Model dependencies and parallelism in dbt - Deep Dive

Overview - Model dependencies and parallelism

What is it?

Model dependencies and parallelism in dbt describe how different data models rely on each other and how dbt runs these models at the same time to save time. Dependencies mean one model needs data from another before it can run. Parallelism means running multiple models together when they don't depend on each other. This helps build data pipelines faster and more efficiently.

Why it matters

Without understanding dependencies, data models might run in the wrong order, causing errors or wrong results. Without parallelism, dbt would run models one by one, making data processing slow and inefficient. Knowing these concepts helps teams build reliable and fast data workflows, which means quicker insights and better decisions.

Where it fits

Before learning this, you should know basic dbt concepts like models, SQL, and how dbt runs projects. After this, you can learn about advanced dbt features like incremental models, snapshots, and testing to improve data quality and performance.

Mental Model

Core Idea

Model dependencies define the order models must run, and parallelism runs independent models at the same time to speed up the process.

Think of it like...

Imagine cooking a meal where some dishes need others to be ready first, like making sauce before pasta. You can't start the pasta until the sauce is done, but you can bake a salad or dessert at the same time. Dependencies are the cooking order, and parallelism is cooking independent dishes together.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│   Model A     │──────▶│   Model B     │──────▶│   Model C     │
└───────────────┘       └───────────────┘       └───────────────┘
       │                      │                      │
       │                      │                      │
       ▼                      ▼                      ▼
┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│   Model D     │       │   Model E     │       │   Model F     │
└───────────────┘       └───────────────┘       └───────────────┘

Models D, E, and F can run in parallel if they don't depend on each other or on Models A, B, C.

Build-Up - 7 Steps

1

Foundation

Understanding dbt Models

Concept: Learn what a dbt model is and how it represents a SQL query that creates a table or view.

In dbt, a model is a SQL file that defines a transformation. When you run dbt, it runs these SQL queries to build tables or views in your database. Each model can be thought of as a step in your data pipeline.

Result

You understand that each model is a building block in your data workflow.

Knowing what a model is helps you see how data flows and transforms step-by-step.

2

Foundation

What Are Model Dependencies?

Concept: Models can depend on other models, meaning one model uses the output of another.

If Model B uses data from Model A, then Model B depends on Model A. dbt figures out these dependencies by looking at references in your SQL code, like ref('model_a'). This tells dbt to run Model A before Model B.

Result

You see how dbt builds a chain of models to run in the right order.

Understanding dependencies prevents errors from running models too early.

3

Intermediate

How dbt Builds Dependency Graphs

Concept: dbt creates a graph showing all models and their dependencies to plan execution order.

When you run dbt, it scans your models and builds a directed graph where nodes are models and edges are dependencies. This graph helps dbt know which models must run first and which can run later or in parallel.

Result

You understand the structure dbt uses to organize model runs.

Knowing the graph concept helps you predict how changes affect the run order.

4

Intermediate

Parallelism in dbt Runs

🤔Before reading on: Do you think dbt runs all models one after another or can it run some at the same time? Commit to your answer.

Concept: dbt runs models in parallel when they don't depend on each other to save time.

dbt uses the dependency graph to find models that can run simultaneously. For example, if Model D and Model E don't depend on each other or on models still running, dbt runs them at the same time. This is called parallelism and speeds up your data builds.

Result

You see how dbt can finish builds faster by running independent models together.

Understanding parallelism helps you optimize your project and hardware use.

5

Intermediate

Configuring Parallelism Settings

Concept: You can control how many models dbt runs in parallel using settings.

dbt lets you set the number of threads in your profile or command line. More threads mean more models run at once, but too many can overload your database. Finding the right balance improves speed without causing errors.

Result

You know how to adjust dbt to run models efficiently on your system.

Knowing how to tune parallelism prevents slow runs or database overload.

6

Advanced

Handling Complex Dependency Chains

🤔Before reading on: Do you think dbt can handle circular dependencies between models? Commit to yes or no.

Concept: dbt detects and prevents circular dependencies to avoid infinite loops.

If Model A depends on Model B, and Model B depends on Model A, dbt will raise an error because it can't decide which to run first. You must fix these cycles by redesigning models or breaking dependencies.

Result

You learn how dbt ensures your dependency graph is valid and runnable.

Understanding this prevents confusing errors and helps design clean data pipelines.

7

Expert

Optimizing Parallelism for Large Projects

🤔Before reading on: Do you think increasing threads always speeds up dbt runs? Commit to yes or no.

Concept: More parallelism is not always better; it depends on database capacity and model complexity.

In large projects, running too many models at once can cause database contention, slow queries, or failures. Experts monitor database performance and adjust threads dynamically. They also organize models to minimize heavy dependencies and balance load.

Result

You understand the tradeoffs in parallelism and how to optimize for real-world systems.

Knowing these limits helps you build scalable, reliable data workflows in production.

Under the Hood

dbt parses your SQL models to find references to other models using the ref() function. It builds a directed acyclic graph (DAG) where each node is a model and edges represent dependencies. dbt then uses this DAG to schedule model runs in order, running independent models in parallel threads. The database executes the SQL queries, creating or updating tables/views as defined.

Why designed this way?

This design ensures data integrity by running models only after their dependencies are ready. Using a DAG avoids cycles that cause infinite loops. Parallelism speeds up builds by using available resources efficiently. Alternatives like manual ordering are error-prone and slow, so dbt automates this with dependency graphs and parallel execution.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│   Parse SQL   │──────▶│ Build DAG     │──────▶│ Schedule Runs │
└───────────────┘       └───────────────┘       └───────────────┘
                                │                      │
                                ▼                      ▼
                      ┌─────────────────┐     ┌─────────────────┐
                      │ Run Independent  │     │ Run Dependent    │
                      │ Models in Parallel│     │ Models in Order │
                      └─────────────────┘     └─────────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does dbt run models strictly one after another, never in parallel? Commit yes or no.

Common Belief: dbt runs models one by one in a fixed order.

Reality: dbt runs models in parallel whenever possible, based on dependencies.

Why it matters: Believing this slows down projects because learners don't use parallelism settings to speed up runs.

Quick: Can circular dependencies exist in dbt without causing errors? Commit yes or no.

Common Belief: You can have circular dependencies between models and dbt will handle them.

Reality: dbt does not allow circular dependencies and will raise errors if they exist.

Why it matters: Ignoring this causes confusing errors and broken pipelines.

Quick: Does increasing the number of threads always make dbt run faster? Commit yes or no.

Common Belief: More threads always mean faster dbt runs.

Reality: Too many threads can overload the database and slow down or fail runs.

Why it matters: Misusing threads can cause performance problems and downtime.

Quick: Does dbt automatically detect dependencies only from ref() calls? Commit yes or no.

Common Belief: dbt detects all dependencies automatically, even if you don't use ref().

Reality: dbt only detects dependencies when you use ref() or similar functions explicitly.

Why it matters: Not using ref() can cause models to run in the wrong order, leading to errors.

Expert Zone

1

dbt's dependency graph is a DAG, so cycles are impossible; detecting cycles early saves debugging time.

2

Parallelism effectiveness depends heavily on database concurrency limits and query complexity, not just thread count.

3

Using ephemeral models can reduce dependency depth and improve parallelism by collapsing intermediate steps.

When NOT to use

If your database cannot handle concurrent queries well, or if models have complex side effects, avoid high parallelism. Instead, use sequential runs or batch models carefully. For very simple pipelines, manual ordering might be simpler.

Production Patterns

Teams often split large projects into smaller sub-projects to manage dependencies better. They use CI/CD pipelines to run dbt with optimized parallelism settings and monitor database load. Incremental models reduce runtime by only processing changed data, improving parallelism benefits.

Connections

Directed Acyclic Graphs (DAGs)

Model dependencies form a DAG, the same structure used in task scheduling and project management.

Understanding DAGs in computer science helps grasp how dbt orders model runs and prevents cycles.

Parallel Computing

Parallelism in dbt applies the same idea as parallel computing: running independent tasks simultaneously to save time.

Knowing parallel computing principles clarifies why dbt runs some models together and how to optimize resource use.

Cooking and Meal Preparation

Like cooking multiple dishes with dependencies and parallel steps, dbt manages data transformations similarly.

This real-world process shows why order and concurrency matter in workflows.

Common Pitfalls

#1 Not using ref() to declare dependencies causes models to run in wrong order.

Wrong approach:

SELECT * FROM raw_data.customers;

Correct approach:

SELECT * FROM {{ ref('raw_data_customers') }};

Root cause:

Learners forget that dbt only tracks dependencies through ref(), so missing it breaks the dependency graph.

#2 Setting too many threads overloads the database causing failures.

Wrong approach:

dbt run --threads 50

Correct approach:

dbt run --threads 8

Root cause:

Assuming more threads always improve speed ignores database concurrency limits.

#3 Creating circular dependencies between models causes dbt to error out.

Wrong approach:

Model A references Model B, and Model B references Model A.

Correct approach:

Redesign models so dependencies flow in one direction only.

Root cause:

Not understanding that dbt requires a DAG structure without cycles.

Key Takeaways

Model dependencies in dbt define the order models must run to ensure data correctness.

Parallelism allows dbt to run independent models at the same time, speeding up data builds.

Using ref() is essential for dbt to detect dependencies and build the correct execution graph.

Too much parallelism can overload your database, so tuning thread count is important.

dbt prevents circular dependencies to keep your data pipeline reliable and maintainable.