Elasticsearch笔记（九）：实践篇-查找附近的人

「爱情、让人受尽委屈。」 2022-03-29 14:08 239阅读 0赞

到这里Elasticsearch的整个系列分享就基本上结束了，当然后续还是会针对某一点进行讲解。为何要在实践篇中讲解"查找附近的人"呢？说实话，想了很久，最终才确定下来，总体希望这个实践对今后的工作过程中有帮助。

## 1. 需求描述 ##

随着移动设备的普及，很多移动APP都提供了LBS（Location Based Service）。其实LBS并不是什么新东西，但它也带来了不一样的改变，一方面可以提高以前做不到的事情，一方面提升用户体验。下面我们将基于位置实现这一个类似的功能：比如微信中附近的人。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2l0c29mdGNoZW5mZWk_size_16_color_FFFFFF_t_70][]

## 2. 实现思路 ##

要实现这样一个需求，可用的技术非常多。我是这么考虑的：

1. 评估实现它的复杂度（技术难度，也可以认为是风险点）

2. 大数据量下的实际情况，技术的成熟度（业界都是怎么玩的）

当然，基于es实现它，es有一个对应的数据类型[Geo-point][]。

### 2.1 geohash ###

geohash基本原理是将地球理解为一个二维平面，将平面递归分解成更小的子块，每个子块在一定经纬度范围内拥有相同的编码，这种方式简单粗暴，可以满足对小规模的数据进行经纬度的检索。它的详细介绍在[wiki][]上，花上5分钟就可以看完。

> 以经纬度值：（116.389550， 39.928167）进行算法说明，对纬度39.928167进行逼近编码 (地球纬度区间是\[-90,90\]）
> 
> **1**.区间\[-90,90\]进行二分为\[-90,0),\[0,90\]，称为左右区间，可以确定39.928167属于右区间\[0,90\]，给标记为1  
> **2**.接着将区间\[0,90\]进行二分为 \[0,45),\[45,90\]，可以确定39.928167属于左区间 \[0,45)，给标记为0  
> **3**.递归上述过程39.928167总是属于某个区间\[a,b\]。随着每次迭代区间\[a,b\]总在缩小，并越来越逼近39.928167  
> **4**.如果给定的纬度x（39.928167）属于左区间，则记录0，如果属于右区间则记录1，序列的长度跟给定的区间划分次数有关，如下图
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2l0c29mdGNoZW5mZWk_size_16_color_FFFFFF_t_70 1][]
> 
> **5**.同理，地球经度区间是\[-180,180\]，可以对经度116.389550进行编码。通过上述计算，纬度产生的编码为1 1 0 1 0 0 1 0 1 1 0 0 0 1 0，经度产生的编码为1 0 1 1 1 0 0 0 1 1 0 0 0 1 1。合并：偶数位放经度，奇数位放纬度，把2串编码组合生成新串：11100 11101 00100 01111 0000  01101。将11100 11101 00100 01111 0000  01101转成十进制，对应着28、29、4、15，0，13 十进制对应的base32编码就是wx4g0e。同理，将编码转换成经纬度的解码算法与之相反。
> 
> ![20190112212050244.png][]

## 3. 代码实现 ##

第一步，建立一个简易的模型

//建立模型
    public class WxUser {
        private String uid;
        private String nickName;
        private String sex = "女";
        private int age;
        /**
         * 它支持4方式，这里用数组
         */
        private double[] location;
    
        public String getUid() {
            return uid;
        }
    
        public void setUid(String uid) {
            this.uid = uid;
        }
    
        public String getNickName() {
            return nickName;
        }
    
        public void setNickName(String nickName) {
            this.nickName = nickName;
        }
    
        public String getSex() {
            return sex;
        }
    
        public void setSex(String sex) {
            this.sex = sex;
        }
    
        public int getAge() {
            return age;
        }
    
        public void setAge(int age) {
            this.age = age;
        }
    
        public double[] getLocation() {
            return location;
        }
    
        public void setLocation(double[] location) {
            this.location = location;
        }
    }

第二步，坐标查询要借助个[在线工具 ][Link 1]

//准备es数据，以上海人民广场为中心点
    public class EsInit_test {
        private Random random = new Random();
        private String[] firstNames = {
                "赵", "钱", "孙", "李", "周", "吴", "郑", "王", "冯", "陈",
                "楮", "卫", "蒋", "沈", "韩", "杨", "朱", "秦", "尤", "许",
                "何", "吕", "施", "张", "孔", "曹", "严", "华", "金", "魏",
                "陶", "姜", "戚", "谢", "邹", "喻", "柏", "水", "窦", "章",
                "云", "苏", "潘", "葛", "奚", "范", "彭", "郎", "鲁", "韦",
                "昌", "马", "苗", "凤", "花", "方", "俞", "任", "袁", "柳",
                "酆", "鲍", "史", "唐", "费", "廉", "岑", "薛", "雷", "贺",
                "倪", "汤", "滕", "殷", "罗", "毕", "郝", "邬", "安", "常",
                "乐", "于", "时", "傅", "皮", "卞", "齐", "康", "伍", "余",
                "元", "卜", "顾", "孟", "平", "黄", "和", "穆", "萧", "尹",
                "姚", "邵", "湛", "汪", "祁", "毛", "禹", "狄", "米", "贝",
                "明", "臧", "计", "伏", "成", "戴", "谈", "宋", "茅", "庞",
                "熊", "纪", "舒", "屈", "项", "祝", "董", "梁", "杜", "阮",
                "蓝", "闽", "席", "季", "麻", "强", "贾", "路", "娄", "危",
                "江", "童", "颜", "郭", "梅", "盛", "林", "刁", "锺", "徐",
                "丘", "骆", "高", "夏", "蔡", "田", "樊", "胡", "凌", "霍",
                "虞", "万", "支", "柯", "昝", "管", "卢", "莫", "经", "房",
                "裘", "缪", "干", "解", "应", "宗", "丁", "宣", "贲", "邓",
                "郁", "单", "杭", "洪", "包", "诸", "左", "石", "崔", "吉",
                "钮", "龚", "程", "嵇", "邢", "滑", "裴", "陆", "荣", "翁",
                "荀", "羊", "於", "惠", "甄", "麹", "家", "封", "芮", "羿",
                "储", "靳", "汲", "邴", "糜", "松", "井", "段", "富", "巫",
                "乌", "焦", "巴", "弓", "牧", "隗", "山", "谷", "车", "侯",
                "宓", "蓬", "全", "郗", "班", "仰", "秋", "仲", "伊", "宫",
                "宁", "仇", "栾", "暴", "甘", "斜", "厉", "戎", "祖", "武",
                "符", "刘", "景", "詹", "束", "龙", "叶", "幸", "司", "韶",
                "郜", "黎", "蓟", "薄", "印", "宿", "白", "怀", "蒲", "邰",
                "从", "鄂", "索", "咸", "籍", "赖", "卓", "蔺", "屠", "蒙",
                "池", "乔", "阴", "郁", "胥", "能", "苍", "双", "闻", "莘",
                "党", "翟", "谭", "贡", "劳", "逄", "姬", "申", "扶", "堵",
                "冉", "宰", "郦", "雍", "郤", "璩", "桑", "桂", "濮", "牛",
                "寿", "通", "边", "扈", "燕", "冀", "郏", "浦", "尚", "农",
                "温", "别", "庄", "晏", "柴", "瞿", "阎", "充", "慕", "连",
                "茹", "习", "宦", "艾", "鱼", "容", "向", "古", "易", "慎",
                "戈", "廖", "庾", "终", "暨", "居", "衡", "步", "都", "耿",
                "满", "弘", "匡", "国", "文", "寇", "广", "禄", "阙", "东",
                "欧", "殳", "沃", "利", "蔚", "越", "夔", "隆", "师", "巩",
                "厍", "聂", "晁", "勾", "敖", "融", "冷", "訾", "辛", "阚",
                "那", "简", "饶", "空", "曾", "毋", "沙", "乜", "养", "鞠",
                "须", "丰", "巢", "关", "蒯", "相", "查", "后", "荆", "红",
                "游", "竺", "权", "逑", "盖", "益", "桓", "公", "仉", "督",
                "晋", "楚", "阎", "法", "汝", "鄢", "涂", "钦", "归", "海",
                "岳", "帅", "缑", "亢", "况", "后", "有", "琴", "商", "牟",
                "佘", "佴", "伯", "赏", "墨", "哈", "谯", "笪", "年", "爱",
                "阳", "佟",
                "万俟", "司马", "上官", "欧阳", "夏侯",
                "诸葛", "闻人", "东方", "赫连", "皇甫",
                "尉迟", "公羊", "澹台", "公冶", "宗政",
                "濮阳", "淳于", "单于", "太叔", "申屠",
                "公孙", "仲孙", "轩辕", "令狐", "锺离",
                "宇文", "长孙", "慕容", "鲜于", "闾丘",
                "司徒", "司空", "丌官", "司寇", "南宫",
                "子车", "颛孙", "端木", "巫马", "公西",
                "漆雕", "乐正", "壤驷", "公良", "拓拔",
                "夹谷", "宰父", "谷梁", "段干", "百里",
                "东郭", "南门", "呼延", "羊舌", "微生",
                "梁丘", "左丘", "东门", "西门"
        };
        private static final String indexName = "weixin";
        private static final String typeName = "user";
        private TransportClient client;
    
        @Before
        public void setUp() throws UnknownHostException {
            if (client == null) {
                // 连接集群的设置
                Settings settings = Settings.builder()
                        .put("client.transport.ignore_cluster_name", true)
                        .build();
                client = new PreBuiltTransportClient(settings)
                        .addTransportAddress(new TransportAddress(InetAddress.getByName("127.0.0.1"), 9300));
            }
        }
    
        @Test
        public void createIndex() throws IOException {
            client.admin().indices().prepareCreate(indexName)
                    .setSettings(Settings.builder().put("index.number_of_shards", 2).put("index.number_of_replicas", 1))
                    .addMapping(typeName, XContentFactory.jsonBuilder().startObject()
                            .startObject("properties")
                            .startObject("uid").field("type", "keyword").endObject()
                            .startObject("nickName").field("type", "text").endObject()
                            .startObject("sex").field("type", "keyword").endObject()
                            .startObject("age").field("type", "integer").endObject()
                            .startObject("location").field("type", "geo_point").endObject()
                            .endObject()
                            .endObject()).get();
            System.out.println("创建完成！");
        }
    
        //上海人民广场
        private double lat = 31.228725;
        private double lon = 121.475186;
        private int nearDistance = 50;
    
        @Test
        public void initData() throws ExecutionException, InterruptedException {
            WxUser user = new WxUser();
            for (int i = 0; i < 200; i++) {
                user.setLocation(randomPoint(lat, lon));
                String id = String.format("wx_%s", UUID.randomUUID().toString().substring(24));
                user.setUid(id);
                String nickName = String.format("%s女士", firstNames[random.nextInt(firstNames.length)]);
                user.setNickName(nickName);
                user.setAge(random.nextInt(35));
                String json = JSON.toJSONString(user);
                IndexResponse response = client.prepareIndex(indexName, typeName).setSource(json, XContentType.JSON).get();
                System.out.println(response.getId());
            }
        }
        /**
         * @param lat 纬度
         * @param lon 经度
         * @return
         */
        private double[] randomPoint(double lat, double lon) {
            double min = 0.000001;//最小1米
            double max = 0.00002;//最大1000米
            double randomNum = random.nextDouble() % (max - min + 1) + max;
            DecimalFormat numFormat = new DecimalFormat("########.000000");
            String slat = numFormat.format(randomNum + lat);
            String sLon = numFormat.format(randomNum + lon);
            double dLat = Double.valueOf(slat);
            double dLon = Double.valueOf(sLon);
            return new double[]{dLon, dLat};//TODO:es存储是经度在前，维度在后
        }
    
    }

第三步，使用es的api执行搜索

//搜索附近
    @Test
    public void searchNear() throws ExecutionException, InterruptedException {
        SearchRequest searchRequest = new SearchRequest(indexName);
        searchRequest.types(typeName);
    
        SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
        sourceBuilder.from(0);
        sourceBuilder.size(15);
        sourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS));
    
        QueryBuilder geoQuery = new GeoDistanceQueryBuilder("location")
                .point(lat, lon)
                .distance(nearDistance, DistanceUnit.KILOMETERS) 指定位置为中心的圆的半径，100km
                .geoDistance(GeoDistance.PLANE); //按平面计算距离，平面(更快，但在长距离和靠近极点的地方是不准确的)而立方(default)
        sourceBuilder.query(geoQuery);
        GeoDistanceSortBuilder geoSort = SortBuilders.geoDistanceSort("location", lat, lon)
                .order(SortOrder.ASC) //最近的排在最前面
                .unit(DistanceUnit.KILOMETERS);
    
        sourceBuilder.sort(geoSort);
    
        searchRequest.source(sourceBuilder);
        SearchResponse searchResponse = client.search(searchRequest).get();
        SearchHits hits = searchResponse.getHits();
        SearchHit[] searchHits = hits.getHits();
        System.out.println("小明，您当前位置为：[" + lon + "," + lat + "]，开始搜索附近 " + nearDistance + "KM 以内的朋友...");
        System.out.println("检索完成!总耗时:" + searchResponse.getTook().getMillis() + "毫秒,符合条件的有 " + searchHits.length + " 个!");
        for (SearchHit hit : searchHits) {
            String sourceAsString = hit.getSourceAsString();
            BigDecimal geoDistance = new BigDecimal((double) hit.getSortValues()[0])
                    .setScale(0, BigDecimal.ROUND_HALF_DOWN);//四舍五入
            Map<String, Object> sourceAsMap = hit.getSourceAsMap();
            System.out.println(sourceAsMap.get("nickName") + " 距您 " + geoDistance + "KM，source:" + sourceAsString);
        }
    }

执行结果：

> 小明，您当前位置为：\[121.475186,31.228725\]，开始搜索附近 50KM 以内的朋友...  
> 检索完成!总耗时:4毫秒,符合条件的有 10 个!  
> 邹女士 距您 5KM，source:\{"age":2,"location":\[121.505997,31.259536\],"nickName":"邹女士","sex":"女","uid":"wx\_0863b4f97cc1"\}  
> 巫马女士 距您 16KM，source:\{"age":29,"location":\[121.582163,31.335702\],"nickName":"巫马女士","sex":"女","uid":"wx\_de170132e6c2"\}  
> 那女士 距您 16KM，source:\{"age":5,"location":\[121.582977,31.336516\],"nickName":"那女士","sex":"女","uid":"wx\_571b83ca7ea1"\}  
> 汪女士 距您 17KM，source:\{"age":26,"location":\[121.58967,31.343209\],"nickName":"汪女士","sex":"女","uid":"wx\_b3a5e216eb7b"\}  
> 祖女士 距您 19KM，source:\{"age":33,"location":\[121.606634,31.360173\],"nickName":"祖女士","sex":"女","uid":"wx\_5cdfd226cf79"\}  
> 倪女士 距您 23KM，source:\{"age":26,"location":\[121.631181,31.38472\],"nickName":"倪女士","sex":"女","uid":"wx\_ed0eeafd07cf"\}  
> 柏女士 距您 34KM，source:\{"age":30,"location":\[121.705156,31.458695\],"nickName":"柏女士","sex":"女","uid":"wx\_13873bdd086d"\}  
> 余女士 距您 36KM，source:\{"age":0,"location":\[121.723644,31.477183\],"nickName":"余女士","sex":"女","uid":"wx\_d3996a3c78de"\}  
> 墨女士 距您 40KM，source:\{"age":3,"location":\[121.745568,31.499107\],"nickName":"墨女士","sex":"女","uid":"wx\_d15e247ad23c"\}  
> 桂女士 距您 44KM，source:\{"age":31,"location":\[121.774825,31.528364\],"nickName":"桂女士","sex":"女","uid":"wx\_828e1ef300e5"\}

到这里就结束了，示例不是目的，它能否带来抛砖引玉的作用取决于您。![85332370][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2l0c29mdGNoZW5mZWk_size_16_color_FFFFFF_t_70]: /images/20220329/12d76a2bca794931ae89dfcfe8053c3c.png
[Geo-point]: https://www.elastic.co/guide/en/elasticsearch/reference/6.2/geo-point.html
[wiki]: https://en.wikipedia.org/wiki/Geohash
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2l0c29mdGNoZW5mZWk_size_16_color_FFFFFF_t_70 1]: /images/20220329/d1292bf3cf2a49c3801e224449adcafd.png
[20190112212050244.png]: /images/20220329/fcca1acb840c499cab41278d7f4e80ea.png
[Link 1]: http://www.gpsspg.com/maps.htm
[85332370]: /images/20220329/5f74e58f76114489aee9a450b03e4612.png