DeFi has moved way past its experimental stages. Today, there are thousands of DeFi platforms that process real-time financial transactions for individuals and corporations, including but not limited to trading, lending, and derivatives. As this usage increases, the workload on existing DeFi infrastructure also increases.
This has made DeFi scaling a crucial part of maintaining reliability, and everything tends to happen in the execution layer through Layer 2 (L2) solutions. This makes execution platform choices one of the most important considerations for any developer to ensure that even sudden spikes in traffic don’t affect reliability.
Understanding Execution in DeFi
Every transaction processed on DeFi platforms, including transactions as little as a token swap or a lending action, is executed on a chain. It goes without saying that this “execution” happens in the execution layer.
It’s here where all transactions are processed and executed without any middleman, and the chain is updated to reflect the transaction.
In DeFi, the execution layer needs to be efficient as you need to process many transactions at once. This means that developers don’t rely on traditional blockchains alone, as they weren’t really built for speed.
Instead, they separate the execution from the main chain (e.g., Ethereum) to boost transaction speed and reduce gas fees. This modular approach allows developers to use faster processing engines that even match the speeds of web 2 apps. The results of transactions are then relayed back to the main chain for finality, which ensures the security of DeFi transactions.
The Scaling Problem
Scaling in DeFi is all about maintaining quality performance as the usage grows over time.
As more users make use of the platform, the volume of transactions gradually increases, smart contract interactions become more complex, and the network demand multiplies.
When this happens without scaling the DeFi system with a more effective execution layer, the network is overloaded with pending transactions, leading to higher gas fees.
These issues are predominant in major blockchain networks. As the demand increases, so does the cost of maintaining the system’s performance in relation to that demand.
This is an inherent problem in major blockchain networks, and it’s what prompted the move to Layer 2 solutions. But then, even Layer 2 chain processing tends not to be fast enough for high-demand DeFi apps as the layers mostly use standard virtual machines (sequential). To solve the issue, developers can connect their execution layer to a faster processing engine in a simple plug-and-play process.
Why Execution Efficiency is Critical
Execution efficiency directly affects how well a platform can handle scaling in several ways:
- Determining throughput: A more efficient execution layer processes more transactions as it uses parallel processing instead of the traditional sequential processing.
- Ensuring cost stability: When execution is optimized in a scalable manner, transaction fees are more predictable even during periods of high demand.
- Improving reliability: The system would be more trustworthy due to fewer failed transactions, which keeps users coming back.
From a business perspective, these are all important as you need to maintain trust.
User Experience and Liquidity
DeFi platforms rely heavily on liquidity, which in turn depends on users’ confidence and experience in processing transactions.
When users experience slow transactions or recurring failed transactions on a DeFi platform as a result of execution inefficiencies, they are unlikely to engage consistently.
On the other hand, DeFi platforms with solid execution layers keep their users for longer periods, attract higher transactions, and support more advanced financial operations.
Overall, execution efficiency is not simply a technical concern as it directly affects how your DeFi platforms grow and compete for users across a wide market. Developers need to prioritize effective execution to stand a better chance at scaling their services without introducing security risks.
