Why does redis single thread execute so fast?

The reason why Redis executes very quickly mainly depends on the following reasons:

(1) Pure memory operation, avoid a large number of database accesses, and reduce direct reads Disk data, redis stores data in memory. When reading and writing data, it is not limited by the hard disk I/O speed, so the speed is fast;

(2) Single-thread operation avoids unnecessary There are no context switching and race conditions, and there is no CPU consumption due to switching caused by multi-process or multi-threading. There is no need to consider various lock issues. There is no locking and releasing lock operations, and there is no performance consumption caused by possible deadlocks;

(3) Non-blocking I/O multiplexing mechanism is adopted

Multiplexing principle:

Users first Add the socket that requires IO operations to the select, and then block and wait for the select system call to return. When data arrives, the socket is activated and the select function returns. The user thread formally initiates a read request, reads the data and continues execution. In this way, users can register multiple sockets, and then continuously call select to read the activated sockets. The redis server places these sockets in the queue, and then the file event dispatcher goes to the queue in turn and forwards them to different event processing in the processor to improve reading efficiency.

The use of multi-channel I/O multiplexing technology allows a single thread to efficiently handle multiple connection requests (minimizing the time consumption of network IO). The multi-channel I/O multiplexing model uses select, poll, epoll has the ability to monitor I/O events of multiple streams at the same time. When it is idle, it will block the current thread. When one or more streams have I/O events, it will wake up from the blocked state, so the program All streams will be polled (epoll only polls those streams that actually emit events), and only ready streams will be processed sequentially. This approach avoids a large number of useless operations, thus improving efficiency.

(4) Flexible and diverse data structures.

Redis uses a redisObject object internally to represent all keys and values. The main information of redisObject includes data type, encoding method, data pointer, virtual memory, etc. It contains five data types: String, Hash, List, Set, and Sorted Set. Corresponding data types are used for different scenarios, which not only reduces memory usage, but also saves network traffic transmission.

(5) Persistence

Since redis data is stored in memory, if persistence is not configured, all data will be lost after redis restarts, so the persistence function of redis needs to be enabled. , save the data to the disk, and when redis is restarted, the data can be restored from the disk. Redis provides two methods for persistence, one is RDB persistence (the principle is to periodically dump the database records of redis in memory to RDB persistence on disk), and the other is AOF (append only file) persistence ( The principle is to write the redis operation log to the file in an appending manner). Persistence does not seem to be directly related to the speed of redis, but it ensures the security and reliability of redis data and also plays a role in data backup.

(6) Summary

Just imagine whether a single thread cannot exert the performance of multi-core CPU. In fact, it is not the case. We can improve it by opening multiple redis instances on a single machine. A single thread can only use one CPU core, so multiple instances can be started in the same multi-core server to form a master-master or master-slave. Time-consuming read commands can be completely performed on the slave, giving full play to redis role.

Single thread refers to the network request module using one thread (so there is no need to consider concurrency security). Other modules will also use multiple threads. When using redis, give full play to its advantages and avoid some Improper operation may lead to performance degradation.

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