Version pinning and updates in dbt - Step-by-Step Execution

Concept Flow - Version pinning and updates

Start: Define dbt project

↓

Specify dbt version in packages.yml

↓

Run dbt commands

↓

dbt uses pinned version

↓

Check for updates

↓

Update version

↓

Run dbt with new version

↓

End

This flow shows how you set a specific dbt version, run commands using that version, check for updates, and optionally update the version.

Execution Sample

dbt

packages:
  - package: dbt-labs/dbt_utils
    version: 0.8.0

# Run dbt deps to install pinned versions

This code pins the dbt_utils package to version 0.8.0 and installs it.

Execution Table

Step	Action	Version Specified	Command Run	Result
1	Read packages.yml	dbt_utils 0.8.0	N/A	Version pinned for dbt_utils
2	Run 'dbt deps'	dbt_utils 0.8.0	dbt deps	Installs dbt_utils 0.8.0
3	Run 'dbt run'	dbt_utils 0.8.0	dbt run	Runs models using pinned version
4	Check for newer version	dbt_utils 0.8.0	Check online	Newer version 0.9.0 available
5	Decide to update	dbt_utils 0.8.0	Update packages.yml to 0.9.0	Version updated to 0.9.0
6	Run 'dbt deps' again	dbt_utils 0.9.0	dbt deps	Installs dbt_utils 0.9.0
7	Run 'dbt run' again	dbt_utils 0.9.0	dbt run	Runs models using updated version
8	End	dbt_utils 0.9.0	N/A	Process complete

💡 Process ends after running dbt with updated version or continuing with pinned version.

Variable Tracker

Variable	Start	After Step 2	After Step 5	After Step 6	Final
dbt_utils_version	Not set	0.8.0	0.9.0	0.9.0	0.9.0
dbt_command	None	dbt deps	None	dbt deps	dbt run

Key Moments - 2 Insights

Why does dbt use the version specified in packages.yml instead of the latest?

dbt uses the pinned version to ensure consistent behavior and avoid unexpected errors from newer versions, as shown in step 2 and 3 where version 0.8.0 is used.

What happens if you update the version in packages.yml but forget to run 'dbt deps'?

The old version remains installed and used because 'dbt deps' installs the specified versions. This is why step 6 reruns 'dbt deps' after updating the version.

Visual Quiz - 3 Questions

Look at the execution table, what version of dbt_utils is installed after step 2?

A 0.9.0

B Not installed

C 0.8.0

D Latest available

💡 Hint

Check the 'Version Specified' and 'Result' columns at step 2.

At which step does the dbt_utils version get updated to 0.9.0?

A Step 5

B Step 3

C Step 7

D Step 2

💡 Hint

Look for the step where 'Update packages.yml to 0.9.0' happens.

If you skip running 'dbt deps' after updating the version, what will happen when you run 'dbt run'?

A It will use the new version anyway

B It will use the old version still installed

C It will fail to run

D It will prompt to install dependencies

💡 Hint

Refer to the key moment about forgetting to run 'dbt deps' after update.

Concept Snapshot

Version pinning in dbt means specifying exact package versions in packages.yml.
Run 'dbt deps' to install these versions.
This ensures consistent runs and avoids surprises.
To update, change version in packages.yml and rerun 'dbt deps'.
Always run 'dbt deps' after changing versions.

Full Transcript

In dbt, version pinning means you fix the version of packages you use by writing it in packages.yml. When you run 'dbt deps', dbt installs exactly those versions. This keeps your project stable and predictable. If a new version is available, you can update the version number in packages.yml and run 'dbt deps' again to install the new version. Without running 'dbt deps', dbt will keep using the old version. This process helps you control when and how you update your tools.

Practice

1. What is the main purpose of version pinning in a dbt project?

easy

A. To keep the project stable by fixing package or dbt core versions

B. To always use the latest version of dbt without restrictions

C. To automatically update all dependencies daily

D. To remove all version constraints from packages

Solution

1. Step 1: Understand version pinning concept

   Version pinning means fixing the version of packages or dbt core to avoid unexpected changes.

2. Step 2: Identify the purpose in dbt projects

   This helps keep the project stable and predictable by avoiding automatic upgrades that might break things.

3. Final Answer:

   To keep the project stable by fixing package or dbt core versions -> Option A

4. Quick Check:

   Version pinning = stability [OK]

Hint: Version pinning means fixing versions to avoid surprises [OK]

Common Mistakes:

• Thinking version pinning means always using latest versions
• Assuming it automatically updates packages
• Confusing pinning with removing constraints

2. Which of the following is the correct syntax to pin a dbt package version to exactly 1.0.0 in packages.yml?

easy

A. - package: dbt-labs/dbt_utils
version: ">=1.0.0,<2.0.0"

B. - package: dbt-labs/dbt_utils
version: "1.0.0"

C. - package: dbt-labs/dbt_utils
version: "=1.0.0"

D. - package: dbt-labs/dbt_utils
version: "~>1.0.0"

Solution

1. Step 1: Review version pinning syntax in packages.yml

   To pin exactly one version, use the version string with the exact version number in quotes, like "1.0.0".

2. Step 2: Analyze options

   - package: dbt-labs/dbt_utils version: ">=1.0.0,<2.0.0" uses a range, not exact pinning. - package: dbt-labs/dbt_utils version: "=1.0.0" uses an invalid syntax with '='. - package: dbt-labs/dbt_utils version: "~>1.0.0" uses a pessimistic operator (~>), which allows patch updates.

3. Final Answer:

   - package: dbt-labs/dbt_utils version: "1.0.0" -> Option B

4. Quick Check:

   Exact version pin = "1.0.0" [OK]

Hint: Exact pin uses just the version string in quotes [OK]

Common Mistakes:

• Using '=' before version number incorrectly
• Confusing range syntax with exact pin
• Using pessimistic operator when exact pin needed

3. Given the following packages.yml snippet, what version of dbt_utils will be installed if versions 0.8.0, 0.9.0, and 1.0.0 are available?

- package: dbt-labs/dbt_utils
  version: ">=0.8.0,<1.0.0"

medium

A. 0.9.0

B. 1.0.0

C. 0.8.0

D. Any version above 1.0.0

Solution

1. Step 1: Understand version range syntax

   The version range ">=0.8.0,<1.0.0" means any version starting from 0.8.0 up to but not including 1.0.0.

2. Step 2: Identify highest version in range

   Among available versions 0.8.0, 0.9.0, and 1.0.0, only 0.8.0 and 0.9.0 fit the range. The highest is 0.9.0.

3. Final Answer:

   0.9.0 -> Option A

4. Quick Check:

   Version <1.0.0 and >=0.8.0 = 0.9.0 [OK]

Hint: Pick highest version within the range [OK]

Common Mistakes:

• Choosing 1.0.0 which is excluded by <1.0.0
• Picking lowest version instead of highest
• Ignoring the upper bound of the range

4. You updated your packages.yml to use version: ">=0.8.0,<1.0.0" but dbt still installs version 1.0.0. What is the most likely cause?

medium

A. The version range syntax is incorrect and ignored

B. dbt always installs the latest version regardless of pinning

C. You forgot to run dbt deps after changing the file

D. The package name is misspelled

Solution

1. Step 1: Check version range syntax

   The syntax ">=0.8.0,<1.0.0" is correct and valid for version pinning.

2. Step 2: Consider dbt workflow

   After changing packages.yml, you must run dbt deps to update installed packages. Without this, old versions remain.

3. Final Answer:

   You forgot to run dbt deps after changing the file -> Option C

4. Quick Check:

   Run dbt deps after changes [OK]

Hint: Always run dbt deps after editing packages.yml [OK]

Common Mistakes:

• Assuming dbt auto-updates packages without command
• Thinking version range syntax is wrong
• Ignoring package installation step

5. You want to allow patch updates but prevent minor or major upgrades for the dbt_utils package. Which version pinning strategy in packages.yml achieves this while balancing stability and updates?

hard

A. - package: dbt-labs/dbt_utils
version: ">=1.0.0"

B. - package: dbt-labs/dbt_utils
version: ">=1.0.0,<2.0.0"

C. - package: dbt-labs/dbt_utils
version: "1.0.0"

D. - package: dbt-labs/dbt_utils
version: "~>1.0.0"

Solution

1. Step 1: Understand version pinning operators

   Exact version ("1.0.0") disallows all updates. Range (">=1.0.0,<2.0.0") allows minor updates (1.x) but blocks major. ">=1.0.0" allows minor and major upgrades with no upper bound.

2. Step 2: Identify the pessimistic operator usage

   The "~>1.0.0" operator allows patch updates (like 1.0.1, 1.0.2) but prevents minor (1.1.0) or major (2.0.0) upgrades, balancing stability and updates.

3. Final Answer:

   - package: dbt-labs/dbt_utils version: "~>1.0.0" -> Option D

4. Quick Check:

   Pessimistic operator (~>) allows patch updates only [OK]

Hint: Use ~> for patch updates only, blocking minor/major [OK]

Common Mistakes:

• Using exact version and missing updates
• Using wide range allowing minor/major updates
• Confusing range with pessimistic operator