Model dependencies and parallelism in dbt - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a model dependency in dbt?

A model dependency in dbt means one model needs the output of another model before it can run. It creates a chain where models run in order based on their connections.

beginner

How does dbt know which models depend on others?

dbt reads the SQL code and looks for references to other models using the {{ ref() }} function. This tells dbt the order to run models.

beginner

What is parallelism in dbt?

Parallelism means running multiple models at the same time if they don’t depend on each other. This speeds up the whole process.

intermediate

Why is understanding model dependencies important for parallelism?

Because dbt can only run models in parallel if they don’t depend on each other. Knowing dependencies helps dbt run models safely and fast.

intermediate

How can you control the number of models running in parallel in dbt?

You can set the 'threads' option in the profiles.yml file. More threads mean more models run at the same time, but too many can slow things down.

What function does dbt use to identify model dependencies?

A {{ source() }}

B {{ run() }}

C {{ config() }}

D {{ ref() }}

dbt uses the {{ ref() }} function to link models and understand dependencies.

If two models do NOT depend on each other, how does dbt run them?

A One after the other

B They merge automatically

C In parallel

D Only one runs

dbt runs models that don’t depend on each other in parallel to save time.

What happens if you set too many threads in dbt?

A May slow down due to resource limits

B Runs faster without limits

C Stops all models

D Ignores dependencies

Too many threads can overload your system and slow down the process.

Which of these is NOT a reason to use model dependencies?

A To ensure correct order of data processing

B To speed up running unrelated models

C To avoid errors from missing data

D To create a chain of data transformations

Model dependencies help order models, but speeding up unrelated models is done by parallelism.

Where do you set the number of threads for parallelism in dbt?

A In the profiles.yml file

B In the SQL model file

C In the database settings

D In the command line only

The 'threads' setting is configured in the profiles.yml file.

Explain how dbt uses model dependencies to decide the order of running models.

Think about how one model needs another's output before it can run.

Describe how parallelism works in dbt and why it is useful.

Consider how dbt speeds up running models that don’t rely on each other.

Practice

1. What does the {{ ref('model_name') }} function do in dbt models?

easy

A. It sets the number of threads for execution.

B. It creates a dependency on another model, ensuring it runs first.

C. It runs the model in parallel with others.

D. It deletes the referenced model from the project.

Solution

1. Step 1: Understand the purpose of {{ ref() }}

   The {{ ref('model_name') }} function tells dbt that the current model depends on another model named 'model_name'.

2. Step 2: Recognize dependency effect

   This means dbt will run the referenced model first before running the current one, ensuring correct order.

3. Final Answer:

   It creates a dependency on another model, ensuring it runs first. -> Option B

4. Quick Check:

   ref() sets dependencies = C [OK]

Hint: Remember: ref() links models to run in order [OK]

Common Mistakes:

• Thinking ref() controls parallelism
• Confusing ref() with thread settings
• Assuming ref() deletes models

2. Which command line option controls how many models dbt runs at the same time?

easy

A. --models

B. --exclude

C. --full-refresh

D. --threads

Solution

1. Step 1: Identify parallelism control option

   The --threads option tells dbt how many models it can run simultaneously.

2. Step 2: Differentiate from other options

   Other options like --models select which models to run, but do not control parallelism.

3. Final Answer:

   --threads -> Option D

4. Quick Check:

   Parallelism = --threads [OK]

Hint: Threads set parallel runs count in dbt [OK]

Common Mistakes:

• Confusing --models with parallelism control
• Using --full-refresh to control threads
• Thinking --exclude affects parallelism

3. Given these models and dependencies:

model_a.sql (no dependencies)
model_b.sql (depends on model_a)
model_c.sql (depends on model_a)
model_d.sql (depends on model_b and model_c)

If you run dbt run --threads 2, which models can run in parallel first?

medium

A. model_a only

B. model_b and model_c

C. model_a and model_b

D. model_c and model_d

Solution

1. Step 1: Identify models without dependencies

   Only model_a has no dependencies, so it must run first.

2. Step 2: Understand parallelism with threads=2

   Since only model_a is ready, it runs alone first. After it finishes, model_b and model_c can run in parallel.

3. Final Answer:

   model_a only -> Option A

4. Quick Check:

   First run models without dependencies = model_a [OK]

Hint: Run models without dependencies first, then parallelize [OK]

Common Mistakes:

• Running dependent models before their dependencies
• Assuming multiple models run before dependencies finish
• Ignoring dependency order

4. You wrote this dbt model:

select * from {{ ref('model_x') }}

But when running, dbt errors saying model_x does not exist. What is the likely problem?

medium

A. You used --threads with too many threads.

B. The ref function is misspelled.

C. You forgot to create or run model_x before this model.

D. You need to add --full-refresh to run.

Solution

1. Step 1: Understand the error meaning

   The error says model_x does not exist, meaning dbt cannot find it to build dependency.

2. Step 2: Check model creation and run order

   This usually happens if model_x was not created or run before this model, so dependency is missing.

3. Final Answer:

   You forgot to create or run model_x before this model. -> Option C

4. Quick Check:

   Missing dependency model causes error = B [OK]

Hint: Ensure all referenced models exist and run first [OK]

Common Mistakes:

• Blaming thread count for missing model error
• Assuming ref() spelling error without checking
• Thinking --full-refresh fixes missing model

5. You have 5 models with these dependencies:

model_1 (no dependencies)
model_2 (depends on model_1)
model_3 (depends on model_1)
model_4 (depends on model_2)
model_5 (depends on model_3)

If you run dbt run --threads 3, what is the minimum number of sequential steps dbt needs to complete all models?

hard

A. 3

B. 4

C. 5

D. 2

Solution

1. Step 1: Identify dependency layers

   Layer 1: model_1 (no dependencies)
   Layer 2: model_2 and model_3 (depend on model_1)
   Layer 3: model_4 (depends on model_2) and model_5 (depends on model_3)

2. Step 2: Calculate steps with 3 threads

   Step 1: Run model_1 alone
   Step 2: Run model_2 and model_3 in parallel (2 threads used)
   Step 3: Run model_4 and model_5 in parallel (2 threads used)

3. Final Answer:

   3 -> Option A

4. Quick Check:

   Dependency layers = 3 steps [OK]

Hint: Count dependency layers, parallelize within each layer [OK]

Common Mistakes:

• Counting each model as separate step ignoring parallelism
• Ignoring dependency order
• Assuming all models run in one step