The evolution of blockchain technology, from its origins as a decentralized ledger for cryptocurrencies to its broader applications in areas like decentralized finance (DeFi), has significantly transformed financial ecosystems while introducing new challenges such as Maximum Extractable Value (MEV). This paper explores MEV on the Polygon blockchain, with a particular focus on Atomic Arbitrage (AA) transactions. We establish criteria for identifying AA transactions and analyze key factors such as searcher behavior, bidding dynamics, and token usage. Utilizing a dataset spanning 22 months and covering 23 million blocks, we examine MEV dynamics with a focus on Spam-based and Auction-based backrunning strategies. Our findings reveal that while Spam-based transactions are more prevalent, Auction-based transactions demonstrate greater profitability. Through detailed examples and analysis, we investigate the interactions between network architecture, transaction sequencing, and MEV extraction, offering comprehensive insights into the evolution and challenges of MEV in decentralized ecosystems. These results emphasize the need for robust transaction ordering mechanisms and highlight the implications of emerging MEV strategies for blockchain networks.

The Hyperledger Fabric is well known and the most prominent enterprise-grade permissioned blockchain. The architecture of the Hyperledger Fabric introduces a new architecture paradigm of simulate-order-validate and pluggable architecture, allowing a greater level of customization where one of the critical components is the world state database, which is responsible for capturing the snapshot of the blockchain application state. Hyperledger Fabric manages the state with the key-value database abstraction and peer updates it after transactions have been validated and read from the state during simulation. Therefore, providing good performance during reading and writing impacts the system's overall performance. Currently, Hyperledger Fabric supports two different implementations of the state database. One is LevelDB, the embedded DB based on LSM trees and CouchDB. In this study, we would like to focus on searching and exploring the alternative implementation of a state database and analyze whenever there are better and more scalable options. We evaluated different databases to be plugged into Hyperledger Fabric, such as RocksDB, Boltdb, and BadgerDB. The study describes how to plug new state databases and performance results based on various workloads.