Lazyfree异步 3命令
UNLINK：异步删除key
FLUSHDB ASYNC：异步清空当前DB
FLUSHALL ASYNC：异步清空所有DB
Lazyfree异步 4配置
lazyfree-lazy-expire：异步删除过期key
lazyfree-lazy-eviction：异步淘汰key
lazyfree-lazy-server-del：隐式删除时采取异步删除，比如rename a b，若b存在则需删除b
slave-lazy-flush：全量同步时，slave异步清空所有DB
热点数据
使用OBJECT FREQ命令即可获取指定key的访问频率，不过需要首先把内存逐出策略设置为allkeys-lfu或者volatile-lfu：
127.0.0.1:6379> config get maxmemory-policy
1) "maxmemory-policy"
2) "noeviction"
127.0.0.1:6379> object freq counter:000000006889
(error) ERR An LFU maxmemory policy is not selected, access frequency not tracked. Please note that when switching between policies at runtime LRU and LFU data will take some time to adjust.
127.0.0.1:6379> config set maxmemory-policy allkeys-lfu
OK
127.0.0.1:6379> object freq counter:000000006889
(integer) 3
使用scan命令遍历所有key，再通过OBJECT FREQ获取访问频率并排序，即可得到热点key。为了方便用户使用，Redis自带的客户端redis-cli也提供了热点key发现功能，执行redis-cli时加上--hotkeys选项即可，示例如下：
$./redis-cli --hotkeys
Scanning the entire keyspace to find hot keys as well as
average sizes per key type. You can use -i 0.1 to sleep 0.1 sec
per 100 SCAN commands (not usually needed).
[00.00%] Hot key 'counter:000000000002' found so far with counter 87
[00.00%] Hot key 'key:000000000001' found so far with counter 254
[00.00%] Hot key 'mylist' found so far with counter 107
[00.00%] Hot key 'key:000000000000' found so far with counter 254
[45.45%] Hot key 'counter:000000000001' found so far with counter 87
[45.45%] Hot key 'key:000000000002' found so far with counter 254
[45.45%] Hot key 'myset' found so far with counter 64
[45.45%] Hot key 'counter:000000000000' found so far with counter 93
-------- summary -------
Sampled 22 keys in the keyspace!
hot key found with counter: 254 keyname: key:000000000001
hot key found with counter: 254 keyname: key:000000000000
hot key found with counter: 254 keyname: key:000000000002
hot key found with counter: 107 keyname: mylist
hot key found with counter: 93 keyname: counter:000000000000
hot key found with counter: 87 keyname: counter:000000000002
hot key found with counter: 87 keyname: counter:000000000001
hot key found with counter: 64 keyname: myset
MEMORY内存分析命令
127.0.0.1:6379> memory help
1) "MEMORY DOCTOR - Outputs memory problems report"
2) "MEMORY USAGE <key> [SAMPLES <count>] - Estimate memory usage of key"
3) "MEMORY STATS - Show memory usage details"
4) "MEMORY PURGE - Ask the allocator to release memory"
5) "MEMORY MALLOC-STATS - Show allocator internal stats"
MEMORY STATS
127.0.0.1:6379> memory stats
1) "peak.allocated"
2) (integer) 423995952
3) "total.allocated"
4) (integer) 11130320
5) "startup.allocated"
6) (integer) 9942928
7) "replication.backlog"
8) (integer) 1048576
9) "clients.slaves"
10) (integer) 16858
11) "clients.normal"
12) (integer) 49630
13) "aof.buffer"
14) (integer) 3253
15) "db.0"
16) 1) "overhead.hashtable.main"
2) (integer) 5808
3) "overhead.hashtable.expires"
4) (integer) 104
17) "overhead.total"
18) (integer) 11063904
19) "keys.count"
20) (integer) 94
21) "keys.bytes-per-key"
22) (integer) 12631
23) "dataset.bytes"
24) (integer) 66416
25) "dataset.percentage"
26) "5.5934348106384277"
27) "peak.percentage"
28) "2.6251003742218018"
29) "fragmentation"
30) "1.1039986610412598"
一共有15项内容，内存使用量均以字节为单位，我们一个一个来看：
- peak.allocated
redis启动到现在，最多使用过多少内存。
- total.allocated
当前使用的内存总量。
- startup.allocated
redis启动初始化时使用的内存，有很多读者会比较奇怪，为什么我的redis启动以后什么都没做就已经占用了几十MB的内存？
这是因为redis本身不仅存储key-value，还有其他的内存消耗，比如共享变量、主从复制、持久化和db元信息，下面各项会有详细介绍。
- replication.backlog
主从复制backlog使用的内存，默认10MB，backlog只在主从断线重连时发挥作用，主从复制本身并不依赖此项。
- clients.slaves
主从复制中所有slave的读写缓冲区，包括output-buffer（也即输出缓冲区）使用的内存和querybuf（也即输入缓冲区），这里简单介绍一下主从复制：
redis把一次事件循环中，所有对数据库发生更改的内容先追加到slave的output-buffer中，在事件循环结束后统一发送给slave。
那么主从之间就难免会有数据的延迟，如果主从之间连接断开，重连时为了保证数据的一致性就要做一次全量同步，这显然是不够高效的。backlog就是为此而设计，master在backlog中缓存一部分主从复制的增量数据，断线重连时如果slave的偏移量在backlog中，那就可以只把偏移量之后的增量数据同步给slave即可，避免了全量同步的开销。
- clients.normal
除slave外所有其他客户端的读写缓冲区。
有时候一些客户端读取不及时，就会造成output-buffer积压占用内存过多的情况，可以通过配置项client-output-buffer-limit来限制，当超过阈值之后redis就会主动断开连接以释放内存，slave亦是如此。
- aof.buffer
此项为aof持久化使用的缓存和aofrewrite时产生的缓存之和，当然如果关闭了appendonly那这项就一直为0：
redis并不是在有写入时就立即做持久化的，而是在一次事件循环内把所有的写入数据缓存起来，待到事件循环结束后再持久化到磁盘。
aofrewrite时缓存增量数据使用的内存，只在aofrewrite时才会使用，aofrewrite机制可以参考之前的文章《redis4.0之利用管道优化aofrewrite》。
可以看出这一项的大小与写入流量成正比。
- db.0
redis每个db的元信息使用的内存，这里只使用了db0，所以只打印了db0的内存使用状态，当使用其他db时也会有相应的信息。
db的元信息有以下三项：
a) redis的db就是一张hash表，首先就是这张hash表使用的内存（redis使用链式hash，hash表中存放所有链表的头指针）；
b) 每一个key-value对都有一个dictEntry来记录他们的关系，元信息便包含该db中所有dictEntry使用的内存；
c) redis使用redisObject来描述value所对应的不同数据类型（string、list、hash、set、zset），那么redisObject占用的空间也计算在元信息中。
overhead.hashtable.main：
db的元信息也即是以上三项之和，计算公式为：
hashtable + dictEntry + redisObject
overhead.hashtable.expires:
对于key的过期时间，redis并没有把它和value放在一起，而是单独用一个hashtable来存储，但是expires这张hash表记录的是key-expire信息，所以不需要redisObject来描述value，其元信息也就少了一项，计算公式为：
hashtable + dictEntry
- overhead.total
3-8项之和：startup.allocated+replication.backlog+clients.slaves+clients.normal+aof.buffer+dbx
- dataset.bytes
所有数据所使用的内存——也即total.allocated - overhead.total——当前内存使用量减去管理类内存使用量。
- dataset.percentage
所有数据占比，这里并没有直接使用total.allocated做分母，而是除去了redis启动初始化的内存，计算公式为：
100 * dataset.bytes / (total.allocated - startup.allocated)
- keys.count
redis当前存储的key总量
- keys.bytes-per-key
平均每个key的内存大小，直觉上应该是用dataset.bytes除以keys.count即可，但是redis并没有这么做，而是把管理类内存也平摊到了每个key的内存使用中，计算公式为：
(total.allocated - startup.allocated) / keys.count
- peak.percentage
当前使用内存与历史最高值比例
- fragmentation
内存碎片率
- MEMORY USAGE
内存评估
MEMORY usage [samples]
以hash为例看下其如果工作：
首先类似于上一节中的overhead.hashtable.main，要计算hash的元信息内存，包括hash表的大小以及所有dictEntry的内存占用信息。
与overhead.hashtable.main不同的是，每个dictEntry中key-value都是字符串，所以没redisObject的额外消耗。在评估真正的数据内存大小时redis并没有去遍历所有key，而是采用的抽样估算：随机抽取samples个key-value对计算其平均内存占用，再乘以key-value对的个数即得到结果。试想一下如果要精确计算内存占用，那么就需要遍历所有的元素，当元素很多时就是使redis阻塞，所以请合理设置samples的大小。
- MEMORY DOCTOR
首先是没问题的情况
运行状态良好：
Hi Sam, I can't find any memory issue in your instance. I can only account for what occurs on this base.
redis的数据量很小，暂无建议：
Hi Sam, this instance is empty or is using very little memory, my issues detector can't be used in these conditions. Please, leave for your mission on Earth and fill it with some data. The new Sam and I will be back to our programming as soon as I finished rebooting.
接下来出现的结果就需要注意了
内存使用峰值1.5倍于目前内存使用量，此时内存碎片率可能会比较高，需要注意：
Peak memory: In the past this instance used more than 150% the memory that is currently using. The allocator is normally not able to release memory after a peak, so you can expect to see a big fragmentation ratio, however this is actually harmless and is only due to the memory peak, and if the Redis instance Resident Set Size (RSS) is currently bigger than expected, the memory will be used as soon as you fill the Redis instance with more data. If the memory peak was only occasional and you want to try to reclaim memory, please try the MEMORY PURGE command, otherwise the only other option is to shutdown and restart the instance.
内存碎片率过高超过1.4，需要注意：
High fragmentation: This instance has a memory fragmentation greater than 1.4 (this means that the Resident Set Size of the Redis process is much larger than the sum of the logical allocations Redis performed). This problem is usually due either to a large peak memory (check if there is a peak memory entry above in the report) or may result from a workload that causes the allocator to fragment memory a lot. If the problem is a large peak memory, then there is no issue. Otherwise, make sure you are using the Jemalloc allocator and not the default libc malloc.
每个slave缓冲区的平均内存超过10MB，原因可能是master写入流量过高，也有可能是主从同步的网络带宽不足或者slave处理较慢：
Big slave buffers: The slave output buffers in this instance are greater than 10MB for each slave (on average). This likely means that there is some slave instance that is struggling receiving data, either because it is too slow or because of networking issues. As a result, data piles on the master output buffers. Please try to identify what slave is not receiving data correctly and why. You can use the INFO output in order to check the slaves delays and the CLIENT LIST command to check the output buffers of each slave.
普通客户端缓冲区的平均内存超过200KB，原因可能是pipeline使用不当或者Pub/Sub客户端处理消息不及时导致：
Big client buffers: The clients output buffers in this instance are greater than 200K per client (on average). This may result from different causes, like Pub/Sub clients subscribed to channels bot not receiving data fast enough, so that data piles on the Redis instance output buffer, or clients sending commands with large replies or very large sequences of commands in the same pipeline. Please use the CLIENT LIST command in order to investigate the issue if it causes problems in your instance, or to understand better why certain clients are using a big amount of memory.
-
MEMORY MALLOC-STATS
打印内存分配器状态，只在使用jemalloc时有用。 - MEMORY PURGE
请求分配器释放内存，同样只对jemalloc生效