尔操作是AND，意味着如果查询字符串中没有指定布尔操作符，那么查询会将所有条件视为AND连接。
- 字段名和值的转义：如果需要在查询中使用特殊字符，可以使用反斜杠\进行转义。
- 性能：由于query_string查询的复杂性，它可能比简单的match查询更消耗资源。因此，在性能敏感的应用中需要谨慎使用。

# 请求
GET /teacher/_search
{"query": {"query_string": {"query": "name:zhansan OR sex:男"}}
}# 返回
{"took" : 9,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 2,"relation" : "eq"},"max_score" : 1.0296195,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0296195,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "002","_score" : 1.0296195,"_source" : {"name" : "lisi","nickname" : "lisi","sex" : "男","age" : 20}}]}
}

分析器的使用：这个查询使用standard分析器来处理查询字符串。

# 请求
GET /teacher/_search
{"query": {"query_string": {"query": "name:zhangsan","analyzer": "standard"}}
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.540445,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.540445,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}}]}
}

3、文档查询结果集处理

（1）查询结果高亮(highlight)

Elasticsearch中的高亮（highlight）功能允许你在搜索结果中突出显示与查询条件匹配的文本片段，以便用户能够快速定位到关键信息。
以下是关于Elasticsearch高亮功能的详细介绍：
- 高亮参数：
 - Elasticsearch提供了多种高亮参数，包括字段设置、自定义标签、片段大小等。
 - 默认情况下，Elasticsearch使用标签来标记高亮的关键字。
- 自定义高亮片段：
 - 用户可以自定义高亮标签，比如使用标签来代替默认的标签。
 - 通过pre_tags和post_tags参数，用户可以指定高亮前后的标签。
- 多字段高亮：
 - 高亮功能可以应用于一个或多个字段，只需在highlight字段中添加相应的字段名称。
- 高亮性能分析：
 - 高亮功能需要字段的实际内容，如果字段未存储（映射未设置store为true），则需要从_source中加载实际值并从中提取相关字段。
高亮查询示例：

# 请求
GET /teacher/_search
{"query": {"term": {"name": {"value": "zhangsan"}}},"highlight": {"fields": {"*":{}}}
}# 返回
{"took" : 33,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.540445,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.540445,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30},"highlight" : {"name" : ["<em>zhangsan</em>"]}}]}
}

自定义标签

# 请求
GET /teacher/_search
{"query": {"term": {"name": {"value": "zhangsan"}}},"highlight": {"pre_tags": "<font color='red'>","post_tags": "</font>","fields": {"name": {}}}
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.540445,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.540445,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30},"highlight" : {"name" : ["<font color='red'>zhangsan</font>"]}}]}
}

（2）指定返回条数(size)

# 请求
GET /teacher/_search
{"query": {"match_all": {}},"size": 2
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30}},{"_index" : "teacher","_type" : "_doc","_id" : "002","_score" : 1.0,"_source" : {"name" : "lisi","nickname" : "lisi","sex" : "男","age" : 20}}]}
}

（3）分页查询(from)

size：取多少条数据
from：从哪条数据开始
size+from 合起来实现分页效果

# 请求
GET /teacher/_search
{"query": {"match_all": {}},"size": 2,"from": 2
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "003","_score" : 1.0,"_source" : {"name" : "wangwu1","nickname" : "wangwu2","sex" : "女","age" : 40}},{"_index" : "teacher","_type" : "_doc","_id" : "004","_score" : 1.0,"_source" : {"name" : "zhaoliu1","nickname" : "zhaoliu2","sex" : "女","age" : 50}}]}
}

（4）指定字段排序(sort)

文本字段（text字段）默认不适用于需要逐文档字段数据的操作，如聚合（aggregations）和排序（sorting）。这是因为text字段会被分词器分词，存储的是词元（tokens），而不是完整的原始字符串。

# 请求
GET /teacher/_search
{"query": {"terms": {"name":["zhangsan","lisi"]}},"sort": [{"age": {"order": "desc"}}]
}# 返回
{"took" : 5,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 2,"relation" : "eq"},"max_score" : null,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : null,"_source" : {"name" : "zhangsan","nickname" : "zhangsan","sex" : "男","age" : 30},"sort" : [30]},{"_index" : "teacher","_type" : "_doc","_id" : "002","_score" : null,"_source" : {"name" : "lisi","nickname" : "lisi","sex" : "男","age" : 20},"sort" : [20]}]}
}

（5）指定返回字段(_source)

includes：来指定想要显示的字段
excludes：来指定不想要显示的字段

# 请求
GET /teacher/_search
{"query": {"terms": {"name":["zhangsan","lisi"]}},"_source": {"includes": ["name","nickname"]}
}# 返回
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 2,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "teacher","_type" : "_doc","_id" : "001","_score" : 1.0,"_source" : {"name" : "zhangsan","nickname" : "zhangsan"}},{"_index" : "teacher","_type" : "_doc","_id" : "002","_score" : 1.0,"_source" : {"name" : "lisi","nickname" : "lisi"}}]}
}

4、文档聚合查询

（1）返回最大值(max)

aggs：表示聚合
- max_age：自定义返回聚合字段名
  - max：取最大值
    - field：聚合的字段是 age
size：0 表示返回聚合结果不返回详细数据

# 请求
GET /teacher/_search
{"aggs":{"max_age":{"max":{"field":"age"}}},"size":0
}# 返回
{"took" : 4,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"max_age" : {"value" : 50.0}}
}

（2）返回最小值(min)

# 请求
GET /teacher/_search
{"aggs":{"min_age":{"min":{"field":"age"}}},"size":0
}# 返回值
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"min_age" : {"value" : 20.0}}
}

（3）返回平均值(avg)

# 请求
GET /teacher/_search
{"aggs":{"avg_age":{"avg":{"field":"age"}}},"size":0
}# 返回值
{"took" : 3,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"avg_age" : {"value" : 33.333333333333336}}
}

（4）返回和(sum)

# 请求
GET /teacher/_search
{"aggs":{"sum_age":{"sum":{"field":"age"}}},"size":0
}# 返回值
{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"sum_age" : {"value" : 200.0}}
}

（5）返回去重行数(cardinality)

# 请求
GET /teacher/_search
{"aggs":{"cardinality_age":{"cardinality":{"field":"age"}}},"size":0
}# 返回值
{"took" : 10,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"cardinality_age" : {"value" : 4}}
}

（6）返回全部聚合(stats)

# 请求
GET /teacher/_search
{"aggs":{"stats_age":{"stats":{"field":"age"}}},"size":0
}# 返回值
{"took" : 2,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"stats_age" : {"count" : 6,"min" : 20.0,"max" : 50.0,"avg" : 33.333333333333336,"sum" : 200.0}}
}

（7）分组聚合(terms)

默认情况下，分组统计的是每个分组的行数

# 请求
GET /teacher/_search
{"aggs":{"terms_age":{"terms":{"field":"age"}}},"size":0
}# 返回值
{"took" : 2,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"terms_age" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : 30,"doc_count" : 3},{"key" : 20,"doc_count" : 1},{"key" : 40,"doc_count" : 1},{"key" : 50,"doc_count" : 1}]}}
}

（8）分组指定聚合(terms+aggs)

# 请求
GET /teacher/_search?size=0
{"aggs": {"group_by_field": {"terms": {"field": "age"},"aggs": {"average_value": {"avg": {"field": "age"}}}}}
}# 返回值
{"took" : 1,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"group_by_field" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : 30,"doc_count" : 3,"average_value" : {"value" : 30.0}},{"key" : 20,"doc_count" : 1,"average_value" : {"value" : 20.0}},{"key" : 40,"doc_count" : 1,"average_value" : {"value" : 40.0}},{"key" : 50,"doc_count" : 1,"average_value" : {"value" : 50.0}}]}}
}

（9）分组聚合排序(terms+aggs+sort)

GET /teacher/_search?size=0
{"aggs": {"group_by_field": {"terms": {"field": "age","order": {"average_value": "desc"}},"aggs": {"average_value": {"avg": {"field": "age"}}}}}
}# 返回值
{"took" : 4,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"group_by_field" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : 50,"doc_count" : 1,"average_value" : {"value" : 50.0}},{"key" : 40,"doc_count" : 1,"average_value" : {"value" : 40.0}},{"key" : 30,"doc_count" : 3,"average_value" : {"value" : 30.0}},{"key" : 20,"doc_count" : 1,"average_value" : {"value" : 20.0}}]}}
}

seo策略是什么_福田区罗湖区最新通告_seo排名如何_南宁seo推广外包

零、文章目录

ElasticSearch04-高级操作

1、文档添加

（1）生成文档ID

2、文档条件查询

（1）准备数据

（2）查询全部(match_all)

（3）分词查询(match)

（4）精确查询(term)

（5）范围查询(range)

（6）前缀查询(prefix)

（7）通配符查询(wildcard)

（8）多id查询(ids)

（9）模糊查询(fuzzy)

（10）组合查询(bool)

（11）多字段查询(multi_match)

（12）多值查询(terms)

（13）复杂查询(query_string)

3、文档查询结果集处理

（1）查询结果高亮(highlight)

（2）指定返回条数(size)

（3）分页查询(from)

（4）指定字段排序(sort)

（5）指定返回字段(_source)

4、文档聚合查询

（1）返回最大值(max)

（2）返回最小值(min)

（3）返回平均值(avg)

（4）返回和(sum)

（5）返回去重行数(cardinality)

（6）返回全部聚合(stats)

（7）分组聚合(terms)

（8）分组指定聚合(terms+aggs)

（9）分组聚合排序(terms+aggs+sort)

最新新闻

热搜词