Why Model dependencies and parallelism in dbt? - Purpose & Use Cases

The Big Idea

What if your data projects could build themselves faster and without mistakes?

The Scenario

Imagine you have a big recipe book where each dish depends on ingredients prepared by other dishes. You try to cook everything step-by-step, waiting for one dish to finish before starting the next, even if some dishes don't depend on each other.

The Problem

This slow, one-by-one cooking wastes time. You might forget which dish needs which ingredient first, causing mistakes or redoing steps. It's hard to keep track and takes much longer than needed.

The Solution

Model dependencies and parallelism in dbt help you organize your recipes so you know exactly which dishes depend on others. dbt then cooks all independent dishes at the same time, saving you lots of time and avoiding confusion.

Before vs After

✗ Before

run model_a
depend on model_a: run model_b
run model_c after model_b

✓ After

dbt run --models model_a model_b model_c --threads 3

What It Enables

It lets you build complex data projects faster by running independent tasks at the same time while respecting their order.

Real Life Example

In a sales dashboard, you first prepare customer data, then sales data, and finally combine them. With model dependencies and parallelism, dbt runs customer and sales data models simultaneously if they don't depend on each other, speeding up the whole process.

Key Takeaways

Manual step-by-step runs are slow and error-prone.

Model dependencies map out what needs to run first.

Parallelism runs independent models at the same time, saving time.

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