Erasure Coding in Hadoop: Reducing Storage Costs Without Losing Data

 


In the era of big data, organisations handle massive volumes of information on a daily basis. Enterprises rely on Hadoop for its ability to store and process massive datasets across clusters, making data management more scalable and efficient. However, storing petabytes of data reliably is a costly endeavour. Traditionally, Hadoop relied on data replication, which, while effective in ensuring data availability, significantly inflated storage costs. Enter Erasure Coding — a game-changing technique designed to reduce storage overhead without compromising on data reliability.

Whether you’re a tech enthusiast or someone pursuing a data scientist course, understanding erasure coding can provide deeper insights into how modern data systems are evolving to become more cost-efficient and scalable.

What is Erasure Coding?

As a fault-tolerant technique, Erasure Coding divides data, adds redundancy, and stores it across multiple nodes to safeguard against loss. If some fragments are lost due to hardware failure, the original data can still be reconstructed using the remaining pieces and the redundant information.

This technique is widely used in storage systems like RAID, cloud storage, and now Hadoop’s Distributed File System (HDFS), particularly in Hadoop 3.0 and later versions. Unlike replication, which stores multiple full copies of data (e.g., three replicas), EC stores partially redundant blocks, allowing for more efficient use of disk space.

Erasure Coding vs. Traditional Replication

Hadoop’s default data protection method has historically been triple replication. For every block of data, Hadoop creates two additional copies, storing them on different nodes. While this approach ensures fault tolerance, it also increases storage usage by 200%.

Erasure coding, on the other hand, significantly reduces this overhead. For example, using a 6+3 EC policy (6 data blocks and three parity blocks), Hadoop can achieve the same level of fault tolerance with only a 50% storage overhead—a significant savings in large-scale systems.

Let’s break that down:

  • Replication: 100 TB of data → 300 TB storage required.

  • Erasure Coding (6+3): 100 TB of data → 150 TB storage required.

For businesses handling hundreds of terabytes or more, this reduction translates to substantial cost savings.

How Does Hadoop Use Erasure Coding?

Erasure Coding, introduced in Hadoop 3.0, is designed to store infrequently accessed data more efficiently than replication, helping with compliance and long-term retention. This feature is especially valuable in archival storage scenarios, where reducing costs is a primary concern.

When applied, Hadoop breaks the data into multiple blocks, calculates parity blocks using algorithms like Reed-Solomon, and then stores these blocks across the cluster. Hadoop ensures fault tolerance by rebuilding corrupted or lost data using parity-based reconstruction from surviving blocks.

However, it’s important to note that erasure coding increases computational complexity during data reconstruction. Therefore, it's typically used for archival rather than real-time or hot data access.

Benefits of Erasure Coding in Hadoop

1. Storage Efficiency

The most obvious benefit is reduced storage overhead. With erasure coding, less redundant information is stored, often resulting in a 50% reduction in total storage space versus traditional replication systems.

2. Fault Tolerance

Despite using less space, EC provides similar or even greater fault tolerance compared to replication. With carefully configured parity schemes, it can tolerate multiple node failures without data loss.

3. Cost Reduction

Less storage means lower costs for hardware, power, and data centre space. This makes erasure coding an attractive solution for organisations managing data at scale.

4. Scalability

Erasure coding allows organisations to scale their Hadoop clusters more efficiently. By optimising how data is stored, companies can expand storage capacity without proportionally increasing costs.

Limitations and Considerations

While erasure coding offers numerous advantages, it’s not without trade-offs:

  • Increased CPU Usage: Encoding and decoding data require significant computational resources.

  • Higher Latency: Reconstructing lost data is slower than reading a replica, making EC unsuitable for frequently accessed data.

  • Complex Management: Setting up and managing erasure-coded data can be more complicated than traditional replication.

These limitations make erasure coding ideal for cold data storage but less suitable for transactional systems or real-time analytics.

Why Should Aspiring Data Scientists Care?

Understanding storage optimisation strategies, such as erasure coding, is crucial for data professionals who design and manage large-scale data pipelines. If you're enrolled in a data scientist course in Pune, particularly one that covers big data technologies, topics like Hadoop and erasure coding will likely feature in your curriculum.

For those considering a data scientist course in Pune, a city that has emerged as a hub for tech education, many training programs offer in-depth modules on distributed systems, Hadoop ecosystem, and modern data storage practices, including erasure coding.

Conclusion

Erasure coding in Hadoop represents a significant leap forward in achieving more cost-effective, scalable, and resilient data storage. While it may not replace replication in all scenarios, its benefits for archival and infrequently accessed data are undeniable. By reducing storage overhead without compromising data durability, erasure coding enables businesses to manage ever-growing datasets more efficiently.

Contact Us:

Business Name: Elevate Data Analytics

Address: Office no 403, 4th floor, B-block, East Court Phoenix Market City, opposite GIGA SPACE IT PARK, Clover Park, Viman Nagar, Pune, Maharashtra 411014

Phone No.:095131 73277

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