How to achieve high concurrency with redis

Redis achieves high concurrency through the following mechanisms: single-threaded event loop, I/O multiplexing, lock-free data structure, lazy deletion, pipelined, client connection pooling, and scalable to cluster mode.

How does Redis achieve high concurrency?

Redis is a popular in-memory database that can handle high concurrent access. It is implemented through the following mechanism:

1. Single-threaded event loop

Redis adopts a single thread architecture, which means it has only one main thread to handle all requests. This architecture simplifies concurrency problems and avoids the overhead of data competition and context switching between multiple threads.

2. I/O multiplexing

Redis is based on the I/O multiplexing model, which allows a single thread to listen to multiple sockets and pipelines simultaneously and handle incoming events efficiently. It uses system calls such as epoll or kqueue to monitor a large number of connections, thereby responding to client requests quickly.

3. Lockless data structure

Redis uses lock-free data structures such as Atomic Reference Counts (RC) and Copy-On-Write (COW) to manage its data. These data structures do not require mutex locks, eliminating the risks of lock contention and deadlock and improving concurrency performance.

4. Lazy deletion

Redis adopts a lazy deletion strategy, that is, when a key is deleted from the database, the key is not immediately removed from memory. Instead, it is marked as deleted and is only truly recycled from memory when space is needed. This avoids frequent memory allocation and release in high concurrency scenarios and improves performance.

5. Pipeline

Redis supports request pipeline, allowing clients to package multiple requests into one request to send to the server. The server will process all requests at the same time, reducing network overhead and server-side processing time.

6. Client connection pool

To avoid frequent client connections and disconnections, Redis provides client connection pooling. Clients can cache connections and reuse them, thereby reducing the overhead of connection establishment and improving concurrency.

7. Cluster

For extremely high concurrency requirements, Redis can be scaled using cluster mode. The cluster connects multiple Redis instances and shards the requests using a consistent hash. This can distribute the load to multiple servers and improve overall concurrent processing capabilities.

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