Setting Up Batch Transaction Execution

Batch transaction execution is a fundamental pattern in modern blockchain development, allowing users to submit multiple distinct operations—like token transfers, swaps, or contract interactions—as a single, atomic transaction. This approach is critical for improving user experience by reducing the number of wallet confirmations and for optimizing gas costs by sharing overhead like base fees and calldata. Protocols like Uniswap's Universal Router, Safe's multi-send, and EIP-4337 Account Abstraction bundles rely on this principle to enable complex, multi-step DeFi strategies and wallet management.

The core technical implementation involves a smart contract that acts as an executor or relayer. Instead of calling target contracts directly, users send a single transaction to this executor contract. The transaction's calldata contains an encoded list of operations: target addresses, ETH values, and function call data. The executor then loops through this list, making each call via a low-level call or delegatecall. Crucially, if any single call in the batch fails (e.g., due to insufficient funds or slippage), the entire transaction can be designed to revert, ensuring atomicity.

Setting up batch execution starts with understanding the data structure. A typical batch is an array of Operation structs. In Solidity, this might look like:

solidityCopy
struct Operation {
    address target;
    uint256 value;
    bytes data;
}
function execute(Operation[] calldata ops) external payable {
    for (uint256 i = 0; i < ops.length; i++) {
        (bool success, ) = ops[i].target.call{value: ops[i].value}(ops[i].data);
        require(success, "Batch call failed");
    }
}

This simple executor iterates through operations, forwarding the specified ETH value and calldata to each target address.

For production systems, security and flexibility are paramount. A robust executor should include: a replay protection mechanism like nonces, gas limit checks per operation to prevent out-of-gas failures mid-batch, and access control to restrict who can call the execute function. Many teams use established audited contracts like OpenZeppelin's Multicall or Gnosis Safe's MultiSend as a foundation. When designing batches, remember that the total gas limit of the blockchain still applies to the entire bundled transaction, so complex batches may require careful gas estimation.

The primary use cases for batch transactions are gas optimization for power users performing several actions, improved UX for dApp workflows that require multiple steps (e.g., approve and swap), and enabling atomic arbitrage or liquidations that depend on a sequence of trades succeeding together. By adopting batch execution, developers can build more efficient and user-friendly applications, moving away from the paradigm of requiring separate transactions for every on-chain state change.

Batch transaction execution allows you to bundle multiple operations—like token approvals, swaps, or NFT transfers—into a single on-chain transaction. This approach offers significant benefits: it reduces gas costs by amortizing the base fee, improves user experience by minimizing wallet confirmations, and ensures atomicity, where all actions succeed or fail together. To work with this pattern, you'll need a basic understanding of Ethereum Virtual Machine (EVM) concepts, a development environment, and a wallet with testnet funds.

Your primary tool will be a smart contract wallet or a relayer service. Standard Externally Owned Accounts (EOAs) cannot natively batch calls. Instead, you'll interact with a contract account that has this capability, such as a Safe (formerly Gnosis Safe) wallet or a custom contract using a library like OpenZeppelin's Multicall. Alternatively, you can use a relayer network like Gelato or Biconomy to sponsor and execute the batched transaction on your behalf, abstracting gas complexity from the end-user.

Set up your development environment with Node.js (v18 or later) and a package manager like npm or yarn. You will need the Ethers.js v6 or viem library for interacting with the blockchain. For testing and deployment, configure a .env file to securely store your private keys and RPC URLs. Use providers like Alchemy or Infura for reliable mainnet and testnet connections. Always perform initial development and testing on a testnet like Sepolia or Goerli to avoid spending real funds.

You will need testnet ETH to pay for gas during development. Faucets for networks like Sepolia can be found on platforms like Alchemy's Faucet or PoW Faucet. For batch execution, the gas cost is a sum of the costs for each internal call plus a base overhead. Use eth_estimateGas RPC calls or built-in library methods to estimate costs before sending transactions. Understanding gas limits and transaction reverts is critical, as one failed call in the batch will cause the entire transaction to fail, protecting users from partial execution states.

Finally, familiarize yourself with the ABI (Application Binary Interface) for the contracts you intend to call. Your batch transaction will consist of an array of target addresses and encoded function call data. Libraries like Ethers.js provide utilities (Interface, encodeFunctionData) to encode these calls. Start by writing a simple script that batches two read-only calls, then progress to state-changing transactions. Always verify contracts on block explorers like Etherscan after deployment to enable direct interaction and transparency.

A UserOperation is a pseudo-transaction object that represents a user's intent for execution by a smart contract account. Unlike a standard Ethereum transaction, it does not originate from an Externally Owned Account (EOA). Instead, it is submitted to a dedicated mempool by a Bundler, which packages it into a real on-chain transaction. This structure enables advanced features like gas sponsorship, signature aggregation, and most importantly, batch execution. The core fields include sender, nonce, initCode, callData, callGasLimit, verificationGasLimit, preVerificationGas, maxFeePerGas, maxPriorityFeePerGas, paymasterAndData, and signature.

Batch execution is achieved by packing multiple calls into the callData field of a single UserOperation. For a smart contract account compliant with ERC-4337, the executeBatch or a similar function is invoked. This function takes arrays of target addresses, values, and calldata payloads. The key advantage is atomicity: either all calls succeed, or the entire batch reverts, preventing partial state changes. This is critical for complex DeFi interactions, like performing a token swap on Uniswap V3 and then depositing the output into Aave in one step, eliminating the risk of being front-run between the two discrete transactions.

Here is a simplified example of constructing a UserOperation for a batch call using the @account-abstraction/sdk and ethers.js. This code bundles a USDC approval and a Uniswap swap into one operation.

javascriptCopy
import { ethers } from 'ethers';
import { UserOperationBuilder } from '@account-abstraction/sdk';

// 1. Encode the batch calls
const usdcContract = new ethers.Contract(usdcAddress, ['function approve(address,uint256)'], provider);
const swapContract = new ethers.Contract(routerAddress, ['function swapExactTokensForTokens(...)'], provider);

const callData = smartAccount.interface.encodeFunctionData('executeBatch', [
  [usdcAddress, routerAddress], // targets
  [0, 0], // values
  [
    usdcContract.interface.encodeFunctionData('approve', [routerAddress, amountIn]),
    swapContract.interface.encodeFunctionData('swapExactTokensForTokens', [amountIn, minAmountOut, path, userAddress, deadline])
  ] // calldata array
]);

// 2. Build the UserOperation
const userOp = new UserOperationBuilder()
  .setSender(smartAccount.address)
  .setNonce(await smartAccount.getNonce())
  .setCallData(callData)
  .setCallGasLimit(200000) // Estimate appropriately
  .setVerificationGasLimit(150000)
  .setPreVerificationGas(50000)
  .setMaxFeePerGas(maxFee)
  .setMaxPriorityFeePerGas(priorityFee)
  .setSignature(signature)
  .build();

To submit this UserOperation for execution, you send it to a bundler service. A common endpoint is the eth_sendUserOperation RPC method exposed by bundlers like those run by Stackup, Alchemy, or Biconomy. The bundler validates the operation, packages it with others into a single bundle transaction, and sends it to the EntryPoint contract (address 0x5FF137D4b0FDCD49DcA30c7CF57E578a026d2789 on mainnet). The EntryPoint handles the orchestration: it verifies the user's signature and paymaster validity, executes the batch calls via the smart account, and compensates the bundler. Always estimate gas limits carefully using tools like the userop.js library's Estimator to avoid failed transactions.

Key considerations for batch execution include gas estimation and error handling. Since you are combining multiple actions, the total callGasLimit must cover the sum of all executions. Underestimating will cause the entire batch to fail. Furthermore, the order of operations matters for state dependencies; a call that relies on the outcome of a previous call in the batch must be placed after it. For developers, thoroughly testing batches on a testnet (like Sepolia) using a verifying paymaster to sponsor gas is essential before mainnet deployment. This setup allows you to debug the execution flow without spending real ETH on gas.

A batch transaction interface allows users to compose and execute multiple distinct operations—such as token approvals, swaps, and transfers—as a single, atomic transaction. This design pattern is fundamental to improving user experience in DeFi and NFT applications by reducing gas fees, minimizing wallet confirmations, and ensuring complex multi-step interactions either succeed completely or fail without side effects. Key protocols like Safe (formerly Gnosis Safe) and Uniswap's Universal Router have popularized this approach, demonstrating its utility for power users and automated systems.

The core technical challenge is managing transaction dependency and state validation. Your interface must construct an array of calldata where each subsequent call can safely assume the state changes of the previous ones. For example, a common batch might first call approve() on an ERC-20 token for a router, then call swapExactTokensForTokens() on that router. The interface logic must ensure the approval is included and ordered correctly. Smart contract wallets and EIP-4337 Account Abstraction bundles are built for this pattern, validating the entire batch's success before submission.

When designing the frontend, clarity is paramount. A well-structured UI should: display a simulated summary of the batch's net effect on the user's portfolio, provide clear error states if a step would revert (e.g., insufficient balance post-step 2), and allow users to review, edit, and reorder steps before signing. Implementing a preview of the total estimated gas cost for the bundle versus executing steps individually provides tangible user value. Libraries like ethers.js or viem are used to encode the individual function calls and aggregate them into a single transaction object.

For developers, the implementation involves interacting with a batch processor contract. You send the target addresses, values, and calldata for each step. A basic Solidity interface for such a processor is:

solidityCopy
function executeBatch(
    address[] calldata targets,
    uint256[] calldata values,
    bytes[] calldata calldatas
) external payable;

Your client-side code must populate these arrays, often using ABI encoding utilities. Security is critical: the interface should simulate the batch via eth_call to a node or a service like Tenderly to preview outcomes and catch reverts before the user signs.

Advanced implementations incorporate conditional execution and gas optimization. For instance, a batch might only execute a limit order if the price reaches a certain level, checked via an oracle call within the transaction. Gas can be saved by using techniques like gas token refunds (e.g., CHI or GST2) or structuring calls to minimize storage slot accesses. The interface can offer users the option to enable these optimizations, explaining the trade-offs between cost, speed, and complexity.

Finally, always provide clear post-execution feedback. Upon successful batch completion, the interface should decode and display the results of each internal call—such as amounts swapped or received—and provide a link to the bundle transaction on a block explorer. For failed batches, the error should be traced to the specific step that caused the revert. This level of transparency turns a complex technical feature into a reliable and user-friendly tool, encouraging adoption for sophisticated on-chain strategies.

Batch execution works by aggregating multiple function calls into a single transaction. Instead of paying base gas fees (21,000 gas for a simple transfer) for each individual operation, you pay it once for the entire batch. This is particularly effective for operations like token approvals, swaps, or interacting with multiple DeFi protocols in sequence. The primary mechanism involves a relayer contract or a multicall contract that receives an array of calldata and executes them sequentially within the same context.

To implement batching, you typically interact with a pre-deployed multicall contract. A common standard is the Multicall3 contract by mds1, deployed on most EVM chains. The core function is aggregate3, which takes an array of Call3 structs. Each struct contains a target address, allowFailure boolean, and callData. The contract loops through the array and makes a low-level call to each target with the provided data, reverting only if allowFailure is false and the call fails.

Here is a basic JavaScript/ethers.js example for constructing and sending a batch transaction. First, encode the individual calls, then pass them to the multicall contract.

javascriptCopy
const { ethers } = require('ethers');
const multicallAbi = [...]; // ABI for Multicall3's aggregate3
const MULTICALL3_ADDRESS = '0xcA11bde05977b3631167028862bE2a173976CA11';

// 1. Encode calls to different contracts
const call1 = {
  target: TOKEN_ADDRESS,
  callData: tokenInterface.encodeFunctionData('approve', [SPENDER, AMOUNT])
};
const call2 = {
  target: SWAP_ROUTER_ADDRESS,
  callData: routerInterface.encodeFunctionData('swapExactTokensForTokens', [...])
};

// 2. Create the multicall
const multicallContract = new ethers.Contract(MULTICALL3_ADDRESS, multicallAbi, signer);
const tx = await multicallContract.aggregate3([
  { ...call1, allowFailure: false },
  { ...call2, allowFailure: false }
]);

Key considerations for gas optimization include calldata packing and execution order. Since calldata costs 16 gas per non-zero byte and 4 gas per zero byte on Ethereum, efficiently encoding your data is crucial. Order operations to minimize state changes between calls that might increase gas costs. Also, use allowFailure: true for non-critical operations to prevent the entire batch from reverting, though this requires careful result parsing. Always estimate gas for the batch and compare it to the sum of individual transactions to confirm savings.

For advanced use cases, consider meta-transactions or account abstraction (ERC-4337) bundlers, which can further abstract gas payment and enable sponsored transactions. Protocols like Gelato Network or OpenZeppelin's Defender can automate batch execution based on off-chain conditions. When designing your system, audit the multicall contract you use and ensure it properly handles delegate calls if used, as this introduces significant security risks. Testing on a fork of mainnet using tools like Foundry or Hardhat is essential before deployment.

In blockchain applications, batch transactions bundle multiple function calls into a single on-chain operation. This pattern is essential for gas efficiency and atomicity, where you want a group of actions to succeed or fail together. However, a critical challenge arises when one call in the batch fails—should the entire transaction revert, or should successful calls be preserved? This is the core problem of partial execution. Protocols like Uniswap's Multicall and OpenZeppelin's ReentrancyGuard approach this differently, forcing developers to make explicit design choices about failure handling.

To set up batch execution, you typically use a dispatcher contract or a library designed for multicall. A basic Solidity implementation involves an array of Call structs, each containing a target address, value, and calldata. The dispatcher loops through these calls using a low-level .call(). The key decision point is error handling within the loop. A simple require(success) will cause a full revert on any failure, ensuring atomicity but sacrificing progress. For partial execution, you must catch the revert, log the failure, and continue the loop, which requires careful state management to avoid leaving the contract in an inconsistent state.

Consider a batch that swaps tokens on a DEX and then deposits them into a lending protocol. If the deposit fails, a full-revert strategy undoes the profitable swap—a poor user experience. A partial execution strategy would complete the swap, refund the user, and report the deposit failure. Implementing this safely requires checks-effects-interactions patterns and potentially using intermediate escrow contracts to hold assets mid-batch. Always emit clear events for each sub-operation's success or failure so off-chain monitors can track the batch's outcome.

Testing is paramount. Use a framework like Foundry or Hardhat to simulate various failure modes: insufficient gas for later calls, reverts in target contracts, and reentrancy attacks. Write tests that verify the contract state after a partially failed batch is still secure and functional. Tools like Slither or MythX can help analyze the control flow for vulnerabilities introduced by the continue-on-failure logic. Remember, allowing partial success increases complexity and attack surface; it should only be used when the benefits outweigh the risks.

In practice, many developers use established libraries. OpenZeppelin's Multicall.sol provides a basic, non-atomic batch function. For more advanced needs, the Gnosis Safe's MultiSend contract and the EIP-2535 Diamonds standard offer modular patterns for delegatecall-based batching. When integrating with these systems, always review their failure handling behavior. The choice between atomic and partial execution fundamentally shapes your application's resilience and user experience, making it a critical architectural decision in smart contract design.

You have now learned how to set up a system for executing multiple operations in a single on-chain transaction. The primary benefits are clear: significant gas savings for users and improved UX by reducing the number of wallet confirmations required for complex interactions. This pattern is widely used in DeFi for actions like multi-asset swaps, collateral management, and governance voting. The core mechanism relies on a smart contract with a multicall or batchExecute function that loops through an array of encoded function calls.

For production use, security is paramount. Your batch executor contract must include robust access control, typically using OpenZeppelin's Ownable or role-based systems. It should also implement checks for reentrancy and validate all inputs to prevent malicious payloads. Consider integrating with a gas estimation service to provide users with accurate cost predictions before they sign. Tools like Tenderly's Simulation API or the eth_estimateGas RPC call are essential for this.

To extend your implementation, explore advanced patterns. You can incorporate gas refunds by using operations that free up storage, integrate signature verification for meta-transactions via EIP-712, or create a relayer network to sponsor transaction fees for users. Studying established contracts like Uniswap V3's Multicall, MakerDAO's DssExecLib, or the Gnosis Safe's batch transaction module provides excellent real-world references for secure and efficient design.

Your next practical steps should be: 1) Deploy your batch executor to a testnet like Sepolia or Goerli, 2) Write comprehensive tests using Foundry or Hardhat covering success cases, failures, and gas usage, and 3) Integrate the frontend logic using libraries like ethers.js or viem to encode the calls. Remember to verify your contract source code on block explorers like Etherscan to build trust with users.

The ecosystem for batch execution is evolving. Keep an eye on new standards like ERC-7579 for modular smart accounts, which formalizes batch execution interfaces. Layer 2 solutions like Arbitrum and Optimism, with their lower gas fees, make complex batches even more practical. By mastering this technique, you are building a critical component for the next generation of efficient and user-friendly decentralized applications.