How to implement multi-threading with redis

Redis implements multi-threading by cleverly combining Reactor mode, thread pool and internal multi-threading mechanisms, thereby effectively utilizing multi-core CPUs, improving throughput, optimizing resource utilization, maintaining low latency and enhancing scalability, and meeting different load needs.

Redis multi-threaded implementation

Redis is a high-performance key-value database that uses a single-threaded model to handle requests. However, to take advantage of the multi-core CPU, Redis cleverly implements multi-threading in its internal data structures and operations.

Reactor mode

Redis uses Reactor mode to handle network requests. Reactor mode delegates all network input and output operations to a single thread (called event loop) that continuously listens to network events. When a network event occurs, the event loop distributes events to the appropriate thread pool (called event handlers) where they handle the actual request.

Thread pool

Redis uses thread pools to handle time-consuming tasks such as persistence and replication. A thread pool contains a certain number of threads that are assigned to process incoming tasks. By using thread pools, Redis avoids the overhead of creating large threads and ensures that tasks are executed in an efficient way.

Internal multithreading

In addition to Reactor mode and thread pool, Redis uses internal multithreading to improve the performance of specific operations. For example:

• Rehash operation of hash table: When a hash is expressed to a certain threshold, Redis will use multiple threads to rehash data into a larger hash table in parallel.
• RDB persistence: Redis uses a separate thread to persist data into an RDB file while continuing to process requests.
• AOF rewrite: Redis rewrites the AOF file using a separate thread to avoid rewrite operations blocking the server.

Advantages

Using multithreading provides Redis with the following advantages:

• Improve throughput: Reactor mode and thread pool can handle multiple requests simultaneously, thereby increasing throughput.
• Optimized resource utilization: Internal multithreading can optimize resource utilization, such as by parallelizing time-consuming operations.
• Keep Low Latency: Using a single-threaded model to handle requests ensures low latency, even under high loads.
• Scalability: Redis can be scaled to meet different load requirements by resizing thread pools and configuring.

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