EFK 配置geo-ip落地实践（三）经纬度数据查询及格式化输出

时间 2019-12-04

标签 efk 配置 geo 落地实践经纬度数据查询格式化输出繁體版

原文原文链接

通过以前的工做，目前已经完成了数据地图的数据格式化和录入记录，目前咱们的数据地图项目已经进行到最后阶段，因此如今须要一个接口，进行格式化数据并输出，其中须要用到Elasticsearch的全文检索，检索出数据后，使用php接口格式化数据输出php

1、全文检索html

搜索条件（时间，空间）
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，有操做行为的，用户个数node

由此需求，能够获得相应的Elasticsearch的搜索语句，以下：json

{
"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"
                    }
                }
            }
        ]
    }
}
}
复制代码

size=0表示不分页
query为搜索主体，其中的必要条件为时间参数，即，搜索此段时间内的全部数据
aggs中至关于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。
aggs嵌套，即上层条件的结果上，继续作筛选
geohash_grid表示，按照你定义的精度计算每个点的 geohash 值而将附近的位置聚合在一块儿，其中field为目前筛选的的字段， precision为经度，单位为km
最后，经过geo_centroid获得key为location的聚合数据

结果数据格式以下：api

{
"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
    }
}
}
复制代码

aggregations中是咱们最终须要的数据
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范围以内的用户数。

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

2、接口书写数据结构

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

代码以下，有注释：elasticsearch

<?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;

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

//须要被筛选的字段名，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;

//建立查询结构体
$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();
复制代码

因为H5页面插件限制，因此须要特定的数据格式。因此最终输出结果以下：ide

[{
    "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"]
    }
}]
复制代码

至此，咱们数据地图项目在数据方面的工做暂且告一段落。ui

参考文档：

www.elastic.co/guide/en/el…

www.elastic.co/guide/en/elasticsearch/reference/current/search.html

www.elastic.co/guide/cn/el…