Geohash算法是一种地址编码,它能把二维的经纬度编码成一维的字符串。比如,成都永丰立交的编码是wm3yr31d2524
1)、利用一个字段,即可存储经纬度;搜索时,只需一条索引,效率较高
2)、编码的前缀可以表示更大的区域,查找附近的,非常方便。 SQL中,LIKE 'wm3yr3%',即可查询附近的所有地点。
3)、通过编码精度可模糊坐标、隐私保护等。
距离和排序需二次运算(筛选结果中运行,其实挺快)
成都永丰立交经纬度(30.63578,104.031601)
1)、纬度范围(-90, 90)平分成两个区间(-90, 0)、(0, 90), 如果目标纬度位于前一个区间,则编码为0,否则编码为1。
由于30.625265属于(0, 90),所以取编码为1。
然后再将(0, 90)分成 (0, 45), (45, 90)两个区间,而39.92324位于(0, 45),所以编码为0
然后再将(0, 45)分成 (0, 22.5), (22.5, 45)两个区间,而39.92324位于(22.5, 45),所以编码为1
依次类推可得永丰立交纬度编码为101010111001001000100101101010。
2)、经度也用同样的算法,对(-180, 180)依次细分,(-180,0)、(0,180) 得出编码110010011111101001100000000000
3)、合并经纬度编码,从高到低,先取一位经度,再取一位纬度;得出结果 111001001100011111101011100011000010110000010001010001000100
4)、用0-9、b-z(去掉a, i, l, o)这32个字母进行base32编码,得到(30.63578,104.031601)的编码为wm3yr31d2524。
11100 10011 00011 11110 10111 00011 00001 01100 00010 00101 00010 00100 => wm3yr31d2524 十进制 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 base32 0 1 2 3 4 5 6 7 8 9 b c d e f g 十进制 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 base32 h j k m n p q r s t u v w x y z
1)、在纬度和经度入库时,数据库新加一字段geohash,记录此点的geohash值
2)、查找附近,利用 在SQL中 LIKE 'wm3yr3%';且此结果可缓存;在小区域内,不会因为改变经纬度,而重新数据库查询
3)、查找出的有限结果,如需要求距离或者排序,可利用距离公式和二维数据排序;此时也是少量数据,会很快的。
geohash.class.php
codingMap[substr($this->coding, $i, 1)] = str_pad(decbin($i), 5, "0", STR_PAD_LEFT); } } public function decode($hash) { $binary = ""; $hl = strlen($hash); for($i = 0; $i < $hl; $i++) { $binary .= $this->codingMap[substr($hash, $i, 1)]; } $bl = strlen($binary); $blat = ""; $blong = ""; for ($i = 0; $i < $bl; $i++) { if ($i%2) { $blat = $blat.substr($binary, $i, 1); } else { $blong = $blong.substr($binary, $i, 1); } } $lat = $this->binDecode($blat, -90, 90); $long = $this->binDecode($blong, -180, 180); $latErr = $this->calcError(strlen($blat), -90, 90); $longErr = $this->calcError(strlen($blong), -180, 180); $latPlaces = max(1, -round(log10($latErr))) - 1; $longPlaces = max(1, -round(log10($longErr))) - 1; $lat = round($lat, $latPlaces); $long = round($long, $longPlaces); return array($lat,$long); } public function encode($lat,$long) { $plat = $this->precision($lat); $latbits = 1; $err = 45; while($err > $plat) { $latbits++; $err/ = 2; } $plong = $this->precision($long); $longbits = 1; $err = 90; while($err > $plong) { $longbits++; $err /= 2; } $bits = max($latbits,$longbits); $longbits = $bits; $latbits = $bits; $addlong = 1; while (($longbits+$latbits) % 5 != 0) { $longbits += $addlong; $latbits += !$addlong; $addlong = !$addlong; } $blat = $this->binEncode($lat, -90, 90, $latbits); $blong = $this->binEncode($long, -180, 180, $longbits); $binary = ""; $uselong = 1; while (strlen($blat)+strlen($blong)) { if ($uselong) { $binary = $binary.substr($blong, 0, 1); $blong = substr($blong, 1); } else { $binary = $binary.substr($blat, 0, 1); $blat = substr($blat, 1); } $uselong = !$uselong; } $hash = ""; for ($i = 0; $i < strlen($binary); $i += 5) { $n = bindec(substr($binary, $i, 5)); $hash = $hash . $this->coding[$n]; } return $hash; } private function calcError($bits, $min, $max) { $err = ($max - $min) / 2; while ($bits--) { $err /= 2; } return $err; } private function precision($number) { $precision = 0; $pt = strpos($number,'.'); if ($pt! == false) { $precision = -(strlen($number) - $pt - 1); } return pow(10, $precision) / 2; } private function binEncode($number, $min, $max, $bitcount) { if ($bitcount == 0) { return ""; } $mid = ($min + $max) / 2; if ($number > $mid) { return "1" . $this->binEncode($number, $mid, $max, $bitcount - 1); } else { return "0" . $this->binEncode($number, $min, $mid, $bitcount - 1); } } private function binDecode($binary, $min, $max) { $mid = ($min + $max) / 2; if (strlen($binary) == 0) { return $mid; } $bit = substr($binary, 0, 1); $binary = substr($binary, 1); if ($bit == 1) { return $this->binDecode($binary, $mid, $max); } else { return $this->binDecode($binary, $min, $mid); } } } ?>
'127.0.0.1', 'port' = > 3306, 'user' = > 'root', 'password' = > '123456', 'database' = > 'mocube', 'charset' = > 'utf8', 'persistent' = > false ); $mysql = new Db_Mysql($conf); $geohash = new Geohash; //经纬度转换成Geohash $sql = 'select shop_id,latitude,longitude from mb_shop_ext'; $data = $mysql->queryAll($sql); foreach($data as $val) { $geohash_val = $geohash->encode($val['latitude'],$val['longitude']); $sql = 'update mb_shop_ext set geohash = "'.$geohash_val.'" where shop_id = '.$val['shop_id']; echo $sql; $re = $mysql->query($sql); var_dump($re); } //获取附近的信息 $n_latitude = $_GET['la']; $n_longitude = $_GET['lo']; //开始 $b_time = microtime(true); //方案A,直接利用数据库存储函数,遍历排序 $sql = 'SELECT *,latitude,longitude,GETDISTANCE(latitude,longitude,'.$n_latitude.','.$n_longitude.') AS distance FROM mb_shop_ext where 1 HAVING distance<1000 ORDER BY distance ASC'; $data = $mysql->queryAll($sql); //结束 $e_time = microtime(true); echo $e_time - $b_time; var_dump($data); exit; //方案B geohash求出附近,然后排序 //当前 geohash值 $n_geohash = $geohash->encode($n_latitude,$n_longitude); //附近,参数n代表Geohash,精确的位数,也就是大概距离;n=6时候,大概为附近1千米 $n = $_GET['n']; $like_geohash = substr($n_geohash, 0, $n); $sql = 'select * from mb_shop_ext where geohash like "'.$like_geohash.'%"'; echo $sql; $data = $mysql->queryAll($sql); //算出实际距离 foreach($data as $key =>$val) { $distance = getDistance($n_latitude, $n_longitude, $val['latitude'], $val['longitude']); $data[$key]['distance'] = $distance; //排序列 $sortdistance[$key] = $distance; } //距离排序 array_multisort($sortdistance,SORT_ASC,$data); //结束 $e_time = microtime(true); echo $e_time - $b_time; var_dump($data); //根据经纬度计算距离 其中A($lat1,$lng1)、B($lat2,$lng2) function getDistance($lat1, $lng1, $lat2, $lng2) { //地球半径 $R = 6378137; //将角度转为狐度 $radLat1 = deg2rad($lat1); $radLat2 = deg2rad($lat2); $radLng1 = deg2rad($lng1); $radLng2 = deg2rad($lng2); //结果 $s = acos(cos($radLat1)*cos($radLat2)*cos($radLng1-$radLng2)+sin($radLat1)*sin($radLat2))*$R; //精度 $s = round($s* 10000)/10000; return round($s); } ?>
方案B的亮点在于:
1、搜索结果可缓存,重复使用,不会因为用户有小范围的移动,直接穿透数据库查询。
2、先缩小结果范围,再运算、排序,可提升性能。
254条记录,性能对比,在实际应用场景中,方案B数据库搜索可内存缓存;且如数据量更大,方案B结果会更优。
方案A: 0.016560077667236 0.032402992248535 0.040318012237549 方案B 0.0079810619354248 0.0079669952392578 0.0064868927001953
两种方案,根据应用场景以及负载情况合理选择,当然推荐方案B。
不管哪种方案,都记得,给列加上索引,利于数据库检索。
注意:在数据库中给Geohash加上索引,用户位置频繁发生改变则会导致索引重建,这势必会给数据库造成很大的压力