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This article mainly introduces the relevant knowledge of what the concept of Redis is, the content is detailed and easy to understand, the operation is simple and fast, and has a certain reference value. I believe you will gain something after reading this Redis concept article. Let's take a look at it.

1. What is Redis?

The result of this problem affects how we use Redis. If you think Redis is a key value store, you may use it instead of MySQL;. If you think it is a persistent cache, it may just hold some temporary data that is accessed frequently. Redis is an acronym for REmote DIctionary Server, and the subtitle of Redis on the official website is A persistent key-value database with built-in net interface written in ANSI-C for Posix systems. This definition is biased towards key value store. Others think that Redis is a memory database because its high performance is based on memory operations. Others think that Redis is a data structure server because Redis supports complex data features such as List, Set, and so on. Different interpretations of the role of Redis determine how you use Redis.

At present, Internet data is basically stored in two ways, relational database or key value. But these Internet services themselves do not belong to these two data types, such as the relationship of users in the social platform, which is a list, which needs to be converted into a form of multi-line records if it is to be stored in a relational database, which has a lot of redundant data, and each row needs to store some duplicate information. If you use key value storage, it is troublesome to modify and delete, and you need to read out and write all the data. Redis designs various data types in memory to enable businesses to access these data structures at a high speed, and does not need to care about persistent storage, which architecturally solves the problem that the first two kinds of storage need to take some detours.

2. Redis can't be faster than Memcache

Many developers believe that Redis cannot be faster than Memcached, Memcached is based entirely on memory, and Redis has persistent storage features, and even if it is asynchronous, Redis cannot be faster than Memcached. But the test result is that Redis is absolutely dominant. I have been thinking about this reason, and there are several reasons that come to mind at present.

Libevent . Unlike Memcached, Redis did not choose libevent. In order to cater to versatility, Libevent results in huge code (currently the Redis code is less than 1 / 3 of libevent) and sacrifices a lot of performance on a particular platform. Redis implements its own epoll event loop (4) with two file modifications in libevent. Many developers in the industry also suggest that Redis use another high-performance libevent to replace libev, but the author still insists on the idea that Redis should be small and dependent. One impressive detail is that you don't need to execute. / configure before compiling Redis.

The problem follows the CAS. CAS is a convenient method in Memcached to prevent competition to modify resources. The implementation of CAS needs to set a hidden cas token,cas equivalent to value version number for each cache key, and each set needs to be incremented by token, thus bringing double overhead of CPU and memory. Although these costs are very small, these costs will bring some slight performance differences between the two sides after 10G + cache and tens of thousands of QPS (5).

3. The storage data of a single Redis must be smaller than the physical memory.

Keeping all Redis data in memory brings high-speed performance, but it also brings some irrationality. For example, if a medium-sized website has 1 million registered users, if these materials are to be stored in Redis, the memory must be able to accommodate these 1 million users. However, the actual situation of the business is that 1 million users have only 50, 000 active users, and only 150000 users have visited it once a week, so it is unreasonable for all 1 million users' data to be included, and RAM needs to pay for cold data.

This is very similar to the operating system, where all applications access data in memory, but if there is no room for new data in physical memory, the operating system will intelligently swap some of the data that has not been accessed for a long time to disk, making room for new applications. What modern operating systems provide to applications is not physical memory, but the concept of virtual memory (Virtual Memory).

Based on the same considerations, Redis 2.0 also adds the VM feature. Let the Redis data capacity break through the limit of physical memory. And the cold and hot separation of data is realized.

4. The VM implementation of Redis is to repeat the wheel.

Redis's VM is still implemented on its own according to the previous epoll implementation idea. But in the previous introduction to the operating system mentioned that OS can also automatically help programs to achieve hot and cold data separation, Redis only needs OS to apply for a large piece of memory, OS will automatically put hot data into physical memory, cold data will be exchanged to the hard disk, another well-known "understanding of the modern operating system (3)" Varnish is implemented in this way, has also achieved very successful results.

The author antirez mentions several reasons (6) in explaining why he should implement VM himself. The VM swap and output of the main OS is based on the concept of Page. For example, OS VM1 Page is 4K. As long as there is one element in 4K, even if only 1 byte is accessed, the page will not be SWAP. By the same token, reading a byte may swap into 4K useless memory. On the other hand, the implementation of Redis can achieve the granularity of controlling swapping. In addition, the block process when accessing the memory area of the operating system SWAP is also one of the reasons why Redis has to implement VM on its own.

5. Using Redis in get/set way

As a key value, many developers naturally use set/get to use Redis, which is not the optimal way to use it. Especially when VM is not enabled, all data of Redis needs to be put into memory, so it is especially important to save memory.

If a key-value unit needs to occupy at least 512 bytes, even if only one byte is saved, it takes up 512 bytes. At this point, there is a design pattern that can reuse key, put several key-value into a key, and then store the value as a set, so that the same 512 bytes will store 10-100 times the capacity.

This is to save memory, it is recommended to use hashset instead of set/get to use Redis.

6. Use aof instead of snapshot

There are two storage methods for Redis. The default is snapshot. The implementation method is to persist the snapshot (snapshot) of memory to the hard disk on a regular basis. The disadvantage of this method is that if crash occurs after persistence, a piece of data will be lost. Therefore, under the promotion of perfectionists, the author adds the aof method. Aof, that is, append only mode, saves operation commands to log files while writing in-memory data. In a system with tens of thousands of concurrent changes, the command log is a very large amount of data, the cost of management and maintenance is very high, and the recovery and reconstruction time will be very long, which leads to the loss of the original intention of aof high availability. What is more important is that Redis is an in-memory data structure model, and all the advantages are based on efficient atomic operations on complex data structures in memory, so it can be seen that aof is a very uncoordinated part.

In fact, the main purpose of aof is data reliability and high availability, and there is another way to achieve it in Redis: Replication. Due to the high performance of Redis, there is basically no latency in replication. In this way, a single point of failure is prevented and high availability is achieved.

This is the end of the article on "what is the concept of Redis?" Thank you for reading! I believe you all have a certain understanding of "what is the concept of Redis". If you want to learn more, you are welcome to follow the industry information channel.

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