1、term&terms查询
1.1 term查询(分页)
ElasticSearch查询语法:
# term查询
POST /sms-logs-index/sms-logs-type/_search
{
"from": 0,
"size": 5,
"query": {
"term": {
"province": {
"value": "北京"
}
}
}
}查询结果中max_score匹配度越高,数据的排名就越靠前
// Java代码实现方式
@Test
public void termQuery() throws IOException {
//1. 创建Request对象
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.from(0);
builder.size(5);
builder.query(QueryBuilders.termQuery("province","北京"));
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 获取到_source中的数据，并展示
for (SearchHit hit : resp.getHits().getHits()) {
Map<String, Object> result = hit.getSourceAsMap();
System.out.println(result);
}
}1.2 terms查询
# terms查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"terms": {
"province": [
"北京",
"山西",
"武汉"
]
}
}
}
## 返回指定的列
POST /sms-logs-index/sms-logs-type/_search
{
"_source": ["province","fee"],
"query": {
"terms": {
"province": [
"北京",
"山西",
"武汉"
]
}
}
}// Java实现
@Test
public void termsQuery() throws IOException {
//1. 创建request
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 封装查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.query(QueryBuilders.termsQuery("province","北京","山西"));
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出_source
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}2 、match查询
2.1 match_all查询
查询全部内容，不指定任何查询条件。
# match_all查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"match_all": {}
}
}java代码实现方式
// java代码实现
@Test
public void matchAllQuery() throws IOException {
//1. 创建Request
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.query(QueryBuilders.matchAllQuery());
builder.size(20); // ES默认只查询10条数据，如果想查询更多，添加size
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
System.out.println(resp.getHits().getHits().length);
}2.2 match查询
# match查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"match": {
"smsContent": "收货安装"
}
}
}@Test
public void matchQuery() throws IOException {
//1. 创建Request
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//-----------------------------------------------
builder.query(QueryBuilders.matchQuery("smsContent","收货安装"));
//-----------------------------------------------
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}2.3 match查询,追加操作,或者,并且
# 布尔match查询,内容既包含中国也包含健康
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"match": {
"smsContent": {
"query": "中国 健康",
"operator": "and"
}
}
}
}
# 布尔match查询,内容包括健康或者包括中国
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"match": {
"smsContent": {
"query": "中国 健康",
"operator": "or"
}
}
}
}java代码实现方式
// Java代码实现
@Test
public void booleanMatchQuery() throws IOException {
//1. 创建Request
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//----------------------------------------------- 选择AND或者OR↓
builder.query(QueryBuilders.matchQuery("smsContent","中国 健康").operator(Operator.OR));
//-----------------------------------------------
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}2.4 multi_match查询,多字段属性查询
# multi_match 查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"multi_match": {
"query": "北京",
"fields": ["province","smsContent"]
}
}
}java代码实现方式（multiMatchQuery）
// java代码实现
@Test
public void multiMatchQuery() throws IOException {
//1. 创建Request
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//-----------------------------------------------
builder.query(QueryBuilders.multiMatchQuery("北京","province","smsContent"));
//-----------------------------------------------
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}3 、其他查询
3.1 id查询
# 查询id为21的数据
GET /sms-logs-index/sms-logs-type/21// Java代码实现
@Test
public void findById() throws IOException {
//1. 创建GetRequest
GetRequest request = new GetRequest(index,type,"21");//查id为21,可以打开看id再写即可
//2. 执行查询
GetResponse resp = client.get(request, RequestOptions.DEFAULT);
//3. 输出结果
System.out.println(resp.getSourceAsMap());
}3.2 ids查询
# ids查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"ids": {
"values": ["21","22","23"]
}
}
}// Java代码实现
@Test
public void findByIds() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//----------------------------------------------------------
builder.query(QueryBuilders.idsQuery().addIds("21","22","23"));
//----------------------------------------------------------
request.source(builder);
//3. 执行
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}3.3 prefix查询,前缀查询
# prefix查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"prefix": {
"corpName": {
"value": "关键词"
}
}
}
}java代码实现方式
// Java实现前缀查询
@Test
public void findByPrefix() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//----------------------------------------------------------
builder.query(QueryBuilders.prefixQuery("corpName","关键词"));
//----------------------------------------------------------
request.source(builder);
//3. 执行
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}3.4 fuzzy查询,模糊,比如不完全输对,也能搜索出来
# fuzzy查询,可以指定前面几个字符是不允许出错
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"fuzzy": {
"corpName": {
"value": "大概内容",
"prefix_length": 2
}
}
}
}java代码实现方式（fuzzyQuery）
// Java代码实现Fuzzy查询
@Test
public void findByFuzzy() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//----------------------------------------------------------
builder.query(QueryBuilders.fuzzyQuery("corpName","大概内容").prefixLength(2));
//----------------------------------------------------------
request.source(builder);
//3. 执行
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}3.5 wildcard查询,外卡,通配符查询
# wildcard查询,可以使用*和？指定通配符和占位符(指定长度)
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"wildcard": {
"corpName": {
"value": "前缀*"
}
}
}
}// Java代码实现Wildcard查询
@Test
public void findByWildCard() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//----------------------------------------------------------
builder.query(QueryBuilders.wildcardQuery("corpName","中国*"));
//----------------------------------------------------------
request.source(builder);
//3. 执行
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}3.6 range查询,范围查询
java代码实现（rangeQuery）
// Java实现range范围查询
@Test
public void findByRange() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//----------------------------------------------------------
builder.query(QueryBuilders.rangeQuery("fee").lte(10).gte(5));
//----------------------------------------------------------
request.source(builder);
//3. 执行
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}3.7 regexp查询,正则查询
# regexp查询,编写正则
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"regexp": {
"mobile": "180[0-9]{8}"
}
}
}// Java代码实现正则查询
@Test
public void findByRegexp() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
//----------------------------------------------------------
builder.query(QueryBuilders.regexpQuery("mobile","180[0-9]{8}"));
//----------------------------------------------------------
request.source(builder);
//3. 执行
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}4、 深分页之Scroll（滚动分页）
# 执行scroll查询,返回第一页数据,并且将文档id信息存放在ES上下文中,指定生存时间为1m,1分钟
POST /sms-logs-index/sms-logs-type/_search?scroll=1m
{
"query": {
"match_all": {}
},
"size": 4,
"sort": [
{
"fee": {
"order": "desc"
}
}
]
}
# 根据scroll,查询下一页数据
POST /_search/scroll
{
"scroll_id": "根据第一步得到的scorll_id去指定",
"scroll": "scorll信息的生存时间"
}
# 删除scroll,在ES上下文中的数据
DELETE /_search/scroll/scroll_idjava代码实现方式
// Java实现scroll分页
@Test
public void scrollQuery() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定scroll信息!
request.scroll(TimeValue.timeValueMinutes(1L));
//3. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.size(4);
builder.sort("fee", SortOrder.DESC);
builder.query(QueryBuilders.matchAllQuery());
request.source(builder);
//4. 获取返回结果scrollId，source
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
String scrollId = resp.getScrollId();
System.out.println("----------首页---------");
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
while(true) {
//5. 循环 - 创建SearchScrollRequest
SearchScrollRequest scrollRequest = new SearchScrollRequest(scrollId);
//6. 指定scrollId的生存时间!
scrollRequest.scroll(TimeValue.timeValueMinutes(1L));
//7. 执行查询获取返回结果
SearchResponse scrollResp = client.scroll(scrollRequest, RequestOptions.DEFAULT);
//8. 判断是否查询到了数据，输出
SearchHit[] hits = scrollResp.getHits().getHits();
if(hits != null && hits.length > 0) {
System.out.println("----------下一页---------");
for (SearchHit hit : hits) {
System.out.println(hit.getSourceAsMap());
}
}else{
//9. 判断没有查询到数据-退出循环
System.out.println("----------结束---------");
break;
}
}
//10. 创建CLearScrollRequest
ClearScrollRequest clearScrollRequest = new ClearScrollRequest();
//11. 指定ScrollId
clearScrollRequest.addScrollId(scrollId);
//12. 删除ScrollId
ClearScrollResponse clearScrollResponse = client.clearScroll(clearScrollRequest, RequestOptions.DEFAULT);
//13. 输出结果
System.out.println("删除scroll：" + clearScrollResponse.isSucceeded());
}5 _delete_by_query,删除,根据查询出来的数据来删除（查询删除）
根据term,match等查询方式去删除大量的文档
Ps：如果你需要删除的内容，是index下的大部分数据，推荐创建一个全新的index，将保留的文档内容，添加到全新的索引
# delete-by-query
POST /sms-logs-index/sms-logs-type/_delete_by_query
{
"query": {
"range": {
"fee": {
"lt": 4
}
}
}
}// Java代码实现
@Test
public void deleteByQuery() throws IOException {
//1. 创建DeleteByQueryRequest
DeleteByQueryRequest request = new DeleteByQueryRequest(index);
request.types(type);
//2. 指定检索的条件 和SearchRequest指定Query的方式不一样
request.setQuery(QueryBuilders.rangeQuery("fee").lt(4));
//3. 执行删除
BulkByScrollResponse resp = client.deleteByQuery(request, RequestOptions.DEFAULT);
//4. 输出返回结果
System.out.println(resp.toString());
}6 、复合查询
6.1 bool查询,布尔查询,组装多个条件
// Java代码实现Bool查询
@Test
public void BoolQuery() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
//# 查询省份为武汉或者北京
boolQuery.should(QueryBuilders.termQuery("province","武汉"));
boolQuery.should(QueryBuilders.termQuery("province","北京"));
//# 运营商不是联通
boolQuery.mustNot(QueryBuilders.termQuery("operatorId",2));
//# smsContent中包含中国和平安
boolQuery.must(QueryBuilders.matchQuery("smsContent","中国"));
boolQuery.must(QueryBuilders.matchQuery("smsContent","平安"));
builder.query(boolQuery);
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}6.2 boosting查询
# boosting查询,收货安装
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"boosting": {
"positive": {
"match": {
"smsContent": "收货安装"
}
},
"negative": {
"match": {
"smsContent": "王五"
}
},
"negative_boost": 0.5
}
}
}java代码实现方式：
// Java实现Boosting查询
@Test
public void BoostingQuery() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
BoostingQueryBuilder boostingQuery = QueryBuilders.boostingQuery(
QueryBuilders.matchQuery("smsContent", "收货安装"),
QueryBuilders.matchQuery("smsContent", "王五")
).negativeBoost(0.5f);
builder.query(boostingQuery);
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}查询结果比如没减分之前王五的分数是1.75...,减分之后,系数写的0.5,就相当于1.75乘以0.5等于0.8
7 、bool查询之过滤查询,filter查询
# filter查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"bool": {
"filter": [
{
"term": {
"corpName": "关键词"
}
},
{
"range": {
"fee": {
"lte": 5
}
}
}
]
}
}
}java实现方式
// Java实现filter操作
@Test
public void filter() throws IOException {
//1. SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 查询条件
SearchSourceBuilder builder = new SearchSourceBuilder();
BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
boolQuery.filter(QueryBuilders.termQuery("corpName","关键词"));
boolQuery.filter(QueryBuilders.rangeQuery("fee").lte(5));
builder.query(boolQuery);
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}8 、高亮查询
# highlight查询
POST /sms-logs-index/sms-logs-type/_search
{
"query": {
"match": {
"smsContent": "关键词"
}
},
"highlight": {
"fields": {
"smsContent": {}
},
"pre_tags": "<font color='red'>",
"post_tags": "</font>",
"fragment_size": 10
}
}说白了就是把要高亮的数据增加一个html标签并加上属性,比如字体的红色属性,这样以后把查询出来的数据在浏览器打开时就是红色的了
java代码实现方式：
// Java实现高亮查询
@Test
public void highLightQuery() throws IOException {
//1. SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定查询条件（高亮）
SearchSourceBuilder builder = new SearchSourceBuilder();
//2.1 指定查询条件
builder.query(QueryBuilders.matchQuery("smsContent","关键词"));
//2.2 指定高亮
HighlightBuilder highlightBuilder = new HighlightBuilder();
highlightBuilder.field("smsContent",10)
.preTags("<font color='red'>")
.postTags("</font>");
builder.highlighter(highlightBuilder);
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 获取高亮数据，输出
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getHighlightFields().get("smsContent"));
}
}9、 聚合查询
# ES聚合查询的RESTful语法
POST /index/type/_search
{
"aggs": {
"名字推荐写agg": {
"agg_type": {
"属性": "值"
}
}
}
}9.1 基数去重,计数查询
去重计数，即Cardinality，第一步先将返回的文档中的一个指定的field进行去重，统计一共有多少条
# 去重计数查询 北京 上海 武汉 山西
POST /sms-logs-index/sms-logs-type/_search
{
"aggs": {
"agg": {
"cardinality": {
"field": "province"
}
}
}
}//Java代码实现去重计数查询
@Test
public void cardinality() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定使用的聚合查询方式
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.aggregation(AggregationBuilders.cardinality("agg").field("province"));
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 获取返回结果
Cardinality agg = resp.getAggregations().get("agg");
long value = agg.getValue();
System.out.println(value);
}9.2 范围统计查询,比如在一定的区间内的数据统计查询出来封装在桶里面
统计一定范围内出现的文档个数，比如，针对某一个Field的值在 0~100,100~200,200~300之间文档出现的个数分别是多少。
范围统计可以针对普通的数值，针对时间类型，针对ip类型都可以做相应的统计。
range数值范围，date_range时间范围，ip_range即ip访问统计↓
# 数值方式范围统计,from有包含当前值的意思
POST /sms-logs-index/sms-logs-type/_search
{
"aggs": {
"agg": {
"range": {
"field": "fee",
"ranges": [
{
"to": 5
},
{
"from": 5,
"to": 10
},
{
"from": 10
}
]
}
}
}
}结果,from5有>=5的意思,而to没有
时间范围统计↓
# 时间方式范围统计
POST /sms-logs-index/sms-logs-type/_search
{
"aggs": {
"agg": {
"date_range": {
"field": "createDate",
"format": "yyyy",
"ranges": [
{
"to": 2000
},
{
"from": 2000
}
]
}
}
}
}结果,2000年以前的有多少个,2000以后的有多少个数据
ip范围统计↓
# ip方式 范围统计
POST /sms-logs-index/sms-logs-type/_search
{
"aggs": {
"agg": {
"ip_range": {
"field": "ipAddr",
"ranges": [
{
"to": "10.126.2.9"
},
{
"from": "10.126.2.9"
}
]
}
}
}
}// Java实现数值 范围统计
@Test
public void range() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定使用的聚合查询方式
SearchSourceBuilder builder = new SearchSourceBuilder();
//---------------------------------------------
builder.aggregation(AggregationBuilders.range("agg").field("fee")
.addUnboundedTo(5)
.addRange(5,10)
.addUnboundedFrom(10));
//---------------------------------------------
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 获取返回结果
Range agg = resp.getAggregations().get("agg");//注意这里用Range才有getBuckets方法↓
for (Range.Bucket bucket : agg.getBuckets()) {
String key = bucket.getKeyAsString();
Object from = bucket.getFrom();
Object to = bucket.getTo();
long docCount = bucket.getDocCount();
System.out.println(String.format("key：%s，from：%s，to：%s，docCount：%s",key,from,to,docCount));//%s理解为占位符的意思
}
}代码怎么写其实和查询出来的结果标签其实是一一对应的,要注意这里用Range才有getBuckets方法
9.3 扩展状态,统计聚合查询,求最值等等
# 统计聚合查询,扩展状态
POST /sms-logs-index/sms-logs-type/_search
{
"aggs": {
"agg": {
"extended_stats": {
"field": "fee"
}
}
}
}结果,使用extended_stats查出来的结果里面就有各种最大值，最小值，平均值，平方和等等
java代码实现方式
// Java实现统计聚合查询
@Test
public void extendedStats() throws IOException {
//1. 创建SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2. 指定使用的聚合查询方式
SearchSourceBuilder builder = new SearchSourceBuilder();
//---------------------------------------------
builder.aggregation(AggregationBuilders.extendedStats("agg").field("fee"));
//---------------------------------------------
request.source(builder);
//3. 执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4. 获取返回结果
ExtendedStats agg = resp.getAggregations().get("agg");
double max = agg.getMax();
double min = agg.getMin();
System.out.println("fee的最大值为：" + max + "，最小值为：" + min);
}其他聚合查询方式看官方文档：Elasticsearch Guide [6.5] | Elastic
10 、地图经纬度搜索
10.1 ES的地图检索方式
| 语法 | 说明 |
|---|---|
| geo_distance | 直线距离检索方式 |
| geo_bounding_box | 以两个点确定一个矩形，获取在矩形内的全部数据 |
| geo_polygon | 以多个点，确定一个多边形，获取多边形内的全部数据 |
10.2 基于RESTful实现地图检索
# geo_distance,确定一个点,表示检索经纬度是北京站北京distance为3000米,方圆的范围的数据,arc圆形
POST /map/map/_search
{
"query": {
"geo_distance": {
"location": {
"lon": 116.433733,
"lat": 39.908404
},
"distance": 3000,
"distance_type": "arc"
}
}
}# geo_bounding_box,左上角中央人民大学的经纬度坐标点,右下角北京建筑大学的经纬度坐标点
POST /map/map/_search
{
"query": {
"geo_bounding_box": {
"location": {
"top_left": {
"lon": 116.326943,
"lat": 39.95499
},
"bottom_right": {
"lon": 116.347783,
"lat": 39.939281
}
}
}
}
}# geo_polygon,指定多个点确定一个多边形,第一个点西苑操场,第二个点巴沟山水园,第三个点中关村
POST /map/map/_search
{
"query": {
"geo_polygon": {
"location": {
"points": [
{
"lon": 116.298916,
"lat": 39.99878
},
{
"lon": 116.29561,
"lat": 39.972576
},
{
"lon": 116.327661,
"lat": 39.984739
}
]
}
}
}
}10.3 Java实现es基于地理位置经纬度范围查询↓
public class Test03 {
RestHighLevelClient client = ESClient.getClient();
String index = "map";//索引库名字
String type = "map";//类型表名字
@Test
public void geoPolygon() throws IOException {
//1.SearchRequest
SearchRequest request = new SearchRequest(index);
request.types(type);
//2.指定检索方式
SearchSourceBuilder builder = new SearchSourceBuilder();
List<GeoPoint> points = new ArrayList<GeoPoint>();
//geo_polygon,多点多边形,以第一个点西苑操场,第二个点巴沟山水园,第三个点中关村构成的多边形,包括海淀公园↓
points.add(new GeoPoint(39.99878,116.298916));
points.add(new GeoPoint(39.972576,116.29561));
points.add(new GeoPoint(39.984739,116.327661));
builder.query(QueryBuilders.geoPolygonQuery("location",points));
//geo_bounding_box,两点矩形,以左上角中央人民大学,右下角北京建筑大学构成的矩形包括北京动物园↓
//GeoBoundingBoxQueryBuilder location1 = QueryBuilders.geoBoundingBoxQuery("location");
//location1.topLeft().reset(39.95499,116.326943);
//location1.bottomRight().reset(39.939281,116.347783);
//builder.query(location1);
//distance,单点方圆,北京站这个点,方圆3000米的范围,包括天安门↓
//GeoDistanceQueryBuilder location = QueryBuilders.geoDistanceQuery("location");
//location.point(39.908404,116.433733).distance("3000");
//builder.query(location);
request.source(builder);
//3.执行查询
SearchResponse resp = client.search(request, RequestOptions.DEFAULT);
//4.输出结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}
}
