redis文档翻译_LRU缓存

Using Redis as an LRU cache使用Redis作为LRU缓存

出处：http://blog.csdn.net/column/details/redisbanli.html

When Redis is used as a cache, sometimes it is handy to let it automatically evict old data as you
add new one. This behavior is very well known in the community of developers, since it is the default behavior of the popular memcached system.

当使用Redis作为缓存时，有时候为了向缓存加入新数据，要让Redis自己主动删除旧数据。在社区，众所周知的 memcached 
系统就有这样的缺省的做法。

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.

LRU其实是Redis唯一支持逐出的方法。

此页包括Redis的maxmemory指令，用于限制Redis使用最大物理内存的大小，也包括Redis深度的LRU算法。实际是近似的LRU算法。

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 SETcommand at runtime.

maxmemory 指令是用作配置指定Redis使用内存的最大值。

能够在redis.conf文件配置，执行过程中还能够使用CONFIG
SET 命令设置。

For example in order to configure a memory limit of 100 megabytes, the following directive can be used inside the  redis.conf file.

比如。为了配置限制内存为100M，能够在redis.conf文件里配置：

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.

设置 maxmemory 
为0 的结果是没有内存限制。这是64位系统的缺省做法，32位系统是限制3G。

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.

当指定内存数量达到最大限制时。Redis能够选择不同的做法，称为
 policies（政策）。

 每当新的数据被加入时，能够仅仅返回一个错误，或者能够逐出一些旧数据让新数据能够被加入。

Eviction policies 逐出政策

The exact behavior Redis follows when the maxmemory limit is reached is configured using the maxmemory-policy configuration
directive.

当Redis内存到达上限时，Redis使用
 maxmemory-policy  指令配置的政策运行。

The
following policies are available:    以下是policies能够配置的 变量

 
  

• 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).
• noeviction：当达到最大内存使用限制而且client尝试运行使用很多其它内存的命令时。将返回一个错误。
• allkeys-lru: evict keys trying to remove the less recently used (LRU) keys first, in order to make space for the new data added.
• allkeys-lru：为了新加入的数据使用内存。将逐出近期最少使用的key。

• volatile-lru: evict keys trying to remove the less recently used (LRU) keys first, but only among keys that have anexpire set, in
  order to make space for the new data added.
• volatile-lru: 为了新加入的数据使用内存，将逐出有过期时间设置而且近期最少使用的key。

• allkeys-random: evict random keys in order to make space for the new data added.
• allkeys-random:： 为了新加入的数据使用内存。将随机逐出key。
• volatile-random: evict random keys in order to make space for the new data added, but only evict keys with anexpire set.
• volatile-random: 为了新加入的数据使用内存，将随机逐出具有过期时间设置的key。
• volatile-ttl: In order to make space for the new data, evict only keys with an expire set, and try to evict keys with a shorter
  time to live (TTL) first.
• 为了新加入的数据使用内存，将逐出有过期时间限制。而且存活时间（TTL）最短的key。

The
policies volatile-lru, volatile-random and volatile-ttl behave
like noeviction if there are no keys to evict matching the prerequisites.

 volatile-lru, volatile-random 和volatile-ttl 在没有匹配key的时候和noeviction是一样的。

To
pick 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.

依赖于你的应用去选择正确的逐出政策是很重要的。然而在Redis执行时，你也能够又一次配置逐出政策，而且你能够使用INFO命令输出内存使用情况然后又一次配置。

In
general as a rule of thumb: 一般的配置规则

• 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.

         使用allkeys-lru，当你希望你的请求是一个 power-law 分布，如此，你期望常驻内存的元素是你可被訪问元素的子集。假设你不确定的话。使用allkeys-lru是好的选择。

• 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).

        假设全部key被周期地訪问或者的当逐出分布不统一时（全部元素被訪问的概率一样），使用allkeys-random。

• 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.

 在创建对象缓存的时候假设你想通过使用TTL的值提供好的逐出候选者，使用volatile-ttl 。

The allkeys-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.

当你使用一个实例作为缓存而且key都是永久性的时候。使用allkeys-lru和 volatile-random。

可是还有更好的做法，能够使用两个Redis实例解决内存不足问题。

It
is also worth to note 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.

另外注意，设置过期时间是消耗内存的，因此使用像allkeys-lru
 的政策比較有内存效率。由于它不须要去为过期的key的逐出添加额外的内存压力。

How the eviction process works  逐出是这样工作的

It is important to understand that the eviction process works like this:

理解逐出工作方式，就像这样：

• A client runs a new command, resulting in more data added.

        一个client执行一个新的命令。导致很多其它数据被加入。

• Redis checks the memory usage, and if it is greater than the maxmemory limit , it evicts keys according to the policy.

    Redis检測内存使用，怎样超过  maxmemory 
限制。就依据政策逐出key。

• A new command is executed, and so forth.

    新的命令被运行。

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.

因此我们持续加入数据达到内存限制，通过上面的步骤，逐出一些key使得内存回到限制之下。

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.

假设命令的结果是导致非常大的内存被使用（比方一个新的key是一个大的set集合）。非常多时候达到内存限制是显而易见的。

Approximated LRU algorithm  近似的LRU算法

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.

Redis的LRU算法不是准确的实现。也就是说Redis没有为逐出选择
最好的候选人 ，也就是没有选择过去最后被訪问离如今最久的。反而 是去运行一个 近似LRU的算法，通过抽样少量的key，而且逐出抽样中最后被訪问离如今最久的key（最老的訪问时间）。

However
since Redis 3.0 (that is currently in beta) 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.

在Redis 3.0（眼下的測试版），算法被改进了。使用了一个逐出最佳候选池。

改进了算法的性能。使它更加近似真正LRU算法。

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:

算法中，关于逐出检測的样品数量。你能够自己去调整。配置參数是：

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.

Redis没有使用真正实现LRU算是的原因是，由于消耗很多其它的内存。然而对于使用Redis的应用来说，其实是等价的。以下是Redis的LRU算法和真正LRU算法的比較：

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.

给出配置数量的key生成上面的图表。

key从第一行到最后一行被訪问，那么第一个key是LUR算法中最好的逐出候选者。

之后有50%的key被加入，那么一半的旧key被逐出。

You can see three kind of dots in the graphs, forming three distinct bands.

在上图中你能够看见3个明显的差别：

• The light gray band are objects that were evicted.

            浅灰色带是被逐出的对象。

• The gray band are objects that were not evicted.

        灰色带是没有被逐出的对象。

• The green band are objects that were added.

        绿色带是被加入的对象。

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.

LRU理论实现是在全部的旧key中前一半被逐出。Redis使用的是近似过期的key被逐出。

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.

如你所见，3.0的工作比2.8更好。然而在2.8版本号中，大多数最新訪问对象的仍然保留。在3.0使用样品为10 时，性能很接近理论上的LRU算法。

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.

注意：LRU不过一个预測模式。给出的key非常可能在未来被訪问。此外，假设你的数据訪问模式类似于幂律（线性的）。大多数key都可能被訪问那么这个LRU算法的处理就是非常好的。

In simulations we found that using a power law access pattern, the difference between true LRU and Redis approximation were minimal or non-existent.

在实战中 。我们发现使用幂律（线性的）的訪问模式，在真正的LRU算法和Redis的LRU算法之间差异非常小或者不存在差异。

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.

你能够提升样品大小配置到10。它将接近真正的LRU算法，而且有不同错过率。可是要消耗很多其它的CPU。

To experiment in production with different values for the sample size by using the CONFIG
SET maxmemory-samples <count> command, is very simple.

在调试时使用不同的样品大小去调试很easy，使用命令CONFIG SET maxmemory-samples <count>  实现。

出处：http://blog.csdn.net/column/details/redisbanli.html