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经纬度在mysql中以什么字段类型存储 数据经纬度

经过之前的工作,目前已经完成了数据地图的数据格式化和录入记录,目前我们的数据地图项目已经进行到最后阶段,所以现在需要一个接口,进行格式化数据并输出,其中需要用到Elasticsearch的全文检索,检索出数据后,使用php接口格式化数据输出

一、全文检索

  1. 搜索条件(时间,空间)
  2. 输出结果(用户数量)

例如,一个小时内,在中国范围内,各个经纬度坐标的,有操作行为的,用户个数

由此需求,可以得到相应的Elasticsearch的搜索语句,如下:

{
"size": 0,
"aggs": {
    "filter_agg": {
        "filter": {
            "geo_bounding_box": {
                "location": {
                    "top_left": {
                        "lat": 90,
                        "lon": -34.453125
                    },
                    "bottom_right": {
                        "lat": -90,
                        "lon": 34.453125
                    }
                }
            }
        },
        "aggs": {
            "2": {
                "geohash_grid": {
                    "field": "location",
                    "precision": 2
                },
                "aggs": {
                    "3": {
                        "geo_centroid": {
                            "field": "location"
                        }
                    }
                }
            }
        }
    }
},
"stored_fields": [
    "*"
],
"docvalue_fields": [
    "@timestamp"
],
"query": {
    "bool": {
        "must": [
            {
                "range": {
                    "@timestamp": {
                        "gte": 1542692193461,
                        "lte": 1542695793461,
                        "format": "epoch_millis"
                    }
                }
            }
        ]
    }
}
}

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  1. size=0表示不分页
  2. query为搜索主体,其中的必要条件为时间参数,即,搜索此段时间内的所有数据
  3. aggs中相当于spl中的where条件,而其中geo_bounding_box为地理范围,由左上角经纬度点到右下角经纬度点所界定的一个矩形方框。
  4. aggs嵌套,即上层条件的结果上,继续做筛选
  5. geohash_grid表示,按照你定义的精度计算每一个点的 geohash 值而将附近的位置聚合在一起,其中field为目前筛选的的字段, precision为经度,单位为km
  6. 最后,通过geo_centroid得到key为location的聚合数据

结果数据格式如下:

{
"took": 428,
"timed_out": false,
"_shards": {
    "total": 131,
    "successful": 126,
    "skipped": 121,
    "failed": 5,
    "failures": [
        {
            "shard": 0,
            "index": "elastalert_status_status",
            "node": "w10b9zEBRpuUEQsWvNqEig",
            "reason": {
                "type": "query_shard_exception",
                "reason": "failed to find geo_point field [location]",
                "index_uuid": "Dm4dpUtTTHitYN-TZFC-1g",
                "index": "elastalert_status_status"
            }
        }
    ]
},
"hits": {
    "total": 360942,
    "max_score": 0,
    "hits": []
},
"aggregations": {
    "filter_agg": {
        "2": {
            "buckets": [
                {
                    "3": {
                        "location": {
                            "lat": 48.58949514372008,
                            "lon": 7.584022147181843
                        },
                        "count": 252
                    },
                    "key": "u0",
                    "doc_count": 252
                },
                {
                    "3": {
                        "location": {
                            "lat": 54.420127907268785,
                            "lon": -3.120888938036495
                        },
                        "count": 181
                    },
                    "key": "gc",
                    "doc_count": 181
                },
                {
                    "3": {
                        "location": {
                            "lat": 42.32862451614172,
                            "lon": 3.7518564593602917
                        },
                        "count": 67
                    },
                    "key": "sp",
                    "doc_count": 67
                },
                {
                    "3": {
                        "location": {
                            "lat": 45.40799999143928,
                            "lon": 11.88589995726943
                        },
                        "count": 21
                    },
                    "key": "u2",
                    "doc_count": 21
                },
                {
                    "3": {
                        "location": {
                            "lat": 46.65579996071756,
                            "lon": 32.61779992841184
                        },
                        "count": 1
                    },
                    "key": "u8",
                    "doc_count": 1
                }
            ]
        },
        "doc_count": 522
    }
}
}

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  1. aggregations中是我们最终需要的数据
  2. 其中location为聚合的经纬度坐标,紧跟着的count则指的是,在此点2km*2km范围之内的用户数。

自此,我们首先明白了,在Elasticsearch,如何书写search语句查询我们想要的东西。 接下来,我们需要书写相应的php接口,来格式化输出数据

二、接口书写

  1. 使用Elasticseach的PHP API
  2. 确定输入参数:时间范围,空间范围
  3. 确定输出数据结构,并格式化数据输出

代码如下,有注释:

<?php
/**
 * Created by PhpStorm.
 * User: ekisong
 * Date: 2018/11/13
 * Time: 15:55
 */
require 'vendor/autoload.php';
ini_set('display_errors','on');
error_reporting(E_ALL);

use Elasticsearch\ClientBuilder;

//创建Elasticsearch 的搜索对象client
$client = ClientBuilder::create()->setHosts(["localhost:9200"])->build();

//需要被筛选的字段名,默认值为location
$fieldName = isset($_GET['field']) ? $_GET['field'] : 'location';

//地理围栏左上角纬度,默认值90
$topLeftLat = isset($_GET['top_left_lat']) ? $_GET['top_left_lat'] : 90;

//地理围栏左上角经度,默认值-180
$topLeftLon = isset($_GET['top_left_lon']) ? $_GET['top_left_lon'] : -180;

//地理围栏右下角纬度,默认值-90
$bottomRightLat = isset($_GET['bottom_right_lat']) ? $_GET['bottom_right_lat'] : -90;

//地理围栏右下角经度,默认值180
$bottomRightLon = isset($_GET['bottom_right_lon']) ? $_GET['bottom_right_lon'] : 180;

//时间范围结束时间,默认当前时间
$endTime = isset($_GET['end_time']) ? $_GET['end_time'] : time()*1000;

//时间范围其实时间,默认当前时间前15分钟
$startTime = isset($_GET['start_time']) ? $_GET['start_time'] : $endTime - 15*60*1000;

//创建查询结构体
$body = [
    'size' => 0,
    'query' => [
        'bool' => [
            'must' => [
                [
                    'range' => [
                        '@timestamp' => [
                            'gte' => $startTime,
                            'lte' => $endTime,
                            'format' => 'epoch_millis'
                        ]
                    ]
                ]
            ]
        ]
    ],
    'aggs' => [
        'filter_agg' => [
            'filter' => [
                'geo_bounding_box' => [
                    'location' => [
                        'top_left' => [
                            'lat' => $topLeftLat,
                            'lon' => $topLeftLon
                        ],
                        'bottom_right' => [
                            'lat' => $bottomRightLat,
                            'lon' => $bottomRightLon
                        ]
                    ]
                ]
            ],
            'aggs' => [
                '2' => [
                    'geohash_grid' => [
                        'field' => $fieldName,
                        'precision' => 1
                    ],
                    'aggs' => [
                        '3' => [
                            'geo_centroid' => [
                                'field' => $fieldName
                            ]
                        ]
                    ]
                ]
            ]
        ]
    ],
    'stored_fields' => [
        '*'
    ],
    'docvalue_fields' => [
        '@timestamp'
    ]
];

//搜索参数
$params = [
    'index' => 'logstash-*',
    'body' => $body
];

//Elasticsearch搜索结果原始数据
$response = $client->search($params);

$resultTmp = $response['aggregations']['filter_agg']['2']['buckets'];

$data = array();

//格式化数据
foreach ($resultTmp as $doc)
{
    $lat = $doc['3'][$fieldName]['lat'];
    $lon = $doc['3'][$fieldName]['lon'];
    $count = $doc['doc_count'];
    $tmp = [
        'count' => $count,
        'geometry' => [
            'type' => 'Point',
            'coordinates' => [$lon,$lat]
        ]
    ];
    $data[] = $tmp;
}

$result = array('data'=>$data,'error_msg'=>'','flag'=>1);

if (empty($data))
{
    $result['error_msg'] = 'no data';
    $result['flag'] = 0;
}

//最终输出
echo json_encode($result);
exit();
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由于H5页面插件限制,所以需要特定的数据格式。所以最终输出结果如下:

[{
    "count": 6,
    "geometry": {
        "type": "Point",
        "coordinates": ["116.395645", "39.929986"]
    }
}, {
    "count": 6,
    "geometry": {
        "type": "Point",
        "coordinates": ["121.487899", "31.249162"]
    }
}, {
    "count": 5,
    "geometry": {
        "type": "Point",
        "coordinates": ["117.210813", "39.14393"]
    }
}, {
    "count": 4,
    "geometry": {
        "type": "Point",
        "coordinates": ["106.530635", "29.544606"]
    }
}]

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至此,我们数据地图项目在数据方面的工作暂且告一段落。


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