MySQL data storage is stored in tables. When looking for data, you must first perform a global scan of the table or search based on the index. This involves a disk search. If the disk search is Searching by points may be faster, but searching sequentially is slower; Redis does not have to be so troublesome. It is stored in the memory and will be retrieved directly according to the location of the data in the memory. (Recommended learning: Redis video tutorial)
Redis is based on memory storage, MySQL is based on disk storage
Redis stores data in k-v format . The time complexity is O(1), constant order, while the underlying implementation of the MySQL engine is B Tree, and the time complexity is O(logn), logarithmic order. Redis will be slightly faster than MySQL.
Redis is a single-threaded multiplexed IO. Single-threaded avoids the overhead of thread switching, while multiplexed IO avoids the overhead of IO waiting, improving processor usage efficiency under multi-core processors. Data can be partitioned and then each processor processes different data.
First of all, we know that mysql is a persistent storage, stored in the disk, and retrieval will involve a certain amount of IO. In order to solve this bottleneck, so Cache appeared, such as memcached (referred to as mc) which is the most used nowadays.
First, the user accesses mc. If it misses, access mysql. Then, like the memory and hard disk, the data is copied to the mc part.
Redis and mc are both caches, and both run in memory, which greatly improves the access speed of web access with high data volume. However, mc only provides simple data structures.
For example, string storage; redis provides a large number of data structures, such as string, list, set, hashset, sorted set, etc., which makes it a lot more convenient for users. After all, it encapsulates a layer of practical functions and simultaneously implements To achieve the same effect, of course use redis and slowly abandon mc.
The relationship between memory and hard disk. The hard disk places the main data for persistent storage, while the memory is the part of the data currently running. The CPU accesses the memory instead of the disk, which greatly improves the running speed. Of course, this It is based on the principle of localized access of the program.
Reasoning to redis mysql, it is a mapping of the memory disk relationship. Mysql is placed on the disk and redis is placed in the memory. In this case, the web application only accesses redis every time. If no data is found, it will be accessed. Mysql.
However, the usage of redis mysql and memory disk is preferably different.
For more Redis-related technical articles, please visit the Introduction to Using Redis Database Tutorial column to learn!
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Redis's database methods include in-memory databases and key-value storage. 1) Redis stores data in memory, and reads and writes fast. 2) It uses key-value pairs to store data, supports complex data structures such as lists, collections, hash tables and ordered collections, suitable for caches and NoSQL databases.

Redis is a powerful database solution because it provides fast performance, rich data structures, high availability and scalability, persistence capabilities, and a wide range of ecosystem support. 1) Extremely fast performance: Redis's data is stored in memory and has extremely fast read and write speeds, suitable for high concurrency and low latency applications. 2) Rich data structure: supports multiple data types, such as lists, collections, etc., which are suitable for a variety of scenarios. 3) High availability and scalability: supports master-slave replication and cluster mode to achieve high availability and horizontal scalability. 4) Persistence and data security: Data persistence is achieved through RDB and AOF to ensure data integrity and reliability. 5) Wide ecosystem and community support: with a huge ecosystem and active community,

Key features of Redis include speed, flexibility and rich data structure support. 1) Speed: Redis is an in-memory database, and read and write operations are almost instantaneous, suitable for cache and session management. 2) Flexibility: Supports multiple data structures, such as strings, lists, collections, etc., which are suitable for complex data processing. 3) Data structure support: provides strings, lists, collections, hash tables, etc., which are suitable for different business needs.

The core function of Redis is a high-performance in-memory data storage and processing system. 1) High-speed data access: Redis stores data in memory and provides microsecond-level read and write speed. 2) Rich data structure: supports strings, lists, collections, etc., and adapts to a variety of application scenarios. 3) Persistence: Persist data to disk through RDB and AOF. 4) Publish subscription: Can be used in message queues or real-time communication systems.

Redis supports a variety of data structures, including: 1. String, suitable for storing single-value data; 2. List, suitable for queues and stacks; 3. Set, used for storing non-duplicate data; 4. Ordered Set, suitable for ranking lists and priority queues; 5. Hash table, suitable for storing object or structured data.

Redis counter is a mechanism that uses Redis key-value pair storage to implement counting operations, including the following steps: creating counter keys, increasing counts, decreasing counts, resetting counts, and obtaining counts. The advantages of Redis counters include fast speed, high concurrency, durability and simplicity and ease of use. It can be used in scenarios such as user access counting, real-time metric tracking, game scores and rankings, and order processing counting.

Use the Redis command line tool (redis-cli) to manage and operate Redis through the following steps: Connect to the server, specify the address and port. Send commands to the server using the command name and parameters. Use the HELP command to view help information for a specific command. Use the QUIT command to exit the command line tool.

Redis cluster mode deploys Redis instances to multiple servers through sharding, improving scalability and availability. The construction steps are as follows: Create odd Redis instances with different ports; Create 3 sentinel instances, monitor Redis instances and failover; configure sentinel configuration files, add monitoring Redis instance information and failover settings; configure Redis instance configuration files, enable cluster mode and specify the cluster information file path; create nodes.conf file, containing information of each Redis instance; start the cluster, execute the create command to create a cluster and specify the number of replicas; log in to the cluster to execute the CLUSTER INFO command to verify the cluster status; make


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