Elasticsearch【基础入门】
2024-10-15 05:16:43
一.操作index
1.查看index
GET /_cat/indices?v
2.增加index
PUT /index名
3.删除index
DELETE /index名
二.操作index
1.新增document
--PUT /index名/type名/id
PUT /movie_index/movie/1
{ "id":1,
"name":"operation red sea",
"doubanScore":8.5,
"actorList":[
{"id":1,"name":"zhang yi"},
{"id":2,"name":"hai qing"},
{"id":3,"name":"zhang han yu"}
]
}
PUT /movie_index/movie/2
{
"id":2,
"name":"operation meigong river",
"doubanScore":8.0,
"actorList":[
{"id":3,"name":"zhang han yu"}
]
}
注意: 如果之前没建过 index 或者 type，es 会自动创建。
2.查询type 全部数据
GET /index名/type名/_search
3.查找指定 id 的 document 数据
GET /index名/type名/id值
4.修改 document
修改分两种: 整体替换和只修改某个字段
整体修改:和新增document差不多
PUT /movie_index/movie/3
{
"id":"3",
"name":"incident red sea",
"doubanScore":"8.0",
"actorList":[
{"id":"1","name":"zhang chen"}
]
}
只修改某个字段 ：使用post方法
POST /movie_index/movie/3/_update
{
"doc": {
--字段值 : 更新后的值
"doubanScore":"8.1"
}
}
5.删除一个 document
DELETE /movie_index/movie/3
6.条件查询
原始数据格式
{ "id":1,
"name":"operation red sea",
"doubanScore":8.5,
"actorList":[
{"id":1,"name":"zhang yi"},
{"id":2,"name":"hai qing"},
{"id":3,"name":"zhang han yu"}
]
}
查询全部
GET /movie_index/movie/_search
{
"query": {
"match_all": {}
}
}
------等价于
GET /movie_index/movie/_search
按照指定字段值查询
GET /movie_index/movie/_search
{
"query": {
"match": {
"name": "sea" --字段值
}
}
}
按照字段子属性查询
GET /movie_index/movie/_search
{
"query": {
"match": {
"actorList.name": "zhang" --json数组取子元素字段
}
}
}
7.按照短语整体查询
按照短语查询的意思是指, 匹配某个 field 的整个内容, 不再利用分词技术
GET /movie_index/movie/_search
{
"query": {
"match_phrase": {
"name": "operation red"
}
}
}
说明: 把operation red作为一个整体来看待
对比一下
--包含 operation 或者 red 的都会被查找出来
GET /movie_index/movie/_search
{
"query": {
"match": {
"name": "operation red"
}
}
}
8.模糊查询
校正匹配分词，当一个单词都无法准确匹配，es 通过一种算法对非常接近的单词也给与一定的评分，能够查询出来，但是消耗更多的性能。
GET /movie_index/movie/_search
{
"query": {
"fuzzy": {
"name": "red"
}
}
}
9.查询后过滤
GET /movie_index/movie/_search
{
"query": {
"match": {
"name": "red"
}
},
"post_filter": {
"term": {
"actorList.id": "3"
}
}
}
10.查询前过滤（推荐）
--条件：actorList.id=3 or actorList.id= 1 && name contains "zhang"
GET movie_index/movie/_search
{
"query": {
"bool": {
"filter": [
{"term":
{"actorList.id": 3}
},
{
"term":
{"actorList.id": 1}
}
],
"must":
{"match": {
"name": "zhang"
}}
}
}
}
must、should、must_not区别
must可以理解为 &，should理解为 |, must_not理解为！【与、或、非的关系】
must+should的使用可以理解为：先按照must过滤，过滤出来的结果的score分数增加。should只是辅助作用
must
年龄39 且 性别'女'
GET /bank/_search
{
"query": {
"bool": {
"must": [
{"match": {
"age": "39"
}},
{"match": {
"gender": "F"
}}
]
}
}
}
should
年龄39 或 性别'女'
GET /bank/_search
{
"query": {
"bool": {
"should": [
{"match": {
"age": "39"
}},
{"match": {
"gender": "F"
}}
]
}
}
}
must_not
年龄不是39 且 性别不为 '女'
GET /bank/_search
{
"query": {
"bool": {
"must_not": [
{"match": {
"age": "39"
}},
{"match": {
"age": "40"
}}
]
}
}
}
must+should
结果和must结果一样，不同就是“must+should”的结果的score增加。
GET /bank/_search
{
"query": {
"bool": {
"must": [
{"match": {
"age": "39"
}}
],
"should": [
{"match": {
"gender": "F"
}}
]
}
}
}
11.按照范围过滤
lt:小于，lte：小于等于，gt：大于，gte：大于等于
GET movie_index/movie/_search
{
"query": {
"bool": {
"filter": {
"range": {
"doubanScore": {
"gt": 5,
"lt": 9
}
}
}
}
}
}
12.排序
GET movie_index/movie/_search
{
"query":{
"match": {"name":"red operation"}
}
, "sort": [
{
"doubanScore": { --指定排序字段
"order": "desc" --指定排序规则
}
}
]
}
13.分页查询
GET movie_index/movie/_search
{
"query": { "match_all": {} },
"from": 10, --从第几条开始查询
"size": 10 --展示几条
}
14.聚合
select count(*) from group by gender
GET /bank/_search
{
"aggs": {
"groupby_gender": {
"terms": {
"field": "gender.keyword",
"size": 1
}
}
}
}
多组聚合
相对于Sql中的两个group by语句的查询结果
selec sum(balance), max(balance) from .. group by gender
selec sum(balance) from .. group by age
GET /bank/_search
{
"query": {
"match": {
"address": "Terrace"
}
},
"aggs": {
--按照gender聚合
"groupby_gender": {
"terms": {
"field": "gender.keyword",
"size": 2
},
"aggs": {
"b_sum": {
"sum": {
"field": "balance"
}
},
"b_max":{
"max": {
"field": "balance"
}
}
}
},
----按照age聚合
"groupby_age": {
"terms": {
"field": "age",
"size": 100
},
"aggs": {
"b_sum": {
"sum": {
"field": "balance"
}
}
}
}
},
"sort": [
{
"age": {
"order": "desc"
}
}
],
"_source": ["balance", "age"]
}
三. Scala API插入数据到ES
使用java API同样可以实现
1.ES中新建测试Index
GET /user/_search --查询
--向Index插入一条数据，同时创建Index
PUT /user/_doc/1
{
"name":"zhangsan",
"age":10
}
2.User样例类
case class User(name:String,age:Int)
3.ES工具类
import io.searchbox.client.{JestClient, JestClientFactory}
import io.searchbox.client.config.HttpClientConfig
import io.searchbox.core.{Bulk, Index}
/**
* @description: ES工具类
* @author: HaoWu
* @create: 2020年09月09日
*/
object ESUtil {
// 构建JestClientFactory
//ES服务器地址 注意：可以设置1个也可以设置1个Collection，要转为java的集合
import scala.collection.JavaConverters._
val esServerUrl = Set("http://hadoop102:9200", "http://hadoop103:9200", "http://hadoop104:9200").asJava
//构建客户端工厂
private val factory = new JestClientFactory
var conf: HttpClientConfig = new HttpClientConfig.Builder(esServerUrl)
.multiThreaded(true)
.maxTotalConnection(100)
.connTimeout(10 * 1000)
.readTimeout(10 * 1000)
.build()
factory.setHttpClientConfig(conf)
/**
* 获取ES客户端
*/
def getESClient(): JestClient = {
factory.getObject
}
/**
* 插入单条数据
*
* @param index :插入的Index
* @param source :满足两种类型参数：1.source 2.(id,source) ,其中source可以是样例类对象 或 json对象字符串
*/
def insertSingle(index: String, source: Any): Unit = {
val client: JestClient = getESClient()
val action =
source match {
case (id, s: Any) => {
new Index.Builder(s)
.index(index)
.`type`("_doc")
.id(id.toString) //ES中的id为String类型，当入参的id为int类型可能插入错误。
.build()
}
case (_) => {
new Index.Builder(source)
.index(index)
.`type`("_doc")
.build()
}
}
client.execute(action)
client.close()
}
/**
* 批量插入数据
*
* @param index :插入的Index
* @param sources :满足两种类型参数：1.source 2.(id,source) ,其中source可以是样例类对象 或 Json对象字符串
* 说明：将来数据使用mapPartition批量写入，所以参数封装为Iterator类型
*/
def insertBluk(index: String, sources: Iterator[Object]) {
// 1.获取ES客户端
val client: JestClient = getESClient()
// 2.构建Builder
val builder: Bulk.Builder = new Bulk.Builder()
.defaultIndex(index)
.defaultType("_doc")
// 3.为Builder添加action
//================== 方式一 ========================================
/* sources.foreach(
source => {
val action =
source match {
case (id: String, s) => { //入参是一个元祖(id， source)
new Index.Builder(s)
.id(id)
.build()
}
case (_) => { //入参是source,样例类，或者 json对象字符串
new Index.Builder(source)
.build()
}
}
//添加action
builder.addAction(action)
}
)*/
//================== 方式二 ========================================
sources.map { //转换为action
case (id: String, s) => {
new Index.Builder(s)
.id(id)
.build()
}
case (source) => {
new Index.Builder(source)
.build()
}
} //往builder添加action
.foreach(builder.addAction)
// 4.执行插入
client.execute(builder.build())
// 5.关闭客户端
client.close()
}
4.测试
插入单条数据
val source1 = User("lisi", 20) //id随机生成
val source2 = ("11",User("lisi", 20)) //id为10
insertSingle("user", source1)
insertSingle("user", source2)
查询结果
{
"_index": "user",
"_type": "_doc",
"_id": "pwvTcXQBrKDUC6YPHEQZ",
"_score": 1,
"_source": {
"name": "lisi",
"age": 20
}
},
{
"_index": "user",
"_type": "_doc",
"_id": "11",
"_score": 1,
"_source": {
"name": "lisi",
"age": 20
}
}
批量插入数据
//不指定id 和 指定id
val sources = Iterator(User("wangwu", 21), (99,User("zhaoliu", 30)))
insertBluk("user",sources)
查询结果
{
"_index": "user",
"_type": "_doc",
"_id": "qwvgcXQBrKDUC6YPbETl",
"_score": 1,
"_source": {
"name": "wangwu",
"age": 21
}
},
{
"_index": "user",
"_type": "_doc",
"_id": "rAvgcXQBrKDUC6YPbETl",
"_score": 1,
"_source": {
"_1": 99,
"_2": {
"name": "zhaoliu",
"age": 30
}
}
}
四.JavaAPI 条件查询ES 解析
需求：根据日期(date)和关键字(sku_name)过滤，并且分别对年龄(age)和性别(gender)分组，根据当前页数(startPage),每页展示条数(pageSize)
DSL查询
GET /gmall0421_sale_detail/_search
{
"query": {
"bool": {
"filter": {
"term": {
"dt": "2020-09-12" //日期date
}
},
"must": [
{"match": {
"sku_name": {
"query": "手机小米", //关键字keyword
"operator": "and"
}
}}
]
}
},
"aggs": {
"group_by_gender": {
"terms": {
"field": "user_gender", //按性别分组(gender)
"size": 10
}
},
"group_by_age": {
"terms": {
"field": "user_age", //按年龄分组(age)
"size": 150
}
}
},
"from": 0, //查询的页数(startPage)
"size": 1000 //每页记录数(pageSize)
}
ESUtil
scala代码，写DSL语句更美观
package com.bigdata.real_time_project.gmallpublisher.uitl
import io.searchbox.client.config.HttpClientConfig
import io.searchbox.client.{JestClient, JestClientFactory}
import io.searchbox.core.{Bulk, Index, Search, SearchResult}
import scala.collection.JavaConverters._
/**
* @description: ES工具类
* @author: HaoWu
* @create: 2020年09月09日
*/
object ESUtil {
// 构建JestClientFactory
//ES服务器地址
val esServerUrl = Set("http://hadoop102:9200", "http://hadoop103:9200", "http://hadoop104:9200")
.asJava //要转为java的集合
private val factory = new JestClientFactory
var conf: HttpClientConfig = new HttpClientConfig.Builder(esServerUrl)
.multiThreaded(true)
.maxTotalConnection(100)
.connTimeout(10 * 1000)
.readTimeout(10 * 1000)
.build()
factory.setHttpClientConfig(conf)
/**
* 获取ES客户端
*/
def getESClient(): JestClient = {
factory.getObject
}
//获取DSL语句
def getQueryDSL(date: String, keyword: String, startPage: Int, pageSize: Int) = {
val dsl =
s"""
|{
| "query": {
| "bool": {
| "filter": {
| "term": {
| "dt": "${date}"
| }
| },
| "must": [
| {"match": {
| "sku_name": {
| "query": "${keyword}",
| "operator": "and"
| }
| }}
| ]
| }
| },
|
| "aggs": {
| "group_by_gender": {
| "terms": {
| "field": "user_gender",
| "size": 10
| }
| },
| "group_by_age": {
| "terms": {
| "field": "user_age",
| "size": 150
| }
| }
|
| },
|
|
| "from": ${startPage},
| "size": ${pageSize}
|}
|""".stripMargin
dsl
}
}
查询并解析
Java代码解析
//前端访问：http://localhost:8070/sale_detail?date=2019-05-20&&keyword=手机小米&&startpage=1&&size=5
public Map<String, Object> getSailDetailAndAggs(String date, String keyword, int startpage, int size) throws IOException {
//1.查询 es
JestClient client = ESUtil.getESClient();
Search.Builder builder = new Search.Builder(ESUtil.getQueryDSL(date, keyword, startpage, size))
.addIndex("gmall0421_sale_detail")
.addType("_doc");
SearchResult searchResult = client.execute(builder.build());
client.close();
//2.解析查询结果
HashedMap result = new HashedMap();
//2.1 total
result.put("total", searchResult.getTotal());
//2.2 details
ArrayList<Map> details = new ArrayList<>();
List<SearchResult.Hit<Map, Void>> hits = searchResult.getHits(Map.class);
for (SearchResult.Hit<Map, Void> hit : hits) {
Map source = hit.source;
details.add(source);
}
result.put("detail", details);
//2.3 gender聚合结果
Map<String, Object> genderMap = new HashMap<>();
List<TermsAggregation.Entry> bucketsGender = searchResult.getAggregations()
.getTermsAggregation("group_by_gender")
.getBuckets();
for (TermsAggregation.Entry bucket : bucketsGender) {
genderMap.put(bucket.getKey(), bucket.getCount());
}
result.put("genderAgg",genderMap);
//2.3 age聚合结果
Map<String, Object> ageMap = new HashMap<>();
List<TermsAggregation.Entry> bucketsAge = searchResult.getAggregations()
.getTermsAggregation("group_by_age")
.getBuckets();
for (TermsAggregation.Entry bucket : bucketsAge) {
ageMap.put(bucket.getKey(), bucket.getCount());
}
result.put("ageAgg",ageMap);
return result;
}
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