What is data recovery in distributed databases?

Data recovery in distributed databases refers to the process of restoring and recovering data in the event of a failure or loss in a distributed database system. It involves recovering the data to a consistent and usable state after a failure, such as hardware failure, software failure, network failure, or human error.

In distributed databases, data is stored and replicated across multiple nodes or sites, making it more resilient to failures. However, failures can still occur, and data recovery mechanisms are necessary to ensure the integrity and availability of the data.

There are several techniques and strategies used for data recovery in distributed databases, including:

1. Replication: Replicating data across multiple nodes ensures that even if one node fails, the data can still be accessed from other nodes. In case of a failure, the system can recover the data from the replicated copies.

2. Redundancy: Redundancy involves storing multiple copies of data on different nodes or sites. This redundancy helps in recovering data in case of failures by retrieving the data from the redundant copies.

3. Checkpoints: Checkpoints are periodic snapshots of the database state. These snapshots capture the current state of the database and can be used to restore the database to a consistent state in case of failures. Checkpoints are typically stored in a separate location to ensure their availability even if the primary database fails.

4. Logging and transaction management: Distributed databases use logging mechanisms to record all the changes made to the database. In case of a failure, the system can use these logs to recover the database by replaying the logged transactions.

5. Distributed commit protocols: Distributed commit protocols ensure that all the nodes in the distributed database agree on the outcome of a transaction. In case of a failure during the commit phase, the protocol can be used to recover the database by coordinating the recovery process across the nodes.

Overall, data recovery in distributed databases involves a combination of replication, redundancy, checkpoints, logging, and transaction management techniques to ensure the availability and consistency of data in the face of failures.