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Metamask: Are MetaMask’s Gas Estimations Unreliable on L2 Testnets and Mainnets?

As a developer working with Layer 2 (L2) testnets, such as Arbitrum sepolia and Unicoin, I’ve noticed that transactions failing consistently. One of the key reasons behind this issue is the reliance on MetaMask’s gas estimations for smart contract deployment. But are these estimations reliable across both L2 testnets and mainnet environments?

What are gas estimations?

Gas estimations in blockchain networks like Ethereum, Polkadot, or Solana are used to predict the computational effort required to execute a particular transaction or smart contract call. These estimates provide an approximate value for the amount of gas needed to deploy a function on the network.

The issue with MetaMask’s gas estimations

In my experience, MetaMask’s gas estimations were not accurately reflecting the actual gas requirements of various functions. This was particularly problematic when I encountered transaction failures during deployment on L2 testnets and mainnet environments. The discrepancies between estimated and actual gas consumption led to inconsistent behavior, which in turn affected the performance of my project.

L2 Testnets: Gas Estimations vs. Actual Consumption

On L2 testnets like Arbitrum sepolia and Unicoin, I found that MetaMask’s gas estimations were significantly lower than the actual gas requirements for deploying functions. This resulted in a substantial difference between estimated and actual transaction times, leading to delays or even complete abandonment of my project.

Mainnet Environment: Gas Estimations vs. Actual Consumption

On mainnet environments like Ethereum or Solana, MetaMask’s gas estimations were generally accurate, but not entirely reliable. The network conditions, such as the available gas supply and transaction fees, could still lead to variability in actual gas consumption compared to estimated values.

Why are gas estimations unreliable?

Several factors contribute to the unreliability of MetaMask’s gas estimations:

• Network congestion: High traffic on L2 testnets can cause network congestion, leading to increased gas prices and reduced accuracy in gas estimation.

• Gas price volatility: Mainnet environments are subject to high gas price fluctuations, which can affect estimated values.

• Transaction complexity: Complex smart contracts or function calls can lead to inaccurate gas estimations.

Conclusion

In conclusion, the reliability of MetaMask’s gas estimations on L2 testnets and mainnet environments is a significant concern for developers working with these networks. While gas estimations are essential for planning and optimizing smart contract deployment, they must be taken with caution due to the potential inaccuracies associated with network congestion, gas price volatility, and transaction complexity.

To mitigate this issue, developers should:

• Monitor and analyze network conditions: Regularly check gas prices, network congestion levels, and transaction fees on both L2 testnets and mainnet environments.

• Use alternative estimation methods

  

  : Utilize third-party tools or develop custom estimation algorithms that account for specific use cases and networks.

• Validate estimated values with actual deployment: Verify the accuracy of gas estimations by deploying smart contracts on L2 testnets and mainnet environments.

By acknowledging these limitations and adopting best practices, developers can reduce the risks associated with gas estimations and ensure more reliable smart contract deployment on L2 testnets and mainnet environments.