Data source vs resource difference in Terraform - Performance Comparison

Time Complexity: Data source vs resource difference

O(n)

Understanding Time Complexity

We want to understand how the time to run Terraform changes when using data sources versus resources.

Specifically, how does Terraform's work grow as we add more data sources or resources?

Scenario Under Consideration

Analyze the time complexity of this Terraform snippet.

    data "aws_ami" "example" {
      most_recent = true
      filter {
        name   = "name"
        values = ["amzn2-ami-hvm-*-x86_64-ebs"]
      }
    }

    resource "aws_instance" "example" {
      ami           = data.aws_ami.example.id
      instance_type = "t2.micro"
    }
    

This code fetches an existing AMI using a data source and then creates an EC2 instance resource using that AMI.

Identify Repeating Operations

Look at what Terraform does repeatedly when applying this configuration.

• Primary operation: API call to fetch AMI details (data source), and API call to create EC2 instance (resource).
• How many times: Each data source triggers one read API call per apply; each resource triggers one create API call per instance.

How Execution Grows With Input

As you add more data sources and resources, the number of API calls grows.

Input Size (n)	Approx. API Calls/Operations
10 data sources + 10 resources	~20 API calls (10 reads + 10 creates)
100 data sources + 100 resources	~200 API calls (100 reads + 100 creates)
1000 data sources + 1000 resources	~2000 API calls (1000 reads + 1000 creates)

Pattern observation: The total API calls grow roughly linearly with the number of data sources and resources.

Final Time Complexity

Time Complexity: O(n)

This means the time to apply grows in direct proportion to how many data sources and resources you have.

Common Mistake

[X] Wrong: "Data sources are free and do not add to execution time."

[OK] Correct: Data sources make API calls to read existing info, so they add to the total work Terraform does.

Interview Connect

Understanding how Terraform scales with data sources and resources helps you design efficient infrastructure code and shows you think about real-world costs.

Self-Check

"What if we replaced many data sources with a single resource that creates and manages all those items? How would the time complexity change?"

Practice

1. What is the main difference between a resource and a data source in Terraform?

easy

A. Resources and data sources both create infrastructure but in different clouds.

B. Resources create or manage infrastructure; data sources only read existing information.

C. Resources only read existing infrastructure; data sources create new infrastructure.

D. Resources are temporary; data sources are permanent.

Solution

1. Step 1: Understand resource role

   Resources in Terraform are used to create or manage cloud infrastructure components.

2. Step 2: Understand data source role

   Data sources are used to read information about existing infrastructure without changing it.

3. Final Answer:

   Resources create or manage infrastructure; data sources only read existing information. -> Option B

4. Quick Check:

   Resource = create/manage, Data source = read [OK]

Hint: Resources build; data sources just look up info [OK]

Common Mistakes:

• Thinking data sources create resources
• Confusing resource and data source roles
• Assuming both modify infrastructure
• Believing data sources are temporary

2. Which of the following is the correct syntax to declare a data source in Terraform?

easy

A. data "aws_ami" "example" {}

B. resource "aws_instance" "example" {}

C. source "aws_vpc" "example" {}

D. resource "data_aws_subnet" "example" {}

Solution

1. Step 1: Identify data source syntax

   Data sources use the keyword data followed by the type and name.

2. Step 2: Check options

   data "aws_ami" "example" {} uses data "aws_ami" "example" {}, which is correct syntax for a data source.

3. Final Answer:

   data "aws_ami" "example" {} -> Option A

4. Quick Check:

   Data source starts with 'data' keyword [OK]

Hint: Data sources start with 'data' keyword [OK]

Common Mistakes:

• Using 'resource' keyword for data sources
• Mixing 'source' keyword which doesn't exist
• Prefixing resource with 'data_' incorrectly
• Confusing resource and data source syntax

3. Given this Terraform snippet:

data "aws_vpc" "selected" {
  filter {
    name   = "tag:Name"
    values = ["main-vpc"]
  }
}

resource "aws_subnet" "example" {
  vpc_id     = data.aws_vpc.selected.id
  cidr_block = "10.0.1.0/24"
}

What does the data.aws_vpc.selected.id represent?

medium

A. The ID of an existing VPC filtered by tag "Name=main-vpc".

B. The ID of a subnet created inside the VPC.

C. A random VPC ID generated by Terraform.

D. The ID of a new VPC created by Terraform.

Solution

1. Step 1: Analyze data source usage

   The data "aws_vpc" "selected" block reads an existing VPC filtered by the tag "Name=main-vpc".

2. Step 2: Understand reference in resource

   The resource aws_subnet.example uses data.aws_vpc.selected.id to get the ID of that existing VPC to create a subnet inside it.

3. Final Answer:

   The ID of an existing VPC filtered by tag "Name=main-vpc". -> Option A

4. Quick Check:

   Data source reads existing VPC ID [OK]

Hint: Data source reads existing resource info [OK]

Common Mistakes:

• Thinking data source creates new VPC
• Confusing subnet ID with VPC ID
• Assuming random ID generation
• Using resource instead of data source for existing info

4. You wrote this Terraform code but get an error:

resource "aws_vpc" "main" {
  cidr_block = "10.0.0.0/16"
}

data "aws_vpc" "main" {
  id = aws_vpc.main.id
}

What is the problem?

medium

A. The data source block is missing a filter instead of id.

B. Data sources cannot reference resources in the same configuration.

C. You cannot use the same name for resource and data source.

D. The resource block is missing a provider configuration.

Solution

1. Step 1: Check naming conflict

   The resource and data source both use the name "main" which causes a naming conflict in Terraform.

2. Step 2: Terraform rules

   Terraform does not allow a resource and a data source to share the same type and name within the same module.

3. Final Answer:

   You cannot use the same name for resource and data source. -> Option C

4. Quick Check:

   Resource and data source names must be unique [OK]

Hint: Resource and data source names must differ [OK]

Common Mistakes:

• Thinking same names cause conflicts
• Believing you always need filters over id
• Assuming data sources can depend on resources
• Blaming missing provider config

5. You want to create a subnet in an existing VPC without recreating the VPC. Which Terraform approach is best?

hard

A. Use a resource block for the VPC and a resource block for the subnet.

B. Use a resource block for the subnet and ignore the VPC.

C. Use a data source block for both VPC and subnet.

D. Use a data source block to read the existing VPC and a resource block to create the subnet.

Solution

1. Step 1: Identify existing infrastructure

   The VPC already exists, so it should not be recreated or managed as a resource.

2. Step 2: Use data source for existing VPC

   Use a data source block to safely read the existing VPC's details without changing it.

3. Step 3: Create subnet resource

   Create a resource block for the subnet to add new infrastructure inside the existing VPC.

4. Final Answer:

   Use a data source block to read the existing VPC and a resource block to create the subnet. -> Option D

5. Quick Check:

   Data source reads existing; resource creates new [OK]

Hint: Read existing with data source; create new with resource [OK]

Common Mistakes:

• Trying to recreate existing VPC as resource
• Using data source to create subnet (not possible)
• Ignoring existing VPC and creating subnet without VPC ID
• Using resource for both causing conflicts