Cassandra-Like Distributed Database
Source: Dev.to
Consistent Hashing and Distributed Partitioning
The system uses consistent hashing for key distribution. Each node maps to a position on a ring via SHA‑1, forming a ring of multiple nodes where each node is an instance of Cassandra. By hashing node IDs with SHA‑1, rebalancing is minimized: only keys near the affected node need to move.
Replication and Fault Tolerance
When the primary node is down, writes are forwarded to the first alive replica, which becomes the coordinator. Dead replicas are skipped during replication. Upon recovery, a node requests missing state from its peers. In the CAP theorem trade‑off, the system chooses availability and partition tolerance, providing eventual consistency.
Gossip Protocol for Membership Management
The Gossip Protocol eliminates a central coordinator. Nodes detect failures through periodic information exchange (default interval: 1000 ms). Each node selects a random peer, exchanges membership state, and marks a peer as “down” if no updates are received. This decentralized, scalable, and fault‑tolerant approach ensures that eventually all nodes share the same membership view.
Crash Recovery
Details on crash recovery mechanisms can be added here.
Conflict Resolution
Details on conflict resolution strategies can be added here.
Tests and Visualizations
A suite of tests covers partition tolerance, replication, and the gossip protocol. The test code and visualizations are available on my GitHub repository.
References
- Cassandra Documentation 2025
- DataStax Developers 2021