Test severity levels in dbt - Time & Space Complexity

Time Complexity: Test severity levels

O(n)

Understanding Time Complexity

When using test severity levels in dbt, it's important to understand how the number of tests affects the time it takes to run them.

We want to know how the execution time grows as we add more tests with different severity levels.

Scenario Under Consideration

Analyze the time complexity of running dbt tests with severity levels.

# Example dbt test configuration with severity
version: 2

models:
  - name: customers
    tests:
      - unique:
          severity: error
      - not_null:
          severity: warn

This snippet shows two tests on a model, each with a severity level that controls how dbt treats failures.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Running each test query on the data model.
• How many times: Once per test defined for the model.

How Execution Grows With Input

As you add more tests, the total time to run all tests grows roughly in direct proportion.

Number of Tests (n)	Approx. Operations
10	10 test queries run
100	100 test queries run
1000	1000 test queries run

Pattern observation: Doubling the number of tests roughly doubles the work done.

Final Time Complexity

Time Complexity: O(n)

This means the time to run tests grows linearly with the number of tests you have.

Common Mistake

[X] Wrong: "Severity levels change how many tests run, so time complexity changes."

[OK] Correct: Severity only changes how failures are reported, not how many tests run. All tests still execute, so time grows with test count.

Interview Connect

Understanding how test counts affect runtime helps you design efficient data quality checks and explain your choices clearly in discussions.

Self-Check

"What if we grouped tests to run in parallel? How would that affect the time complexity?"

Practice

1. What does setting a dbt test severity level to ERROR do?

easy

A. It stops the dbt run if the test fails.

B. It only logs a warning but continues the run.

C. It ignores the test result completely.

D. It retries the test automatically.

Solution

1. Step 1: Understand dbt test severity levels

   dbt uses severity levels to decide what happens when a test fails.

2. Step 2: Identify the effect of ERROR severity

   When severity is set to ERROR, dbt stops the run immediately on failure.

3. Final Answer:

   It stops the dbt run if the test fails. -> Option A

4. Quick Check:

   ERROR severity = stops run [OK]

Hint: ERROR severity stops the run; WARN just warns [OK]

Common Mistakes:

• Confusing ERROR with WARN severity
• Thinking ERROR retries the test
• Assuming ERROR ignores failures

2. Which of the following is the correct way to set a test severity to WARN in a dbt YAML test configuration?

easy

A. severity: warn

B. severity = WARN

C. severity: WARN

D. severity: Warning

Solution

1. Step 1: Recall YAML syntax for dbt test severity

   dbt expects severity as a key-value pair with uppercase values like WARN or ERROR.

2. Step 2: Identify correct syntax

   The correct syntax uses a colon and uppercase WARN: severity: WARN.

3. Final Answer:

   severity: WARN -> Option C

4. Quick Check:

   YAML key-value with uppercase WARN = correct [OK]

Hint: Use colon and uppercase WARN for severity [OK]

Common Mistakes:

• Using lowercase 'warn' instead of uppercase
• Using equals sign instead of colon
• Spelling severity value incorrectly

3. Given this dbt test configuration snippet:

tests:
  - unique:
      column_name: id
      severity: WARN

What happens if the test fails during a dbt run?

medium

A. The failure is logged as a warning, but the run continues.

B. The test automatically retries until it passes.

C. The test is ignored and no message is shown.

D. The run stops immediately with an error.

Solution

1. Step 1: Analyze the severity level in the test config

   The severity is set to WARN, which means dbt should warn but not stop.

2. Step 2: Understand dbt behavior on WARN severity

   When a test fails with WARN severity, dbt logs a warning and continues the run.

3. Final Answer:

   The failure is logged as a warning, but the run continues. -> Option A

4. Quick Check:

   WARN severity = warn and continue [OK]

Hint: WARN severity warns but lets run continue [OK]

Common Mistakes:

• Thinking WARN stops the run
• Assuming WARN ignores failures
• Believing tests retry automatically

4. You wrote this test in your dbt model YAML:

tests:
  - not_null:
      column_name: user_id
      severity: ERROR

But your dbt run does not stop when the test fails. What is the likely problem?

medium

A. The test name 'not_null' is invalid.

B. The severity key is misplaced; it should be under 'config'.

C. The severity value should be lowercase 'error'.

D. The test is not actually failing.

Solution

1. Step 1: Check correct placement of severity in dbt tests

   Severity must be set inside the config block, not directly under the test.

2. Step 2: Identify why run does not stop

   Since severity is misplaced, dbt ignores it and uses default behavior, so run does not stop.

3. Final Answer:

   The severity key is misplaced; it should be under 'config'. -> Option B

4. Quick Check:

   Severity must be inside config = The severity key is misplaced; it should be under 'config'. [OK]

Hint: Put severity inside config block to take effect [OK]

Common Mistakes:

• Using lowercase severity value
• Misplacing severity outside config
• Assuming test name is wrong

5. You want to run a dbt test that warns on missing emails but errors on duplicate emails. Which YAML configuration correctly sets different severities for these tests on the email column?

hard

A. tests:
- not_null:
column_name: email
severity: WARN
- unique:
column_name: email
severity: ERROR

B. tests:
- not_null:
column_name: email
config:
severity: ERROR
- unique:
column_name: email
config:
severity: WARN

C. tests:
- not_null:
column_name: email
severity: ERROR
- unique:
column_name: email
severity: WARN

D. tests:
- not_null:
column_name: email
config:
severity: WARN
- unique:
column_name: email
config:
severity: ERROR

Solution

1. Step 1: Recall correct severity placement in dbt YAML

   Severity must be inside a config block under each test.

2. Step 2: Match severities to tests as required

   Set not_null severity to WARN and unique severity to ERROR inside their config blocks.

3. Final Answer:

   Tests with severity inside config: not_null WARN, unique ERROR. -> Option D

4. Quick Check:

   Severity inside config with correct WARN/ERROR = tests: - not_null: column_name: email config: severity: WARN - unique: column_name: email config: severity: ERROR [OK]

Hint: Use config block for different severities per test [OK]

Common Mistakes:

• Placing severity outside config block
• Swapping WARN and ERROR severities
• Using lowercase severity values