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Scenarios of applying Redis Php in projects, project redisphp scenarios
Home Backend Development PHP Tutorial Scenarios for applying Redis Php in projects, project redisphp scenarios_PHP tutorial

Scenarios for applying Redis Php in projects, project redisphp scenarios_PHP tutorial

Jul 12, 2016 am 08:50 AM
php redis

Scenarios of applying Redis Php in projects, project redisphp scenarios

Foreword

In some cases, some students said why the string type cannot be used. The string type can be completely implemented

I suggest you read my column "Advanced Usage of Redis", which introduces the benefits of using hash type

Product dimension count

Count product likes, comments, appraisals, and views
Speaking of e-commerce, products are definitely inseparable, and the accompanying products have various counts (number of likes, number of comments, number of appraisals, number of views, etc.)
Redis commands are all atomic, and you can easily use INCR, DECR and other commands to count.

Use the Redis type: Hash. If you are not familiar with the redis data type, you can refer to
http://redis.io/topics/data-types-intro

Define a key product: for product, and define a hashkey for each value, such as like number like_num

$redis->hSet('product:123', 'like_num ', 5); // 添加 id為123的商品 like_num 為5
 
$redis->hIncrBy('product:123', 'like_num ', 1); // 添加 id為123的商品like_num +1

$redis->hGetAll('product:123'); // 獲取id為123的商品相關(guān)信息
                  array('like_num '=> 1)

User dimension count

Count the number of user updates, number of followers, number of fans, number of liked products, number of posts, etc.
Both user dimension counting and product dimension counting use Hash. Define a key for User as user:
Define a hashkey for each value, such as the number of followers follow

$redis->hSet('user:100000', 'follow ', 5); // 添加uid為10000的用戶follow 為5
 
$redis->hIncrBy('user:100000', 'follow ', 1); // 更新uid為10000的用戶follow +1

$redis->hGetAll('user:100000'); // 獲取uid為10000的用戶
                  array('like_num '=> 1)

???????????????????????????????????????????????????????
Storing social relationships

For example, the user’s friends/fans/follows can be stored in a sorted set, and the score can be timestamp

The default collection is sorted by increasing score
To find mutual friends between two people, you may only need to use the intersection command

$redis->zAdd('user:1000:follow', 1463557212, '1001'); 

                #uid為1000用戶關(guān)注uid為1001 , score值設(shè)定時(shí)間戳1463557212

$redis->zAdd('user:1000:follow', 1463557333, '1002'); 

$redis->zAdd('user:2000:follow', 1463577568, '1001'); 

$redis->zAdd('user:2000:follow', 1463896964, '1003');
 
                #uid為2000用戶關(guān)注1001和1003用戶 , score值設(shè)定時(shí)間戳

$redis->zInter('com_fllow:1000:2000', array('user:1000:follow', 'user:2000:follow')); 

    #對(duì)集合'user:1000:follow'和'user:2000:follow'取交集'com_fllow:1000:2000'
    #獲得共同關(guān)注的uid 

$redis->zRange('com_fllow:1000:2000',0,-1); // 獲取全部集合元素
    #array('10001','10002')

Used as cache instead of memcached

Apply to product list, comment list, @tip list

Compared with memcached’s simple key-value storage, redis has numerous data structures (list, set, sorted set, hash,

etc)

It can cache various business data more conveniently, and its performance is no less than memcached.

NOTE: RPUSH pagewviews.user: EXPIRE pagewviews.user: 60 //Pay attention to update timeout

Anti-spam system

Spam control for application system comments, product publishing, and forum postings

As an e-commerce website, it is inevitable to be attacked by various spam (spam comments, posting spam products, advertisements, brushing the ranking of your own products, etc.)

Develop a series of anti-spam rules for these spams, some of which can use redis for real-time analysis

For example: no more than 2 comments in 1 minute, less than 5 comments in 5 minutes, etc. (More mechanisms/rules need to be combined with drools)

Regular sorted set records user operations in the last day
(Why not record everything? To save memory, all operations will be recorded in the log, and Hadoop will be used for more comprehensive analysis and statistics in the future)

#獲取5秒內(nèi)操作記錄
$res = $redis->zRangeByScore('user:1000:comment', time() - 5, time());
#判斷5秒內(nèi)不能評(píng)論
if (!$res) {
  $redis->zAdd('user:1000:comment', time(), '評(píng)論內(nèi)容');
} else {
  echo '5秒之內(nèi)不能評(píng)論';
}

#5秒內(nèi)評(píng)論不得超過(guò)2次
if($redis->zRangeByScore('user:1000:comment',time()-5 ,time())==1)
echo '5秒之內(nèi)不能評(píng)論2次';

#5秒內(nèi)評(píng)論不得少于2次

if(count($redis->zRangeByScore('user:1000:comment',time()-5 ,time()))<2)
echo '5秒之內(nèi)不能評(píng)論2次';


 

User Timeline/Feeds

Apply to people, topics, brands and columns you follow

Redis is mainly used as cache here

$redis->zAdd('user:2000:feed:topic', time(), '13');
//score 為timestamp uid為2000的用戶關(guān)注tid為13的topic

$redis->expire('user:2000:feed:topic',24*60*60);
#關(guān)注有效期為24小時(shí)
# ttl 30天之內(nèi)按秒數(shù)計(jì)算 30天之外以timestamp為準(zhǔn)

Latest List & Ranking

Used to record business scenarios such as the latest list or ranking of products that the user just liked

The latest list of products-sorted set structure presentation

    $redis->zAdd('user:1000:product:like', time(), '3002');
    $redis->zAdd('user:1000:product:like', time(), '3001');
    $redis->zAdd('user:1000:product:like', time(), '3004');
    $redis->zAdd('user:1000:product:like', time(), '3003');
    
    $redis->zRange('user:1000:product:like', 0, -1,true); 
    #默認(rèn)喜歡時(shí)間升序序排列
    #
      Array(
        [3002] => 1463565179
        [3001] => 1463565189
        [3004] => 1463565199
        [3003] => 1463565209
      )
    
    $redis->zRevRange('user:1000:product:like', 0, -1,true); 
    #以喜歡時(shí)間降序排列
    #
      Array
      (
        [3003] => 1463565424
        [3004] => 1463565414
        [3001] => 1463565404
        [3002] => 1463565394
      )
    

Ranking list-list data structure presentation

    $redis->lPush('user:1000:product:like', '3002');
    $redis->lPush('user:1000:product:like', '3001');
    $redis->lPush('user:1000:product:like', '3004');
    $redis->lPush('user:1000:product:like', '3003');
    
    $redis->lRange('user:1000:product:like', 0, -1);
    
    Array
    (
      [0] => 3003
      [1] => 3004
      [2] => 3001
      [3] => 3002
    )

Message notification

Use Hash structure to count message notification business scenarios


$redis->hSet('user:1000:message:notice', 'system', 1);
#設(shè)置1條未讀系統(tǒng)消息

$redis->hIncrBy('user:1000:message:notice', 'system', 1);
#未讀系統(tǒng)消息+1

$redis->hSet('user:1000:message:notice', 'comment', 1);
#設(shè)置1條未讀評(píng)論

$redis->hIncrBy('user:1000:message:notice', 'comment', 1);
#未讀評(píng)論+1

$redis->hGetAll('user:1000:message:notice');
#查看所有消息通知數(shù)量

Array
(
  [system] => 2
  [comment] => 2
)



Use Redis as a message queue

Using Redis’ List data structure to implement distributed message queue

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