Building a DAG of models in dbt - Time & Space Complexity

Time Complexity: Building a DAG of models

O(n)

Understanding Time Complexity

When building a DAG of models in dbt, we want to understand how the time to run all models grows as we add more models.

We ask: How does the total work increase when the number of models grows?

Scenario Under Consideration

Analyze the time complexity of the following dbt model dependencies.

-- model_a.sql
select * from source_table

-- model_b.sql
select * from {{ ref('model_a') }}

-- model_c.sql
select * from {{ ref('model_b') }}

-- model_d.sql
select * from {{ ref('model_a') }}

-- model_e.sql
select * from {{ ref('model_c') }} join {{ ref('model_d') }} on ...
    

This code shows models depending on others, forming a Directed Acyclic Graph (DAG) of dependencies.

Identify Repeating Operations

Look at how dbt runs models based on dependencies.

• Primary operation: Running each model once after its dependencies.
• How many times: Each model runs exactly one time.

How Execution Grows With Input

As you add more models, the total work grows roughly by the number of models.

Input Size (n)	Approx. Operations
10 models	10 runs
100 models	100 runs
1000 models	1000 runs

Pattern observation: The total work grows linearly as you add more models.

Final Time Complexity

Time Complexity: O(n)

This means the total time to build all models grows directly with the number of models.

Common Mistake

[X] Wrong: "Running one model means running all its dependencies multiple times."

[OK] Correct: dbt runs each model once and reuses results, so dependencies are not rerun repeatedly.

Interview Connect

Understanding how work grows with model count helps you design efficient data pipelines and explain your approach clearly.

Self-Check

"What if some models depend on many others and run slower? How would that affect the overall time complexity?"

Practice

1.

What does a DAG represent in dbt?

easy

A. The configuration settings for dbt profiles

B. The syntax rules for writing SQL queries

C. The order in which models depend on each other

D. The list of all tables in the database

Solution

1. Step 1: Understand what DAG means in dbt context

   A DAG (Directed Acyclic Graph) shows how models are connected by dependencies.

2. Step 2: Identify the role of DAG in dbt

   dbt uses the DAG to know which models to run first based on dependencies.

3. Final Answer:

   The order in which models depend on each other -> Option C

4. Quick Check:

   DAG = model dependency order [OK]

Hint: DAG shows model dependencies and run order [OK]

Common Mistakes:

• Confusing DAG with SQL syntax
• Thinking DAG lists all tables
• Mixing DAG with dbt config files

2.

Which of the following is the correct way to reference another model in a dbt SQL file?

SELECT * FROM ___

easy

A. ref(model_name)

B. ref('model_name')

C. 'ref(model_name)'

D. ref:"model_name"

Solution

1. Step 1: Recall the syntax for referencing models in dbt

   dbt uses the function ref() with the model name as a string inside parentheses.

2. Step 2: Check each option for correct syntax

   ref('model_name') uses ref('model_name') which is correct; others have syntax errors or wrong quotes.

3. Final Answer:

   ref('model_name') -> Option B

4. Quick Check:

   Use ref('model_name') with quotes [OK]

Hint: Use ref('model_name') with quotes and parentheses [OK]

Common Mistakes:

• Omitting quotes around model name
• Using wrong quote types
• Using colons or other symbols

3.

Given these two models, what is the order dbt will run them?

-- model_a.sql
SELECT * FROM source_table

-- model_b.sql
SELECT * FROM {{ ref('model_a') }}

medium

A. model_a runs first, then model_b

B. model_b runs first, then model_a

C. Both run simultaneously

D. dbt will error due to circular dependency

Solution

1. Step 1: Identify dependencies from ref()

   model_b references model_a using ref(), so model_b depends on model_a.

2. Step 2: Determine run order based on dependencies

   dbt runs model_a first, then model_b to ensure data is ready.

3. Final Answer:

   model_a runs first, then model_b -> Option A

4. Quick Check:

   Dependency order = model_a before model_b [OK]

Hint: Models run in dependency order: referenced first [OK]

Common Mistakes:

• Assuming ref() means reverse dependency
• Thinking models run simultaneously
• Confusing circular dependency errors

4.

What is wrong with this dbt model code snippet?

SELECT * FROM {{ ref(model_a) }}

medium

A. Model name should be uppercase

B. ref() cannot be used inside SELECT

C. Missing FROM keyword

D. Missing quotes around model name in ref()

Solution

1. Step 1: Check syntax of ref() usage

   ref() requires the model name as a string with quotes inside the parentheses.

2. Step 2: Identify the error in the code snippet

   model_a is not quoted, causing a syntax error in dbt compilation.

3. Final Answer:

   Missing quotes around model name in ref() -> Option D

4. Quick Check:

   ref('model_name') needs quotes [OK]

Hint: Always put model names in quotes inside ref() [OK]

Common Mistakes:

• Forgetting quotes around model names
• Thinking ref() can't be in SELECT
• Assuming case sensitivity causes error

5.

You have three models: model_x, model_y, and model_z. model_y references model_x, and model_z references both model_x and model_y. Which of the following is the correct order dbt will run these models?

hard

A. model_x, model_y, model_z

B. model_y, model_x, model_z

C. model_z, model_y, model_x

D. model_x, model_z, model_y

Solution

1. Step 1: Analyze dependencies among models

   model_y depends on model_x; model_z depends on both model_x and model_y.

2. Step 2: Determine run order respecting dependencies

   model_x runs first (no dependencies), then model_y (depends on model_x), then model_z (depends on both).

3. Final Answer:

   model_x, model_y, model_z -> Option A

4. Quick Check:

   Run order respects dependencies [OK]

Hint: Run models so dependencies are built before dependents [OK]

Common Mistakes:

• Running dependent models before their dependencies
• Ignoring multiple dependencies
• Assuming any order works if models reference each other