Elasticsearch的增删改查
Elasticsearch的增删改查
在使用elasticsearch-head建立索引的时候就咱们查看索引的时候能够看到mappings的关键字，该关键字是建立结构化索引的关键字。若是使用head来建立索引，那么该索引是非结结构化的。若是要建立结构化的索引，须要使用如下的方式app
除此以外咱们也能够使用postman去建立结构化的索引less
在elasticsearch-head能够看到以下的信息：elasticsearch
此时咱们就建立好了一个结构化的索引post
插入数据
插入自定义id的文档
查看elasticsearch-head的数据以下:spa
elasticsearch分配id
elasticsearch一样能够看到我刚刚插入的数据3d
修改数据
直接修改文档
在elastisearch-head能够查看到code
脚本修改文档
http://localhost:9200/people/_doc/1/_updatecdn
{
"script":{
#ES内置的脚本
"lang":"painless",
#ctx是es的上下文，这里是将年龄+10
"inline":"ctx._source.age+=10"
}
}
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返回结果blog
{
"_index": "people",
"_type": "_doc",
"_id": "1",
"_version": 3,
"result": "updated",
"_shards": {
"total": 2,
"successful": 2,
"failed": 0
},
"_seq_no": 3,
"_primary_term": 3
}
在elasticsearch-head也能够看到数据的变化
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除了上面的方式更新以外，咱们还能够把更新的数据做为参数提出来索引
{
"script":{
"lang":"painless",
"inline":"ctx._source.age=params.age",
"params":{
"age":30
}
}
}
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在elasticsearch-head就能够发现shuhu 变化
删除
删除一个文档
此时elasticsearch-head已经没有了这个文档
删除一个索引
再去elasticsearch-head查看发现已经不存在该索引以及该索引如下的数据了，索引删除操做时一个比较危险的操做，必定要很当心
查询
简单查询
咱们只须要http://localhost:9200/book/_doc/1发送请求就能够获取到id为1的文档
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条件查询
聚合查询
须要注意的是Elasticsearch没法对文本进行聚合
{
"aggs":{
"group_by_word_count":{
"terms":{
"field":"word_count"
}
},
"group_by_publish_date":{
"terms":{
"field":"publish_date"
}
}
}
}
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返回结果以下:
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 3,
"successful": 3,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "book",
"_type": "_doc",
"_id": "5",
"_score": 1,
"_source": {
"author": "王八",
"word_count": 3000,
"title": "Python入门",
"publish_date": "2017-03-10"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "7",
"_score": 1,
"_source": {
"author": "石仔",
"word_count": 10000,
"title": "石仔的吃货史",
"publish_date": "2018-06-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "2",
"_score": 1,
"_source": {
"author": "李四",
"word_count": 2000,
"title": "李启明的成长记",
"publish_date": "2018-06-01"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "3",
"_score": 1,
"_source": {
"author": "王五",
"word_count": 2000,
"title": "Elasticsearch入门",
"publish_date": "2018-10-01"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "4",
"_score": 1,
"_source": {
"author": "赵六",
"word_count": 100000,
"title": "Elasticsearch进阶",
"publish_date": "2017-11-11"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "10",
"_score": 1,
"_source": {
"author": "jack",
"word_count": 5000,
"title": "Elasticsearch的深刻理解",
"publish_date": "2018-05-09"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "1",
"_score": 1,
"_source": {
"author": "张三",
"word_count": 1000,
"title": "旺仔的成长记",
"publish_date": "2018-06-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "6",
"_score": 1,
"_source": {
"author": "老九",
"word_count": 3000,
"title": "老干妈的回忆录",
"publish_date": "2018-03-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "8",
"_score": 1,
"_source": {
"author": "李启明",
"word_count": 2000,
"title": "旺仔回忆录",
"publish_date": "2017-06-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "9",
"_score": 1,
"_source": {
"author": "旺仔",
"word_count": 2000,
"title": "李启明回忆录",
"publish_date": "2017-06-01"
}
}
]
},
"aggregations": {
"group_by_publish_date": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 1527897600000,
"key_as_string": "2018-06-02 00:00:00",
"doc_count": 2
},
{
"key": 1489104000000,
"key_as_string": "2017-03-10 00:00:00",
"doc_count": 1
},
{
"key": 1496275200000,
"key_as_string": "2017-06-01 00:00:00",
"doc_count": 1
},
{
"key": 1496361600000,
"key_as_string": "2017-06-02 00:00:00",
"doc_count": 1
},
{
"key": 1510358400000,
"key_as_string": "2017-11-11 00:00:00",
"doc_count": 1
},
{
"key": 1519948800000,
"key_as_string": "2018-03-02 00:00:00",
"doc_count": 1
},
{
"key": 1525824000000,
"key_as_string": "2018-05-09 00:00:00",
"doc_count": 1
},
{
"key": 1527811200000,
"key_as_string": "2018-06-01 00:00:00",
"doc_count": 1
},
{
"key": 1538352000000,
"key_as_string": "2018-10-01 00:00:00",
"doc_count": 1
}
]
},
"group_by_word_count": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 2000,
"doc_count": 4
},
{
"key": 3000,
"doc_count": 2
},
{
"key": 1000,
"doc_count": 1
},
{
"key": 5000,
"doc_count": 1
},
{
"key": 10000,
"doc_count": 1
},
{
"key": 100000,
"doc_count": 1
}
]
}
}
}
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还能够这样聚合
{
"aggs":{
"grade_word_count":{
"stats":{
"field":"word_count"
}
}
}
}
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获得以下的聚合结果：
"aggregations": {
"grade_word_count": {
"count": 10,
"min": 1000,
"max": 100000,
"avg": 13000,
"sum": 130000
}
}
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