Table creation with AWS SDK in DynamoDB - Time & Space Complexity

Time Complexity: Table creation with AWS SDK

O(1)

Understanding Time Complexity

When creating a table using the AWS SDK for DynamoDB, it's important to understand how the time taken grows as we add more details to the table.

We want to know how the number of steps or calls changes when we create tables with different sizes or settings.

Scenario Under Consideration

Analyze the time complexity of the following operation sequence.

const AWS = require('aws-sdk');
const dynamodb = new AWS.DynamoDB();

const params = {
  TableName: "MyTable",
  KeySchema: [
    { AttributeName: "Id", KeyType: "HASH" }
  ],
  AttributeDefinitions: [
    { AttributeName: "Id", AttributeType: "S" }
  ],
  ProvisionedThroughput: {
    ReadCapacityUnits: 5,
    WriteCapacityUnits: 5
  }
};

await dynamodb.createTable(params).promise();
    

This code creates a DynamoDB table with a simple key schema and provisioned throughput settings.

Identify Repeating Operations

Identify the API calls, resource provisioning, data transfers that repeat.

• Primary operation: One API call to createTable to request table creation.
• How many times: Exactly once per table creation request.

How Execution Grows With Input

Creating a table involves a single API call regardless of table size or settings.

Input Size (n)	Approx. Api Calls/Operations
1 table with 1 attribute	1
1 table with 10 attributes	1
1 table with 100 attributes	1

Pattern observation: The number of API calls stays the same no matter how many attributes or settings the table has.

Final Time Complexity

Time Complexity: O(1)

This means creating a table takes a fixed number of steps, no matter how big or complex the table is.

Common Mistake

[X] Wrong: "Creating a table takes longer if it has more attributes or indexes because it needs more API calls."

[OK] Correct: Actually, the SDK sends one single request to create the table with all details included, so the number of API calls stays the same.

Interview Connect

Understanding how AWS SDK operations scale helps you explain cloud resource management clearly and confidently in real-world situations.

Self-Check

"What if we created multiple tables in a loop? How would the time complexity change?"

Practice

1. What is the main purpose of specifying a primary key when creating a DynamoDB table using the AWS SDK?

easy

A. To set the table's read and write capacity

B. To specify the table's encryption settings

C. To define the table's region

D. To uniquely identify each item in the table

Solution

1. Step 1: Understand the role of a primary key in DynamoDB

   The primary key uniquely identifies each item in the table, ensuring no duplicates.

2. Step 2: Differentiate from other table settings

   Read/write capacity, region, and encryption are important but unrelated to item uniqueness.

3. Final Answer:

   To uniquely identify each item in the table -> Option D

4. Quick Check:

   Primary key = unique item ID [OK]

Hint: Primary key means unique ID for each item [OK]

Common Mistakes:

• Confusing primary key with capacity settings
• Thinking primary key sets region or encryption
• Ignoring the uniqueness requirement

2. Which of the following is the correct way to specify the primary key attribute when creating a DynamoDB table using AWS SDK for JavaScript?

easy

A. "KeySchema": [{ "AttributeName": "UserId", "KeyType": "HASH" }]

B. "KeySchema": [{ "AttributeName": "UserId", "KeyType": "PRIMARY" }]

C. "KeySchema": [{ "AttributeName": "UserId", "KeyType": "PRIMARY_KEY" }]

D. "KeySchema": [{ "AttributeName": "UserId", "KeyType": "KEY" }]

Solution

1. Step 1: Recall AWS SDK syntax for KeySchema

   The correct key type for the partition key is "HASH" in the KeySchema array.

2. Step 2: Identify incorrect key types

   "PRIMARY", "PRIMARY_KEY", and "KEY" are not valid KeyType values in AWS SDK.

3. Final Answer:

   "KeySchema": [{ "AttributeName": "UserId", "KeyType": "HASH" }] -> Option A

4. Quick Check:

   KeyType for partition key = HASH [OK]

Hint: Use "HASH" for partition key in KeySchema [OK]

Common Mistakes:

• Using invalid KeyType values like PRIMARY or KEY
• Confusing KeyType with attribute types
• Missing the array structure for KeySchema

3. Given the following AWS SDK code snippet for creating a DynamoDB table, what will be the output if the table creation is successful?

const params = {
  TableName: "Products",
  KeySchema: [
    { AttributeName: "ProductId", KeyType: "HASH" }
  ],
  AttributeDefinitions: [
    { AttributeName: "ProductId", AttributeType: "S" }
  ],
  ProvisionedThroughput: {
    ReadCapacityUnits: 5,
    WriteCapacityUnits: 5
  }
};

const result = await dynamodb.createTable(params).promise();
console.log(result.TableDescription.TableName);

medium

A. Products

B. Table creation failed

C. undefined

D. Error: Missing KeySchema

Solution

1. Step 1: Understand the createTable response structure

   On success, createTable returns an object with TableDescription including TableName.

2. Step 2: Check the console.log statement

   It prints result.TableDescription.TableName, which is "Products" as specified.

3. Final Answer:

   Products -> Option A

4. Quick Check:

   Successful createTable logs table name [OK]

Hint: Successful createTable returns TableDescription with TableName [OK]

Common Mistakes:

• Expecting error messages on success
• Confusing undefined with valid output
• Missing await causing promise object logging

4. You try to create a DynamoDB table with the following parameters but get an error. What is the most likely cause?

const params = {
  TableName: "Orders",
  KeySchema: [
    { AttributeName: "OrderId", KeyType: "HASH" },
    { AttributeName: "OrderDate", KeyType: "RANGE" }
  ],
  AttributeDefinitions: [
    { AttributeName: "OrderId", AttributeType: "S" }
  ],
  ProvisionedThroughput: {
    ReadCapacityUnits: 10,
    WriteCapacityUnits: 10
  }
};

await dynamodb.createTable(params).promise();

medium

A. TableName is invalid

B. Missing AttributeDefinition for "OrderDate"

C. ProvisionedThroughput values are too high

D. KeyType "RANGE" is not allowed

Solution

1. Step 1: Check KeySchema and AttributeDefinitions consistency

   Both "OrderId" and "OrderDate" are in KeySchema, but only "OrderId" is defined in AttributeDefinitions.

2. Step 2: Identify missing attribute definition

   "OrderDate" must be defined in AttributeDefinitions to avoid error.

3. Final Answer:

   Missing AttributeDefinition for "OrderDate" -> Option B

4. Quick Check:

   All key attributes need AttributeDefinitions [OK]

Hint: Define all key attributes in AttributeDefinitions [OK]

Common Mistakes:

• Forgetting to define all key attributes
• Assuming RANGE is invalid KeyType
• Blaming ProvisionedThroughput or TableName

5. You want to create a DynamoDB table named "Employees" with a composite primary key consisting of "EmployeeId" (string) as the partition key and "Department" (string) as the sort key. You also want to set the read capacity to 3 and write capacity to 2. Which of the following AWS SDK parameter objects correctly creates this table?

hard

A. { TableName: "Employees", KeySchema: [{ AttributeName: "EmployeeId", KeyType: "HASH" }], AttributeDefinitions: [{ AttributeName: "EmployeeId", AttributeType: "S" }, { AttributeName: "Department", AttributeType: "S" }], ProvisionedThroughput: { ReadCapacityUnits: 3, WriteCapacityUnits: 2 } }

B. { TableName: "Employees", KeySchema: [{ AttributeName: "EmployeeId", KeyType: "RANGE" }, { AttributeName: "Department", KeyType: "HASH" }], AttributeDefinitions: [{ AttributeName: "EmployeeId", AttributeType: "S" }, { AttributeName: "Department", AttributeType: "S" }], ProvisionedThroughput: { ReadCapacityUnits: 3, WriteCapacityUnits: 2 } }

C. { TableName: "Employees", KeySchema: [{ AttributeName: "EmployeeId", KeyType: "HASH" }, { AttributeName: "Department", KeyType: "RANGE" }], AttributeDefinitions: [{ AttributeName: "EmployeeId", AttributeType: "S" }, { AttributeName: "Department", AttributeType: "S" }], ProvisionedThroughput: { ReadCapacityUnits: 3, WriteCapacityUnits: 2 } }

D. { TableName: "Employees", KeySchema: [{ AttributeName: "EmployeeId", KeyType: "HASH" }, { AttributeName: "Department", KeyType: "RANGE" }], AttributeDefinitions: [{ AttributeName: "EmployeeId", AttributeType: "N" }, { AttributeName: "Department", AttributeType: "S" }], ProvisionedThroughput: { ReadCapacityUnits: 3, WriteCapacityUnits: 2 } }

Solution

1. Step 1: Verify KeySchema order and types

   Partition key must have KeyType "HASH" and sort key "RANGE" in correct order: EmployeeId (HASH), Department (RANGE).

2. Step 2: Check AttributeDefinitions types

   Both EmployeeId and Department are strings, so AttributeType "S" is correct for both.

3. Step 3: Confirm ProvisionedThroughput values

   ReadCapacityUnits: 3 and WriteCapacityUnits: 2 match the requirement.

4. Final Answer:

   { TableName: "Employees", KeySchema: [{ AttributeName: "EmployeeId", KeyType: "HASH" }, { AttributeName: "Department", KeyType: "RANGE" }], AttributeDefinitions: [{ AttributeName: "EmployeeId", AttributeType: "S" }, { AttributeName: "Department", AttributeType: "S" }], ProvisionedThroughput: { ReadCapacityUnits: 3, WriteCapacityUnits: 2 } } -> Option C

5. Quick Check:

   Correct key order and attribute types = { TableName: "Employees", KeySchema: [{ AttributeName: "EmployeeId", KeyType: "HASH" }, { AttributeName: "Department", KeyType: "RANGE" }], AttributeDefinitions: [{ AttributeName: "EmployeeId", AttributeType: "S" }, { AttributeName: "Department", AttributeType: "S" }], ProvisionedThroughput: { ReadCapacityUnits: 3, WriteCapacityUnits: 2 } } [OK]

Hint: Partition key = HASH, sort key = RANGE, types must match [OK]

Common Mistakes:

• Swapping HASH and RANGE key types
• Mismatching attribute types (string vs number)
• Omitting sort key in KeySchema