Installing packages with packages.yml in dbt - Mechanics & Internals

Overview - Installing packages with packages.yml

What is it?

Installing packages with packages.yml in dbt means adding external reusable code modules to your project. These packages contain pre-built models, macros, or tests that you can use to speed up your work. The packages.yml file is where you list these packages and their versions so dbt knows what to download and include. This helps you avoid rewriting common logic and keeps your project organized.

Why it matters

Without packages.yml, you would have to write all your data transformations and tests from scratch, which takes a lot of time and can lead to errors. Using packages lets you build on others' work, making your projects faster and more reliable. It also helps teams share best practices and maintain consistency across projects. Imagine building a house without any ready-made tools or parts—packages are like those helpful tools and parts that make construction easier.

Where it fits

Before learning about packages.yml, you should understand basic dbt project structure and how to write models and macros. After mastering packages.yml, you can explore advanced package management, version control, and creating your own reusable packages to share with others.

Mental Model

Core Idea

Packages.yml is a list that tells dbt which external code bundles to fetch and include in your project automatically.

Think of it like...

It's like a shopping list for your kitchen: you write down the ingredients (packages) you need, and the store (dbt) delivers them to your home (project) so you can cook (build models) without hunting for each item.

┌───────────────┐
│ packages.yml  │
│  - package A  │
│  - package B  │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ dbt fetches   │
│ packages from │
│ remote repos  │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Packages added│
│ to your project│
└───────────────┘

Build-Up - 6 Steps

1

Foundation

What is packages.yml file

Concept: Introducing the packages.yml file as the place to list external packages.

The packages.yml file is a simple text file in your dbt project folder. It lists the packages you want to use by name and version. For example: packages: - package: dbt-labs/dbt_utils version: 0.8.0 This tells dbt to include the dbt_utils package version 0.8.0 in your project.

Result

A clear place to specify which external packages your project needs.

Understanding that packages.yml is the single source of truth for external code helps keep your project organized and reproducible.

2

Foundation

How dbt uses packages.yml

Concept: Explaining how dbt reads packages.yml and downloads packages.

When you run 'dbt deps', dbt reads packages.yml and downloads the listed packages into a 'dbt_modules' folder inside your project. These packages become part of your project and can be referenced in your models and macros.

Result

Your project now includes external code ready to use.

Knowing that 'dbt deps' syncs your packages ensures you keep your project dependencies up to date.

3

Intermediate

Specifying package versions

🤔Before reading on: do you think specifying no version installs the latest package or causes an error? Commit to your answer.

Concept: How to control which version of a package you install to avoid unexpected changes.

In packages.yml, you can specify exact versions or version ranges for packages. For example: packages: - package: dbt-labs/dbt_utils version: 0.8.0 If you omit the version, dbt installs the latest version available, which might introduce breaking changes. Using fixed versions helps keep your project stable.

Result

You control package versions to ensure consistent behavior.

Understanding version control in packages prevents surprises from automatic updates that could break your project.

4

Intermediate

Using packages in your models

🤔Before reading on: do you think you can use package macros without importing or referencing them explicitly? Commit to your answer.

Concept: How to call macros and models from installed packages in your own dbt code.

Once packages are installed, you can use their macros by prefixing with the package name. For example, if dbt_utils has a macro called 'surrogate_key', you call it like this: {{ dbt_utils.surrogate_key(['id']) }} This lets you reuse tested code without rewriting it.

Result

Your models can use external package functions easily.

Knowing how to reference package macros unlocks powerful reusable code and speeds up development.

5

Advanced

Managing package conflicts and overrides

🤔Before reading on: do you think you can override package models directly in your project without special steps? Commit to your answer.

Concept: How to handle situations when package models conflict with your own or need customization.

If a package includes models you want to customize, you can override them by creating a model with the same name in your project. dbt will use your version instead. Also, if two packages have conflicting dependencies, you may need to adjust versions or avoid installing both.

Result

You can customize or replace package code safely.

Understanding overrides helps you adapt packages to your needs without losing control or causing errors.

6

Expert

Creating and sharing your own packages

🤔Before reading on: do you think creating a package is just about writing code or involves special structure and metadata? Commit to your answer.

Concept: How to build your own reusable dbt packages to share with others or across projects.

A dbt package is a dbt project with a specific structure and a packages.yml file listing its dependencies. To create one, organize your models, macros, and tests, then publish it to a git repository. Others can then add your package to their packages.yml to reuse your work.

Result

You can build reusable components and share best practices.

Knowing how to create packages empowers you to contribute to the dbt community and improve team productivity.

Under the Hood

When you run 'dbt deps', dbt reads the packages.yml file and fetches the specified packages from their git repositories. It clones these repositories into the 'dbt_modules' directory inside your project. During compilation, dbt merges your project code with the package code, resolving references to macros and models. This merging allows your project to use package code as if it were local, while keeping the package code separate for easy updates.

Why designed this way?

This design keeps external code modular and separate, avoiding clutter in your main project. Using git repositories for packages leverages existing version control tools and workflows. It also allows easy updates and rollbacks by changing versions in packages.yml. Alternatives like copying code manually were error-prone and hard to maintain, so this approach balances flexibility and control.

┌───────────────┐
│ packages.yml  │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ dbt deps cmd  │
│ reads packages│
│ and fetches   │
│ git repos     │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ dbt_modules   │
│ folder holds  │
│ package code  │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ dbt compile   │
│ merges your   │
│ code + packages│
└───────────────┘

Myth Busters - 3 Common Misconceptions

Quick: Does omitting the version in packages.yml always install the latest package? Commit yes or no.

Common Belief: If you don't specify a version, dbt will install the latest package version automatically and safely.

Reality: Omitting the version installs the latest available version, which may introduce breaking changes or incompatibilities.

Why it matters: This can cause your project to break unexpectedly when packages update, leading to downtime or bugs.

Quick: Can you edit package code directly inside dbt_modules and have changes persist? Commit yes or no.

Common Belief: You can modify package code inside dbt_modules to fix bugs or customize behavior directly.

Reality: Changes inside dbt_modules are overwritten every time you run 'dbt deps', so edits do not persist.

Why it matters: Editing package code directly leads to lost work and inconsistent behavior across environments.

Quick: Does installing multiple packages with overlapping dependencies always work without conflicts? Commit yes or no.

Common Belief: dbt automatically resolves all dependency conflicts between packages, so you don't need to worry about versions.

Reality: dbt does not resolve conflicting package dependencies automatically; you must manage versions to avoid conflicts.

Why it matters: Ignoring dependency conflicts can cause build failures or unexpected behavior in your project.

Expert Zone

1

Some packages include macros that depend on specific database adapters; knowing this helps avoid runtime errors when switching databases.

2

Packages can include tests that run automatically; understanding how to enable or disable these tests is key for project stability.

3

Using semantic versioning in packages.yml allows safe upgrades by specifying version ranges instead of fixed versions.

When NOT to use

If your project requires highly customized logic that differs significantly from available packages, relying on packages.yml may limit flexibility. In such cases, writing custom models and macros directly is better. Also, for very small projects, adding packages can add unnecessary complexity.

Production Patterns

Teams often create internal packages with shared business logic and add them via packages.yml to all projects. This ensures consistency and reduces duplicated code. Continuous integration pipelines run 'dbt deps' to fetch packages before building models, ensuring reproducible builds.

Connections

Dependency Management in Software Development

Packages.yml in dbt is similar to package.json in JavaScript or requirements.txt in Python, listing external dependencies.

Understanding how dependency files work in other languages helps grasp the importance of packages.yml for managing external code in dbt.

Modular Design in Engineering

Using packages is like using modular parts in engineering to build complex systems from reusable components.

Recognizing packages as modular building blocks clarifies why they improve maintainability and scalability.

Supply Chain Management

Packages.yml acts like a supply chain order list, ensuring the right parts arrive on time for assembly.

Seeing package installation as supply chain logistics highlights the importance of version control and dependency tracking.

Common Pitfalls

#1 Not running 'dbt deps' after changing packages.yml

Wrong approach:

Edit packages.yml to add a package but run 'dbt run' without 'dbt deps'.

Correct approach:

After editing packages.yml, run 'dbt deps' to fetch new packages before running dbt commands.

Root cause:

Forgetting that 'dbt deps' is required to download and install packages after changes.

#2 Using conflicting package versions causing build errors

Wrong approach:

Specify incompatible versions of two packages that depend on different versions of the same sub-package.

Correct approach:

Adjust versions in packages.yml to compatible ranges or avoid installing conflicting packages together.

Root cause:

Not understanding dependency conflicts and version compatibility.

#3 Editing code inside dbt_modules folder directly

Wrong approach:

Modify macros or models inside dbt_modules to fix bugs or add features.

Correct approach:

Fork the package repository, make changes there, and update packages.yml to point to your fork.

Root cause:

Misunderstanding that dbt_modules is a managed folder overwritten by 'dbt deps'.

Key Takeaways

The packages.yml file is the central place to list external dbt packages your project needs.

Running 'dbt deps' downloads and installs these packages into your project for use.

Specifying package versions prevents unexpected breaking changes from automatic updates.

You can use package macros and models by referencing them with the package name prefix.

Creating your own packages lets you share reusable code and best practices across projects.