People in the crypto community who had paid attention to Ethereum’s Shanghai upgrade must have heard a regularly discussed term called sharding. It was believed to be an answer to Ethereum’s scalability problem.
Ultimately, the developers behind the Ethereum blockchain decided to scrap the idea because they found alternative solutions that eliminated the need for sharding. The concept is still prevalent among blockchain enthusiasts who believe that it has the capabilities to improve blockchain performance.
We will look into the practical applications of this concept along with its benefits and challenges and provide you with the answer to the question: What is Sharding?
What is Sharding?
Sharding is a groundbreaking concept in the realm of distributed databases. It has emerged as a vital solution to address the most recurring problem heard across the blockchain world: the scalability problem.
The scalability problem in the context of blockchain refers to the challenge of increasing a network’s capacity to handle a growing number of transactions or users, which strains the network’s ability to handle large volumes of data efficiently, compromising performance.
Blockchains that have implemented sharding have found relative success in addressing this problem. Sharding has transformed the traditional approach of managing databases by decentralizing data storage.
In conventional setups, you have a single database that is tasked to serve the role of storing information on a single platform. As the volume of the data grows, the centralized structure starts to face bottleneck issues or network congestion, which can cause a decline in system response time; therefore, users of the system face delay issues.
Sharding addresses the problem by dividing these databases into smaller, distinct shards, each functioning as independent data with its own schema and data subset.
These shards are then distributed across multiple servers or nodes within a network. By doing so, the workload is distributed, and each shard operates independently, significantly boosting database efficiency and overall system performance.
How Sharding Works
We have discussed the core principle of how sharding works; we will now break down the steps in breaking down a central database into smaller shards.
Data Partitioning
The very first step involved in the process of sharding is data partitioning. Data portioning entails an extensive database divided into smaller, more manageable subsections called shards. The division is based on predetermined criteria like range-based partitioning, hash-based partitioning, or geographic partitioning.
Range-Based Partitioning:
In range-based partitioning, data is divided based on specific ranges or intervals of values within a chosen attribute. Consider a database of time-stamped transactions. Range-based partitioning might involve dividing the data into shards based on time intervals, such as transactions from January to March in one shard and April to June in another.
Hash-Based Partitioning:
Hash-based partitioning involves applying a hash function to a chosen attribute’s values, and the hash result determines the shard assignment.
For example, If the chosen attribute is a user ID, a hash function could be applied to the user IDs, and the shards could be assigned based on the hash values. This method helps distribute data uniformly across shards.
Geographic Partitioning:
Geographic partitioning involves dividing data based on specific attributes’ geographical location or proximity.
Suppose, in a system dealing with global user data, geographic partitioning might involve assigning shards based on the physical location of users.
Shards could be dedicated to users from specific regions, ensuring that data related to users in a particular geographical area is stored together.
These are different criteria that can determine how a database is sharded. Sharding can be carried out using two other techniques, namely horizontal and parallel partitioning.
Horizontal Partitioning
Horizontal sharding involves dividing the data in a set of rows on top of each other and distributing the subsets of data across different shards.
In a blockchain network, horizontal partitioning separates the data based on a specific characteristic like the type of asset or transaction history.
It ensures that each shard contains a distinct set of transaction records, as it will allow for efficient results when a query is passed for a specific subset of the transaction category within the blockchain.
Parallel Partitioning
Parallel partitioning includes partitioning data stored in a shard into further columns of information.
The further division in a parallel pattern allows for parallel processing within these shards. Multiple queries can be entertained concurrently in a single shard, which helps achieve enhanced performance.
For instance, within a shard responsible for handling user data, parallel partitioning might involve subdividing the data based on user activity, allowing simultaneous processing of queries related to different user segments.
Shard Distribution
Once the data is partitioned, the shards are distributed across different servers or nodes within a network.
The distribution ensures that the overall workload is spread across multiple resources, preventing any single server from becoming a performance bottleneck.
Independent Operation
Each shard operates as an autonomous database, capable of handling its queries and transactions independently.
The independence is a critical factor in achieving parallelism and improved performance. It also contributes to fault tolerance, as the failure of one shard does not impact the entire database.
Query Routing
Query routing becomes a crucial aspect of sharded systems. When a query is issued, a mechanism must be in place to determine which shard(s) should handle the request.
The process involves utilizing a query router or coordinator that directs the query to the relevant shard(s), collects the results, and presents them to the user or application.
Scaling Out
Sharding facilitates horizontal scaling, allowing organizations to scale out by adding more servers as the data volume grows.
It is an optimal solution compared to traditional vertical scaling, which involves upgrading a single server’s capacity. This solution is costly and provides a limited solution to a problem.
Benefits of Sharding
- Sharding changes how we manage blockchain databases and brings many benefits compared to traditional extensive databases.
- It helps a blockchain increase by adding more parts (shards) and sharing the work among different servers while avoiding the limitations of enhancing database size by vertical scaling.
- The cost-effective method keeps the system efficient even as more data is added. Sharding makes the blockchain work faster by dividing tasks between different shards, ensuring quick responses even during phases of high-volume traffic.
- The process also ensures that no bottleneck is created if one shard is not responding to a query at a given time.
- Sharded systems also exhibit enhanced fault tolerance. If one shard fails to perform the required action, the whole system doesn’t crash, and we can even replicate data to other nodes to ensure all necessary information is available.
Challenges and Considerations
While the benefits of sharding are compelling, it is crucial to acknowledge the challenges and considerations accompanying its implementation.
Data Consistency
Maintaining data consistency across distributed shards can pose a significant challenge. As transactions span multiple shards, ensuring consistency becomes complex.
Firms implementing this concept must also implement robust mechanisms, such as distributed transactions or eventual consistency models, to address this challenge effectively.
Implementation Complexity
Sharding introduces complexity in database management that may be unfamiliar to organizations accustomed to traditional databases.
Data partitioning, shard distribution, and query routing require careful planning and execution.
Implementation complexity can increase with the need for dynamic shard re-balancing and handling shard failures.
Potential Points of Failure
Although sharding enhances fault tolerance, it introduces new potential points of failure. Issues such as network partitions, shard unavailability, or misconfigurations can impact the overall system’s reliability.
Companies must implement robust monitoring and failover mechanisms to promptly identify and address potential points of failure.
Query Routing Overhead
The introduction of query routing mechanisms adds an overhead to the system. Coordinating queries and directing them to the appropriate shards introduces latency, impacting overall query response times. Efficient query routing strategies and optimizations are essential to minimize this overhead.
Dynamic Scaling Challenges
While sharding allows for dynamic scaling by adding more shards, managing this process seamlessly can be challenging.
Dynamic scaling involves shard splitting, merging, and re-balancing, which must be executed precisely to avoid service disruptions.
Companies need well-defined strategies and tools for handling dynamic scaling challenges effectively.
Real-world Applications and Case Studies
We have already discussed how sharding enhances blockchain scalability, so we will look at some practical examples in the context of blockchain and cryptocurrencies where sharding has proved very helpful in resolving different issues.
Smart Contract Execution
Sharding plays a crucial role in improving the execution of smart contracts on blockchain platforms. By sharding the data and computational workload associated with smart contracts, decentralized applications (DApps) can achieve parallel processing, resulting in faster and more efficient execution.
This is particularly impactful in crypto ecosystems where the execution of smart contracts is integral to various decentralized services.
Decentralized Finance (DeFi)
Sharding has found application in the growing field of decentralized finance (DeFi). Blockchain projects aim to create scalable and efficient decentralized exchanges, lending platforms, or liquidity pools leveraging sharding to handle the increasing volume of transactions seamlessly. It ensures that DeFi applications can provide users with a fluid and responsive experience.
Cross-Chain Interoperability
Sharding contributes to achieving cross-chain interoperability, a key consideration in the crypto space. Blockchain projects are exploring sharding solutions to facilitate communication and data exchange between different blockchains.
The approach enables a more connected and interoperable ecosystem, allowing assets and information to flow seamlessly across multiple blockchain networks.
Blockchains that have implemented sharding
These are some of the well-known blockchain networks that have implemented sharding to improve network scalability.
Polkadot
The Polkadot network features a unique sharded architecture called Parachains. These are independent blockchains connected to a central relay chain that handles cross-chain communication and security. The design enables high scalability and flexibility for various dApps to be built on top of Polkadot.
NEAR Protocol
NEAR protocol utilizes a unique sharding approach called Nightshade. It combines sharding with validator checkpoints to achieve fast and secure transaction processing while maintaining decentralization.
NEAR is known for its user-friendly development environment and is attracting a growing community of developers.
Cosmos
The Cosmos ecosystem consists of multiple interconnected blockchains called Zones. Each zone can be built with its custom consensus mechanism and sharding configuration, allowing for tailored solutions for specific use cases.
The interoperability between zones facilitated by the Cosmos Hub enables seamless communication and data exchange.
Zilliqa
Zilliqa blockchain network was one of the pioneers in implementing sharding technology. It uses a hybrid approach called Practical Byzantine Fault Tolerance (pBFT) for consensus, allowing fast transaction finality within individual shards.
Zilliqa is known for its high transaction throughput and is well-suited for real-world applications.
Why didn’t Ethereum implement sharding?
If you go to the official Ethereum.org website, you will see that their roadmap for upgrading Ethereum included sharding, but they later announced that they had abandoned that plan.
The explanation provided by the organization was that they no longer needed sharding because the Layer 2 rollups they have introduced eliminated the need for sharding because they have provided a lot of scaling already, so they don’t need sharding anymore.
Layer 2 (L2) rollups are scaling solutions built on existing blockchains. They tackle blockchain congestion and high fees by taking transaction processing off the main chain (L1) and onto a separate, dedicated sidechain (L2).
They create a fast lane for transactions, allowing them to be processed much quicker and cheaper than on the mainnet.
Closing Thoughts
Sharding is a significant step forward in addressing the blockchain scalability issue in the grand scheme. Although it brings about new intricacies and potential drawbacks, its ability to enhance scalability while maintaining decentralization offers tremendous potential for evolving blockchain networks in the times ahead.
