排查Redis大key的方法

排查Redis大key的方法

redis-cli --bigkeys

特点：

• 使用–bigkeys参数会扫描整个Redis数据库，应该在低流量峰值时执行

• 这个方法只能返回每种类型中最大的那个bigkey，无法得到大小排到前N位的bigkey

• 对于集合类型来说，这个方法只统计集合元素的多少，而不是实际占用的内存量。因为一个集合中元素个数多，并不一定占用内存就多

    @GetMapping("init")
    @Async
    public void initData(@RequestParam (name = "size", defaultValue = "5000") Integer size){

        redisTemplate.opsForValue().set("string_large_key1", generateTestData(10* 1024));
        redisTemplate.opsForValue().set("string_large_key2", generateTestData(10* 1024));

        redisTemplate.opsForSet().add("set_large_key1", new HashSet<>(50000));
        redisTemplate.opsForSet().add("set_large_key2", new HashSet<>(50000));

        redisTemplate.opsForHash().putAll("hash_large_key1", buildMapData(50000));
        redisTemplate.opsForHash().putAll("hash_large_key2", buildMapData(50000));

        redisTemplate.opsForList().rightPushAll("list_large_key1", buildListData(50000));
        redisTemplate.opsForList().rightPushAll("list_large_key2", buildListData(50000));
    }



    private Map buildMapData(int initialCapacity){

        Map<String, String> result =new HashMap<>(initialCapacity);
        for (int i = 0; i < initialCapacity; i++) {
            result.put("kevin_" + i, "123");
        }
        return result;

    }

    private List<String> buildListData(int initialCapacity){
        List<String> result = new ArrayList<>(initialCapacity);
        for (int i = 0; i < initialCapacity; i++) {
            result.add("kevin_" + i );
        }
        return result;

    }

• 使用–bigkeys查询

root@DESKTOP-0JS7U4E:~# redis-cli -h 127.0.0.1 -p 16379 --bigkeys

# Scanning the entire keyspace to find biggest 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%] Biggest hash   found so far 'hash_large_key2' with 50000 fields

# 只返回了最大的那个bigkey
[00.00%] Biggest string found so far 'string_large_key2' with 10485762 bytes
[00.00%] Biggest set    found so far 'set_large_key2' with 1 members
[00.00%] Biggest list   found so far 'list_large_key1' with 50000 items

-------- summary -------

Sampled 9 keys in the keyspace!
Total key length in bytes is 135 (avg len 15.00)

Biggest   list found 'list_large_key1' has 50000 items
Biggest   hash found 'hash_large_key2' has 50000 fields
Biggest string found 'string_large_key2' has 10485762 bytes
Biggest    set found 'set_large_key2' has 1 members

# 返回了list的容量
2 lists with 100000 items (22.22% of keys, avg size 50000.00)
# 返回了hash的容量
2 hashs with 100000 fields (22.22% of keys, avg size 50000.00)
2 strings with 20971524 bytes (22.22% of keys, avg size 10485762.00)
0 streams with 0 entries (00.00% of keys, avg size 0.00)
3 sets with 3 members (33.33% of keys, avg size 1.00)
0 zsets with 0 members (00.00% of keys, avg size 0.00)
root@DESKTOP-0JS7U4E:~#

redis-cli scan VS memory usage组合

实际上bigkey的底层也使用SCAN命令执行。

SCAN命令可以用于迭代遍历所有key。它是一个非阻塞操作，支持游标(cursor)的方式来逐步遍历所有key。使用SCAN命令可以避免阻塞，减少对Redis性能的影响。

Redis Scan命令说明

1、先使用scan扫描出key

127.0.0.1:16379> scan 1000 MATCH "string*"
1) "0"
2) 1) "string_large_key2"
   2) "string_large_key1"
127.0.0.1:16379>
127.0.0.1:16379> scan 0 MATCH "string*" count 20
1) "0"
2) 1) "string_large_key2"
   2) "string_large_key1"
127.0.0.1:16379>

2、使用 memory usage查询key占用的内存大小

127.0.0.1:16379> memory usage string_large_key1
(integer) 10485831
127.0.0.1:16379>

这样组合的方式操作比较复杂，需要对命令使用非常熟悉。在生产环境需要更快，更高效的发现问题还是建议使用成熟的分析工具，毕竟也都是用这些命令组合起来的。

使用云上的Redis可以直接使用CloundDBA功能

redis-rdb-tools

该三方工具Github地址redis-rdb-tools。

安装该分析工具

python setup.py install

要使用memory功能，需要安装

pip3 install python-lzf

如果出现没有权限的问题，那就以管理员打开cmd再运行

error: [Errno 13] Permission denied: 'C:\\Python310\\Scripts\\rdb-script.py'

安装完成之后目录下面多出这几个文件。

C:\Python310\Scripts>dir
....................
2024/08/08  13:38               996 rdb-script.py
2024/08/08  13:38            74,752 rdb.exe
2024/08/08  13:38             1,030 redis-memory-for-key-script.py
2024/08/08  13:38            74,752 redis-memory-for-key.exe
2024/08/08  13:38             1,018 redis-profiler-script.py
2024/08/08  13:38            74,752 redis-profiler.exe

C:\Python310\Scripts>

使用rdb进行分析

C:\Python310\Scripts>rdb --command memory --bytes 102400 \\wsl.localhost\Ubuntu-20.04\var\lib\redis\dump.rdb
database,type,key,size_in_bytes,encoding,num_elements,len_largest_element,expiry
0,hash,hash_large_key2,3186588,hashtable,50000,11,
0,hash,hash_large_key1,3186588,hashtable,50000,11,
0,string,string_large_key1,12582976,string,10485762,10485762,
0,list,list_large_key1,744355,quicklist,50000,13,
0,string,string_large_key2,12582976,string,10485762,10485762,
0,list,list_large_key2,744355,quicklist,50000,13,

C:\Python310\Scripts>

也可以加上-f参数，将结果输出到本地文件中。

rdb --command memory --bytes 102400 \\wsl.localhost\Ubuntu-20.04\var\lib\redis\dump.rdb -f d:\kevin.csv