Abstract
Hyperledger Fabric is a leading permissioned blockchain platform known for its flexibility and customization. A crucial yet often overlooked component is its state database, which records the current state of blockchain applications. While the platform currently supports LevelDB and CouchDB, this study argues that there is an unmet need for exploring alternative databases to enhance performance and scalability. We evaluate RocksDB, Boltdb, and BadgerDB under various workloads, focusing on memory and CPU utilization. Our findings reveal that each alternative outperforms the existing options: RocksDB excels in throughput and latency, Boltdb minimizes CPU usage, and BadgerDB is most memory-efficient. This research not only provides a roadmap for integrating new state databases into Hyperledger Fabric but also offers critical insights for those aiming to optimize enterprise blockchain systems. The study underscores the significant gains in scalability and performance that can be achieved by reconsidering the choice of state database.
| Original language | English |
|---|---|
| Pages (from-to) | 1526-1556 |
| Number of pages | 31 |
| Journal | Advances in Artificial Intelligence and Machine Learning |
| Volume | 3 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2023 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 Ivan Laishevskiy, et al.
Keywords
- BadgerDB
- Blockchain
- BoltDB
- Database
- Hyperledger caliper
- Hyperledger fabric
- Key value store
- LevelDB
- RocksDB
- State database
ASJC Scopus subject areas
- Artificial Intelligence