Batch operations in Blockchain / Solidity - Time & Space Complexity

Time Complexity: Batch operations

O(n)

Understanding Time Complexity

When working with blockchain, batch operations let us handle many tasks at once. Understanding how the time needed grows as we add more tasks is important.

We want to know: how does doing many operations together affect the time it takes?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

function batchTransfer(address[] memory recipients, uint256 amount) public {
  for (uint i = 0; i < recipients.length; i++) {
    token.transfer(recipients[i], amount);
  }
}

This code sends tokens to many addresses one after another in a loop.

Identify Repeating Operations

• Primary operation: The for-loop that calls token.transfer for each recipient.
• How many times: Once for each address in the recipients list.

How Execution Grows With Input

As the number of recipients grows, the number of transfers grows the same way.

Input Size (n)	Approx. Operations
10	10 transfers
100	100 transfers
1000	1000 transfers

Pattern observation: The time grows directly with the number of recipients. Double the recipients, double the work.

Final Time Complexity

Time Complexity: O(n)

This means the time needed grows in a straight line with the number of recipients.

Common Mistake

[X] Wrong: "Batch operations always run in constant time because they are done together."

[OK] Correct: Even if done in one function, each operation inside the batch still runs one after another, so time grows with the number of operations.

Interview Connect

Understanding how batch operations scale helps you explain efficiency clearly. This skill shows you can think about real blockchain costs and user experience.

Self-Check

"What if the batch function called another function that itself loops over the recipients? How would the time complexity change?"