Erasure Coding

Definition of Erasure Coding

Erasure coding is a data protection and fault-tolerance technique used in distributed storage systems. It involves splitting original data into fragments, then encoding and expanding these fragments with redundant data pieces. This process allows for data recovery even when multiple fragments are lost or corrupted, improving reliability and reducing the need for additional copies.


E-r-a-s-u-r-e C-o-d-i-n-g: /ɪˈreɪʒər ˈkoʊdɪŋ/

Key Takeaways

  1. Erasure Coding is a data protection technique that provides higher reliability and fault tolerance by fragmenting and encoding data across multiple storage devices.
  2. It uses mathematical algorithms to create redundant data pieces, allowing for the reconstruction of lost or corrupted data from a smaller subset of the original fragments.
  3. Compared to traditional data protection methods like RAID, Erasure Coding offers better storage efficiency and resiliency, making it an ideal choice for large-scale, distributed storage systems.

Importance of Erasure Coding

Erasure coding is important in the realm of technology because it ensures data protection and reliability by using redundancy and fault tolerance techniques.

It involves breaking data into fragments, encoding them with additional parity data, and distributing them across multiple storage devices or systems.

This process allows for the recovery of the original data even if some fragments are lost, damaged, or inaccessible.

By reducing the risk of data loss, erasure coding enhances the performance and dependability of storage systems, making them more suitable for mission-critical applications and large-scale distributed systems.

In essence, erasure coding plays a vital role in maintaining the integrity, availability, and resilience of data in today’s increasingly data-driven world.


Erasure coding serves as a crucial data protection method, particularly for distributed storage systems, aiming to ensure the reliability and durability of information in case of node or disk failures. With the continuous growth of data-driven applications, safeguarding data becomes increasingly important, as it helps organizations prevent data loss and minimize downtime.

Erasure coding achieves this by dividing data into smaller chunks, encoding these fragments, and then, adding redundant data pieces, thereby enabling data restoration even when some of these fragments go missing or become corrupted. In the context of storage systems, erasure coding can be effectively applied to maintain data integrity while optimizing storage space and network overhead.

For instance, when dealing with large-scale data storage solutions like Hadoop HDFS or object storage, erasure coding methods such as Reed-Solomon algorithm can provide a higher fault tolerance while consuming less storage capacity than traditional data replication techniques. In fact, erasure coding enables substantial cost savings, as well as efficient and scalable data protection for businesses managing massive data sets in highly distributed environments, like cloud-based services.

Ultimately, erasure coding becomes a pivotal tool to meet the growing requirements of data durability and reliable storage infrastructure, providing a solid foundation for various applications in the technology landscape.

Examples of Erasure Coding

Erasure coding is a data protection technique used in various fields to ensure data reliability and fault tolerance. Here are three real-world examples of its applications:

Cloud Storage Services: Erasure coding is widely implemented in cloud storage systems such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) to improve data durability and protect against data loss. By dividing data into smaller fragments, encoding these fragments with redundant data pieces, and storing them across different nodes, erasure coding allows these platforms to recover lost or corrupted data efficiently, even with high server failure rates. For instance, AWS uses a technique called Reed-Solomon erasure coding to support its Amazon S3 service.

Distributed Storage Systems: Erasure coding is employed by distributed storage systems like Hadoop Distributed File System (HDFS) and Ceph to increase data durability across distributed environments. In such systems, data is dispersed across nodes or data centers, enabling the reconstruction of lost or damaged data fragments by leveraging other available fragments. The implementation of erasure coding in these systems reduces storage overhead without sacrificing data durability.

Telecommunications and Data Transmission: Erasure coding plays a crucial role in improving the reliability of data transmission across communication channels. For instance, in satellite communication systems, erasure coding algorithms like Tornado codes and LDPC (low-density parity-check) codes are utilized to counteract the loss of data packets due to factors such as interference, noise, and signal fading. By employing erasure coding, these systems can achieve reliable data communication even in the presence of high packet loss rates.

FAQ: Erasure Coding

1. What is Erasure Coding?

Erasure Coding is a technique used in the field of data protection and storage to enable data to be recovered even if some data is lost or corrupted. It works by encoding the original data into multiple fragments which are stored separately. This self-healing capability not only maintains data integrity but also enhances storage efficiency.

2. How does Erasure Coding work?

Erasure Coding works by dividing data into smaller fragments and creating redundant coded fragments from the original data. These coded fragments are distributed across multiple storage nodes or disks to ensure fault tolerance. In case of data loss or corruption, the missing fragments can be reconstructed from the remaining fragments, allowing data to be recovered optimally.

3. What are the advantages of Erasure Coding over traditional data protection methods?

Erasure Coding offers multiple advantages over traditional methods like mirroring and RAID. These include increased storage efficiency, reduced storage overhead, and better fault tolerance. Erasure Coding is highly scalable, making it suitable for distributed and large-scale systems. It also enables data recovery with minimal performance impact, ensuring better data availability and protection.

4. When should I use Erasure Coding?

Erasure Coding is highly recommended when dealing with large-scale distributed systems or environments with high data durability requirements. It is ideal for cloud storage, archival storage, and big data applications. However, it might not be suitable for high latency-sensitive applications or those with low fault-tolerance requirements.

5. Are there any downsides or limitations to Erasure Coding?

While Erasure Coding provides significant benefits for data protection and storage efficiency, it does have certain downsides and limitations. These include increased computational complexity, potential performance impact during data reconstruction, and bandwidth requirements for the encoding and decoding process. Additionally, it may not be suitable for all types of applications or infrastructures.

Related Technology Terms

  • Data Fragmentation
  • Redundancy
  • Reed-Solomon Algorithm
  • Fault Tolerance
  • Distributed Storage Systems

Sources for More Information


About The Authors

The DevX Technology Glossary is reviewed by technology experts and writers from our community. Terms and definitions continue to go under updates to stay relevant and up-to-date. These experts help us maintain the almost 10,000+ technology terms on DevX. Our reviewers have a strong technical background in software development, engineering, and startup businesses. They are experts with real-world experience working in the tech industry and academia.

See our full expert review panel.

These experts include:


About Our Editorial Process

At DevX, we’re dedicated to tech entrepreneurship. Our team closely follows industry shifts, new products, AI breakthroughs, technology trends, and funding announcements. Articles undergo thorough editing to ensure accuracy and clarity, reflecting DevX’s style and supporting entrepreneurs in the tech sphere.

See our full editorial policy.

More Technology Terms

Technology Glossary

Table of Contents