一起聊聊redis的scan操作

redis系列

• redis的发布订阅功能
• redis消息队列
• redis的pipeline
• redis的scan操作

序

在redis的db存在大量key或者db里头的某个set、zset、hash里头的元素非常多的话，用普通的get all操作很可能导致redis因为这个操作阻塞了，导致不能响应其他操作，特别是在高并发、海量数据的背景下，这个问题显得尤其严重。那么能不能像数据库那样有个分页的功能呢，答案就是scan操作。本文主要展示怎么在redis-cli以及SpringDataRedis中的使用。【推荐：redis视频教程】

scan语法

scan之后返回两部分，第一部分是下次scan的参数，第二部分就是scan出来的项

作用对象(db、set、zset、hash)

• db(key)

127.0.0.1:6379> scan 0 1) "120" 2)  1) "articleMap:63"     2) "articleMap:37"     3) "counter:__rand_int__"     4) "articleMap:60"     5) "tagSet:tag5"     6) "articleMap:80"     7) "messageCache~keys"     8) "mymap"     9) "articleMap:46"    10) "articleMap:55" 127.0.0.1:6379> scan 120 1) "28" 2)  1) "articleMap:17"     2) "tagSet:tag1"     3) "articleMap:18"     4) "articleMap:81"     5) "xacxedx00x05tx00btest-cas"     6) "articleMap:51"     7) "articleMap:94"     8) "articleMap:26"     9) "articleMap:71"    10) "user-abcde"

• set(value)

127.0.0.1:6379> sscan myset 0 1) "3" 2)  1) "m"     2) "j"     3) "c"     4) "h"     5) "f"     6) "i"     7) "a"     8) "g"     9) "n"    10) "e"    11) "b" 127.0.0.1:6379> sscan myset 3 1) "0" 2) 1) "l"    2) "k"    3) "d"

• zset(value & score)

127.0.0.1:6379> zscan sortset 0 1) "0" 2) 1) "tom"    2) "89"    3) "jim"    4) "90"    5) "david"    6) "100"

• hash(key & value)

127.0.0.1:6379> hscan mymap 0 1) "0" 2)  1) "name"     2) "codecraft"     3) "email"     4) "pt@g.cn"     5) "age"     6) "20"     7) "desc"     8) "hello"     9) "sex"    10) "male"

SCAN的额外参数

• count(指定每次取多少条)

127.0.0.1:6379> scan 0 count 5 1) "240" 2) 1) "articleMap:63"    2) "articleMap:37"    3) "counter:__rand_int__"    4) "articleMap:60"    5) "tagSet:tag5"

• match(匹配key)

127.0.0.1:6379> scan 0 match article* 1) "120" 2) 1) "articleMap:63"    2) "articleMap:37"    3) "articleMap:60"    4) "articleMap:80"    5) "articleMap:46"    6) "articleMap:55"

RedisTemplate操作

遍历数据库key

@Test     public void scanDbKeys(){         template.execute(new RedisCallback<Iterable<byte[]>>() {             @Override             public Iterable<byte[]> doInRedis(RedisConnection connection) throws DataAccessException {                  List<byte[]> binaryKeys = new ArrayList<byte[]>();                  Cursor<byte[]> cursor = connection.scan(ScanOptions.scanOptions().count(5).build());                 while (cursor.hasNext()) {                     byte[] key = cursor.next();                     binaryKeys.add(key);                     System.out.println(new String(key, StandardCharsets.UTF_8));                 }                  try {                     cursor.close();                 } catch (IOException e) {                     // do something meaningful                 }                  return binaryKeys;             }         });     }

遍历set

/**      * sadd myset a b c d e f g h i j k l m n      */     @Test     public void scanSet(){         Cursor<String> cursor = template.opsForSet().scan("myset",ScanOptions.NONE);         while (cursor.hasNext()){             System.out.println(cursor.next());         }     }

遍历zset

/**      * zadd sortset 89 tom 90 jim 100 david      */     @Test     public void scanZSet(){         Cursor<ZSetOperations.TypedTuple<String>> cursor = template.opsForZSet().scan("sortset",ScanOptions.NONE);         while (cursor.hasNext()){             ZSetOperations.TypedTuple<String> item = cursor.next();             System.out.println(item.getValue() + ":" + item.getScore());         }     }

遍历hash

/**      *  hset mymap name "codecraft"      *  hset mymap email "pt@g.cn"      *  hset mymap age 20      *  hset mymap desc "hello"      *  hset mymap sex "male"      */     @Test     public void scanHash(){         Cursor<Map.Entry<Object, Object>> curosr = template.opsForHash().scan("mymap", ScanOptions.NONE);         while(curosr.hasNext()){             Map.Entry<Object, Object> entry = curosr.next();             System.out.println(entry.getKey()+":"+entry.getValue());         }     }