探寻 Redis 内存诡异增长的元凶

原文链接： https://yq.aliyun.com/articles/644788

一、现象

• 实例名：r-bp1cxxxxxxxxxd04(主从)

• 时间：2017-11-16 12:26~12:27

• 问题：一分钟内存上涨了2G，如下图所示：

• 键值规模：6000万左右

二、Redis内存分析

1. 内存组成

上图中的内存统计的是Redis的info memory命令中的used_memory属性，例如：

1. redis> info memory# Memoryused_memory:9195978072used_memory_human:8.56Gused_memory_rss:9358786560used_memory_peak:10190212744used_memory_peak_human:9.49Gused_memory_lua:38912mem_fragmentation_ratio:1.02mem_allocator:jemalloc-3.6.0

每个属性的详细说明

属性名	属性说明
used_memory	Redis 分配器分配的内存量，也就是实际存储数据的内存总量
used_memory_human	以可读格式返回 Redis 使用的内存总量
used_memory_rss	从操作系统的角度，Redis进程占用的总物理内存
used_memory_peak	内存分配器分配的最大内存，代表used_memory的历史峰值
used_memory_peak_human	以可读的格式显示内存消耗峰值
used_memory_lua	Lua引擎所消耗的内存
mem_fragmentation_ratio	used_memory_rss /used_memory比值，表示内存碎片率
mem_allocator	Redis 所使用的内存分配器。默认: jemalloc

计算公式如下：

1. used_memory = 自身内存+对象内存+缓冲内存+lua内存used_rss = used_memory + 内存碎片

如下图所示：

2. 内存分析

(1) 自身内存：一个空的Redis占用很小，可以忽略不计
(2) kv内存：key对象 + value对象
(3) 缓冲区：客户端缓冲区(普通 + slave伪装 + pubsub)以及aof缓冲区(比较固定，一般没问题)
(4) Lua：Lua引擎所消耗的内存

3. 内存突增常见问题

(1) kv内存：bigkey、大量写入
(2) 客户端缓冲区：一般常见的有普通客户端缓冲区(例如monitor命令)或者pubsub客户端缓冲区

三、问题排查

(1) bigkey ? 经扫描未发现bigkey

1. Sampled 67234427 keys in the keyspace!

2. Total key length in bytes is 1574032382 (avg len 23.41)

4. Biggest string found 'CCARD_DEVICE_CARD_REF_MAP_KEY_016817000004209' has 20862 bytes

5. Biggest   list found 'CCARD_VALID_DEVICE_TRAIN_QUEUE_KEY' has 51 items

6. Biggest   hash found 'CCARD_VALID_DEVICE_TRAIN_MAP_KEY' has 51 fields67234359 strings with 71767890 bytes (100.00% of keys, avg size 1.07)67 lists with 151 items (00.00% of keys, avg size 2.25)0 sets with 0 members (00.00% of keys, avg size 0.00)1 hashs with 51 fields (00.00% of keys, avg size 51.00)0 zsets with 0 members (00.00% of keys, avg size 0.00)

(2) 键值个数增加？未发现键值有明显变化

(3) 客户端缓冲区

由于内存增上去后，长时间没下落，如果是因为缓冲区问题，会从info clients找到明显问题，执行后发现：

1. redis> info clients# Clientsconnected_clients:43client_longest_output_list:0client_biggest_input_buf:0blocked_clients:0admin_clients:6rejected_vpc_conn_count:0close_idle_unknown_conn_count:0

执行client中也没有明显的omem大于0的情况

1. id=80207 addr=10.xx.0.4:63920 fd=46 name= age=624 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

2. id=80215 addr=10.xx.0.23:43489 fd=36 name= age=591 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

3. id=80366 addr=10.xx.0.8:59785 fd=18 name= age=84 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=del read=0 write=0 type=user

4. id=80356 addr=10.xx.0.33:32117 fd=13 name= age=114 idle=0 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

5. id=80064 addr=10.xx.59.4:53446 fd=38 name= age=1070 idle=1070 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=NULL read=0 write=0 type=admin

6. id=80276 addr=10.xx.0.23:48511 fd=8 name= age=387 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

7. id=80188 addr=10.xx.0.33:16265 fd=42 name= age=681 idle=3 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

8. id=80326 addr=10.xx.0.32:59779 fd=16 name= age=209 idle=0 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

9. id=80065 addr=10.xx.59.4:53447 fd=45 name= age=1070 idle=1070 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=NULL read=0 write=0 type=admin

10. id=79936 addr=10.xx.0.22:10607 fd=30 name= age=1480 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

11. id=80174 addr=10.xx.0.5:60914 fd=6 name= age=722 idle=2 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

12. id=80300 addr=10.xx.0.22:22757 fd=48 name= age=298 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

13. id=80037 addr=10.xx.0.5:55189 fd=15 name= age=1143 idle=2 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

14. id=80330 addr=10.xx.0.8:48533 fd=17 name= age=199 idle=10 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

15. id=79896 addr=10.xx.0.30:26814 fd=11 name= age=1616 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

16. id=80299 addr=10.xx.0.24:11227 fd=44 name= age=303 idle=3 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

17. id=80086 addr=10.xx.0.32:52526 fd=40 name= age=1002 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

18. id=80202 addr=10.xx.0.33:16658 fd=26 name= age=636 idle=3 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

19. id=80256 addr=10.xx.0.24:60496 fd=19 name= age=448 idle=2 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

20. id=79908 addr=10.xx.0.29:18975 fd=12 name= age=1583 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

21. id=80365 addr=10.xx.0.29:46429 fd=14 name= age=85 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

22. id=79869 addr=10.xx.27.4:48455 fd=35 name= age=1700 idle=1700 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=NULL read=0 write=0 type=admin

23. id=80334 addr=10.xx.0.23:50012 fd=39 name= age=189 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

24. id=80041 addr=10.xx.0.32:51107 fd=33 name= age=1132 idle=3 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

25. id=79992 addr=10.xx.0.22:12068 fd=28 name= age=1289 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

26. id=80251 addr=10.xx.0.30:44213 fd=23 name= age=468 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

27. id=80006 addr=10.xx.0.2:45895 fd=31 name= age=1242 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

28. id=80321 addr=10.xx.0.30:48048 fd=5 name= age=224 idle=3 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

29. id=80381 addr=10.xx.0.8:13360 fd=22 name= age=24 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=del read=0 write=0 type=user

30. id=80200 addr=10.xx.0.24:59183 fd=24 name= age=640 idle=0 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

31. id=80113 addr=10.xx.0.2:52492 fd=21 name= age=915 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

32. id=174 addr=11.216.117.242:53027 fd=9 name= age=281390 idle=0 flags=S db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=replconf read=0 write=0 type=admin

33. id=79991 addr=10.xx.0.4:48412 fd=25 name= age=1296 idle=0 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

34. id=80301 addr=127.0.0.1:47869 fd=49 name= age=291 idle=261 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=strlen read=0 write=0 type=admin

35. id=80047 addr=10.xx.59.4:53184 fd=41 name= age=1114 idle=1114 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=NULL read=0 write=0 type=admin

36. id=80236 addr=10.xx.0.5:62546 fd=47 name= age=516 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

37. id=80364 addr=10.xx.0.4:18794 fd=7 name= age=85 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

38. id=80175 addr=10.xx.0.4:62245 fd=29 name= age=718 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

39. id=80336 addr=10.xx.0.29:45701 fd=50 name= age=180 idle=1 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

40. id=80050 addr=10.xx.59.4:53188 fd=43 name= age=1114 idle=1114 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=NULL read=0 write=0 type=admin

41. id=79765 addr=10.xx.0.2:33832 fd=37 name= age=2027 idle=177 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=info read=0 write=0 type=user

42. id=80170 addr=10.xx.0.2:57853 fd=20 name= age=728 idle=24 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=0 obl=0 oll=0 omem=0 events=r cmd=ping read=0 write=0 type=user

43. id=80390 addr=127.0.0.1:49449 fd=27 name= age=0 idle=0 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=0 qbuf-free=32768 obl=0 oll=0 omem=0 events=r cmd=client read=0 write=0 type=admin

四、揪出元凶

常用的几招都用了，还是不行，同事@径远帮忙一起分析，怀疑是不是因为Redis的kv哈希表做了 rehash。

1. Redis的kv存储结构

如下图所示，Redis的所有kv保存在dict中，其中ht对应两个哈希表ht[0]和ht[1]，平时一个空闲，一个用于存储数据，只有当需要rehash时，ht[1]才会用到。

2. Redis的字典rehash

为了保证哈希表的负载，当哈希表的元素个数等于哈希表槽数时候，会进行rehash扩容。扩容后h[1]的容量等于第一个大于等于ht[0].size*2的2n，例如hash表的初始化容量是4，那么下一次扩容就是8，以此类推。

3. 测试

(1) 测试方法

先批量写入到rehash阈值附近，然后在逐条去写，观察内存变化

1. // 为每个键设置1天过期时间int expireTime = 60 * 60 * 24;// rehash阈值 - 50为了方便观察rehash内存变化int rehashThreshold = (int) Math.pow(2, 25) - 50;// 1.批量写入：pipeline批量写入，由于是本机测试，这里用10000，实际生产不要这么用Pipeline pipeline = jedis.pipelined();

2. pipeline = jedis.pipelined();for (int i = 0; i < rehashThreshold; i++) {

3.    pipeline.setex(String.valueOf(i), expireTime, String.valueOf(i));    if (i % 10000 == 0) {

4.        pipeline.sync();

5.    }

6. }

7. pipeline.sync();// 2.等待写增量TimeUnit.SECONDS.sleep(5);for (int i = rehashThreshold; i < rehashThreshold + 200; i++) {

8.    jedis.setex(String.valueOf(i), expireTime, String.valueOf(i));

9.    TimeUnit.SECONDS.sleep(1);

10. }

(2) 开始测试

(a) 当阈值=215=32768，从下面可以看出到key的个数为32769时，内存涨了一些，但是还不明显。

1. keys       mem      clients blocked requests            connections32766      4.69M    3       0       32797 (+2)          4

2. 32767      4.69M    3       0       32799 (+2)          4

3. 32768      4.69M    3       0       32801 (+2)          4

4. 32769      5.44M    3       0       32803 (+2)          4

(b) 当阈值=220=1048576，从下面可以看出到key的个数为1048577时，内存涨了32M。因为rehash会扩容，所以新的哈希表中的槽位变为了221 * 2（因为每个key都设置了过期时间，expires表），指针为8个字节，221 ️ 2 ️ 8 = 225 = 32MB。

1. keys       mem      clients blocked requests            connections1048574    128.69M  3       0       3364129 (+2)        16

2. 1048575    128.69M  3       0       3364131 (+2)        16

3. 1048576    128.69M  3       0       3364133 (+2)        16

4. 1048577    160.69M  3       0       3364135 (+2)        16

5. 1048578    160.69M  3       0       3364137 (+2)        16

(c) 当阈值=226=67108864，从下面可以看出到key的个数为67108865时，内存涨了2GB。因为rehash会扩容，所以新的哈希表中的槽位变为了227 * 2（因为每个key都设置了过期时间，expires表），指针为8个字节，227 ️ 2 ️ 8 = 231 = 2GB。

1. keys       mem      clients blocked requests            connections67108862   9.70G    3       0       70473683 (+2)       18

2. 67108863   9.70G    3       0       70473685 (+2)       18

3. 67108864   9.70G    3       0       70473687 (+2)       18

4. 67108865   11.70G   3       0       70473689 (+2)       18

5. 67108866   11.70G   3       0       70473691 (+2)       18

6. 67108867   11.70G   3       0       70473693 (+2)       18

回过来看r-bp1c15fd9b142d04的key和内存变化图，可以发现上面的规则是正确的：

4. 后续观察

17点时，rehash结束，内存降了增加的2G的一半。

五、总结

由于哈希表的特性，Redis 中键值数量大，不会对存取造成性能影响，但是会出现本文提到的问题。控制键个数有几个建议：无用的键值设置过期时间或者定期删除。优化键值设计：例如可以使用 ziplist hash合并优化部分字符串类型。未来改进：内核层面支持 rehash 的审计日志以及增强 rehash 的速度。

原文发布时间为：2018-09-25

本文作者：付磊

本文来自云栖社区合作伙伴“云时代架构”，了解相关信息可以关注“云时代架构”。