Elasticsearch Query DSL (语法)
Elasticsearch 提供了一个可以执行查询的 Json 风格的 DSL（domain-specific language 领域特
定语言）。这个被称为 Query DSL。
一. 基本数据格式
举个栗子
GET movies/_search
{
"query": {
"bool": {
"filter": [
{
"terms": {
"title": [
"beautiful"
]
}
},
{
"range": {
"year": {
"gte": 1990,
"lte": 1992
}
}
}
]
}
}
}
//------------------------结果-----------------------------------
{
"took" : 38,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : "movies",
"_type" : "_doc",
"_id" : "130996",
"_score" : 0.0,
"_source" : { // _source: 需要返回的字段
"id" : "130996",
"year" : 1992,
"@version" : "1",
"title" : "The Beautiful Story",
"genre" : [
"Adventure",
"Drama",
"Fantasy"
]
}
}
]
}
}
二. match (匹配查询)
- 对于数据类型的(非字符串), 进行精确匹配
# 找出2012年播放的电影
GET movies/_search
{
"query": {
"match": {
"year": 2012
}
}
}
- 对于字符串类型的字段, 进行全文搜索, 模糊匹配
# 找出标题含有beautiful的电影
GET movies/_search
{
"query": {
"match": {
"title": "beautiful"
}
}
}
- 字符串，多个单词 (分词+全文检索)
# 找出标题含有beautiful mind的电影
GET movies/_search
{
"query": {
"match": {
"title": "beautiful mind" // 包含beautiful 或 mind 或 beautiful mind 的
}
}
}
- 精确匹配字符串
# 精确匹配字符串
GET movies/_search
{
"query": {
"match": {
"title.keyword": "beautiful mind"
}
}
}
三. match_phrase(短语匹配)
将需要匹配的值当成一个整体单词（不分词）进行检索
# 将需要匹配的值当成一个整体单词（不分词）进行检索
GET movies/_search
{
"query": {
"match_phrase": {
"title": "beautiful mind"
}
}
}
四. multi_match(多字段匹配)
# 多字段匹配
GET movies/_search
{
"query": {
"multi_match": {
"query": "beautiful 2010",
"fields": ["title","year"]
}
}
}
五. bool(复合查询)
bool 用来做复合查询： 复合语句可以合并任何其它查询语句，包括复合语句，复合语句之间可以互相嵌套，可以表达非常复杂的逻辑
- must: 必须达到must所列举的所有条件
GET movies/_search
{
"query": {
"bool": {
"must": [
{
"range": {
"year": {
"gte": 1990,
"lte": 1992
}
}
}, //条件1 年份范围内
{
"match": {
"title": "beautiful mind"
}
} // 条件2 标题包含关键词
]
}
}
}
- must_not 必须不是指定的情况
GET movies/_search
{
"size": 1,
"query": {
"bool": {
"must_not": [
{
"range": {
"year": {
"gte": 1990,
"lte": 1992
}
}
},
{
"match": {
"title": "beautiful mind"
}
}
]
}
}
}
- should 应该达到的条件- (匹配到能提高文档得分, 匹配不到不会影响查询结果)
GET movies/_search
{
"size": 1,
"query": {
"bool": {
"must": [
{
"range": {
"year": {
"gte": 1990,
"lte": 1992
}
}
},
{
"match": {
"title": "beautiful"
}
}
],
"should": [
{
"match": {
"title": "mind"
}
}
]
}
}
}
- filter (结果过滤) 为了不计算分数 Elasticsearch 会自动检查场景并且优化查询的执行。
GET movies/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"title": "beautiful"
}
}
],
"filter": [
{
"range": {
"year": {
"gte": 2010,
"lte": 2012
}
}
}
]
}
}
}
六. term(精确匹配)
全文检索字段用 match，其他非 text 字段匹配用 term
举个栗子
GET movies/_search
{
"query": {
"term": {
"year": "2010"
}
}
}
七. aggs(聚合)
创建employee 索引 并准备数据
// 创建索引
PUT employee
{
"mappings": {
"properties": {
"id":{
"type": "integer"
},
"name":{
"type": "keyword"
},
"job":{
"type": "keyword"
},
"age":{
"type": "integer"
},
"gender":{
"type": "keyword"
}
}
}
}
// 准备数据
PUT employee/_bulk
{"index": {"_id": 1}}
{"id": 1, "name": "Bob", "job": "java", "age": 21, "sal": 8000, "gender": "female"}
{"index": {"_id": 2}}
{"id": 2, "name": "Rod", "job": "html", "age": 31, "sal": 18000, "gender": "female"}
{"index": {"_id": 3}}
{"id": 3, "name": "Gaving", "job": "java", "age": 24, "sal": 12000, "gender": "male"}
{"index": {"_id": 4}}
{"id": 4, "name": "King", "job": "dba", "age": 26, "sal": 15000, "gender": "female"}
{"index": {"_id": 5}}
{"id": 5, "name": "Jonhson", "job": "dba", "age": 29, "sal": 16000, "gender": "male"}
{"index": {"_id": 6}}
{"id": 6, "name": "Douge", "job": "java", "age": 41, "sal": 20000, "gender": "female"}
{"index": {"_id": 7}}
{"id": 7, "name": "cutting", "job": "dba", "age": 27, "sal": 7000, "gender": "male"}
{"index": {"_id": 8}}
{"id": 8, "name": "Bona", "job": "html", "age": 22, "sal": 14000, "gender": "female"}
{"index": {"_id": 9}}
{"id": 9, "name": "Shyon", "job": "dba", "age": 20, "sal": 19000, "gender": "female"}
{"index": {"_id": 10}}
{"id": 10, "name": "James", "job": "html", "age": 18, "sal": 22000, "gender": "male"}
{"index": {"_id": 11}}
{"id": 11, "name": "Golsling", "job": "java", "age": 32, "sal": 23000, "gender": "female"}
{"index": {"_id": 12}}
{"id": 12, "name": "Lily", "job": "java", "age": 24, "sal": 2000, "gender": "male"}
{"index": {"_id": 13}}
{"id": 13, "name": "Jack", "job": "html", "age": 23, "sal": 3000, "gender": "female"}
{"index": {"_id": 14}}
{"id": 14, "name": "Rose", "job": "java", "age": 36, "sal": 6000, "gender": "female"}
{"index": {"_id": 15}}
{"id": 15, "name": "Will", "job": "dba", "age": 38, "sal": 4500, "gender": "male"}
{"index": {"_id": 16}}
{"id": 16, "name": "smith", "job": "java", "age": 32, "sal": 23000, "gender": "male"}
单值: sum, avg, min, max, cardinality
- sum 求和
# 查询工资的总和
GET employee/_search
{
"size": 0,
"aggs": {
"sum_sal": {
"sum": {
"field": "sal"
}
}
}
}
- avg 平均
# 查询平均工资
GET employee/_search
{
"size": 0,
"aggs": {
"avg_sal": {
"avg": {
"field": "sal"
}
}
}
}
- min 最小值
# 查询最低工资
GET employee/_search
{
"size": 0,
"aggs": {
"min_sal": {
"min": {
"field": "sal"
}
}
}
}
- max 最大值
# 查询最高工资
GET employee/_search
{
"size": 0,
"aggs": {
"max_sal": {
"max": {
"field": "sal"
}
}
}
}
- cardinality (去重)
# 查询总共有多少个岗位(对属性去重后count查询)
GET employee/_search
{
"size": 0,
"aggs": {
"sum_job": {
"cardinality": {
"field": "job"
}
}
}
}
返回多值 : stats , terms
- stats
# 查询员工工资信息(数值类型)
GET employee/_search
{
"size": 0,
"aggs": {
"sal_info": {
"stats": {
"field": "sal"
}
}
}
}
//----------------------------结果--------------------------------
{
"took" : 9,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 16,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"sal_info" : {
"count" : 16,
"min" : 2000.0,
"max" : 23000.0,
"avg" : 13281.25,
"sum" : 212500.0
}
}
}
- terms
# 查询每个岗位有多少人
GET employee/_search
{
"size": 0,
"aggs": {
"job_emps_num": {
"terms": {
"field": "job"
}
}
}
}
复杂聚合嵌套
举个栗子
# 查询每个岗位下工资的信息(平均、最高、最少等)
GET employee/_search
{
"size": 0,
"aggs": {
"job_info": {
"terms": {
"field": "job"
},
"aggs": {
"diff_job_sal_info": {
"stats": {
"field": "sal"
}
}
}
}
}
}
# 查询不同工种的男女员工数量、然后统计不同工种下男女员工的工资信息
GET employee/_search
{
"size": 0,
"aggs": {
"job_info": {
"terms": {
"field": "job"
},
"aggs": {
"diff_job_gender_no": {
"terms": {
"field": "gender"
},
"aggs": {
"diff_job_gender_sal_info": {
"stats": {
"field": "sal"
}
}
}
}
}
}
}
}
# 查询年龄最大的两位员工的信息
GET employee/_search
{
"size": 0,
"aggs": {
"older_two_emp": {
"top_hits": {
"size": 2,
"sort": [
{
"age": {
"order": "desc"
}
}
]
}
}
}
}
# 查询不同工资区间员工工资的统计信息
GET employee/_search
{
"size": 0,
"aggs": {
"rang_sal_info": {
"range": {
"field": "sal",
"ranges": [
{
"key": "0 <= sal < 10001",
"to": 10001
},
{
"key": "10001 <= sal < 20001",
"from": 10001,
"to": 20001
},
{
"key": "20001 <= sal < 30001",
"from": 20001,
"to": 30001
}
]
}
}
}
}
# 以直方图的方式以每5000元为一个区间查询员工工资信息
GET employee/_search
{
"size": 0,
"aggs": {
"range_sal_info": {
"histogram": {
"field": "sal",
"interval": 5000,
"extended_bounds": {
"min": 0,
"max": 15000
}
}
}
}
}
# 查询平均工资最低的工种
GET employee/_search
{
"size": 0,
"aggs": {
"job_info": {
"terms": {
"field": "job"
},
"aggs": {
"diff_job_avg_sal": {
"avg": {
"field": "sal"
}
}
}
},
"min_avg_sal_job": {
"min_bucket": {
"buckets_path": "job_info>diff_job_avg_sal"
}
}
}
}
# 查询年龄大于30岁的员工的平均工资
GET employee/_search
{
"size": 0,
"query": {
"range": {
"age": {
"gt": 30
}
}
},
"aggs": {
"gt_30_emp_avg_sal": {
"avg": {
"field": "sal"
}
}
}
}
# 查询Java员工的平均工资(不进行相关性算法，效率更高)
GET employee/_search
{
"size": 0,
"query": {
"constant_score": {
"filter": {
"term": {
"job": "java"
}
},
"boost": 1.2
}
},
"aggs": {
"java_emp_avg_sal": {
"avg": {
"field": "sal"
}
}
}
}
# 求30岁以上的员工平均工资和所有员工的平均工资
GET employee/_search
{
"size": 0,
"aggs": {
"all_emp_avg_sal": {
"avg": {
"field": "sal"
}
},
"gt_30_emp_avg_info": {
"filter": {
"range": {
"age": {
"gt": 30
}
}
},
"aggs": {
"gt_30_emp_avg_sal": {
"avg": {
"field": "sal"
}
}
}
}
}
}