Why S3 matters for object storage in AWS - Performance Analysis

Time Complexity: Why S3 matters for object storage

O(n)

Understanding Time Complexity

We want to understand how the time to store and retrieve objects in S3 changes as we add more objects.

How does the number of objects affect the speed of operations?

Scenario Under Consideration

Analyze the time complexity of uploading multiple objects to an S3 bucket.

// Upload multiple files to S3 bucket
for (let i = 0; i < n; i++) {
  s3.putObject({
    Bucket: 'my-bucket',
    Key: `file_${i}.txt`,
    Body: 'file content'
  }, function(err, data) {
    if (err) console.log(err, err.stack); // an error occurred
    else     console.log(data);           // successful response
  });
}

This sequence uploads n separate objects to the same S3 bucket, one by one.

Identify Repeating Operations

• Primary operation: The putObject API call to upload each file.
• How many times: Exactly once per file, so n times for n files.

How Execution Grows With Input

Each new file means one more upload call, so the total work grows directly with the number of files.

Input Size (n)	Approx. Api Calls/Operations
10	10 uploads
100	100 uploads
1000	1000 uploads

Pattern observation: The number of upload operations grows linearly as we add more files.

Final Time Complexity

Time Complexity: O(n)

This means the time to upload all files grows in direct proportion to how many files you have.

Common Mistake

[X] Wrong: "Uploading many files at once takes the same time as uploading one file."

[OK] Correct: Each file requires its own upload call, so more files mean more time overall.

Interview Connect

Understanding how operations scale with input size helps you design efficient storage solutions and explain your reasoning clearly in interviews.

Self-Check

What if we uploaded multiple files in parallel instead of one by one? How would the time complexity change?