In distributed database systems (DDBMS), reliability refers to the ability of the system to function
correctly and consistently, even in the presence of failures or unexpected events. It ensures that the
system can continue to provide accurate, consistent, and available data under various conditions, such as
network failures, hardware crashes, and data corruption.
Measures to Ensure Reliability
1. Data Replication:
o Replication: Copies of data are stored on multiple nodes to ensure that even if one node
fails, the data is still accessible from other nodes.
o Consistency Models: Implementing consistency models like eventual consistency or
strong consistency to manage how and when data updates are propagated across
replicas.
2. Fault Tolerance:
o Redundancy: Building redundancy into the system’s components (e.g., multiple servers,
storage devices) to ensure that a failure in one component does not lead to a system
wide failure.
o Quorum-Based Protocols: Requiring a majority of nodes to agree on a transaction
before it is committed, ensuring that the system can tolerate failures of some nodes
without losing consistency.
3. Consensus Algorithms:
o Paxos and Raft: Algorithms designed to achieve consensus among distributed nodes,
ensuring that all nodes agree on the system’s state even in the presence of failures.
o Leader Election: Mechanisms for electing a leader node to coordinate actions and
maintain system consistency.
Protocols to Ensure Reliability
4. Two-Phase Commit (2PC):
o Preparation Phase: All participating nodes prepare to commit the transaction and vote
on whether to proceed.
o Commit Phase: Based on the votes, the transaction is either committed or aborted,
ensuring atomicity across the distributed system.
5. Three-Phase Commit (3PC):
o Preparation Phase: Similar to 2PC, but adds an additional step to ensure that all nodes
are in sync before committing.
o Pre-Commit Phase: Nodes confirm readiness, providing an extra layer of safety.
o Commit Phase: The transaction is committed if all nodes agree.
Failure Recovery Strategies
1. Checkpoints and Logging:
o Periodic Checkpoints: Regularly saving the system’s state to stable storage, allowing the
system to recover from failures by rolling back to the last checkpoint.
o Write-Ahead Logging (WAL): Recording changes in a log before applying them to the
database, ensuring that the system can recover by replaying or undoing logged
operations.
2. Rollback and Rollforward:
o Rollback: Reverting the system to a previous consistent state in case of a failure.
o Rollforward: Applying logged changes to bring the system to a consistent state after a
failure.
3. Replica Recovery:
o Failover Mechanisms: Automatically switching to a standby replica if the primary node
fails, ensuring continued availability.
o State Transfer: Synchronizing the state of a new or recovering replica with the rest of the
system to maintain consistency.
Hence, reliability in DDBMS is achieved through data replication, fault tolerance, consensus algorithms, and robust failure recovery strategies.