redis内存使用率排查

RedisDB内存使用率分析

查看当前实例有哪些DB

redis> info keyspace
# Keyspace
db0:keys=4,expires=0,avg_ttl=0
db5:keys=5,expires=0,avg_ttl=0
db6:keys=24848,expires=24847,avg_ttl=1814866
db7:keys=53216,expires=53209,avg_ttl=103466773

查看当前实例的内存

redis> info memory
# Memory
used_memory:227329144
used_memory_human:216.80M
used_memory_rss:361926656
used_memory_rss_human:345.16M
used_memory_peak:2176294808
used_memory_peak_human:2.03G
used_memory_peak_perc:10.45%
used_memory_overhead:50345278
used_memory_startup:7995640
used_memory_dataset:176983866
used_memory_dataset_perc:80.69%
used_memory_lua:66560
used_memory_lua_human:65.00K
used_memory_scripts:10080
used_memory_scripts_human:9.84K
number_of_cached_scripts:16
maxmemory:2147483648
maxmemory_human:2.00G
maxmemory_policy:volatile-lru
mem_fragmentation_ratio:1.59
mem_allocator:jemalloc-5.1.0
active_defrag_running:0
lazyfree_pending_objects:0
oom_err_count:0

列出实例当前使用的DB内存使用率

redis> EVAL "
  -- 允许在脚本里混用 SCAN + UNLINK/SELECT
  redis.replicate_commands()

  local total = tonumber(ARGV[1])     -- 第一个参数：INFO memory 里的 used_memory
  local dbs   = {}                     -- 剩下的参数就是 DB 索引
  for i = 2, #ARGV do
    dbs[#dbs+1] = tonumber(ARGV[i])
  end

  local out = {}
  for _, db in ipairs(dbs) do
    -- 切库
    redis.call('SELECT', db)
    local cursor, sum = '0', 0
    -- 扫描并累加用量
    repeat
      local res = redis.call('SCAN', cursor, 'COUNT', 1000)
      cursor = res[1]
      for _, key in ipairs(res[2]) do
        sum = sum + (redis.call('MEMORY','USAGE', key) or 0)
      end
    until cursor == '0'
    -- 转 MB 并格式化百分比
    local mb  = sum / 1024 / 1024
    local pct = sum * 100 / total
    out[#out+1] = string.format('DB%d: %.2f MB (%.2f%%)', db, mb, pct)
  end

  --（可选）切回 DB0
  redis.call('SELECT', 0)

  return out
" 0 227329144 0 5 6 7

-- 返回的数据
1) DB0: 115.00 MB (53.04%)
2) DB5: 0.03 MB (0.01%)
3) DB6: 5.33 MB (2.46%)
4) DB7: 29.57 MB (13.64%)

RedisKey内存使用率分析

标题一的方法已经定位到redisdb使用的内存分析, 接下来分析具体的db内存占用情况

定位至DB7
```
redis> SELECT 7
OK
```

排序本DB前100的大key

EVAL "
-- 把所有 key 扫描出来，记录 {key, usage, ttl} 三元组，排序后只取前 100
local cursor = '0'
local all = {}
repeat
  local scan = redis.call('SCAN', cursor)
  cursor = scan[1]
  for _, k in ipairs(scan[2]) do
    local u = redis.call('MEMORY','USAGE',k)      or 0
    local t = redis.call('TTL',     k)           or -1
    table.insert(all, {k, u, t})
  end
until cursor == '0'
-- 按内存用量降序
table.sort(all, function(a,b) return a[2] > b[2] end)
-- 只返回前 100 条（如果你想看更多可以改这里的 100）
local out = {}
for i = 1, math.min(100, #all) do
  table.insert(out, all[i])
end
return out
" 0

解释

SCAN 循环取出所有 key
MEMORY USAGE key 拿到该 key 占用的字节数
TTL key 拿到剩余秒数（-1 表示无过期，-2 表示 key 不存在）
按占用内存排序，最后截取前 100

查看以CASES_WORK前缀的KEY占用内存使用率

EVAL "
  local prefix = 'CASES_WORK'
  local cursor, sum = '0', 0
  repeat
    local res = redis.call('SCAN', cursor, 'MATCH', prefix..'*', 'COUNT', 1000)
    cursor = res[1]
    for _, key in ipairs(res[2]) do
      sum = sum + (redis.call('MEMORY','USAGE', key) or 0)
    end
  until cursor == '0'
  
  -- 取总内存
  local info = redis.call('INFO','memory')
  local total = tonumber(string.match(info,'used_memory:(%d+)'))
  
  -- 计算百分比
  local pct = sum * 100 / total
  
  -- 返回：{前缀 key 总占用, Redis 总占用, 百分比}
  return { sum, total, string.format('%.2f%%', pct) }
" 0 

-- 返回数据
1) 2616464
2) 220134304
3) 1.19%

查看并列出以CASES_WORK前缀的KEY, 限制1000个

EVAL "
  local cursor, all = '0', {}
  repeat
    local res = redis.call('SCAN', cursor, 'MATCH', 'CASES_WORK*', 'COUNT', 1000)
    cursor = res[1]
    for _, k in ipairs(res[2]) do
      table.insert(all, k)
    end
  until cursor == '0'
  return all
" 0

-- 返回的数据
...
288) CASES_WORK:a9508eba-80b4-4783-91ca-255a906f29d7
289) CASES_WORK:5e321dbf-b655-4371-8275-310779c37737
290) CASES_WORK:38519e5d-7cad-4400-83cc-6271cffe7839
291) CASES_WORK:21158dfc-874f-4e5e-ae17-e43e8e846b45
292) CASES_WORK:10690943-5790-4f31-a65b-3d54e75a5541
293) CASES_WORK:26c3f7a6-a838-4016-bcf6-ebb2e4651eee
294) CASES_WORK:9303d821-d5d2-471c-a83d-c21145afb5a4
295) CASES_WORK:c7a721a1-4557-4372-9804-511a0f07d63c
296) CASES_WORK:b8ffa9f6-0860-4dd3-9133-5622c572133e
297) CASES_WORK:afaa29a1-79d3-489b-850a-05e8a3ae58fb
298) CASES_WORK:487f5ce0-c47d-409b-8776-4e456d433fbd
299) CASES_WORK:c9481de2-c6fa-442b-8f4e-3e817eea7cfc

批量删除以CASES_WORK前缀的KEY

-- 切到目标库
SELECT 7

-- EVAL 脚本批量删除所有 CASES_WORK* 前缀的 key
EVAL "
  -- 打开命令模式复制，允许 SCAN + UNLINK 混用
  redis.replicate_commands()
  
  local cursor = '0'
  repeat
    -- 分批扫描匹配
    local res = redis.call('SCAN', cursor, 'MATCH', 'CASES_WORK*', 'COUNT', 1000)
    cursor = res[1]
    for _, k in ipairs(res[2]) do
      redis.call('UNLINK', k)
    end
  until cursor == '0'
  
  return 'DELETED ALL CASES_WORK*'
" 0

如果怕误操作以至于不影响全部数据, 可以分批进行清理

-- 1. 先切到目标库（以 DB7 为例）
SELECT 7

-- 2. 每次运行下面这条 EVAL，修改最后的数字为你想删的“本批限额”：
EVAL "
  -- 切换到命令复制模式，允许 SCAN + UNLINK 混用
  redis.replicate_commands()

  local cursor = '0'
  local deleted = 0
  local limit = tonumber(ARGV[1])

  repeat
    local res = redis.call('SCAN', cursor, 'MATCH', 'CASES_WORK*', 'COUNT', 1000)
    cursor = res[1]
    for _, k in ipairs(res[2]) do
      if deleted < limit then
        redis.call('UNLINK', k)
        deleted = deleted + 1
      end
    end
  until cursor == '0' or deleted >= limit

  return deleted
" 0 1000

统计以CASE_WORK前缀的KEY数量

EVAL "
  local cursor = '0'
  local total = 0
  repeat
    -- 扫描匹配 CASES_WORK* 的一批 key
    local res = redis.call('SCAN', cursor, 'MATCH', 'CASES_WORK*', 'COUNT', 1000)
    cursor = res[1]
    -- 累加本批 key 数量
    total = total + #res[2]
  until cursor == '0'
  return total
" 0

-- 返回数据
OK
299

posted @ 2025-05-26 11:36 MacoPlus 阅读(77) 评论(0) 收藏举报

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WeChatID as below

ParkLeah

redis内存使用率排查

RedisDB内存使用率分析

RedisKey内存使用率分析

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