Loss of Volatile Storage
In a database management system, volatile storage refers to the portion of the memory that is used to store temporary data or data that needs to be frequently accessed. This type of storage is volatile in nature, which means that the data stored in it is lost when the system is turned off or restarted.
A loss of volatile storage can occur due to a system crash, power outage, or hardware failure, among other reasons. When a loss of volatile storage occurs, any data that was stored in it is lost, and the database can become inconsistent. This can lead to a variety of problems, such as lost transactions, data corruption, and system downtime.
To mitigate the risk of data loss due to a loss of volatile storage, most modern database management systems use a combination of techniques, such as checkpointing and write-ahead logging.
Checkpointing is a technique used to periodically save the state of the database to non-volatile storage, such as a hard disk or solid-state drive. During checkpointing, the database management system flushes all modified data to non-volatile storage and updates the metadata to reflect the current state of the database. This ensures that in the event of a loss of volatile storage, the database can be restored to a consistent state by reloading the data from the most recent checkpoint.
Write-ahead logging is another technique used to ensure data consistency in the event of a loss of volatile storage. In this technique, every modification to the database is first recorded in a log file before it is written to the database. The log file is stored in non-volatile storage, so even if the volatile storage is lost, the modifications can be replayed from the log file to restore the database to a consistent state.
For example, suppose a database management system is using write-ahead logging to ensure data consistency in the event of a loss of volatile storage. When a transaction modifies a record in the database, the modification is first written to a log file on non-volatile storage. The database management system then writes the modification to the volatile storage. If a loss of volatile storage occurs before the modification is written to non-volatile storage, the database can be restored to a consistent state by replaying the log file and applying the modifications to the database.
In summary, a loss of volatile storage can lead to data loss and inconsistencies in a database management system. To mitigate this risk, modern database management systems use techniques such as checkpointing and write-ahead logging to ensure data consistency in the event of a loss of volatile storage.
Recovery
There are two main types of recovery techniques used in database management systems: forward recovery and backward recovery.
Forward recovery, also known as redo recovery, is used to recover from failures that occur during normal database operations. In this technique, the database management system uses a log file to track all the changes made to the database, and in case of a failure, it replays the log file to recover any lost or corrupted data. This technique ensures that the database remains consistent and up-to-date after the failure.
Backward recovery, also known as undo recovery, is used to recover from failures that occur during transaction processing. In this technique, the database management system uses a log file to undo any incomplete or partially executed transactions, so that the database can be restored to a consistent state before the failure occurred. This technique ensures that the database remains consistent and free of any partially executed transactions.
The recovery process typically involves the following steps:
- Identification of the failure: The first step in the recovery process is to identify the cause of the failure. This may involve examining log files, error messages, and system logs to determine the extent and nature of the failure.
- Analysis of the database: The second step is to analyze the state of the database and identify any lost or corrupted data. This may involve comparing the state of the database before and after the failure and identifying any discrepancies.
- Redo or undo: Depending on the type of failure, the database management system will use either forward or backward recovery techniques to recover lost or corrupted data. In redo recovery, the system replays the log file to recover any lost or corrupted data. In undo recovery, the system uses the log file to undo any incomplete or partially executed transactions.
- Restoration: Once the lost or corrupted data has been recovered, the system can restore the database to a consistent state. This may involve rebuilding indexes, restoring backups, and performing other maintenance tasks.
In summary, recovery is a critical aspect of database management systems that involves restoring lost or corrupted data and bringing the database back to a consistent state after a failure or error. The recovery process typically involves identifying the cause of the failure, analyzing the database state, using redo or undo techniques to recover lost or corrupted data, and restoring the database to a consistent state.
Database Backup & Recovery from Catastrophic Failure
- Define Recovery Objectives: Determine what data is critical to the business and establish recovery objectives for each critical application, such as recovery time objectives (RTO) and recovery point objectives (RPO). These objectives will help determine the frequency and type of backups needed.
- Backup Strategy: Develop a backup strategy that meets the defined recovery objectives. This should include a backup schedule that specifies the frequency of backups, retention period, and backup location. The backup strategy may include full backups, incremental backups, or a combination of both.
- Backup Verification: Ensure that backups are valid and can be used for recovery by periodically testing the backup and recovery process. This may involve restoring data from backups and verifying the integrity of the data.
- Disaster Recovery Plan: Develop a disaster recovery plan that outlines the steps needed to recover from a catastrophic failure, such as a power outage, natural disaster, or cyberattack. The plan should include procedures for restoring backups, recovering databases, and restoring applications.
- Recovery Testing: Periodically test the disaster recovery plan to ensure that it can be executed effectively. This may involve simulating a catastrophic failure and executing the recovery plan to verify that critical data can be restored.
- Continuous Improvement: Continuously monitor and evaluate the backup and recovery strategy to ensure that it remains effective and meets the defined recovery objectives. This may involve implementing new backup technologies, revising the backup schedule, or updating the disaster recovery plan.
In summary, a backup and recovery strategy is critical for ensuring that critical data can be restored quickly and effectively in the event of a catastrophic failure. A comprehensive strategy should include defining recovery objectives, developing a backup strategy, verifying backups, creating a disaster recovery plan, testing the recovery plan, and continuously improving the strategy. By following these steps, organizations can minimize the impact of catastrophic failures and maintain business continuity.
Crash Recovery
DBMS is a highly complex system with hundreds of transactions being executed every second. The durability and robustness of a DBMS depends on its complex architecture and its underlying hardware and system software. If it fails or crashes amid transactions, it is expected that the system would follow some sort of algorithm or techniques to recover lost data.
Failure Classification
The following are some common failure classifications:
- Transaction failure: A transaction can fail due to logical or system errors. Logical errors occur when there is an error in the transaction’s code or internal error condition, while system errors occur when the DBMS is unable to execute the transaction or has to stop due to some system condition like deadlock or resource unavailability.
- System crash: This type of failure occurs when external factors like power supply interruptions or software failures cause the system to stop abruptly.
- Disk failure: This type of failure occurs when the hard-disk drives or storage drives used in the DBMS fail. It can be caused by the formation of bad sectors, unreachability to the disk, disk head crash, or any other failure that destroys all or a part of disk storage.
By classifying failures, a DBMS can implement appropriate recovery mechanisms, such as maintaining logs of transactions or shadow paging, to ensure the atomicity and consistency of transactions and prevent data loss.
Log-based Recovery
The transaction log contains a record of every operation that is performed on the database, such as updates, inserts, and deletes. When a transaction is started, a new log record is created, and as the transaction progresses, the log records all the changes made to the database.
In the event of a system failure or crash, the transaction log is used to recover lost or corrupted data. During the recovery process, the transaction log is scanned from the last checkpoint, and all the changes made since the checkpoint are redone or undone as required. This process ensures that the database is brought back to a consistent state.
Log-based recovery is a critical component of any database management system as it ensures data integrity and consistency in the event of a failure or crash. It is also important for ensuring that transactions are processed in the correct order, as the log records the order in which the transactions were executed.
