As blockchain technology evolves, the demand for the performance of decentralized systems has significantly increased. While consensus and execution mechanisms like MonadBFT and Parallel Execution have brought substantial improvements in throughput and speed, the data layer of the blockchain—MonadDB—plays an equally important role in ensuring the stability, efficiency, and scalability of the entire system.

In this research paper, we will explore how MonadDB works, its significance to the Monad blockchain, and the advanced techniques it employs to manage vast amounts of data while supporting high-performance parallel execution and rapid transaction validation.

1. Overview of MonadDB

MonadDB is a custom-built database designed specifically for the Monad system, a blockchain capable of processing transactions in parallel (Parallel Execution). The role of MonadDB is crucial in managing the blockchain’s state, optimizing data storage and retrieval processes, and enhancing transaction processing efficiency. In this paper, we will dive deeper into MonadDB, how it operates, and its advantages over other databases like LMDB, LevelDB, and RocksDB, commonly used in many blockchain systems today, including Ethereum.

2. MonadDB and Traditional Databases

Most blockchain systems, such as Ethereum, use key-value databases to store the blockchain’s state. Common examples include B-Tree structures (like LMDB) or LSM-Tree structures (like LevelDB and RocksDB). However, Ethereum employs a different data structure known as the Merkle Patricia Trie (MPT) to store its state. This creates an inefficient solution, as MPT is embedded within another data structure, leading to incompatibility between the two and reducing overall performance.

MonadDB solves this issue by natively implementing the Patricia Trie data structure, both in memory and on disk. This not only optimizes storage processes but also ensures consistency between data layers, enhancing data retrieval and storage speed within the blockchain system.

3. Parallel Execution and Storage Requirements

Monad employs a Parallel Execution mechanism, allowing multiple transactions to be processed simultaneously instead of sequentially. This requires the database to support asynchronous I/O (async I/O) to prevent transactions from waiting for data read/write operations from the disk. If a transaction needs to read the state from the disk, the system should not pause to wait but rather initiate the read and proceed to handle other transactions while waiting for completion.

Most traditional key-value databases do not provide adequate support for asynchronous I/O, although there have been some efforts to improve in this area. In contrast, MonadDB fully leverages the latest asynchronous I/O support from the Linux operating system, particularly io_uring. This allows the system to handle I/O requests quickly and efficiently without spawning multiple kernel threads to process the requests, significantly reducing processing overhead.

4. I/O Optimizations in MonadDB

In addition to supporting asynchronous I/O, MonadDB implements several I/O optimizations, such as bypassing the filesystem to eliminate expensive overhead. Traditional filesystems can slow down the read/write process due to complex data management procedures. By removing the filesystem from the I/O process, MonadDB significantly reduces data access time and enhances overall system performance.

5. Illustrative Example

Imagine managing a large library with millions of books. You need to find a specific book while also handling dozens of other requests from readers. In a traditional library, you might need to wait to find the book before addressing other requests. But with a modern system like MonadDB, you can simultaneously search for the book and process other requests flexibly.

If two readers request to change information about the same book (e.g., editing content or updating its status), MonadDB detects the conflict and resolves it, while other requests continue to be processed without interruption. This accelerates library operations and ensures that data remains tightly and accurately managed.

6. Deferred Execution and MonadDB’s Role in Parallel Execution

Deferred Execution is a mechanism that separates the consensus and execution processes within the Monad system. This means that after a block is accepted during the consensus process, the transactions within that block can be executed in parallel without waiting for completion before moving on to the next block’s consensus.

MonadDB plays a critical role in supporting Deferred Execution through its ability to handle Parallel Execution. By efficiently managing data storage and retrieval, MonadDB enables transactions from previous blocks to be processed simultaneously while consensus for new blocks continues. This optimizes system performance, reduces wait times, and increases transaction throughput.

For instance, imagine organizing a conference with multiple activities happening simultaneously. You have a management system (MonadDB) to track all these activities. While one presentation is ongoing, a panel discussion and a workshop can occur simultaneously without interrupting each other. This system allows you to monitor all activities at once without being overwhelmed, ensuring everything runs smoothly and efficiently.

7. Conclusion

MonadDB is not just an ordinary blockchain database but an optimized solution for storing and processing data in a blockchain system with parallel execution capabilities. By natively implementing the Patricia Trie and supporting asynchronous I/O, MonadDB enhances the performance of the Monad system while ensuring data stability and integrity. Through I/O optimizations and efficient resource management, MonadDB helps the Monad blockchain become a more advanced, robust, and efficient platform compared to traditional blockchains.

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🔹MonadBFT: A Consensus Mechanism Delivering High Performance for the Monad Blockchain

🔹Deferred Execution: Optimizing Consensus and Parallel Transaction Processing

🔹Parallel Execution: Enhancing Multithreaded Transaction Throughput

🔹MonadDB: A Data Layer Optimized for Parallel Execution