Durability in Distributive Systems | Learn System Design

Distributed System term defines the concept that multiple independent Computer Systems are been distributed to various locations and all these nodes can be operated from a single system. Due to their capacity to offer high scalability, fault tolerance, and load balancing, these systems are growing in popularity. They do, however, also have their own set of difficulties, such as assuring data durability. 

Data Durability is an important factor in Distributed Systems. This factor assures that the data is even safe after the system failure or crashed state. 3 different techniques can be applied to recover the data in the situation of system failure. 

• Replication, 
• Backup, and 
• Write-Ahead Logging(WAL)

provide a reliable approach for ensuring data durability in distributed systems. By applying this technique, organizations can safeguard their data and recover the data in the situation of system failure.

Replication in Systems:

The practice of making and keeping numerous copies of data on various nodes in a distributed system is known as replication. 

Data is often dispersed over several nodes in distributed systems, and replication makes sure that each node has a copy of the data. The replication technique can increase the Scalability of System Performance and Availability which results in restoring the data in case of failure. Replication also provides the feature of Fault Tolerance which is discussed below in the article.

Key Features of Replication:

1. Fault tolerance: Replication can help ensure that data is long-lasting and resistant to failure. With multiple copies of the data stored on different nodes, if one copy becomes corrupted or lost, the data can be recovered using another copy.
2. Improved performance: The replication technique can be used to increase the performance of the system by decreasing the amount of data that is transferred across the network channel. By storing copies of the data on multiple nodes, data can be read from the closest node, reducing network latency and improving response times. 
3. High availability: Replication can help to ensure that data remains accessible even if a node fails. Because multiple copies of the data are stored on different nodes, if one node fails, another node can take over and provide access to the requested data avoiding the situation of system failure.

Applications of Replication:

1. Data replication: In a distributed system, replication is commonly used to replicate information across different computer systems. This helps to ensure that data is available even if a node fails.
2. Load balancing: Replication can be used to distribute workload across multiple nodes, assisting in load balancing and system performance.
3. Disaster recovery: By creating copies of data in different geographical locations, replication can be used for disaster recovery. Even after the occurrence of the disaster or cyber-attack the data can be recovered from various replicated copies. These copies hold the same information that is being lost.

Replication is an important technique for ensuring the durability of distributed systems. Replication ensures that data remains available and durable even in the event of failures or other disruptions by maintaining multiple copies of the data.

Fault tolerance is one of the most important advantages of replication in terms of durability. If one node fails, another node can take over and provide access to the data because data is replicated across multiple nodes. This helps to ensure that data remains available and durable even if a node fails.

Data consistency is another advantage of replication in terms of durability. Maintaining data consistency in a distributed system can be difficult. By ensuring that all nodes have the same copy of the data, replication can help ensure consistency. This helps to prevent data inconsistencies and ensures that the data is long-lasting and dependable.

Backup in Systems:

In the concept of Distributed System, backups are one of the most significant aspects of safeguarding data durability. The ability of information to persist and be accessible in situations of failure or any other interruptions is known as Data Durability. Backups are nothing but copies of information that are been preserved in various geographical locations or data centers, which aims to recover the data in the event of data corruption or loss.

Key Features of Backups:

1. Redundancy: Redundancy is the process of creating multiple copies or replicas of data. Redundancy assures that if any one copy of the data is been lost or additional due to various factors like cyber threats, corruption, disaster, etc. then still the additional copies of data can be restored and reused to access the sensitive information without any problem.
2. Automatic Backups: In the current scenario, many distributed systems come with the mechanism of Automated backups which results in allowing the information to be backed up at regular periods. This guarantees that the backups of data are always updated and can be restored at any point of failure without disruption.
3. Incremental Backups: Some backup systems only backup changes to data that have occurred since the last backup. The incremental Backup approach decreases the amount of information that must be backed up, leading to a faster and more efficient backup operation. This results in the faster retrieval of data and the performance of the distributed systems is enhanced.

Applications of Backups: 

1. Compliance: Many industries and government regulations necessitate the creation and storage of backups for a set period. Backups ensure that data adheres to these regulations. For Example, to comply with HIPAA regulations, the healthcare industry requires backups to be made and stored for seven years.
2. Archiving: Backups provide the facility to keep the data in an Archive state. As the data which is Archived is no longer necessary for use in day-to-day functioning. But this can also be used for various historical or legal purposes. Achieving backups can be beneficial for information that is needed to be restored for a longer period, such as Economical Records, Research and development data.
3. Testing and Development: For testing and development, backups are helpful. Through the use of backups, developers can test new software or updates without interfering with the live environment. In order to give developers precise data and configurations to work with, backups can also be utilized to construct development environments that are exact replicas of the production environment.

Backups are used in a variety of applications and are a crucial component of ensuring data durability. Backups are essential for data protection and sustaining business operations, from disaster recovery and compliance to data replication and archiving.

WAL or Write-Ahead Logging in Systems:

WAL in Distributive Systems stands for Write-Ahead Logging, which is a method for ensuring Data Durability in Distributed Systems. In this process, the data which is being changed is first written into the log file, before saving the data to data centers or stores. The goal of this is to ensure that, if the data is being lost while committing to a data store, still it can be retrieved from the log file. This achieves Data Durability in Distributed Systems. In the sectors of Finance and healthcare, the most important factor is data consistency, so to maintain this consistency WAL can be used. 

Below we have mentioned some key features of WAL:

Key Features of WAL:

1. Atomicity: In the process of WAL, Atomicity specifies that the means of change in the data store are either complete or are fully rolled back and remain consistent and reliable.
2. High Write Performance: WAL needs writing to a log file instead of immediately updating the data storage, this can provide enhanced writing performance. The reason for better performance is that the process of writing into a log file is much faster than updating the data store. Additional processing and I/O operations are needed while updating the Data Store.
3. Scalability: WAL has the ability to scale up or scale down a large amount of data volumes and has high write throughput by distributing the log across different multiple nodes. This assures that logs can be written and accessed quickly from larger distributed systems without any disruption.

Applications of WAL:

1. Databases: Write-Ahead Logging can be used in databases to assure data durability and consistency. Many databases like PostgreSQL, SQLite, and Oracle databases use the WAL approach for ensuring data durability. WAF guarantees that the change in the data store is properly written in the log file before saving it to the database.
2. Messaging Systems: WAL in messaging systems assures that the message is been processed and delivered to the authorized person properly. Changes in the message queue are first written into a log file before saving the message queue. Due to this process, the risk of data loss and system failure has been minimized.
3. Financial Applications: WAL in the Financial sector is considered one of the important approaches as the transactions in banks are processed and recorded rapidly. Changes to the bank transaction are initially written into the log file and then committed to the transaction log.  This maintains the durability and also integrity of the data. 

Conclusion

Distributed Systems are responsible for fault-tolerant, which means that if any failure occurs then the complete system should not be crashed. The operation is transferred to another node rather than stopping the operation. Because distributed systems are intended to be fault-tolerant, or to continue operating even if one or more of their components fail, durability is a crucial criterion. To preserve the system’s general availability and dependability, the data stored in it must be robust and resilient to errors.

Because data is typically kept across several nodes, which may be spread out over various physical locations and connected by unreliable networks, ensuring durability in distributed systems is difficult.  To ensure that data is persistent even in the face of such errors, distributed systems must use sophisticated approaches to conserve the durability of the system.