Redis
2021-10-26
这篇文章讨论的是 Reids 数据到期之后发生了什么。
Redis 并不会立即删除过期数据，而是根据配置的策略来处理。
config get maxmemory-policy
# maxmemory-policy
# volatile-ttl
原文
When Redis is used as a cache, often it is handy to let it automatically evict old data as you add new data. This behavior is very well known in the community of developers, since it is the default behavior of the popular memcached system.
Redis 和 Memcached 一样会自动清除旧数据。
LRU is actually only one of the supported eviction methods. This page covers the more general topic of the Redis maxmemory directive that is used in order to limit the memory usage to a fixed amount, and it also covers in depth the LRU algorithm used by Redis, that is actually an approximation of the exact LRU.
Starting with Redis version 4.0, a new LFU (Least Frequently Used) eviction policy was introduced. This is covered in a separated section of this documentation.
Maxmemory configuration directive 最大内存配置指令
The maxmemory configuration directive is used in order to configure Redis to use a specified amount of memory for the data set. It is possible to set the configuration directive using the redis.conf file, or later using the CONFIG SET command at runtime.
For example in order to configure a memory limit of 100 megabytes, the following directive can be used inside the redis.conf file.
maxmemory 100mb
Setting maxmemory to zero results into no memory limits. This is the default behavior for 64 bit systems, while 32 bit systems use an implicit memory limit of 3GB.
Markjour 注释：设置成 0 的话，64 位系统上表示没有限制，32 位系统则隐式地使用 3GB 内存限制。
When the specified amount of memory is reached, it is possible to select among different behaviors, called policies. Redis can just return errors for commands that could result in more memory being used, or it can evict some old data in order to return back to the specified limit every time new data is added.
Eviction policies 清理策略
The exact behavior Redis follows when the maxmemory limit is reached is configured using the maxmemory-policy configuration directive.
The following policies are available:
- noeviction: return errors when the memory limit was reached and the client is trying to execute commands that could result in more memory to be used (most write commands, but DEL and a few more exceptions).
Markjour 注释：内存占用超过限制之后，执行写命令报错，除了 DEL 和其他少数命令。
- allkeys-lru: evict keys by trying to remove the less recently used (LRU) keys first, in order to make space for the new data added.
Markjour 注释：常规的 LRU 策略。
- volatile-lru: evict keys by trying to remove the less recently used (LRU) keys first, but only among keys that have an expire set, in order to make space for the new data added.
Markjour 注释：只对设置了 TTL 的 key 进行 LRU 策略。
- allkeys-random: evict keys randomly in order to make space for the new data added.
Markjour 注释：随机删除。
- volatile-random: evict keys randomly in order to make space for the new data added, but only evict keys with an expire set.
Markjour 注释：只对设置了 TTL 的 key 进行随机删除。
- volatile-ttl: evict keys with an expire set, and try to evict keys with a shorter time to live (TTL) first, in order to make space for the new data added.
Markjour 注释：按照 TTL 升序依次删除。
The policies volatile-lru, volatile-random and volatile-ttl behave like noeviction if there are no keys to evict matching the prerequisites.
Picking the right eviction policy is important depending on the access pattern of your application, however you can reconfigure the policy at runtime while the application is running, and monitor the number of cache misses and hits using the Redis INFO output in order to tune your setup.
In general as a rule of thumb:
- Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests, that is, you expect that a subset of elements will be accessed far more often than the rest. This is a good pick if you are unsure.
- Use the allkeys-random if you have a cyclic access where all the keys are scanned continuously, or when you expect the distribution to be uniform (all elements likely accessed with the same probability).
- Use the volatile-ttl if you want to be able to provide hints to Redis about what are good candidate for expiration by using different TTL values when you create your cache objects.
The volatile-lru and volatile-random policies are mainly useful when you want to use a single instance for both caching and to have a set of persistent keys. However it is usually a better idea to run two Redis instances to solve such a problem.
It is also worth noting that setting an expire to a key costs memory, so using a policy like allkeys-lru is more memory efficient since there is no need to set an expire for the key to be evicted under memory pressure.
Markjour 注释：TTL 的处理需要占用额外的内存，如果是用 allkeys-lru 策略则可以不需要设置 TTL，可以节约内存。
How the eviction process works 清理进程如何工作
It is important to understand that the eviction process works like this:
- A client runs a new command, resulting in more data added.
- Redis checks the memory usage, and if it is greater than the
maxmemory limit , it evicts keys according to the policy.
- A new command is executed, and so forth.
So we continuously cross the boundaries of the memory limit, by going over it, and then by evicting keys to return back under the limits.
If a command results in a lot of memory being used (like a big set intersection stored into a new key) for some time the memory limit can be surpassed by a noticeable amount.
Markjour 注释: 常用的 LRU 过期删除策略：定时，Lazy Expire，LFU，FIFO，RANDOM，TTL。
Approximated LRU algorithm 近似 LRU 算法
Markjour 注释: 出于节约资源（内存）的考虑，Redis LRU 算法并非精确的 LRU 实现。
Redis LRU algorithm is not an exact implementation. This means that Redis is not able to pick the best candidate for eviction, that is, the access that was accessed the most in the past. Instead it will try to run an approximation of the LRU algorithm, by sampling a small number of keys, and evicting the one that is the best (with the oldest access time) among the sampled keys.
However since Redis 3.0 the algorithm was improved to also take a pool of good candidates for eviction. This improved the performance of the algorithm, making it able to approximate more closely the behavior of a real LRU algorithm.
What is important about the Redis LRU algorithm is that you are able to tune the precision of the algorithm by changing the number of samples to check for every eviction. This parameter is controlled by the following configuration directive:
maxmemory-samples 5
The reason why Redis does not use a true LRU implementation is because it costs more memory. However the approximation is virtually equivalent for the application using Redis. The following is a graphical comparison of how the LRU approximation used by Redis compares with true LRU.
The test to generate the above graphs filled a Redis server with a given number of keys. The keys were accessed from the first to the last, so that the first keys are the best candidates for eviction using an LRU algorithm. Later more 50% of keys are added, in order to force half of the old keys to be evicted.
You can see three kind of dots in the graphs, forming three distinct bands.
- The light gray band are objects that were evicted.
- The gray band are objects that were not evicted.
- The green band are objects that were added.
In a theoretical LRU implementation we expect that, among the old keys, the first half will be expired. The Redis LRU algorithm will instead only probabilistically expire the older keys.
As you can see Redis 3.0 does a better job with 5 samples compared to Redis 2.8, however most objects that are among the latest accessed are still retained by Redis 2.8. Using a sample size of 10 in Redis 3.0 the approximation is very close to the theoretical performance of Redis 3.0.
Note that LRU is just a model to predict how likely a given key will be accessed in the future. Moreover, if your data access pattern closely resembles the power law, most of the accesses will be in the set of keys that the LRU approximated algorithm will be able to handle well.
In simulations we found that using a power law access pattern, the difference between true LRU and Redis approximation were minimal or non-existent.
However you can raise the sample size to 10 at the cost of some additional CPU usage in order to closely approximate true LRU, and check if this makes a difference in your cache misses rate.
To experiment in production with different values for the sample size by using the CONFIG SET maxmemory-samples <count> command, is very simple.
Markjour 注释：1. 在 Redis 3.0 中，LRU 算法的精确度提高了。2. 按照默认的 5 个样本就足够了。
The new LFU mode 频率计数模式
Starting with Redis 4.0, a new Least Frequently Used eviction mode is available. This mode may work better (provide a better hits/misses ratio) in certain cases, since using LFU Redis will try to track the frequency of access of items, so that the ones used rarely are evicted while the one used often have an higher chance of remaining in memory.
If you think at LRU, an item that was recently accessed but is actually almost never requested, will not get expired, so the risk is to evict a key that has an higher chance to be requested in the future. LFU does not have this problem, and in general should adapt better to different access patterns.
To configure the LFU mode, the following policies are available:
- volatile-lfu Evict using approximated LFU among the keys with an expire set.
Markjour 注释：对配置了 TTL 的键执行 LFU 清理。
- allkeys-lfu Evict any key using approximated LFU.
Markjour 注释：对所有键执行 LFU 清理。
LFU is approximated like LRU: it uses a probabilistic counter, called a Morris counter in order to estimate the object access frequency using just a few bits per object, combined with a decay period so that the counter is reduced over time: at some point we no longer want to consider keys as frequently accessed, even if they were in the past, so that the algorithm can adapt to a shift in the access pattern.
Markjour 注释：概率计数器（莫里斯计数器）。
近似计数算法允许使用少量内存计算大量事件。1977年由贝尔实验室的罗伯特·莫里斯（密码学家）发明，它使用概率技术来增加计数器。
Those informations are sampled similarly to what happens for LRU (as explained in the previous section of this documentation) in order to select a candidate for eviction.
However unlike LRU, LFU has certain tunable parameters: for instance, how fast should a frequent item lower in rank if it gets no longer accessed? It is also possible to tune the Morris counters range in order to better adapt the algorithm to specific use cases.
By default Redis 4.0 is configured to:
- Saturate the counter at, around, one million requests. Markjour 注释：饱和计数器，一百万请求。
- Decay the counter every one minute. Markjour 注释：每分钟衰减一次。
Markjour 注释: LFU 有可调节参数，上面是 Redis 4.0 的两个默认配置。
Those should be reasonable values and were tested experimental, but the user may want to play with these configuration settings in order to pick optimal values.
Instructions about how to tune these parameters can be found inside the example redis.conf file in the source distribution, but briefly, they are:
lfu-log-factor 10
lfu-decay-time 1
The decay time is the obvious one, it is the amount of minutes a counter should be decayed, when sampled and found to be older than that value. A special value of 0 means: always decay the counter every time is scanned, and is rarely useful.
The counter logarithm factor changes how many hits are needed in order to saturate the frequency counter, which is just in the range 0-255. The higher the factor, the more accesses are needed in order to reach the maximum. The lower the factor, the better is the resolution of the counter for low accesses, according to the following table:
| factor |
100 hits |
1000 hits |
100K hits |
1M hits |
10M hits |
| 0 |
104 |
255 |
255 |
255 |
255 |
| 1 |
18 |
49 |
255 |
255 |
255 |
| 10 |
10 |
18 |
142 |
255 |
255 |
| 100 |
8 |
11 |
49 |
143 |
255 |
So basically the factor is a trade off between better distinguishing items with low accesses VS distinguishing items with high accesses. More informations are available in the example redis.conf file self documenting comments.
Since LFU is a new feature, we'll appreciate any feedback about how it performs in your use case compared to LRU.
总结
TTL (过期时间) 相关命令：
SET key value [EX seconds|PX milliseconds|EXAT timestamp|PXAT milliseconds-timestamp|KEEPTTL] [NX|XX] [GET]
SETEX key seconds value
PSETEX key milliseconds value
GETEX key [EX seconds|PX milliseconds|EXAT timestamp|PXAT milliseconds-timestamp|PERSIST]
TTL key
PTTL key
EXPIRETIME key # 获取过期时间戳，单位为秒
PEXPIRETIME key # 获取过期时间戳，单位为毫秒
EXPIRE key seconds
PEXPIRE key milliseconds
EXPIREAT key timestamp
PEXPIREAT key milliseconds-timestamp
相对于 LRU (Least Recently Used), Redis 采用 LFU (Least Frequently Used) 算法对于其应用场景来说确实是个不错的选择。
一共八种策略：
noeviction 拒绝新值写入，不自动删除volatile-ttl 按 TTL 值删除volatile-lru / allkeys-lru LRUvolatile-lfu / allkeys-lfu LFUvolatile-random / allkeys-random 随机删除
evict [ɪˈvɪkt] vt. 驱逐；逐出
eviction [ɪˈvɪkʃn] n. 逐出；赶出；收回
volatile [ˈvɒlətaɪl]
adj. [化学] 挥发性的；不稳定的；爆炸性的；反复无常的
n. 挥发物；有翅的动物
n. (Volatile)人名；(意)沃拉蒂莱
Golang Redis
2021-06-05
package main
import (
"log"
"time"
"github.com/go-redis/redis"
)
func main() {
client := redis.NewClient(&redis.Options{
Addr: "localhost:6379",
Password: "",
DB: 0,
})
var err error
// 使用 Get/Set/Del =====================================
err = client.Set("key", "value", 0).Err()
if err != nil {
panic(err)
}
val, err := client.Get("key").Result()
if err != nil {
panic(err)
}
log.Println("key", val)
err = client.Del("key").Err()
if err != nil {
panic(err)
}
// 使用 Pipeline ========================================
pipeline := client.Pipeline()
pipeline.Set("key1", "value1", time.Minute*5)
pipeline.Set("key2", "value2", time.Minute*5)
pipeline.Set("key3", "value3", time.Minute*5)
_, err = pipeline.Exec()
if err != nil {
panic(err)
}
pipeline.Get("key1")
pipeline.Get("key2")
pipeline.Get("key3")
vals, err := pipeline.Exec()
if err != nil {
panic(err)
}
val1, _ := vals[0].(*redis.StringCmd).Result()
val2, _ := vals[1].(*redis.StringCmd).Result()
val3, _ := vals[2].(*redis.StringCmd).Result()
log.Println("key1", val1)
log.Println("key2", val2)
log.Println("key3", val3)
pipeline.Del("key1", "key2", "key3")
_, err = pipeline.Exec()
if err != nil {
panic(err)
}
}
package main
import (
"log"
"github.com/go-redis/redis"
)
func main() {
client := redis.NewClient(&redis.Options{
Addr: "localhost:6379",
Password: "",
DB: 0,
})
log.Println(client)
var err error
var items []string
// List ===============================================
{
err = client.RPush("mylist", "item1", "item2", "item3").Err()
if err != nil {
panic(err)
}
listLen, err := client.LLen("mylist").Result()
if err != nil {
panic(err)
}
log.Println("Length of mylist:", listLen)
items, err = client.LRange("mylist", 0, -1).Result()
if err != nil {
panic(err)
}
log.Println("===== Items in mylist:", items)
}
// Hash ===============================================
{
err = client.HSet("myhash", "field1", "value1").Err()
if err != nil {
panic(err)
}
value1, err := client.HGet("myhash", "field1").Result()
if err != nil {
panic(err)
}
log.Println("Value of field1:", value1)
allFields, err := client.HGetAll("myhash").Result()
if err != nil {
panic(err)
}
log.Println("===== All fields in myhash:", allFields)
}
// Set ================================================
{
err = client.SAdd("myset", "item1", "item2", "item3").Err()
if err != nil {
panic(err)
}
setLen, err := client.SCard("myset").Result()
if err != nil {
panic(err)
}
log.Println("Length of myset:", setLen)
items, err = client.SMembers("myset").Result()
if err != nil {
panic(err)
}
log.Println("===== Items in myset:", items)
}
// ZSet ===============================================
{
err = client.ZAdd("myzset", redis.Z{Score: 1.0, Member: "one"}, redis.Z{Score: 2.0, Member: "two"}).Err()
if err != nil {
panic(err)
}
setLen, err := client.ZCard("myzset").Result()
if err != nil {
panic(err)
}
log.Println("Length of myzset:", setLen)
items, err = client.ZRange("myzset", 0, -1).Result()
if err != nil {
panic(err)
}
log.Println("===== Items in myzset:", items)
}
}
哨兵
这样理论上来说，肯定会有性能损耗，毕竟增加了和哨兵的通信。
具体能差多少，还得实验。
package main
import (
"fmt"
"github.com/go-redis/redis/v8"
)
func main() {
failoverClient := redis.NewFailoverClient(&redis.FailoverOptions{
SentinelAddrs: []string{"sentinel1:26379", "sentinel2:26379", "sentinel3:26379"},
MasterName: "mymaster",
})
pong, err := failoverClient.Ping().Result()
if err != nil {
panic(err)
}
fmt.Println(pong)
}
集群
package main
import (
"fmt"
"github.com/go-redis/redis/v8"
)
func main() {
clusterClient := redis.NewClusterClient(&redis.ClusterOptions{
Addrs: []string{"redis1:6379", "redis2:6379", "redis3:6379"},
})
pong, err := clusterClient.Ping().Result()
if err != nil {
panic(err)
}
fmt.Println(pong)
}
