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This is a theoretical document about the Reids interview. It takes about 3 minutes to read it. It is strongly recommended that you bookmark it for emergencies!
Let me first declare here that our so-called fast Redis is actually faster than traditional relational databases, for example, it is faster than MySQL. As for why it is fast, there must be some reasons.
Reason 1: Reids is an in-memory database
Redis is a memory-based database, and its data is stored in memory, which can achieve very fast read and write speeds. Compared with traditional disk databases, in-memory databases can greatly improve data read and write performance, making Redis faster.
Reason 2: Redis is single-threaded
The Redis process is single-threaded, which means that Redis does not need to consider issues such as thread synchronization and lock competition, avoiding the additional overhead and complexity caused by multi-threading. Therefore, Redis can better utilize CPU and memory resources, thereby achieving higher performance.
Reason Three: Efficient Data Structures
Redis provides a variety of efficient data structures, such as strings, hash tables, lists, sets, ordered sets, etc. These data structures can not only meet various application scenarios, but also their implementations are highly optimized to achieve extremely fast read and write speeds.
On this point, I need to say more in detail.
1) Integer encoding
For integer type data stored in Redis, Redis will choose different encoding methods according to the size of the integer. In Redis, integers can be represented by 8 bits, 16 bits or 32 bits. According to the size of the data, Redis will choose the most memory-saving encoding method to store the data. In this way, the memory usage can be significantly reduced, and the data access speed can be improved.
2) Compress the list
For list type data stored in Redis, Redis uses compressed list storage. A compressed list is a compact, efficient data structure that minimizes storage space and enables fast access to data. In Redis, compressed lists are widely used to store various types of data, including lists, hash tables, sets, etc.
3) Hash table
For hash table type data stored in Redis, Redis uses a hash table data structure for storage. A hash table is an efficient data structure that can quickly perform operations such as data insertion, deletion, and lookup.
4) Jump table
For the ordered collection type data stored in Redis, Redis uses the jump table data structure to store. A jump table is an efficient ordered data structure that can quickly perform operations such as data insertion, deletion, and lookup.
Reason 4: Asynchronous I/O
Redis uses an asynchronous I/O model, which means it is able to handle multiple concurrent requests at the same time without blocking and waiting. This allows Redis to respond to client requests faster, thereby improving the throughput of the system.
To sum up, the reason why Redis is so fast is that it uses various optimization strategies such as memory database, single thread, efficient data structure and asynchronous I/O. These optimizations enable Redis to quickly process a large number of read and write requests. , thus becoming a very popular high-performance key-value database.