This article brings you relevant knowledge about PHP. It mainly introduces how to count the number of people online. You can use table statistics, redis ordered set statistics, and hyperloglog. Do statistics and so on. Let’s take a look at it. I hope it will be helpful to everyone.
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How many people visit a business system website every day, and how many people are online How many? We must reserve this kind of business during development, and it is also within our design scope! Because a running website uses statistics every day.
How to count the number of people online? There are several solutions. The code uses the laravel framework. Can be used as a reference during development.
Use a data table to count the number of people online. This method can only be used when the amount of concurrency is not large.
First we create a new table: user_login
Edit
user_login table
Simulate user login, no user exists Just store it in the table, and update the login information if it exists
// 客户端唯一的识别码
$client_id = session()->getId();
//用户是否已存在
$user = DB::table('user_login')
->where('token', $client_id)
->first();
//不存在则插入数据
if (empty($user)) {
$data = [
'token' => $client_id,
'username' => 'user_' . $client_id, // 模拟用户
'uid' => mt_rand(10000000, 99999999), //模拟用户id
'create_time' => date('Y-m-d H:i:s'),
'update_time' => date('Y-m-d H:i:s')
];
DB::table('user_login')->insert($data);
} else {
// 存在则更新用户登录信息
DB::table('user_login')
->where('token', $client_id)
->update([
'update_time' => date('Y-m-d H:i:s')
]);
}Here we also need to regularly clean up users who have no operations. If the user has no operations within an hour, we can record it as an invalid user
The code is as follows:
// 客户端唯一的识别码
$client_id = session()->getId();
//用户是否已存在
$user = DB::table('user_login')
->where('token', $client_id)
->first();
//不存在则插入数据
if (empty($user)) {
$data = [
'token' => $client_id,
'username' => 'user_' . $client_id, // 模拟用户
'uid' => mt_rand(10000000, 99999999), //模拟用户id
'create_time' => date('Y-m-d H:i:s'),
'update_time' => date('Y-m-d H:i:s')
];
DB::table('user_login')->insert($data);
} else {
// 存在则更新用户登录信息
DB::table('user_login')
->where('token', $client_id)
->update([
'update_time' => date('Y-m-d H:i:s')
]);
}Functions we can implement:
1) Current number of people online
2) Number of people online within a certain time period
3) Latest Online users
4) Specify whether the user is online
// 可实现功能一：当前总共在线人数
$c = DB::table('user_login')->count();
echo '当前在线人数：' . $c . '
';
// 可实现功能二：某时间段内在线人数
$begin_date = '2020-08-13 09:00:00';
$end_date = '2020-08-13 18:00:00';
$c = DB::table('user_login')
->where('create_time', '>=', $begin_date)
->where('create_time', '<=', $end_date)
->count();
echo $begin_date . '-' . $end_date . '在线人数：' . $c . '
';
// 可实现功能三：最新上线的用户
$newest = DB::table('user_login')
->orderBy('create_time', 'DESC')
->limit(10)
->get();
echo '最新上线的用户有：';
foreach ($newest as $value) {
echo $value->username . ' ';
}
echo '
';
// 可实现功能四：指定用户是否在线
$username = 'user_1111';
$online = DB::table('user_login')
->where('username', $username)
->exists();
echo $username . ($online ? '在线' : '不在线');Because it is in memory, it is very efficient and can be counted Various aggregation operations can be performed on the number of people online within a certain period of time. But if there are a lot of people online, it will take up more memory. Another point:
Invalid users cannot be cleared through user operation time. Only users who log out manually will be deleted from the collection.
The code is as follows:
// 客户端唯一的识别码 $client_id = session()->getId(); echo $client_id . '
'; // 按日期生成key $day = date('Ymd'); $key = 'online:' . $day; // 是否在线 $is_online = Redis::zScore($key, $client_id); if (empty($is_online)) { // 不在线，加入当前客户端 Redis::zAdd($key, time(), $client_id); } // 可实现功能一：当前总共在线人数 $c = Redis::zCard($key); echo '当前在线人数：' . $c . '
'; // 可实现功能二：某时间段内在线人数 $begin_date = '2020-08-13 09:00:00'; $end_date = '2020-08-13 18:00:00'; $c = Redis::zCount($key, strtotime($begin_date), strtotime($end_date)); echo $begin_date . '-' . $end_date . '在线人数：' . $c . '
'; // 可实现功能三：最新上线的用户,时间从小到大排序 $newest = Redis::zRangeByScore($key, '-inf', '+inf', ['limit' => [0, 50]]); echo '最新上线的用户有：'; foreach ($newest as $value) { echo $value . ' '; } echo '
'; // 可实现功能四：指定用户是否在线 $username = $client_id; $online = Redis::zScore($key, $client_id);; echo $username . ($online ? '在线' : '不在线') . '
'; // 可实现功能五：昨天和今天都上线的客户 $yestoday = Carbon::yesterday()->toDateString(); $yes_key = str_replace('-', '', $yestoday); $members = []; Redis::pipeline(function ($pipe) use ($key, $yes_key, &$members) { Redis::zinterstore('new_key', [$key, $yes_key], ['aggregate' => 'min']); $members = Redis::zRangeByScore('new_key', '-inf', '+inf', ['limit' => [0, 50]]); //dump($members); }); echo '昨天和今天都上线的用户有：'; foreach ($members as $value) { echo $value . ' '; }
Different from the ordered collection method, hyperloglog saves space very much, but the function it implements is also very simple and can only count. The number of people online cannot realize any other functions.
Redis HyperLogLog is an algorithm used for cardinality statistics. The advantage of HyperLogLog is that when the number or volume of input elements is very, very large, the space required to calculate the cardinality is always fixed and very small. .
In Redis, each HyperLogLog key only costs 12 KB of memory to calculate the cardinality of nearly 2^64 different elements. This is in sharp contrast to a collection that consumes more memory when calculating cardinality. The more elements there are, the more memory is consumed.
However, because HyperLogLog will only calculate the cardinality based on the input elements and will not store the input elements themselves, HyperLogLog cannot return each element of the input like a collection.
// note HyperLogLog 只需要知道在线总人数
for ($i=0; $i < 6; $i++) {
$online_user_num = mt_rand(10000000, 99999999); //模拟在线人数
var_dump($online_user_num);
for ($j=1; $j < $online_user_num; $j++) {
$user_id = mt_rand(1, 100000000);
$redis->pfadd('002|online_users_day_'.$i, [$user_id]);
}
}
$count = 0;
for ($i=0; $i < 3; $i++) {
$count += $redis->pfcount('002|online_users_day_'.$i);
print_r($redis->pfcount('002|online_users_day_'.$i). "\n");
}
var_dump($count);
//note 3 days total online num
var_dump($redis->pfmerge('002|online_users_day_both_3', ['002|online_users_day_0', '002|online_users_day_1', '002|online_users_day_2']));
var_dump($redis->pfcount('002|online_users_day_both_3'));This solution can only count the total number of online people in a certain period of time, but cannot do anything about the list of online users, but it saves memory. When there are not many statistical data requirements, we You can consider this option.
Bitmap uses a bit to represent the value or status corresponding to an element, and the key is the corresponding element itself. We know that 8 bits can form a Byte, so the bitmap itself will greatly save storage space.
bitmap is commonly used for functions such as user check-in, active users, and online users.
The code is as follows
// 模拟当前用户
$uid = request('uid');
$key = 'online_bitmap_' . date('Ymd');
// 设置当前用户在线
Redis::setBit($key, $uid, 1);
// 可实现功能1：在线人数
$c = Redis::bitCount($key);
echo '在线人数：' . $c . '
';
// 可实现功能2：指定用户是否在线
$online = Redis::getBit($key, $uid);
echo $uid . ($online ? '在线' : '不在线') . '
';
// 可实现功能3：昨天和今天均上线的用户总数
$yestoday = Carbon::yesterday()->toDateString();
$yes_key = str_replace('-', '', $yestoday);
$c = 0;
Redis::pipeline(function ($pipe) use ($key, $yes_key, &$c) {
Redis::bitOp('AND', 'yest', $key, $yes_key);
$c = Redis::bitCount('yest');
});
echo '昨天和今天都上线的用户数量有：' . $c . '
';bitmap does not consume much memory space, but provides a lot of statistical information. This solution is worth recommending.
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