ElasticSearch查询笔记目录
涉及的常用查询内容较多，将分多个章节进行笔记整理，具体如下：
主要是依据精准的查询条件来查询，查询速度快，也是最常用的几类查询方式，具体种类如下:
- term查询
- terms查询
- match_all查询
- match查询
- 布尔match查询
- multi_match查询
- 根据文档id查询（单个id）
- 根据文档ids查询（多个id）
主要是涉及ElasticSearch查询条件相对模糊，查询速度相对慢，实时查询时应尽量避免这些方式，但是这些查询方式又具有自己独特不可代替的功能，还是还有必要，具体如下：
- prefix查询
- fuzzy查询
- wildcard查询
- range查询
- regexp查询
主要涉及ElasticSearch的一些常用的杂项查询；
- 深分页scroll查询
- delete-by-query
- bool查询
- boosting查询
- filter查询
- highlight(高亮)查询
主要涉及ES的聚合查询Aggregations；
- cardinality(去重计数)查询
- range(范围统计)查询
- extended_stats(统计聚合)查询
. 主要涉及ES的地图检索geo相关的查询；
- geo_distance查询
- geo_bounding_box查询
- geo_polygon查询
整体Java代码的测试用例项目
整个章节的Java代码放在CSDN资源ElasticSearch常用查询的Java实现；路径效果如下图，欢迎下载访问；
geo_point索引、数据准备
ES中特有的类型geo_point，用来存储地图类型，某平台外卖。快递体现的配送人员距离你多少米，就是这个数据类型做出来的。
新建索引map
RESTFul风格的创建索引my_badu_map如下；
PUT /my_badu_map
{
"settings": {
"number_of_replicas": 3,
"number_of_shards": 5
}
, "mappings": {
"properties": {
"name":
{
"type": "text"
}
,"location":
{
"type": "geo_point"
}
}
}
}
RESTFul风格给索引my_badu_map添加数据；
PUT /my_badu_map/_doc/1
{
"name":"天安门",
"location":
{
"lon":116.403981,
"lat":39.914492
}
}
PUT /my_badu_map/_doc/2
{
"name":"海淀公园",
"location":
{
"lon":116.302509,
"lat":39.991152
}
}
PUT /my_badu_map/_doc/3
{
"name":"北京动物园",
"location":
{
"lon":116.343184,
"lat":39.947468
}
}
RESTFul风格预览数据如下；
POST /my_badu_map/_search
#数据结果如下
{
"took" : 815,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "my_badu_map",
"_type" : "_doc",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"name" : "北京动物园",
"location" : {
"lon" : 116.343184,
"lat" : 39.947468
}
}
},
{
"_index" : "my_badu_map",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"name" : "海淀公园",
"location" : {
"lon" : 116.302509,
"lat" : 39.991152
}
}
},
{
"_index" : "my_badu_map",
"_type" : "_doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "天安门",
"location" : {
"lon" : 116.403981,
"lat" : 39.914492
}
}
}
]
}
}
ES的地图检索方式
ES支持的地图检索方式有以下几种；
geo_distance:直线距离检索，如给定点A，要求返回地图上距离点A三千米的商家（点外卖场景），如图1；
geo_bounding_box:以两个点确定一个矩形，获取在矩形内的全部数据，如图2；
geo_polygon：以多个点，确定多边形，获取多边形内的全部数据，如图3；
基于RESTFul风格实现地图检索
geo_distance
geo_distance涉及的参数如下
location:确定一个点；
distance:确定一个半径，单位米
distance_type:确定一个图形的类型，一般是圆形,arc
实现要求，查找索引内距离北京站(116.433733,39.908404)3000米内的点;
RESTFUL代码如下；
POST /my_badu_map/_search
{
"query": {
"geo_distance":
{
"location":
{
"lon":116.433733
,"lat":39.908404
},
"distance":3000,
"distance_type":"arc"
}
}
}
RESTFUL代码效果如下,在3千米内，是包含了北京天安门的；
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "my_badu_map",
"_type" : "_doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "天安门",
"location" : {
"lon" : 116.403981,
"lat" : 39.914492
}
}
}
]
}
}
geo_bounding_box
geo_bounding_box涉及的参数如下
top_left:左上角的矩形起始点经纬度；
bottom_right右下角的矩形结束点经纬度
实现要求，查找索引内位于中央民族大学(116.326943,39.95499)以及京站(116.433733,39.908404)矩形的点;
RESTFUL代码如下；
POST /my_badu_map/_search
{
"query": {
"geo_bounding_box":
{
"location":
{
"top_left":
{
"lon":116.326943,
"lat":39.95499
}
,"bottom_right":
{
"lon":116.433446,
"lat":39.908737
}
}
}
}
}
RESTFUL代码效果如下，北京动物园和天安门在中央民族大学(116.326943,39.95499)以及京站(116.433733,39.908404)矩形内；
{
"took" : 37,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "my_badu_map",
"_type" : "_doc",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"name" : "北京动物园",
"location" : {
"lon" : 116.343184,
"lat" : 39.947468
}
}
},
{
"_index" : "my_badu_map",
"_type" : "_doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "天安门",
"location" : {
"lon" : 116.403981,
"lat" : 39.914492
}
}
}
]
}
}
geo_polygon
geo_polygon涉及的参数如下
points：是个数组，存储多变形定点的经纬度，每个点用大括号包起来
实现要求，查找索引内位于西苑桥(116.300209,40.003423)，巴沟山水园(116.29561,39.976004)以及北京科技大学(116.364528,39.996348)三角形内的点;
RESTFUL代码如下；
POST /my_badu_map/_search
{
"query": {
"geo_polygon":
{
"location":
{
"points":
[
{
"lon":116.29561,
"lat":39.976004
}
,{
"lon":116.364528,
"lat":39.996348
}
,{
"lon":116.300209,
"lat":40.003423
}
]
}
}
}
}
RESTFUL代码效果如下，海淀公园位于西苑桥(116.300209,40.003423)，巴沟山水园(116.29561,39.976004)以及北京科技大学(116.364528,39.996348)三角形内；
{
"took" : 204,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "my_badu_map",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"name" : "海淀公园",
"location" : {
"lon" : 116.302509,
"lat" : 39.991152
}
}
}
]
}
}
基于Java代码实现地图检索
geo_distance
geo_distance涉及的参数如下
location:确定一个点；
distance:确定一个半径，单位米
distance_type:确定一个图形的类型，一般是圆形,arc
实现要求，查找索引内距离北京站(116.433733,39.908404)3000米内的点;
Java代码如下；
static RestHighLevelClient myClient= EsClient.getClient(); //获取操作ES的
String index="sms-logs-index";
String index2="my_badu_map";
@Test
public void geoDistance() throws IOException {
//1.SearchRequest
SearchRequest request=new SearchRequest(index2);
//2.指定检索方式
SearchSourceBuilder builder=new SearchSourceBuilder();
builder.query(QueryBuilders.geoDistanceQuery("location").distance("3000").point(39.908404,116.433733));
request.source(builder);
//3.执行查询
SearchResponse resp = myClient.search(request, RequestOptions.DEFAULT);
//4.返回结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}
Java代码实现geo_distance如下图4；
geo_bounding_box
geo_bounding_box涉及的参数如下
top_left:左上角的矩形起始点经纬度；
bottom_right右下角的矩形结束点经纬度
实现要求，查找索引内位于中央民族大学(116.326943,39.95499)以及京站(116.433733,39.908404)矩形的点;
Java代码如下；
static RestHighLevelClient myClient= EsClient.getClient(); //获取操作ES的
String index="sms-logs-index";
String index2="my_badu_map";
@Test
public void geoBoundingBox() throws IOException {
//1.SearchRequest
SearchRequest request=new SearchRequest(index2);
//2.指定检索方式
SearchSourceBuilder builder=new SearchSourceBuilder();
builder.query(QueryBuilders.geoBoundingBoxQuery("location").setCorners(39.95499,116.326943,39.908737,116.433446));
request.source(builder);
//3.执行查询
SearchResponse resp = myClient.search(request, RequestOptions.DEFAULT);
//4.返回结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}
Java实现geo_bounding_box的效果如图5；
geo_polygon
geo_polygon涉及的参数如下
points：是个数组，存储多变形定点的经纬度，每个点用大括号包起来
实现要求，查找索引内位于西苑桥(116.300209,40.003423)，巴沟山水园(116.29561,39.976004)以及北京科技大学(116.364528,39.996348)三角形内的点;
Java实现代码代码如下；
static RestHighLevelClient myClient= EsClient.getClient(); //获取操作ES的
String index="sms-logs-index";
String index2="my_badu_map";
@Test
public void geoPolygon() throws IOException {
//1.SearchRequest
SearchRequest request=new SearchRequest(index2);
//2.指定检索方式
SearchSourceBuilder builder=new SearchSourceBuilder();
List<GeoPoint> points=new ArrayList<>();
//注意java里面是纬经度的顺序，不要搞反了
points.add(new GeoPoint(39.976004,116.29561));
points.add(new GeoPoint(39.996348,116.364528));
points.add(new GeoPoint(40.003423,116.300209));
builder.query(QueryBuilders.geoPolygonQuery("location",points));
request.source(builder);
//3.执行查询
SearchResponse resp = myClient.search(request, RequestOptions.DEFAULT);
//4.返回结果
for (SearchHit hit : resp.getHits().getHits()) {
System.out.println(hit.getSourceAsMap());
}
}
Java实现geo_polygon的效果如图6；
更多的geo地图检索，可以参考官网geo-queries;
本文参考链接：https://blog.csdn.net/LXWalaz1s1s/article/details/109028745
