Elasticsearch的增刪改查
Elasticsearch的增刪改查
在使用elasticsearch-head建立索引的時候就咱們查看索引的時候能夠看到mappings的關鍵字，該關鍵字是建立結構化索引的關鍵字。若是使用head來建立索引，那麼該索引是非結結構化的。若是要建立結構化的索引，須要使用如下的方式app
除此以外咱們也能夠使用postman去建立結構化的索引less
在elasticsearch-head能夠看到以下的信息：elasticsearch
此時咱們就建立好了一個結構化的索引post
插入數據
插入自定義id的文檔
查看elasticsearch-head的數據以下:spa
elasticsearch分配id
elasticsearch一樣能夠看到我剛剛插入的數據3d
修改數據
直接修改文檔
在elastisearch-head能夠查看到code
腳本修改文檔
http://localhost:9200/people/_doc/1/_updatecdn
{
"script":{
#ES內置的腳本
"lang":"painless",
#ctx是es的上下文，這裏是將年齡+10
"inline":"ctx._source.age+=10"
}
}
複製代碼
返回結果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也能夠看到數據的變化
複製代碼
除了上面的方式更新以外，咱們還能夠把更新的數據做爲參數提出來索引
{
"script":{
"lang":"painless",
"inline":"ctx._source.age=params.age",
"params":{
"age":30
}
}
}
複製代碼
在elasticsearch-head就能夠發現shuhu 變化
刪除
刪除一個文檔
此時elasticsearch-head已經沒有了這個文檔
刪除一個索引
再去elasticsearch-head查看發現已經不存在該索引以及該索引如下的數據了，索引刪除操做時一個比較危險的操做，必定要很當心
查詢
簡單查詢
咱們只須要http://localhost:9200/book/_doc/1發送請求就能夠獲取到id爲1的文檔
複製代碼
條件查詢
聚合查詢
須要注意的是Elasticsearch沒法對文本進行聚合
{
"aggs":{
"group_by_word_count":{
"terms":{
"field":"word_count"
}
},
"group_by_publish_date":{
"terms":{
"field":"publish_date"
}
}
}
}
複製代碼
返回結果以下:
{
"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入門",
"publish_date": "2017-03-10"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "7",
"_score": 1,
"_source": {
"author": "石仔",
"word_count": 10000,
"title": "石仔的吃貨史",
"publish_date": "2018-06-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "2",
"_score": 1,
"_source": {
"author": "李四",
"word_count": 2000,
"title": "李啓明的成長記",
"publish_date": "2018-06-01"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "3",
"_score": 1,
"_source": {
"author": "王五",
"word_count": 2000,
"title": "Elasticsearch入門",
"publish_date": "2018-10-01"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "4",
"_score": 1,
"_source": {
"author": "趙六",
"word_count": 100000,
"title": "Elasticsearch進階",
"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": "張三",
"word_count": 1000,
"title": "旺仔的成長記",
"publish_date": "2018-06-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "6",
"_score": 1,
"_source": {
"author": "老九",
"word_count": 3000,
"title": "老乾媽的回憶錄",
"publish_date": "2018-03-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "8",
"_score": 1,
"_source": {
"author": "李啓明",
"word_count": 2000,
"title": "旺仔回憶錄",
"publish_date": "2017-06-02"
}
},
{
"_index": "book",
"_type": "_doc",
"_id": "9",
"_score": 1,
"_source": {
"author": "旺仔",
"word_count": 2000,
"title": "李啓明回憶錄",
"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
}
]
}
}
}
複製代碼
還能夠這樣聚合
{
"aggs":{
"grade_word_count":{
"stats":{
"field":"word_count"
}
}
}
}
複製代碼
獲得以下的聚合結果：
"aggregations": {
"grade_word_count": {
"count": 10,
"min": 1000,
"max": 100000,
"avg": 13000,
"sum": 130000
}
}
複製代碼
相關文章
- 1. Elasticsearch增刪改查
- 2. elasticsearch增刪改查
- 3. ElasticSearch增刪改查
- 4. elasticsearch的增刪改查
- 5. ElasticSearch增刪改查原理
- 6. Elasticsearch Javascript API增刪改查
- 7. Java之Elasticsearch 增刪改查
- 8. Elasticsearch 增刪改
- 9. Elasticsearch增刪改查 之 —— Delete刪除
- 10. Elasticsearch增刪改查 之 —— Get查詢
- 更多相關文章...
- • Hibernate實現增刪改查 - Hibernate教程
- • MySQL修改和刪除索引（DROP INDEX） - MySQL教程
- • 算法總結-二分查找法
- • 漫談MySQL的鎖機制
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
