分布式高级篇
1 ES
1.1 ELASTIC SEARCH
简介
mysql用作持久化存储，ES用作检索
基本概念：
index索引
动词：相当于mysql的insert
名词：相当于mysql的db
Type类型
在index中，可以定义一个或多个类型
类似于mysql的table，每一种类型的数据放在一起
index库>type表>document文档
Document文档
保存在某个index下，某种type的一个数据document，文档是json格式的，document就像是mysql中的某个table里面的内容。每一行对应的列叫属性
为什么ES搜索快？倒排索引
保存的记录
红海行动
探索红海行动
红海特别行动
红海记录片
特工红海特别探索
将内容分词就记录到索引中
检索：
1 红海特工行动？查出后计算相关性得分：3号记录命中了2次，且3号本身才有3个单词，2/3，所以3号最匹配
2 红海行动？
关系型数据库中两个数据表示是独立的，即使他们里面有相同名称的列也不影响使用，但ES中不是这样的。elasticsearch是基于Lucene开发的搜索引擎，而ES中不同type下名称相同的filed最终在Lucene中的处理方式是一样的。
• 两个不同type下的两个user_name，在ES同一个索引下其实被认为是同一个filed，你必须在两个不同的type中定义相同的filed映射。否则，不同type中的相同字段名称就会在处理中出现冲突的情况，导致Lucene处理效率下降。
去掉type就是为了提高ES处理数据的效率。
Elasticsearch 7.x
URL中的type参数为可选。比如，索引一个文档不再要求提供文档类型。
Elasticsearch 8.x
不再支持URL中的type参数。
解决：将索引从多类型迁移到单类型，每种类型文档一个独立索引
1.2 安装elastic search
dokcer中安装elastic search
下载ealastic search（存储和检索）和kibana（可视化检索）
docker pull elasticsearch:7.4.2
docker pull kibana:7.4.2
注意版本要统一
配置
# 将docker里的目录挂载到linux的/usr/local/elasticsearch/data目录中，修改/mydata就可以改掉docker里的
mkdir -p /usr/local/elasticsearch/config
mkdir -p /usr/local/elasticsearch/data
# es可以被远程任何机器访问
echo "http.host: 0.0.0.0" >/usr/local/elasticsearch/config/elasticsearch.yml
# 递归更改权限，es需要访问
chmod -R 777 /mydata/elasticsearch/
启动Elastic search
# 9200是用户交互端口 9300是集群心跳端口
# -e指定是单阶段运行
# -e指定占用的内存大小，生产时可以设置32G
sudo docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" \
-e ES_JAVA_OPTS="-Xms64m -Xmx512m" \
-v /usr/local/elasticsearch/config/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml \
-v /usr/local/elasticsearch/data:/usr/share/elasticsearch/data \
-v /usr/local/elasticsearch/plugins:/usr/share/elasticsearch/plugins \
-d elasticsearch:7.4.2
1.3 安装kibana
docker pull kibana:7.4.2
启动kibana：
sudo docker run --name kibana -e ELASTICSEARCH_HOSTS=http://192.168.11.129:9200 -p 5601:5601 -d kibana:7.4.2
1.4 测试
查看elasticsearch版本信息： http://192.168.11.129:9200
{
"name": "66718a266132",
"cluster_name": "elasticsearch",
"cluster_uuid": "xhDnsLynQ3WyRdYmQk5xhQ",
"version": {
"number": "7.4.2",
"build_flavor": "default",
"build_type": "docker",
"build_hash": "2f90bbf7 b93631e52bafb59b3b049cb44ec25e96",
"build_date": "2019-10-28T20:40:44.881551Z",
"build_snapshot": false,
"lucene_version": "8.2.0",
"minimum_wire_compatibility_version": "6.8.0",
"minimum_index_compatibility_version": "6.0.0-beta1"
},
"tagline": "You Know, for Search"
}
显示elasticsearch 节点信息http://192.168.11.129:9200/_cat/nodes
127.0.0.1 14 99 25 0.29 0.40 0.22 dilm * 66718a266132
66718a266132代表上面的结点
*代表是主节点
访问Kibana： http://192.168.56.10:5601/app/kibana
1.5 初步检索
1 _CAT
GET /_cat/nodes：查看所有节点
http://192.168.56.10:9200/_cat/nodes
127.0.0.1 12 97 3 0.00 0.01 0.05 dilm * 66718a266132
66718a266132代表结点
*代表是主节点
2 GET /_cat/health：查看es健康状况
http://192.168.56.10:9200/_cat/health
1613741055 13:24:15 elasticsearch green 1 1 0 0 0 0 0 0 - 100.0%
注：green表示健康值正常
GET /_cat/master：查看主节点
http://192.168.56.10:9200/_cat/master
089F76WwSaiJcO6Crk7MpA 127.0.0.1 127.0.0.1 66718a266132
主节点唯一编号
虚拟机地址
GET/_cat/indicies：查看所有索引 ，等价于mysql数据库的show databases;
http://192.168.56.10:9200/_cat/indices
green open .kibana_task_manager_1 DhtDmKrsRDOUHPJm1EFVqQ 1 0 2 3 40.8kb 40.8kb
green open .apm-agent-configuration vxzRbo9sQ1SvMtGkx6aAHQ 1 0 0 0 230b 230b
green open .kibana_1 rdJ5pejQSKWjKxRtx-EIkQ 1 0 5 1 18.2kb 18.2kb
这3个索引是kibana创建的
索引一个文档
保存一个数据，保存在哪个索引的哪个类型下（哪张数据库哪张表下），保存时用唯一标识指定
PUT customer/external/1
http://192.168.56.10:9200/customer/external/1
在customer索引下的external类型下保存1号数据为
{
"name":"John Doe"
}
PUT和POST都可以
POST新增。如果不指定id，会自动生成id。指定id就会修改这个数据，并新增版本号；
PUT可以新增也可以修改。PUT必须指定id；由于PUT需要指定id，我们一般用来做修改操作，不指定id会报错。
唯一区分是post不指定id时永远为创建
创建数据成功后，显示201 created表示插入记录成功。
返回数据：
带有下划线开头的，称为元数据，反映了当前的基本信息。
{
"_index": "customer", 表明该数据在哪个数据库下；
"_type": "external", 表明该数据在哪个类型下；
"_id": "1", 表明被保存数据的id；
"_version": 1, 被保存数据的版本
"result": "created", 这里是创建了一条数据，如果重新put一条数据，则该状态会变为updated，并且版本号也会发生变化。
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 0,
"_primary_term": 1
}
下面选用POST方式：
添加数据的时候，不指定ID，会自动的生成id，并且类型是新增：
{
"_index": "customer",
"_type": "external",
"_id": "5MIjvncBKdY1wAQm-wNZ",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 11,
"_primary_term": 6
}
再次使用POST插入数据，不指定ID，仍然是新增的：
{
"_index": "customer",
"_type": "external",
"_id": "5cIkvncBKdY1wAQmcQNk",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 12,
"_primary_term": 6
}
添加数据的时候，指定ID，会使用该id，并且类型是新增：
{
"_index": "customer",
"_type": "external",
"_id": "2",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 13,
"_primary_term": 6
}
再次使用POST插入数据，指定同样的ID，类型为updated
{
"_index": "customer",
"_type": "external",
"_id": "2",
"_version": 2,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 14,
"_primary_term": 6
}
查看文档
GET /customer/external/1
http://192.168.56.10:9200/customer/external/1
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 10,
"_seq_no": 18,//并发控制字段，每次更新都会+1，用来做乐观锁
"_primary_term": 6,//同上，主分片重新分配，如重启，就会变化
"found": true,
"_source": {
"name": "John Doe"
}
}
通过“if_seq_no=1&if_primary_term=1”，当序列号匹配的时候，才进行修改，否则不修改。
实例：将id=1的数据更新为name=1，然后再次更新为name=2，起始1_seq_no=18，_primary_term=6
将name更新为1
PUT http://192.168.56.10:9200/customer/external/1?if_seq_no=18&if_primary_term=6
将name更新为2，更新过程中使用seq_no=18
PUT http://192.168.56.10:9200/customer/external/1?if_seq_no=18&if_primary_term=6
结果为：
{
"error": {
"root_cause": [
{
"type": "version_conflict_engine_exception",
"reason": "[1]: version conflict, required seqNo [18], primary term [6]. current document has seqNo [19] and primary term [6]",
"index_uuid": "mG9XiCQISPmfBAmL1BPqIw",
"shard": "0",
"index": "customer"
}
],
"type": "version_conflict_engine_exception",
"reason": "[1]: version conflict, required seqNo [18], primary term [6]. current document has seqNo [19] and primary term [6]",
"index_uuid": "mG9XiCQISPmfBAmL1BPqIw",
"shard": "0",
"index": "customer"
},
"status": 409
}
出现更新错误。
查询新的数据
GET http://192.168.56.10:9200/customer/external/1
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 11,
"_seq_no": 19,
"_primary_term": 6,
"found": true,
"_source": {
"name": "1"
}
}
能够看到_seq_no变为19
再次更新，更新成功
PUT http://192.168.56.10:9200/customer/external/1?if_seq_no=19&if_primary_term=1
更新文档_update
POST customer/externel/1/_update
{
"doc":{
"name":"111"
}
}
或者
POST customer/externel/1
{
"doc":{
"name":"222"
}
}
或者
PUT customer/externel/1
{
"doc":{
"name":"222"
}
}
不同：带有update情况下
POST操作会对比源文档数据，如果相同不会有什么操作，文档version不增加。
PUT操作总会重新保存并增加version版本
POST时带_update对比元数据如果一样就不进行任何操作。
看场景：
对于大并发更新，不带update
对于大并发查询偶尔更新，带update；对比更新，重新计算分配规则
POST更新文档，带有_update
http://192.168.56.10:9200/customer/external/1/_update
如果再次执行更新，则不执行任何操作，序列号也不发生变化
返回
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 12,
"result": "noop", // 无操作
"_shards": {
"total": 0,
"successful": 0,
"failed": 0
},
"_seq_no": 20,
"_primary_term": 6
}
POST更新方式，会对比原来的数据，和原来的相同，则不执行任何操作（version和_seq_no）都不变。
POST更新文档，不带_update
在更新过程中，重复执行更新操作，数据也能够更新成功，不会和原来的数据进行对比。
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 13,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 21,
"_primary_term": 6
}
删除文档或索引
DELETE customer/external/1
DELETE customer
注：elasticsearch并没有提供删除类型的操作，只提供了删除索引和文档的操作。
实例：删除id=1的数据，删除后继续查询
DELETE http://192.168.56.10:9200/customer/external/1
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 14,
"result": "deleted",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 22,
"_primary_term": 6
}
再次执行DELETE http://192.168.56.10:9200/customer/external/1
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 15,
"result": "not_found",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 23,
"_primary_term": 6
}
GET http://192.168.56.10:9200/customer/external/1
{
"_index": "customer",
"_type": "external",
"_id": "1",
"found": false
}
实例：删除整个costomer索引数据
删除前，所有的索引http://192.168.56.10:9200/_cat/indices
green open .kibana_task_manager_1 DhtDmKrsRDOUHPJm1EFVqQ 1 0 2 0 31.3kb 31.3kb
green open .apm-agent-configuration vxzRbo9sQ1SvMtGkx6aAHQ 1 0 0 0 283b 283b
green open .kibana_1 rdJ5pejQSKWjKxRtx-EIkQ 1 0 8 3 28.8kb 28.8kb
yellow open customer mG9XiCQISPmfBAmL1BPqIw 1 1 9 1 8.6kb 8.6kb
删除“ customer ”索引
DELTE http://192.168.56.10:9200/customer
响应
{
"acknowledged": true
}
删除后，所有的索引http://192.168.56.10:9200/_cat/indices
green open .kibana_task_manager_1 DhtDmKrsRDOUHPJm1EFVqQ 1 0 2 0 31.3kb 31.3kb
green open .apm-agent-configuration vxzRbo9sQ1SvMtGkx6aAHQ 1 0 0 0 283b 283b
green open .kibana_1 rdJ5pejQSKWjKxRtx-EIkQ 1 0 8 3 28.8kb 28.8kb
ES的批量操作——bulk
匹配导入数据
POST http://192.168.56.10:9200/customer/external/_bulk
两行为一个整体
{"index":{"_id":"1"}}
{"name":"a"}
{"index":{"_id":"2"}}
{"name":"b"}
注意格式json和text均不可，要去kibana里Dev Tools
语法格式：
{action:{metadata}}\n
{request body }\n
{action:{metadata}}\n
{request body }\n
这里的批量操作，当发生某一条执行发生失败时，其他的数据仍然能够接着执行，也就是说彼此之间是独立的。
bulk api以此按顺序执行所有的action（动作）。如果一个单个的动作因任何原因失败，它将继续处理它后面剩余的动作。当bulk api返回时，它将提供每个动作的状态（与发送的顺序相同），所以您可以检查是否一个指定的动作是否失败了。
实例1: 执行多条数据
POST /customer/external/_bulk
{"index":{"_id":"1"}}
{"name":"John Doe"}
{"index":{"_id":"2"}}
{"name":"John Doe"}
执行结果
#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
"took" : 318, 花费了多少ms
"errors" : false, 没有发生任何错误
"items" : [ 每个数据的结果
{
"index" : { 保存
"_index" : "customer", 索引
"_type" : "external", 类型
"_id" : "1", 文档
"_version" : 1, 版本
"result" : "created", 创建
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 0,
"_primary_term" : 1,
"status" : 201 新建完成
}
},
{
"index" : { 第二条记录
"_index" : "customer",
"_type" : "external",
"_id" : "2",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 1,
"_primary_term" : 1,
"status" : 201
}
}
]
}
实例2：对于整个索引执行批量操作
POST /_bulk
{"delete":{"_index":"website","_type":"blog","_id":"123"}}
{"create":{"_index":"website","_type":"blog","_id":"123"}}
{"title":"my first blog post"}
{"index":{"_index":"website","_type":"blog"}}
{"title":"my second blog post"}
{"update":{"_index":"website","_type":"blog","_id":"123"}}
{"doc":{"title":"my updated blog post"}}
运行结果：
#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
"took" : 304,
"errors" : false,
"items" : [
{
"delete" : { 删除
"_index" : "website",
"_type" : "blog",
"_id" : "123",
"_version" : 1,
"result" : "not_found", 没有该记录
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 0,
"_primary_term" : 1,
"status" : 404 没有该
}
},
{
"create" : { 创建
"_index" : "website",
"_type" : "blog",
"_id" : "123",
"_version" : 2,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 1,
"_primary_term" : 1,
"status" : 201
}
},
{
"index" : { 保存
"_index" : "website",
"_type" : "blog",
"_id" : "5sKNvncBKdY1wAQmeQNo",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 2,
"_primary_term" : 1,
"status" : 201
}
},
{
"update" : { 更新
"_index" : "website",
"_type" : "blog",
"_id" : "123",
"_version" : 3,
"result" : "updated",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 3,
"_primary_term" : 1,
"status" : 200
}
}
]
}
样本测试数据
准备了一份顾客银行账户信息的虚构的JSON文档样本。每个文档都有下列的schema（模式）。
{
"account_number": 1,
"balance": 39225,
"firstname": "Amber",
"lastname": "Duke",
"age": 32,
"gender": "M",
"address": "880 Holmes Lane",
"employer": "Pyrami",
"email": "[email protected]",
"city": "Brogan",
"state": "IL"
}
https://github.com/elastic/elasticsearch/blob/master/docs/src/test/resources/accounts.json ，导入测试数据
POST bank/account/_bulk
上面的数据
http://192.168.56.10:9200/_cat/indices
刚导入了1000条
yellow open bank 99m64ElxRuiH46wV7RjXZA 1 1 1000 0 427.8kb 427.8kb
1.6 进阶检索
1 search Api
ES支持两种基本方式检索；
通过REST request uri 发送搜索参数 （uri +检索参数）；
通过REST request body 来发送它们（uri+请求体）；
信息检索
API： https://www.elastic.co/guide/en/elasticsearch/reference/7.x/getting-started-search.html
请求参数方式检索
GET bank/_search?q=*&sort=account_number:asc
检索bank下所有信息，包括type和docs
GET bank/_search
took – 花费多少ms搜索
timed_out – 是否超时
_shards – 多少分片被搜索了，以及多少成功/失败的搜索分片
max_score –文档相关性最高得分
hits.total.value - 多少匹配文档被找到
hits.sort - 结果的排序key，没有的话按照score排序
hits._score - 相关得分 (not applicable when using match_all)
q=* 查询所有
sort 排序字段
asc升序
GET bank/_search?q=*&sort=account_number:asc
检索了1000条数据，但是根据相关性算法，只返回10条
uri+请求体进行检索
GET /bank/_search
{
"query": { "match_all": {} },
"sort": [
{ "account_number": "asc" },
{"balance":"desc"}
]
}
2 Query DSL
什么get的请求体叫query DSL
（1）基本语法格式
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，该查询语言非常全面。
一个查询语句的典型结构
QUERY_NAME:{
ARGUMENT:VALUE,
ARGUMENT:VALUE,
...
}
如果针对于某个字段，那么它的结构如下：
{
QUERY_NAME:{
FIELD_NAME:{
ARGUMENT:VALUE,
ARGUMENT:VALUE,...
}
}
}
示例
GET bank/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 5,
"_source":["balance"],
"sort": [
{
"account_number": {
"order": "desc"
}
}
]
}
_source为要返回的字段
query定义如何查询；
match_all查询类型【代表查询所有的所有】，es中可以在query中组合非常多的查询类型完成复杂查询；
除了query参数之外，我们可也传递其他的参数以改变查询结果，如sort，size；
from+size限定，完成分页功能；
sort排序，多字段排序，会在前序字段相等时后续字段内部排序，否则以前序为准；
(2) 返回部分字段
GET bank/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 5,
"sort": [
{
"account_number": {
"order": "desc"
}
}
],
"_source": ["balance","firstname"]
}
查询结果：
{
"took" : 18,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "999",
"_score" : null,
"_source" : {
"firstname" : "Dorothy",
"balance" : 6087
},
"sort" : [
999
]
},
省略。。。
3 match匹配查询
基本类型（非字符串），精确控制
GET bank/_search
{
"query": {
"match": {
"account_number": "20"
}
}
}
match返回account_number=20的数据。
查询结果：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1, // 得到一条
"relation" : "eq"
},
"max_score" : 1.0, 最大得分
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 1.0,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "[email protected]",
"city" : "Ribera",
"state" : "WA"
}
}
]
}
}
字符串，全文检索
GET bank/_search
{
"query": {
"match": {
"address": "kings"
}
}
}
全文检索，最终会按照评分进行排序，会对检索条件进行分词匹配。
查询结果：
{
"took" : 30,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 5.990829,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 5.990829,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "[email protected]",
"city" : "Ribera",
"state" : "WA"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "722",
"_score" : 5.990829,
"_source" : {
"account_number" : 722,
"balance" : 27256,
"firstname" : "Roberts",
"lastname" : "Beasley",
"age" : 34,
"gender" : "F",
"address" : "305 Kings Hwy",
"employer" : "Quintity",
"email" : "[email protected]",
"city" : "Hayden",
"state" : "PA"
}
}
]
}
}
(4) match_phrase [短句匹配]
将需要匹配的值当成一整个单词（不分词）进行检索
前面的是包含mill或road就查出来，我们现在要都包含才查出
GET bank/_search
{
"query": {
"match_phrase": {
"address": "mill road"
}
}
}
查处address中包含mill road的所有记录，并给出相关性得分
查看结果：
{
"took" : 32,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 8.926605,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 8.926605,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
match_phrase和match的区别，观察如下实例：
GET bank/_search
{
"query": {
"match_phrase": {
"address": "990 Mill"
}
}
}
查询结果：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 10.806405,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 10.806405,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
使用match的keyword
GET bank/_search
{
"query": {
"match": {
"address.keyword": "990 Mill"
}
}
}
查询结果，一条也未匹配到
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}
修改匹配条件为“990 Mill Road”
GET bank/_search
{
"query": {
"match": {
"address.keyword": "990 Mill Road"
}
}
}
查询出一条数据
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 6.5032897,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.5032897,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
文本字段的匹配，使用keyword，匹配的条件就是要显示字段的全部值，要进行精确匹配的。
match_phrase是做短语匹配，只要文本中包含匹配条件，就能匹配到。
（5）multi_math【多字段匹配】
state或者address中包含mill，并且在查询过程中，会对于查询条件进行分词。
GET bank/_search
{
"query": {
"multi_match": {
"query": "mill",
"fields": [
"state",
"address"
]
}
}
}
查询结果：
{
"took" : 28,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 5.4032025,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "[email protected]",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 5.4032025,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "[email protected]",
"city" : "Blackgum",
"state" : "KY"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "472",
"_score" : 5.4032025,
"_source" : {
"account_number" : 472,
"balance" : 25571,
"firstname" : "Lee",
"lastname" : "Long",
"age" : 32,
"gender" : "F",
"address" : "288 Mill Street",
"employer" : "Comverges",
"email" : "[email protected]",
"city" : "Movico",
"state" : "MT"
}
}
]
}
}
（6）bool用来做复合查询
复合语句可以合并，任何其他查询语句，包括符合语句。这也就意味着，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
must_not：必须不匹配must_not所列举的所有条件。
should：应该满足should所列举的条件。满足条件最好，不满足也可以，满足得分更高
实例：查询gender=m，并且address=mill的数据
GET bank/_search
{
"query":{
"bool":{
"must":[
{"match":{"address":"mill"}},
{"match":{"gender":"M"}}
]
}
}
}
查询结果：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 6.0824604,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.0824604,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 6.0824604,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "[email protected]",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 6.0824604,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "[email protected]",
"city" : "Blackgum",
"state" : "KY"
}
}
]
}
}
must_not：必须不是指定的情况
实例：查询gender=m，并且address=mill的数据，但是age不等于38的
GET bank/_search
{
"query": {
"bool": {
"must": [
{ "match": { "gender": "M" }},
{ "match": {"address": "mill"}}
],
"must_not": [
{ "match": { "age": "38" }}
]
}
}
查询结果：
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 6.0824604,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.0824604,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
should：应该达到should列举的条件，如果到达会增加相关文档的评分，并不会改变查询的结果。如果query中只有should且只有一种匹配规则，那么should的条件就会被作为默认匹配条件二区改变查询结果。
实例：匹配lastName应该等于Wallace的数据
GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"gender": "M"
}
},
{
"match": {
"address": "mill"
}
}
],
"must_not": [
{
"match": {
"age": "18"
}
}
],
"should": [
{
"match": {
"lastname": "Wallace"
}
}
]
}
}
}
查询结果：
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 12.585751,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 12.585751,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 6.0824604,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "[email protected]",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 6.0824604,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "[email protected]",
"city" : "Blackgum",
"state" : "KY"
}
}
]
}
}
能够看到相关度越高，得分也越高。
（7）Filter【结果过滤】
上面的must和should影响相关性得分，而must_not仅仅是一个filter ，不贡献得分
must改为filter就使must不贡献得分
如果只有filter条件的话，我们会发现得分都是0
一个key多个值可以用terms
并不是所有的查询都需要产生分数，特别是哪些仅用于filtering过滤的文档。为了不计算分数，elasticsearch会自动检查场景并且优化查询的执行。
不参与评分更快
GET bank/_search
{
"query": {
"bool": {
"must": [
{ "match": {"address": "mill" } }
],
"filter": { # query.bool.filter
"range": {
"balance": {
"gte": "10000",
"lte": "20000"
}
}
}
}
}
}
这里先是查询所有匹配address=mill的文档，然后再根据10000<=balance<=20000进行过滤查询结果
查询结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "[email protected]",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
Each must, should, and must_not element in a Boolean query is referred to as a query clause. How well a document meets the criteria in each must or should clause contributes to the document’s relevance score. The higher the score, the better the document matches your search criteria. By default, Elasticsearch returns documents ranked by these relevance scores.
在boolean查询中，must, should 和must_not 元素都被称为查询子句 。 文档是否符合每个“must”或“should”子句中的标准，决定了文档的“相关性得分”。 得分越高，文档越符合您的搜索条件。 默认情况下，Elasticsearch返回根据这些相关性得分排序的文档。
The criteria in a must_not clause is treated as a filter. It affects whether or not the document is included in the results, but does not contribute to how documents are scored. You can also explicitly specify arbitrary filters to include or exclude documents based on structured data.
“must_not”子句中的条件被视为“过滤器”。 它影响文档是否包含在结果中， 但不影响文档的评分方式。 还可以显式地指定任意过滤器来包含或排除基于结构化数据的文档。
filter在使用过程中，并不会计算相关性得分：
GET bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"address": "mill"
}
}
],
"filter": {
"range": {
"balance": {
"gte": "10000",
"lte": "20000"
}
}
}
}
}
}
查询结果：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 213,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 0.0,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "[email protected]",
"city" : "Ribera",
"state" : "WA"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "37",
"_score" : 0.0,
"_source" : {
"account_number" : 37,
"balance" : 18612,
"firstname" : "Mcgee",
"lastname" : "Mooney",
"age" : 39,
"gender" : "M",
"address" : "826 Fillmore Place",
"employer" : "Reversus",
"email" : "[email protected]",
"city" : "Tooleville",
"state" : "OK"
}
},
省略。。。
能看到所有文档的 “_score” : 0.0。
（8）term
和match一样。匹配某个属性的值。
全文检索字段用match，
其他非text字段匹配用term。
不要使用term来进行文本字段查询
es默认存储text值时用分词分析，所以要搜索text值，使用match
https://www.elastic.co/guide/en/elasticsearch/reference/7.6/query-dsl-term-query.html
字段.keyword：要一一匹配到
match_phrase：子串包含即可
使用term匹配查询
GET bank/_search
{
"query": {
"term": {
"address": "mill Road"
}
}
}
查询结果：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0, 没有
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}
一条也没有匹配到
而更换为match匹配时，能够匹配到32个文档
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 32,
"relation" : "eq"
},
"max_score" : 8.926605,
"hits" : [
也就是说，全文检索字段用match，其他非text字段匹配用term。
(9) Aggregation（聚合）
前面介绍了存储、检索，但还没介绍分析
聚合提供了从数据中分组和提取数据的能力。最简单的聚合方法大致等于SQL Group by和SQL聚合函数。
在elasticsearch中，执行搜索返回this（命中结果），并且同时返回聚合结果，把以响应中的所有hits（命中结果）分隔开的能力。这是非常强大且有效的，你可以执行查询和多个聚合，并且在一次使用中得到各自的（任何一个的）返回结果，使用一次简洁和简化的API啦避免网络往返。
aggs：执行聚合。聚合语法如下：
"aggs":{ # 聚合
"aggs_name这次聚合的名字，方便展示在结果集中":{
"AGG_TYPE聚合的类型(avg,term,terms)":{}
}
}
terms：看值的可能性分布
avg：看值的分布平均
例：搜索address中包含mill的所有人的年龄分布以及平均年龄，但不显示这些人的详情
# 分别为包含mill、，平均年龄、
GET bank/_search
{
"query": { # 查询出包含mill的
"match": {
"address": "Mill"
}
},
"aggs": { #基于查询聚合
"ageAgg": { # 聚合的名字，随便起
"terms": { # 看值的可能性分布
"field": "age",
"size": 10
}
},
"ageAvg": {
"avg": { # 看age值的平均
"field": "age"
}
},
"balanceAvg": {
"avg": { # 看balance的平均
"field": "balance"
}
}
},
"size": 0 # 不看详情
}
查询结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4, // 命中4条
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"ageAgg" : { // 第一个聚合的结果
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 38,
"doc_count" : 2
},
{
"key" : 28,
"doc_count" : 1
},
{
"key" : 32,
"doc_count" : 1
}
]
},
"ageAvg" : { // 第二个聚合的结果
"value" : 34.0
},
"balanceAvg" : {
"value" : 25208.0
}
}
}
子聚合
复杂：
按照年龄聚合，并且求这些年龄段的这些人的平均薪资
写到一个聚合里是基于上个聚合进行子聚合。
下面求每个age分布的平均balance
GET bank/_search
{
"query": {
"match_all": {}
},
"aggs": {
"ageAgg": {
"terms": { # 看分布
"field": "age",
"size": 100
},
"aggs": { # 与terms并列
"ageAvg": { #平均
"avg": {
"field": "balance"
}
}
}
}
},
"size": 0
}
输出结果：
{
"took" : 49,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 31,
"doc_count" : 61,
"ageAvg" : {
"value" : 28312.918032786885
}
},
{
"key" : 39,
"doc_count" : 60,
"ageAvg" : {
"value" : 25269.583333333332
}
},
{
"key" : 26,
"doc_count" : 59,
"ageAvg" : {
"value" : 23194.813559322032
}
},
{
"key" : 32,
"doc_count" : 52,
"ageAvg" : {
"value" : 23951.346153846152
}
},
{
"key" : 35,
"doc_count" : 52,
"ageAvg" : {
"value" : 22136.69230769231
}
},
{
"key" : 36,
"doc_count" : 52,
"ageAvg" : {
"value" : 22174.71153846154
}
},
{
"key" : 22,
"doc_count" : 51,
"ageAvg" : {
"value" : 24731.07843137255
}
},
{
"key" : 28,
"doc_count" : 51,
"ageAvg" : {
"value" : 28273.882352941175
}
},
{
"key" : 33,
"doc_count" : 50,
"ageAvg" : {
"value" : 25093.94
}
},
{
"key" : 34,
"doc_count" : 49,
"ageAvg" : {
"value" : 26809.95918367347
}
},
{
"key" : 30,
"doc_count" : 47,
"ageAvg" : {
"value" : 22841.106382978724
}
},
{
"key" : 21,
"doc_count" : 46,
"ageAvg" : {
"value" : 26981.434782608696
}
},
{
"key" : 40,
"doc_count" : 45,
"ageAvg" : {
"value" : 27183.17777777778
}
},
{
"key" : 20,
"doc_count" : 44,
"ageAvg" : {
"value" : 27741.227272727272
}
},
{
"key" : 23,
"doc_count" : 42,
"ageAvg" : {
"value" : 27314.214285714286
}
},
{
"key" : 24,
"doc_count" : 42,
"ageAvg" : {
"value" : 28519.04761904762
}
},
{
"key" : 25,
"doc_count" : 42,
"ageAvg" : {
"value" : 27445.214285714286
}
},
{
"key" : 37,
"doc_count" : 42,
"ageAvg" : {
"value" : 27022.261904761905
}
},
{
"key" : 27,
"doc_count" : 39,
"ageAvg" : {
"value" : 21471.871794871793
}
},
{
"key" : 38,
"doc_count" : 39,
"ageAvg" : {
"value" : 26187.17948717949
}
},
{
"key" : 29,
"doc_count" : 35,
"ageAvg" : {
"value" : 29483.14285714286
}
}
]
}
}
}
复杂子聚合：查出所有年龄分布，并且这些年龄段中M的平均薪资和F的平均薪资以及这个年龄段的总体平均薪资
GET bank/_search
{
"query": {
"match_all": {}
},
"aggs": {
"ageAgg": {
"terms": { # 看age分布
"field": "age",
"size": 100
},
"aggs": { # 子聚合
"genderAgg": {
"terms": { # 看gender分布
"field": "gender.keyword" # 注意这里，文本字段应该用.keyword
},
"aggs": { # 子聚合
"balanceAvg": {
"avg": { # 男性的平均
"field": "balance"
}
}
}
},
"ageBalanceAvg": {
"avg": { #age分布的平均（男女）
"field": "balance"
}
}
}
}
},
"size": 0
}
输出结果：
{
"took" : 119,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"ageAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 31,
"doc_count" : 61,
"genderAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "M",
"doc_count" : 35,
"balanceAvg" : {
"value" : 29565.628571428573
}
},
{
"key" : "F",
"doc_count" : 26,
"balanceAvg" : {
"value" : 26626.576923076922
}
}
]
},
"ageBalanceAvg" : {
"value" : 28312.918032786885
}
}
]
.......//省略其他
}
}
}
nested对象聚合
GET articles/_search
{
"size": 0,
"aggs": {
"nested": {
"nested": {
"path": "payment"
},
"aggs": {
"amount_avg": {
"avg": {
"field": "payment.amount"
}
}
}
}
}
}
(3) Mapping
映射定义文档如何被存储检索的
（1）字段类型
https://www.elastic.co/guide/en/elasticsearch/reference/7.x/mapping-types.html
核心类型
复合类型
地理类型
特定类型
核心数据类型
（1）字符串
text ⽤于全⽂索引，搜索时会自动使用分词器进⾏分词再匹配
keyword 不分词，搜索时需要匹配完整的值
（2）数值型
整型： byte，short，integer，long
浮点型： float, half_float, scaled_float，double
（3）日期类型：date
（4）范围型
integer_range， long_range， float_range，double_range，date_range
gt是大于，lt是小于，e是equals等于。
age_limit的区间包含了此值的文档都算是匹配。
（5）布尔
boolean
（6）⼆进制
binary 会把值当做经过 base64 编码的字符串，默认不存储，且不可搜索
复杂数据类型
（1）对象
object一个对象中可以嵌套对象。
（2）数组
Array
嵌套类型
nested 用于json对象数组
（2）映射
Mapping(映射)
Maping是用来定义一个文档（document），以及它所包含的属性（field）是如何存储和索引的。比如：使用maping来定义：
哪些字符串属性应该被看做全文本属性（full text fields）；
哪些属性包含数字，日期或地理位置；
文档中的所有属性是否都嫩被索引（all 配置）；
日期的格式；
自定义映射规则来执行动态添加属性；
查看mapping信息：GET bank/_mapping
{
"bank" : {
"mappings" : {
"properties" : {
"account_number" : {
"type" : "long" # long类型
},
"address" : {
"type" : "text", # 文本类型，会进行全文检索，进行分词
"fields" : {
"keyword" : { # addrss.keyword
"type" : "keyword",
"ignore_above" : 256
}
}
},
"age" : {
"type" : "long"
},
"balance" : {
"type" : "long"
},
"city" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"email" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"employer" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"firstname" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"gender" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"lastname" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"state" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
}
}
修改mapping信息
（3）新版本改变
ElasticSearch7-去掉type概念
关系型数据库中两个数据表示是独立的，即使他们里面有相同名称的列也不影响使用，但ES中不是这样的。elasticsearch是基于Lucene开发的搜索引擎，而ES中不同type下名称相同的filed最终在Lucene中的处理方式是一样的。
两个不同type下的两个user_name，在ES同一个索引下其实被认为是同一个filed，你必须在两个不同的type中定义相同的filed映射。否则，不同type中的相同字段名称就会在处理中出现冲突的情况，导致Lucene处理效率下降。
去掉type就是为了提高ES处理数据的效率。
Elasticsearch 7.x URL中的type参数为可选。比如，索引一个文档不再要求提供文档类型。
Elasticsearch 8.x 不再支持URL中的type参数。
解决：
将索引从多类型迁移到单类型，每种类型文档一个独立索引
将已存在的索引下的类型数据，全部迁移到指定位置即可。详见数据迁移
Elasticsearch 7.x
Specifying types in requests is deprecated. For instance, indexing a document no longer requires a document type. The new index APIs are PUT {index}/_doc/{id} in case of explicit ids and POST {index}/_doc for auto-generated ids. Note that in 7.0, _doc is a permanent part of the path, and represents the endpoint name rather than the document type.
The include_type_name parameter in the index creation, index template, and mapping APIs will default to false. Setting the parameter at all will result in a deprecation warning.
The _default_ mapping type is removed.
Elasticsearch 8.x
Specifying types in requests is no longer supported.
The include_type_name parameter is removed.
创建映射PUT /my_index
第一次存储数据的时候es就猜出了映射
第一次存储数据前可以指定映射
创建索引并指定映射
PUT /my_index
{
"mappings": {
"properties": {
"age": {
"type": "integer"
},
"email": {
"type": "keyword" # 指定为keyword
},
"name": {
"type": "text" # 全文检索。保存时候分词，检索时候进行分词匹配
}
}
}
}
输出：
{
"acknowledged" : true,
"shards_acknowledged" : true,
"index" : "my_index"
}
查看映射GET /my_index
GET /my_index
输出结果：
{
"my_index" : {
"aliases" : { },
"mappings" : {
"properties" : {
"age" : {
"type" : "integer"
},
"email" : {
"type" : "keyword"
},
"employee-id" : {
"type" : "keyword",
"index" : false
},
"name" : {
"type" : "text"
}
}
},
"settings" : {
"index" : {
"creation_date" : "1588410780774",
"number_of_shards" : "1",
"number_of_replicas" : "1",
"uuid" : "ua0lXhtkQCOmn7Kh3iUu0w",
"version" : {
"created" : "7060299"
},
"provided_name" : "my_index"
}
}
}
}
添加新的字段映射/my_index/_mapping
PUT /my_index/_mapping
{
"properties": {
"employee-id": {
"type": "keyword",
"index": false # 字段不能被检索。检索
}
}
}
这里的 “index”: false，表明新增的字段不能被检索，只是一个冗余字段。
不能更新映射
对于已经存在的字段映射，我们不能更新。更新必须创建新的索引，进行数据迁移。
数据迁移
先创建new_twitter的正确映射。
然后使用如下方式进行数据迁移。
6.0以后写法
POST reindex
{
"source":{
"index":"twitter"
},
"dest":{
"index":"new_twitters"
}
}
老版本写法
POST reindex
{
"source":{
"index":"twitter",
"twitter":"twitter"
},
"dest":{
"index":"new_twitters"
}
}
案例：原来类型为account，新版本没有类型了，所以我们把他去掉
GET /bank/_search
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",//原来类型为account，新版本没有类型了，所以我们把他去掉
"_id" : "1",
"_score" : 1.0,
"_source" : {
"account_number" : 1,
"balance" : 39225,
"firstname" : "Amber",
"lastname" : "Duke",
"age" : 32,
"gender" : "M",
"address" : "880 Holmes Lane",
"employer" : "Pyrami",
"email" : "[email protected]",
"city" : "Brogan",
"state" : "IL"
}
},
...
GET /bank/_search
查出
"age":{"type":"long"}
想要将年龄修改为integer
先创建新的索引
PUT /newbank
{
"mappings": {
"properties": {
"account_number": {
"type": "long"
},
"address": {
"type": "text"
},
"age": {
"type": "integer"
},
"balance": {
"type": "long"
},
"city": {
"type": "keyword"
},
"email": {
"type": "keyword"
},
"employer": {
"type": "keyword"
},
"firstname": {
"type": "text"
},
"gender": {
"type": "keyword"
},
"lastname": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"state": {
"type": "keyword"
}
}
}
}
查看“newbank”的映射：
GET /newbank/_mapping
能够看到age的映射类型被修改为了integer.
"age":{"type":"integer"}
将bank中的数据迁移到newbank中
POST _reindex
{
"source": {
"index": "bank",
"type": "account"
},
"dest": {
"index": "newbank"
}
}
运行输出：
#! Deprecation: [types removal] Specifying types in reindex requests is deprecated.
{
"took" : 768,
"timed_out" : false,
"total" : 1000,
"updated" : 0,
"created" : 1000,
"deleted" : 0,
"batches" : 1,
"version_conflicts" : 0,
"noops" : 0,
"retries" : {
"bulk" : 0,
"search" : 0
},
"throttled_millis" : 0,
"requests_per_second" : -1.0,
"throttled_until_millis" : 0,
"failures" : [ ]
}
查看newbank中的数据
GET /newbank/_search
输出
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "newbank",
"_type" : "_doc", # 没有了类型
4）分词
一个tokenizer（分词器）接收一个字符流，将之分割为独立的tokens（词元，通常是独立的单词），然后输出tokens流。
例如：whitespace tokenizer遇到空白字符时分割文本。它会将文本"Quick brown fox!"分割为[Quick,brown,fox!]
该tokenizer（分词器）还负责记录各个terms(词条)的顺序或position位置（用于phrase短语和word proximity词近邻查询），以及term（词条）所代表的原始word（单词）的start（起始）和end（结束）的character offsets（字符串偏移量）（用于高亮显示搜索的内容）。
elasticsearch提供了很多内置的分词器（标准分词器），可以用来构建custom analyzers（自定义分词器）。
关于分词器： https://www.elastic.co/guide/en/elasticsearch/reference/7.6/analysis.html
POST _analyze
{
"analyzer": "standard",
"text": "The 2 Brown-Foxes bone."
}
执行结果：
{
"tokens" : [
{
"token" : "the",
"start_offset" : 0,
"end_offset" : 3,
"type" : "<ALPHANUM>",
"position" : 0
},
{
"token" : "2",
"start_offset" : 4,
"end_offset" : 5,
"type" : "<NUM>",
"position" : 1
},
{
"token" : "brown",
"start_offset" : 6,
"end_offset" : 11,
"type" : "<ALPHANUM>",
"position" : 2
},
{
"token" : "foxes",
"start_offset" : 12,
"end_offset" : 17,
"type" : "<ALPHANUM>",
"position" : 3
},
{
"token" : "bone",
"start_offset" : 18,
"end_offset" : 22,
"type" : "<ALPHANUM>",
"position" : 4
}
]
}
对于中文，我们需要安装额外的分词器
(1) 安装ik分词器
所有的语言分词，默认使用的都是“Standard Analyzer”，但是这些分词器针对于中文的分词，并不友好。为此需要安装中文的分词器。
在前面安装的elasticsearch时，我们已经将elasticsearch容器的“/usr/share/elasticsearch/plugins”目录，映射到宿主机的“ /usr/local/elasticsearch/plugins”目录下，所以比较方便的做法就是下载“/elasticsearch-analysis-ik-7.4.2.zip”文件，然后解压到目录ik下即可。安装完毕后，需要重启elasticsearch容器。
确认是否安装好了分词器
(2) 测试分词器
使用默认分词器
GET _analyze
{
"text":"我是中国人"
}
请观察执行结果：
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "<IDEOGRAPHIC>",
"position" : 0
},
{
"token" : "是",
"start_offset" : 1,
"end_offset" : 2,
"type" : "<IDEOGRAPHIC>",
"position" : 1
},
{
"token" : "中",
"start_offset" : 2,
"end_offset" : 3,
"type" : "<IDEOGRAPHIC>",
"position" : 2
},
{
"token" : "国",
"start_offset" : 3,
"end_offset" : 4,
"type" : "<IDEOGRAPHIC>",
"position" : 3
},
{
"token" : "人",
"start_offset" : 4,
"end_offset" : 5,
"type" : "<IDEOGRAPHIC>",
"position" : 4
}
]
}
GET _analyze
{
"analyzer": "ik_smart",
"text":"我是中国人"
}
输出结果：
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "CN_CHAR",
"position" : 0
},
{
"token" : "是",
"start_offset" : 1,
"end_offset" : 2,
"type" : "CN_CHAR",
"position" : 1
},
{
"token" : "中国人",
"start_offset" : 2,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 2
}
]
}
GET _analyze
{
"analyzer": "ik_max_word",
"text":"我是中国人"
}
输出结果：
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "CN_CHAR",
"position" : 0
},
{
"token" : "是",
"start_offset" : 1,
"end_offset" : 2,
"type" : "CN_CHAR",
"position" : 1
},
{
"token" : "中国人",
"start_offset" : 2,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "中国",
"start_offset" : 2,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 3
},
{
"token" : "国人",
"start_offset" : 3,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 4
}
]
}
(3) 自定义词库
比如我们要把尚硅谷算作一个词
修改/usr/local/elasticsearch/plugins/ik/config中的IKAnalyzer.cfg.xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
<comment>IK Analyzer 扩展配置</comment>
<!--用户可以在这里配置自己的扩展字典 -->
<entry key="ext_dict"></entry>
<!--用户可以在这里配置自己的扩展停止词字典-->
<entry key="ext_stopwords"></entry>
<!--用户可以在这里配置远程扩展字典 -->
<entry key="remote_ext_dict">http://192.168.11.129/es/fenci.txt</entry>
<!--用户可以在这里配置远程扩展停止词字典-->
<!-- <entry key="remote_ext_stopwords">words_location</entry> -->
</properties>
修改完成后，需要重启elasticsearch容器，否则修改不生效。docker restart elasticsearch
更新完成后，es只会对于新增的数据用更新分词。历史数据是不会重新分词的。如果想要历史数据重新分词，需要执行：
POST my_index/_update_by_query?conflicts=proceed
安装Nginx
随便启动一个nginx实例，只是为了复制出配置
docker run -p80:80 --name nginx -d nginx:1.10
将容器内的配置文件拷贝到/usr/local/nginx/conf/ 下
mkdir -p/usr/local/nginx/html
mkdir -p /usr/local/nginx/logs
mkdir -p /usr/local/nginx/conf
docker container cp nginx:/etc/nginx/* /usr/local/nginx/conf/
#由于拷贝完成后会在config中存在一个nginx文件夹，所以需要将它的内容移动到conf中
mv /usr/local/nginx/conf/nginx/* /usr/local/nginx/conf/
rm -rf /usr/local/nginx/conf/nginx
终止原容器：
docker stop nginx
执行命令删除原容器：
docker rm nginx
创建新的Nginx，执行以下命令
docker run -p 80:80 --name nginx \
-v /usr/local/nginx/html:/usr/share/nginx/html \
-v /usr/local/nginx/logs:/var/log/nginx \
-v /usr/local/nginx/conf/:/etc/nginx \
-d nginx:1.10
创建“/mydata/nginx/html/index.html”文件，测试是否能够正常访问
访问：http://ngix所在主机的IP:80/index.html
安装好nginx,把Nginx当做tomcat来用
mkdir //usr/local/nginx/html/es
cd /usr/local/nginx/html/es
vim fenci.txt
输入尚硅谷
测试http://192.168.11.129/es/fenci.txt
然后创建“fenci.txt”文件，内容如下：
echo "樱桃萨其马，带你甜蜜入夏" > /mydata/nginx/html/fenci.txt
测试效果：
GET _analyze
{
"analyzer": "ik_max_word",
"text":"樱桃萨其马，带你甜蜜入夏"
}
输出结果：
{
"tokens" : [
{
"token" : "樱桃",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "萨其马",
"start_offset" : 2,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 1
},
{
"token" : "带你",
"start_offset" : 6,
"end_offset" : 8,
"type" : "CN_WORD",
"position" : 2
},
{
"token" : "甜蜜",
"start_offset" : 8,
"end_offset" : 10,
"type" : "CN_WORD",
"position" : 3
},
{
"token" : "入夏",
"start_offset" : 10,
"end_offset" : 12,
"type" : "CN_WORD",
"position" : 4
}
]
}
1.7 elasticsearch-Rest-Client
java操作es有两种方式
1）9300: TCP
spring-data-elasticsearch:transport-api.jar;
springboot版本不同，ransport-api.jar不同，不能适配es版本
7.x已经不建议使用，8以后就要废弃
2）9200: HTTP
有诸多包
jestClient: 非官方，更新慢；
RestTemplate：模拟HTTP请求，ES很多操作需要自己封装，麻烦；
HttpClient：同上；
Elasticsearch-Rest-Client：官方RestClient，封装了ES操作，API层次分明，上手简单；
最终选择Elasticsearch-Rest-Client（elasticsearch-rest-high-level-client）
1.8 SpringBoot整合ElasticSearch
搭建elasticsearch模块
1、导入依赖
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.4.2</version>
</dependency>
在spring-boot-dependencies中所依赖的ES版本位6.8.5，要改掉
<properties>
<java.version>1.8</java.version>
<elasticsearch.version>7.4.2</elasticsearch.version>
</properties>
请求测试项，比如es添加了安全访问规则，访问es需要添加一个安全头，就可以通过requestOptions设置
官方建议把requestOptions创建成单实例
@Configuration
public class GuliESConfig {
public static final RequestOptions COMMON_OPTIONS;
static {
RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();
COMMON_OPTIONS = builder.build();
}
}
2、编写测试类
1）测试保存数据
https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-document-index.html
保存方式分为同步和异步，异步方式多了个listener回调
@Test
public void indexData() throws IOException {
// 设置索引
IndexRequest indexRequest = new IndexRequest ("users");
indexRequest.id("1");
User user = new User();
user.setUserName("张三");
user.setAge(20);
user.setGender("男");
String jsonString = JSON.toJSONString(user);
//设置要保存的内容，指定数据和类型
indexRequest.source(jsonString, XContentType.JSON);
//执行创建索引和保存数据
IndexResponse index = client.index(indexRequest, GulimallElasticSearchConfig.COMMON_OPTIONS);
System.out.println(index);
}
2）测试获取数据
https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-search.html
@Test
public void find() throws IOException {
// 1 创建检索请求
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 构造检索条件
// sourceBuilder.query();
// sourceBuilder.from();
// sourceBuilder.size();
// sourceBuilder.aggregation();
sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
System.out.println(sourceBuilder.toString());
searchRequest.source(sourceBuilder);
// 2 执行检索
SearchResponse response = client.search(searchRequest, GuliESConfig.COMMON_OPTIONS);
// 3 分析响应结果
System.out.println(response.toString());
}
{"took":198,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},
"hits":{
"total":{"value":4,"relation":"eq"},
"max_score":5.4032025,"hits":[{"_index":"bank","_type":"account","_id":"970","_score":5.4032025,"_source":{"account_number":970,"balance":19648,"firstname":"Forbes","lastname":"Wallace","age":28,"gender":"M","address":"990 Mill Road","employer":"Pheast","email":"[email protected]","city":"Lopezo","state":"AK"}},{"_index":"bank","_type":"account","_id":"136","_score":5.4032025,"_source":{"account_number":136,"balance":45801,"firstname":"Winnie","lastname":"Holland","age":38,"gender":"M","address":"198 Mill Lane","employer":"Neteria","email":"[email protected]","city":"Urie","state":"IL"}},{"_index":"bank","_type":"account","_id":"345","_score":5.4032025,"_source":{"account_number":345,"balance":9812,"firstname":"Parker","lastname":"Hines","age":38,"gender":"M","address":"715 Mill Avenue","employer":"Baluba","email":"[email protected]","city":"Blackgum","state":"KY"}},{"_index":"bank","_type":"account","_id":"472","_score":5.4032025,"_source":{"account_number":472,"balance":25571,"firstname":"Lee","lastname":"Long","age":32,"gender":"F","address":"288 Mill Street","employer":"Comverges","email":"[email protected]","city":"Movico","state":"MT"}}]}}
@Test
public void find() throws IOException {
// 1 创建检索请求
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 构造检索条件
// sourceBuilder.query();
// sourceBuilder.from();
// sourceBuilder.size();
// sourceBuilder.aggregation();
sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
//AggregationBuilders工具类构建AggregationBuilder
// 构建第一个聚合条件:按照年龄的值分布
TermsAggregationBuilder agg1 = AggregationBuilders.terms("agg1").field("age").size(10);// 聚合名称
// 参数为AggregationBuilder
sourceBuilder.aggregation(agg1);
// 构建第二个聚合条件:平均薪资
AvgAggregationBuilder agg2 = AggregationBuilders.avg("agg2").field("balance");
sourceBuilder.aggregation(agg2);
System.out.println("检索条件"+sourceBuilder.toString());
searchRequest.source(sourceBuilder);
// 2 执行检索
SearchResponse response = client.search(searchRequest, GuliESConfig.COMMON_OPTIONS);
// 3 分析响应结果
System.out.println(response.toString());
}
把检索结果封装为java bean
// 3.1 获取java bean
SearchHits hits = response.getHits();
SearchHit[] hits1 = hits.getHits();
for (SearchHit hit : hits1) {
hit.getId();
hit.getIndex();
String sourceAsString = hit.getSourceAsString();
Account account = JSON.parseObject(sourceAsString, Account.class);
System.out.println(account);
}
Account(accountNumber=970, balance=19648, firstname=Forbes, lastname=Wallace, age=28, gender=M, address=990 Mill Road, employer=Pheast, [email protected], city=Lopezo, state=AK)
Account(accountNumber=136, balance=45801, firstname=Winnie, lastname=Holland, age=38, gender=M, address=198 Mill Lane, employer=Neteria, [email protected], city=Urie, state=IL)
Account(accountNumber=345, balance=9812, firstname=Parker, lastname=Hines, age=38, gender=M, address=715 Mill Avenue, employer=Baluba, [email protected], city=Blackgum, state=KY)
Account(accountNumber=472, balance=25571, firstname=Lee, lastname=Long, age=32, gender=F, address=288 Mill Street, employer=Comverges, [email protected], city=Movico, state=MT)
获取检索到的分析信息
// 3.2 获取检索到的分析信息
Aggregations aggregations = response.getAggregations();
Terms agg21 = aggregations.get("agg2");
for (Terms.Bucket bucket : agg21.getBuckets()) {
String keyAsString = bucket.getKeyAsString();
System.out.println(keyAsString);
}
搜索address中包含mill的所有人的年龄分布以及平均年龄，平均薪资
GET bank/_search
{
"query": {
"match": {
"address": "Mill"
}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 10
}
},
"ageAvg": {
"avg": {
"field": "age"
}
},
"balanceAvg": {
"avg": {
"field": "balance"
}
}
}
}
1.9 product-es准备
ES在内存中，所以在检索中优于mysql。ES也支持集群，数据分片存储。
需求：
上架的商品才可以在网站展示。
上架的商品需要可以被检索。
分析sku在es中如何存储
商品mapping
分析：商品上架在es中是存sku还是spu？
1）检索的时候输入名字，是需要按照sku的title进行全文检索的
2）检素使用商品规格，规格是spu的公共属性，每个spu是一样的
3）按照分类id进去的都是直接列出spu的，还可以切换。
4〕我们如果将sku的全量信息保存到es中（包括spu属性〕就太多字段了
方案1：
{
skuId:1
spuId:11
skyTitile:华为xx
price:999
saleCount:99
attr:[
{尺寸:5},
{CPU:高通945},
{分辨率:全高清}
]
缺点：如果每个sku都存储规格参数(如尺寸)，会有冗余存储，因为每个spu对应的sku的规格参数都一样
方案2：
sku索引
{
spuId:1
skuId:11
}
attr索引
{
skuId:11
attr:[
{尺寸:5},
{CPU:高通945},
{分辨率:全高清}
]
}
先找到4000个符合要求的spu，再根据4000个spu查询对应的属性，封装了4000个id，long 8B*4000=32000B=32KB
1K个人检索，就是32MB
结论：如果将规格参数单独建立索引，会出现检索时出现大量数据传输的问题，会引起网络网络
因此选用方案1，以空间换时间
建立product索引
最终选用的数据模型：
PUT product
{
"mappings":{
"properties": {
"skuId":{ "type": "long" },
"spuId":{ "type": "keyword" }, # 不可分词
"skuTitle": {
"type": "text",
"analyzer": "ik_smart" # 中文分词器
},
"skuPrice": { "type": "keyword" },
"skuImg" : { "type": "keyword" },
"saleCount":{ "type":"long" },
"hasStock": { "type": "boolean" },
"hotScore": { "type": "long" },
"brandId": { "type": "long" },
"catalogId": { "type": "long" },
"brandName": {"type": "keyword"},
"brandImg":{
"type": "keyword",
"index": false, # 不可被检索，不生成index
"doc_values": false # 不可被聚合
},
"catalogName": {"type": "keyword" },
"attrs": {
"type": "nested",
"properties": {
"attrId": {"type": "long" },
"attrName": {
"type": "keyword",
"index": false,
"doc_values": false
},
"attrValue": {"type": "keyword" }
}
}
}
}
}
其中
“type”: “keyword” 保持数据精度问题，可以检索，但不分词
“index”:false 代表不可被检索
“doc_values”: false 不可被聚合，es就不会维护一些聚合的信息
冗余存储的字段：不用来检索，也不用来分析，节省空间
库存是bool。
检索品牌id，但是不检索品牌名字、图片
用skuTitle检索
nested嵌入式对象
属性是"type": “nested”,因为是内部的属性进行检索
数组类型的对象会被扁平化处理（对象的每个属性会分别存储到一起）
user.name=["aaa","bbb"]
user.addr=["ccc","ddd"]
这种存储方式，可能会发生如下错误：
错误检索到{aaa,ddd}，这个组合是不存在的
数组的扁平化处理会使检索能检索到本身不存在的，为了解决这个问题，就采用了嵌入式属性，数组里是对象时用嵌入式属性（不是对象无需用嵌入式属性）
nested阅读：https://blog.csdn.net/weixin_40341116/article/details/80778599
使用聚合：https://blog.csdn.net/kabike/article/details/101460578
2 商品上架
POST /product/spuinfo/{
spuId}/up
@GetMapping("/skuId/{id}")
public R getSkuInfoBySkuId(@PathVariable("id") Long skuId){
SpuInfoEntity entity = spuInfoService.getSpuInfoBySkuId(skuId);
return R.ok().setData(entity);
}
product里组装好，search里上架
商品上架entity
商品上架需要在es中保存spu信息并更新spu的状态信息，由于SpuInfoEntity与索引的数据模型并不对应，所以我们要建立专门的vo进行数据传输
@Data
public class SkuEsModel {
//common中
private Long skuId;
private Long spuId;
private String skuTitle;
private BigDecimal skuPrice;
private String skuImg;
private Long saleCount;
private boolean hasStock;
private Long hotScore;
private Long brandId;
private Long catalogId;
private String brandName;
private String brandImg;
private String catalogName;
private List<Attr> attrs;
@Data
public static class Attr{
private Long attrId;
private String attrName;
private String attrValue;
}
}
商品上架service
sku的规格参数相同，因此我们要将查询规格参数提前，只查询一次
1)在ware微服务里添加"查询sku是否有库存"的controller
/**
* 查询sku是否有库存
* 返回skuId 和 stock库存量
*/
@PostMapping("/hasStock")
public R getSkuHasStock(@RequestBody List<Long> SkuIds){
List<SkuHasStockVo> vos = wareSkuService.getSkuHasStock(SkuIds);
return R.ok().setData(vos);
}
然后用feign调用
2)设置R的时候最后设置成泛型的
3)收集成map的时候，toMap()参数为两个方法，如SkyHasStockVo::getSkyId,item->item.getHasStock()
将封装好的SkuInfoEntity，调用search的feign，保存到es中
public boolean productStatusUp(List<SkuEsModel> skuEsModels) throws IOException {
// 1.给ES建立一个索引 product
BulkRequest bulkRequest = new BulkRequest();
// 2.构造保存请求
for (SkuEsModel esModel : skuEsModels) {
// 设置索引
IndexRequest indexRequest = new IndexRequest(EsConstant.PRODUCT_INDEX);
// 设置索引id
indexRequest.id(esModel.getSkuId().toString());
String jsonString = JSON.toJSONString(esModel);
indexRequest.source(jsonString, XContentType.JSON);
// add
bulkRequest.add(indexRequest);
}
// bulk批量保存
BulkResponse bulk = client.bulk(bulkRequest, GuliESConfig.COMMON_OPTIONS);
// TODO 是否拥有错误
boolean hasFailures = bulk.hasFailures();
if(hasFailures){
List<String> collect = Arrays.stream(bulk.getItems()).map(item -> item.getId()).collect(Collectors.toList());
log.error("商品上架错误：{}",collect);
}
return hasFailures;
}
5）上架失败返回R.error(错误码,消息)
此时再定义一个错误码枚举。
在接收端获取他返回的状态码
6）上架后再让数据库中变为上架状态
7）mybatis为了能兼容接收null类型，要把long改为Long
debug时很容易远程调用异常，因为超时了
商品上架代码
public void upSpuForSearch(Long spuId) {
//1、查出当前spuId对应的所有sku信息,品牌的名字
List<SkuInfoEntity> skuInfoEntities=skuInfoService.getSkusBySpuId(spuId);
//TODO 4、根据spu查出当前sku的所有可以被用来检索的规格属性
List<ProductAttrValueEntity> productAttrValueEntities = productAttrValueService.list(new QueryWrapper<ProductAttrValueEntity>().eq("spu_id", spuId));
List<Long> attrIds = productAttrValueEntities.stream().map(attr -> {
return attr.getAttrId();
}).collect(Collectors.toList());
List<Long> searchIds=attrService.selectSearchAttrIds(attrIds);
Set<Long> ids = new HashSet<>(searchIds);
List<SkuEsModel.Attr> searchAttrs = productAttrValueEntities.stream().filter(entity -> {
return ids.contains(entity.getAttrId());
}).map(entity -> {
SkuEsModel.Attr attr = new SkuEsModel.Attr();
BeanUtils.copyProperties(entity, attr);
return attr;
}).collect(Collectors.toList());
//TODO 1、发送远程调用，库存系统查询是否有库存
Map<Long, Boolean> stockMap = null;
try {
List<Long> longList = skuInfoEntities.stream().map(SkuInfoEntity::getSkuId).collect(Collectors.toList());
List<SkuHasStockVo> skuHasStocks = wareFeignService.getSkuHasStocks(longList);
stockMap = skuHasStocks.stream().collect(Collectors.toMap(SkuHasStockVo::getSkuId, SkuHasStockVo::getHasStock));
}catch (Exception e){
log.error("远程调用库存服务失败,原因{}",e);
}
//2、封装每个sku的信息
Map<Long, Boolean> finalStockMap = stockMap;
List<SkuEsModel> skuEsModels = skuInfoEntities.stream().map(sku -> {
SkuEsModel skuEsModel = new SkuEsModel();
BeanUtils.copyProperties(sku, skuEsModel);
skuEsModel.setSkuPrice(sku.getPrice());
skuEsModel.setSkuImg(sku.getSkuDefaultImg());
//TODO 2、热度评分。0
skuEsModel.setHotScore(0L);
//TODO 3、查询品牌和分类的名字信息
BrandEntity brandEntity = brandService.getById(sku.getBrandId());
skuEsModel.setBrandName(brandEntity.getName());
skuEsModel.setBrandImg(brandEntity.getLogo());
CategoryEntity categoryEntity = categoryService.getById(sku.getCatalogId());
skuEsModel.setCatalogName(categoryEntity.getName());
//设置可搜索属性
skuEsModel.setAttrs(searchAttrs);
//设置是否有库存
skuEsModel.setHasStock(finalStockMap==null?false:finalStockMap.get(sku.getSkuId()));
return skuEsModel;
}).collect(Collectors.toList());
//TODO 5、将数据发给es进行保存：gulimall-search
R r = searchFeignService.saveProductAsIndices(skuEsModels);
if (r.getCode()==0){
this.baseMapper.upSpuStatus(spuId, ProductConstant.ProductStatusEnum.SPU_UP.getCode());
}else {
log.error("商品远程es保存失败");
}
}
Feign
// ReflectiveFeign
public Object invoke(Object proxy, Method method, Object[] args) throws Throwable {
if (!"equals".equals(method.getName())) {
if ("hashCode".equals(method.getName())) {
return this.hashCode();
} else {
return "toString".equals(method.getName()) ? this.toString() : ((MethodHandler)this.dispatch.get(method)).invoke(args);
}
} else {
try {
Object otherHandler = args.length > 0 && args[0] != null ? Proxy.getInvocationHandler(args[0]) : null;
return this.equals(otherHandler);
} catch (IllegalArgumentException var5) {
return false;
}
}
}
R
因为是个hashmap，所以setData不成功
public class R<T> extends HashMap<String,Object>{
// 把setData重写成PUT
public R setData(Object data){
put("data", data);
return this;
}
public <T> T getData(TypeReference<T> typeReference){
// get("data") 默认是map类型 所以再由map转成string再转json
Object data = get("data");//得到list，list每个值是map类型
// list<Map>转json
String s = JSON.toJSONString(data);
// json转list<T>
return JSON.parseObject(s, typeReference);
}
}
在其他处是new TypeReference<List<T>>
data的值对应的是List，而list的每个值是map
3 商城系统首页
不使用前后端分离开发了，管理后台用vue
nginx发给网关集群，网关再路由到微服务
静态资源放到nginx中
依赖
导入thymeleaf依赖、热部署依赖devtools使页面实时生效
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-devtools</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-thymeleaf</artifactId>
</dependency>
html\首页资源\index放到gulimall-product下的static文件夹
把index.html放到templates中
关闭thymeleaf缓存，方便开发实时看到更新
thymeleaf:
cache: false
suffix: .html
prefix: classpath:/templates/
web开发放到web包下，原来的controller是前后分离对接手机等访问的，所以可以改成app，对接app应用
渲染一级分类菜单
刚导入index.html时，里面的分类菜单都是写死的，我们要访问数据库拿到放到model中，然后在页面foreach填入
@GetMapping({"/", "index.html"})
public String getIndex(Model model) {
//获取所有的一级分类
List<CategoryEntity> catagories = categoryService.getLevel1Catagories();
model.addAttribute("catagories", catagories);
return "index";
}
页面遍历菜单数据
<li th:each="catagory:${catagories}" >
<a href="#" class="header_main_left_a" ctg-data="3" th:attr="ctg-data=${catagory.catId}"><b th:text="${catagory.name}"></b></a>
</li>
渲染三级分类菜单
@ResponseBody
@RequestMapping("index/catalog.json")
public Map<String, List<Catelog2Vo>> getCatlogJson() {
Map<String, List<Catelog2Vo>> map = categoryService.getCatelogJson();
return map;
}
@Override
public Map<String, List<Catelog2Vo>> getCatelogJson() {
List<CategoryEntity> entityList = baseMapper.selectList(null);
// 查询所有一级分类
List<CategoryEntity> level1 = getCategoryEntities(entityList, 0L);
Map<String, List<Catelog2Vo>> parent_cid = level1.stream().collect(Collectors.toMap(k -> k.getCatId().toString(), v -> {
// 拿到每一个一级分类 然后查询他们的二级分类
List<CategoryEntity> entities = getCategoryEntities(entityList, v.getCatId());
List<Catelog2Vo> catelog2Vos = null;
if (entities != null) {
catelog2Vos = entities.stream().map(l2 -> {
Catelog2Vo catelog2Vo = new Catelog2Vo(v.getCatId().toString(), l2.getName(), l2.getCatId().toString(), null);
// 找当前二级分类的三级分类
List<CategoryEntity> level3 = getCategoryEntities(entityList, l2.getCatId());
// 三级分类有数据的情况下
if (level3 != null) {
List<Catalog3Vo> catalog3Vos = level3.stream().map(l3 -> new Catalog3Vo(l3.getCatId().toString(), l3.getName(), l2.getCatId().toString())).collect(Collectors.toList());
catelog2Vo.setCatalog3List(catalog3Vos);
}
return catelog2Vo;
}).collect(Collectors.toList());
}
return catelog2Vos;
}));
return parent_cid;
}
3.1 Nginx配置文件
nginx.conf：
全局块：配置影响nginx全局的指令。如：用户组，nginx进程pid存放路径，日志存放路径，配置文件引入，允许生成worker process故障等
events块：配置影响 Nginx 服务器与用户的网络连接，常用的设置包括是否开启对多 work process下的网络连接进行序列化，是否允许同时接收多个网络连接，选取哪种事件驱动模型来处理连接请求，每个 word process 可以同时支持的最大连接数等。
http块：
http全局块：配置的指令包括文件引入、MIME-TYPE 定义、日志自定义、连接超时时间、单链接请求数上限等。错误页面等
server块：这块和虚拟主机有密切关系，虚拟主机从用户角度看，和一台独立的硬件主机是完全一样的。每个 http 块可以包括多个 server 块，而每个 server 块就相当于一个虚拟主机。
location1：配置请求的路由，以及各种页面的处理情况
location2
server {
listen 80;
server_name gulimall.com;
#charset koi8-r;
#access_log /var/log/nginx/log/host.access.log main;
location / {
#又转回到本机
proxy_pass http://192.168.56.1:10000;
}
#error_page 404 /404.html;
# redirect server error pages to the static page /50x.html
#
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root /usr/share/nginx/html;
}
# proxy the PHP scripts to Apache listening on 127.0.0.1:80
#
#location ~ \.php$ {
# proxy_pass http://127.0.0.1;
#}
# pass the PHP scripts to FastCGI server listening on 127.0.0.1:9000
#
#location ~ \.php$ {
# root html;
# fastcgi_pass 127.0.0.1:9000;
# fastcgi_index index.php;
# fastcgi_param SCRIPT_FILENAME /scripts$fastcgi_script_name;
# include fastcgi_params;
#}
# deny access to .htaccess files, if Apache's document root
# concurs with nginx's one
#
#location ~ /\.ht {
# deny all;
#}
}
3.2 Nginx+网关
修改hosts，映射gulimall.com到192.168.56.10(本机地址)。关闭防火墙
修改nginx/conf/nginx.conf，将upstream映射到我们的网关服务
upstream gulimall{
# 88是网关
server 192.168.56.1:88;
}
修改nginx/conf/conf.d/gulimall.conf，接收到gulimall.com的访问后，如果是/，转交给指定的upstream，由于nginx的转发会丢失host头，造成网关不知道原host，所以我们添加头信息
location / {
proxy_pass http://gulimall;
proxy_set_header Host $host;
}
配置gateway为服务器，将域名为**.gulimall.com转发至商品服务。配置的时候注意 网关优先匹配的原则，所以要把这个配置放到后面
- id: gulimall_host_route
uri: lb://gulimall-product
predicates:
- Host=**.gulimall.com
测试：http://gulimall.com/api/product/attrgroup/list/1
http://localhost:88/api/product/attrgroup/list/1
请求结果相同
此时请求接口和请求页面都是gulimall.com
3.3 压力测试
JVM参数、工具、调优笔记：https://blog.csdn.net/hancoder/article/details/108312012
Jmeter
下载：https://jmeter.apache.org/download_jmeter.cgi
创建测试计划，添加线程组
线程数==用户
ramp-up 多长时间内发送完
添加-取样器-HTTP请求
添加-监听器-查看结果树
添加-监听器-汇总报告
优化
SQL耗时越小越好，一般情况下微秒级别
命中率越高越好，一般情况下不能低于95%
锁等待次数越低越好，等待时间越短越好
中间件越多，性能损失雨大，大多都损失在网络交互了
3.4 Nginx动静分离
由于动态资源和静态资源目前都处于服务端，所以为了减轻服务器压力，我们将js、css、img等静态资源放置在Nginx端，以减轻服务器压力
静态文件上传到 mydata/nginx/html/static/index/css，这种格式
修改index.html的静态资源路径，加上static前缀src="/static/index/img/img_09.png"
修改/mydata/nginx/conf/conf.d/gulimall.conf
如果遇到有/static为前缀的请求，转发至html文件夹
location /static {
root /usr/share/nginx/html;
}
location / {
proxy_pass http://gulimall;
proxy_set_header Host $host;
}
3.5 优化三级分类
优化前
对二级菜单的每次遍历都需要查询数据库，浪费大量资源
优化后
仅查询一次数据库，剩下的数据通过遍历得到并封装
//优化业务逻辑，仅查询一次数据库
List<CategoryEntity> categoryEntities = this.list();
//查出所有一级分类
List<CategoryEntity> level1Categories = getCategoryByParentCid(categoryEntities, 0L);
Map<String, List<Catalog2Vo>> listMap = level1Categories.stream().collect(Collectors.toMap(k->k.getCatId().toString(), v -> {
//遍历查找出二级分类
List<CategoryEntity> level2Categories = getCategoryByParentCid(categoryEntities, v.getCatId());
List<Catalog2Vo> catalog2Vos=null;
if (level2Categories!=null){
//封装二级分类到vo并且查出其中的三级分类
catalog2Vos = level2Categories.stream().map(cat -> {
//遍历查出三级分类并封装
List<CategoryEntity> level3Catagories = getCategoryByParentCid(categoryEntities, cat.getCatId());
List<Catalog2Vo.Catalog3Vo> catalog3Vos = null;
if (level3Catagories != null) {
catalog3Vos = level3Catagories.stream()
.map(level3 -> new Catalog2Vo.Catalog3Vo(level3.getParentCid().toString(), level3.getCatId().toString(), level3.getName()))
.collect(Collectors.toList());
}
Catalog2Vo catalog2Vo = new Catalog2Vo(v.getCatId().toString(), cat.getCatId().toString(), cat.getName(), catalog3Vos);
return catalog2Vo;
}).collect(Collectors.toList());
}
return catalog2Vos;
}));
return listMap;
4 分布式锁redisson与缓存
缓存
本地缓存：和微服务同一个进程。缺点：分布式时本都缓存不能共享
分布式缓存：缓存中间件