目录
增
删:
改:
查:
1)新增商品:新增文档,建立索引
格式:
PUT /index/type/id
{
"json数据"
}
例子:
PUT /ecommerce/product/1
{
"name" : "gaolujie yagao",
"desc" : "gaoxiao meibai",
"price" : 30,
"producer" : "gaolujie producer",
"tags": [ "meibai", "fangzhu" ]
}
es会自动建立index和type,不需要提前创建,而且es默认会对document每个field都建立倒排索引,让其可以被搜索
(5)删除商品:删除文档
DELETE /ecommerce/product/1
一般对应到应用程序中,每次的执行流程基本是这样的:
(1)应用程序先发起一个get请求,获取到document,展示到前台界面,供用户查看和修改
(2)用户在前台界面修改数据,发送到后台
(3)后台代码,会将用户修改的数据在内存中进行执行,然后封装好修改后的全量数据
(4)然后发送PUT请求,到es中,进行全量替换
(5)es将老的document标记为deleted,然后重新创建一个新的document
(3)修改商品:两种
替换文档:
PUT /ecommerce/product/1
{
"name" : "jiaqiangban gaolujie yagao",
"desc" : "gaoxiao meibai",
"price" : 30,
"producer" : "gaolujie producer",
"tags": [ "meibai", "fangzhu" ]
}
替换方式有一个不好,即使必须带上所有的field,才能去进行信息的修改(否则只会保留第二次输入的内容)
partial update:更新文档,只用输入需要更改的部分
post /index/type/id/_update
{
"doc": {
"要修改的少数几个field即可,不需要全量的数据"
}
}
partial update,操作起来很方便的操作,实际内部的原理是什么样子的,然后它的优点是
例子:
PUT /test_index/test_type/10
{
"test_field1": "test1",
"test_field2": "test2"
}
POST /test_index/test_type/10/_update
{
"doc": {
"test_field2": "updated test2"
}
}
全查询+过滤器:
query
过滤查询中的match_all
查询,这是一个查询,只是匹配一切。 格式:
curl -XPOST "http://localhost:9200/_search" -d'
{
"query": {
"filtered": {
"query": {
"match_all": { ##查询条件
}
},
"filter": {
"term": { "year": 1962 } ##过滤条件
}
}
}
}'
curl -XPOST "http://localhost:9200/_search" -d'
{
"query": {
"constant_score": {
"filter": {
"term": { "year": 1962 }
}
}
}
}'
(2)查询商品:检索文档
格式:
GET /index/type/id
例子:
GET /ecommerce/product/1
查询方式的分类:
1、query string search:query string search的由来,因为search参数都是以http请求的query string来附带的。适用于临时的在命令行使用一些工具,比如curl,快速的发出请求,来检索想要的信息;但是如果查询请求很复杂,是很难去构建的。在生产环境中,几乎很少使用query string search
例子:GET /ecommerce/product/_search?q=name:yagao&sort=price:desc
2、query DSL:DSL:Domain Specified Language,特定领域的语言
3对查询出来的JSON的分析:
加入全查询:
请求:
GET /ecommerce/product/_search
查询结果如下:
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1,
"hits": [
{
"_index": "ecommerce",
"_type": "product",
"_id": "2",
"_score": 1,
"_source": {
"name": "jiajieshi yagao",
"desc": "youxiao fangzhu",
"price": 25,
"producer": "jiajieshi producer",
"tags": [
"fangzhu"
]
}
},
{
"_index": "ecommerce",
"_type": "product",
"_id": "1",
"_score": 1,
"_source": {
"name": "gaolujie yagao",
"desc": "gaoxiao meibai",
"price": 30,
"producer": "gaolujie producer",
"tags": [
"meibai",
"fangzhu"
]
}
},
{
"_index": "ecommerce",
"_type": "product",
"_id": "3",
"_score": 1,
"_source": {
"name": "zhonghua yagao",
"desc": "caoben zhiwu",
"price": 40,
"producer": "zhonghua producer",
"tags": [
"qingxin"
]
}
}
]
}
}
其中返回的元数据的意义:
格式:
curl -XPOST "http://localhost:9200/_search" -d'
{
"query": {
"query_string": {
"query": "ford",
"fields": ["title"]
}
}
}'
query
和filter
)的查询。执行时,它使用过滤器过滤查询的结果。要完成这样的查询还需要添加一个过滤器格式:
curl -XPOST "http://localhost:9200/_search" -d'
{
"query": {
"filtered": {
"query": {
"query_string": {
"query": "drama"
}
},
"filter": {
"term": { "year": 1962 }
}
}
}
}'
例子:
1.查询并排序
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"name" : "yagao"
}
},
"sort": [
{ "price": "desc" }
]
}
2.分页查询
GET /ecommerce/product/_search
{
"query": { "match_all": {} },
"from": 1,
"size": 1
}
3.指定查询出来的filde
GET /ecommerce/product/_search
{
"query": { "match_all": {} },
"_source": ["name", "price"]
}
高亮搜索:
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"producer" : "producer"
}
},
"highlight": {
"fields" : {
"producer" : {}
}
}
}
phrase search:
GET /ecommerce/product/_search
{
"query" : {
"match_phrase" : {
"producer" : "yagao producer"
}
}
}
全文检索:
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"producer" : "yagao producer"
}
}
}
过滤查询:
GET /ecommerce/product/_search
{
"query" : {
"bool" : {
"must" : {
"match" : {
"name" : "yagao"
}
},
"filter" : {
"range" : {
"price" : { "gt" : 25 }
}
}
}
}
}
第一个分析需求:计算每个tag下的商品数量
GET /ecommerce/product/_search
{
"aggs": {
"group_by_tags": {
"terms": { "field": "tags" }
}
}
}
将文本field的fielddata属性设置为true
PUT /ecommerce/_mapping/product
{
"properties": {
"tags": {
"type": "text",
"fielddata": true
}
}
}
GET /ecommerce/product/_search
{
"size": 0,
"aggs": {
"all_tags": {
"terms": { "field": "tags" }
}
}
}
{
"took": 20,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 4,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "fangzhu",
"doc_count": 2
},
{
"key": "meibai",
"doc_count": 2
},
{
"key": "qingxin",
"doc_count": 1
}
]
}
}
}
----------------------------------------------------------------------------------------------------------------
第二个聚合分析的需求:对名称中包含yagao的商品,计算每个tag下的商品数量
GET /ecommerce/product/_search
{
"size": 0,
"query": {
"match": {
"name": "yagao"
}
},
"aggs": {
"all_tags": {
"terms": {
"field": "tags"
}
}
}
}
----------------------------------------------------------------------------------------------------------------
第三个聚合分析的需求:先分组,再算每组的平均值,计算每个tag下的商品的平均价格
GET /ecommerce/product/_search
{
"size": 0,
"aggs" : {
"group_by_tags" : {
"terms" : { "field" : "tags" },
"aggs" : {
"avg_price" : {
"avg" : { "field" : "price" }
}
}
}
}
}
{
"took": 8,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 4,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "fangzhu",
"doc_count": 2,
"avg_price": {
"value": 27.5
}
},
{
"key": "meibai",
"doc_count": 2,
"avg_price": {
"value": 40
}
},
{
"key": "qingxin",
"doc_count": 1,
"avg_price": {
"value": 40
}
}
]
}
}
}
----------------------------------------------------------------------------------------------------------------
第四个数据分析需求:计算每个tag下的商品的平均价格,并且按照平均价格降序排序
GET /ecommerce/product/_search
{
"size": 0,
"aggs" : {
"all_tags" : {
"terms" : { "field" : "tags", "order": { "avg_price": "desc" } },
"aggs" : {
"avg_price" : {
"avg" : { "field" : "price" }
}
}
}
}
}
我们现在全部都是用es的restful api在学习和讲解es的所欲知识点和功能点,但是没有使用一些编程语言去讲解(比如java),原因有以下:
----------------------------------------------------------------------------------------------------------------
第五个数据分析需求:按照指定的价格范围区间进行分组,然后在每组内再按照tag进行分组,最后再计算每组的平均价格
GET /ecommerce/product/_search
{
"size": 0,
"aggs": {
"group_by_price": {
"range": {
"field": "price",
"ranges": [
{
"from": 0,
"to": 20
},
{
"from": 20,
"to": 40
},
{
"from": 40,
"to": 50
}
]
},
"aggs": {
"group_by_tags": {
"terms": {
"field": "tags"
},
"aggs": {
"average_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}
定制返回结果
定制返回的结果,指定_source中,返回哪些field
GET /test_index/test_type/1?_source=test_field1,test_field2
{
"_index": "test_index",
"_type": "test_type",
"_id": "1",
"_version": 2,
"found": true,
"_source": {
"test_field2": "test field2"
}
}
1、图解Elasticsearch
(1)
PUT /test_index/test_type/6
{
"test_field": "test test"
}
{
"_index": "test_index",
"_type": "test_type",
"_id": "6",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true
}
{
"found": true,
"_index": "test_index",
"_type": "test_type",
"_id": "6",
"_version": 4,
"result": "deleted",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
}
}
es,其实是有个内置的脚本支持的,可以基于groovy脚本实现各种各样的复杂操作。
基于groovy脚本,如何执行partial update:
PUT /test_index/test_type/11
{
"num": 0,
"tags": []
}
(1)内置脚本
POST /test_index/test_type/11/_update
{
"script" : "ctx._source.num+=1"
}
{
"_index": "test_index",
"_type": "test_type",
"_id": "11",
"_version": 2,
"found": true,
"_source": {
"num": 1,
"tags": []
}
}
(2)外部脚本
ctx._source.tags+=new_tag
POST /test_index/test_type/11/_update
{
"script": {
"lang": "groovy",
"file": "test-add-tags",
"params": {
"new_tag": "tag1"
}
}
}
(3)用脚本删除文档
ctx.op = ctx._source.num == count ? 'delete' : 'none'
POST /test_index/test_type/11/_update
{
"script": {
"lang": "groovy",
"file": "test-delete-document",
"params": {
"count": 1
}
}
}
(4)upsert操作
POST /test_index/test_type/11/_update
{
"doc": {
"num": 1
}
}
{
"error": {
"root_cause": [
{
"type": "document_missing_exception",
"reason": "[test_type][11]: document missing",
"index_uuid": "6m0G7yx7R1KECWWGnfH1sw",
"shard": "4",
"index": "test_index"
}
],
"type": "document_missing_exception",
"reason": "[test_type][11]: document missing",
"index_uuid": "6m0G7yx7R1KECWWGnfH1sw",
"shard": "4",
"index": "test_index"
},
"status": 404
}
如果指定的document不存在,就执行upsert中的初始化操作;如果指定的document存在,就执行doc或者script指定的partial update操作
POST /test_index/test_type/11/_update
{
"script" : "ctx._source.num+=1",
"upsert": {
"num": 0,
"tags": []
}
}