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@@ -12,37 +12,61 @@ The implementation of tf-raft is based on the ["CONSENSUS: BRIDGING THEORY AND P
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In short, Raft achieves consensus through a leader-follower model, where one node serves as the leader and others as followers. The leader is responsible for coordinating the consensus process, and all updates go through the leader to ensure consistency.
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tf-raft implements the three core components of the RAFT Consensus Protocol:
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1.**Leader Election:** The process by which a leader is chosen among the nodes.
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2.**Log Replication:** Ensuring that the logs across nodes are consistent through replication.
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3.**Cluster Membership Changes:** Handling dynamic changes in the cluster, such as adding or removing nodes.
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The core of tf-raft is fully isolated and independent from the infrastructure, relying on ports and adapters for high flexibility.
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tf-raft currently supports `gRPC` and `In-Memory` adapters for the network layer & `In-Memory` and `JSON-Based` adapters for volatile and non-volatile states, respectively.
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tf-raft currently supports `gRPC` and `In-Memory` adapters for the network layer & `In-Memory` and `JSON-Based` adapters for volatile and non-volatile states, respectively.
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## Commands and Usage
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## Store Commands
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Below are the commands supported by tf-raft, along with their descriptions and example usage:
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