How to Optimize Gas in Ethereum Smart Contracts

Over the years, there have been concerns about the gas fees on the Ethereum platform, especially when the network gets congested. The increased demand for blockspaces translates to higher transaction costs at such times. And if you write smart contracts, there are many transactions that will be taking place through your program.

Although when someone transfers an amount such as 3 ETH to USD the fee may be minimal, it will accumulate over time and become significant.

This means that you need to find a way to ensure that your program reduces costs, enhances efficiency, and ensures scalability. One of the best ways of doing this is by optimizing gas fees, and we’ll walk you through different ways you can do that.

What Is Gas And Why Is Optimizing It a Challenge?

In the context of Ethereum, ‘gas’ is a name for the effort spent to execute transactions. Each transaction consumes computational resources, necessitating the need for a fee to prevent endless DoS attacks and loops. The payment required to complete a transaction is known as a ‘gas fee.’

So, why is optimizing Ethereum gas challenging for developers? First, the optimization process requires knowledge of how EVM works and its complexities. Then, the associated gas fee can change over time based on the degree of complexity in the smart contract as well as the data that must be sorted out. The result is that it becomes difficult to predict gas costs accurately.

On top of that, most smart contract audit company are complicated and involve a lot of calculations. As a result, they consume high amounts of gas. With gas prices being volatile in nature, this makes it a bit tricky to estimate how much a smart contract transaction will cost. Developers therefore have to regularly optimize their smart contracts for gas consumption.

6 Ultimate Ethereum Gas Optimization Ultimate Practices

Now that you fully understand the concept of Ethereum gas and the importance of optimizing its consumption, it is time to learn the various ways to optimize it.

Minimize Storage Usage

Storage is a finite resource and is much more costly in terms of gas usage than memory. Significant gas costs are incurred every time a smart contract writes to or reads from storage. You can lessen gas costs by avoiding unnecessary writes. For instance, you can move constant values to memory. In addition, do not store colossal data pieces on-chain. You can consider alternative patterns such as IPFS.


Variable Packing

Another factor that significantly affects gas usage is the number of storage slots used and how developers represent the data in the smart contract. Variable packing means arranging variables so that a single slot can accommodate lists of them. Since every storage slot has cost implications, variable packing optimizes gas usage by utilizing fewer slots than required.

Use Optimized Data Structures

Another way to significantly reduce gas consumption is by using optimized data structures. Data lists can be represented using two data types: mappings and arrays. However, they have different structures and syntaxes. Mappings are mostly more efficient and less costly, while arrays are packable and iterable. It’s advisable to opt for mappings when managing data lists, except when there is need for iteration.

Go for Calldata Instead of Memory

Variables that are function parameters are stored as either ‘memory’ or ‘calldata.’ The major difference between the two terms is that while ‘memory’ allows for modification by the function, ‘calldata’ is immutable.

The principle here is to use ‘call data’ rather than ‘memory’ if it is a read-only function argument. Doing so avoids unnecessary copies from function ‘call data’ to ‘memory.’


Use Constant/Immutable Keywords

For variables, use constant or immutable keywords, as they aren’t housed in contract storage. Instead, they are evaluated at compile-time, and you can find them in the contract’s bytecode. This way, accessing them is cheaper.

Minimize Data Processing

Minimizing the amount of data that needs processing also helps reduce gas consumption. For instance, you can forego processing data in real-time and instead store it in a database and then process it offline.

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