Redis uses a replication model where data is copied from a master node to any number of slave nodes. This allows for high read scalability and data redundancy.
The replication process in Redis is asynchronous, meaning that the master will continue to handle requests while sending updates to its replicas. This behavior leads to eventual consistency wherein all replicas will eventually reflect the changes made on the master given enough time.
However, this also means that there can be temporary inconsistencies between the master and the slave nodes. For instance, if a client writes data to the master and immediately reads the same data from a replica, it might not see the updated data because the replica may not yet have received the update from the master.
To ensure strong consistency, Redis offers a couple of options:
In this code, we wait for at least one replica to acknowledge the receipt of the write operation within 1000 milliseconds.
In this example, both the write and read operations are performed on the master, ensuring that you always get the most recent data.
Keep in mind that while these methods can help ensure strong consistency, they come at the cost of increased latency and reduced throughput. Therefore, it's crucial to balance between consistency requirements and performance based on your application's needs.