Testing model outputs in dbt - Time & Space Complexity

Time Complexity: Testing model outputs

O(n)

Understanding Time Complexity

When testing model outputs in dbt, we want to know how the time to run tests changes as data grows.

We ask: How does the test execution time grow with more data?

Scenario Under Consideration

Analyze the time complexity of the following dbt test code.

-- test to check for nulls in a column
select
  count(*) as null_count
from {{ ref('my_model') }}
where important_column is null
    

This test counts how many rows have nulls in a specific column of a model.

Identify Repeating Operations

Look for repeated work in the test query.

• Primary operation: Scanning all rows in the model table.
• How many times: Once per test run, but it checks every row.

How Execution Grows With Input

The test reads every row to count nulls, so more rows mean more work.

Input Size (n)	Approx. Operations
10	10 row checks
100	100 row checks
1000	1000 row checks

Pattern observation: The work grows directly with the number of rows.

Final Time Complexity

Time Complexity: O(n)

This means the test time grows linearly as the data size grows.

Common Mistake

[X] Wrong: "The test only checks a few rows, so it runs in constant time."

[OK] Correct: The test scans every row to count nulls, so it must look at all data, making time grow with data size.

Interview Connect

Understanding how test queries scale helps you write efficient checks and explain their impact clearly in real projects.

Self-Check

"What if the test checked for nulls only in a small indexed subset of rows? How would the time complexity change?"

Practice

1. What is the main purpose of testing model outputs in dbt?

easy

A. To ensure the data is accurate and reliable

B. To speed up the data loading process

C. To create new tables automatically

D. To delete old data from the database

Solution

1. Step 1: Understand the goal of testing in dbt

   Testing checks if the data produced by models is correct and trustworthy.

2. Step 2: Identify the main benefit of testing outputs

   Accurate and reliable data helps users make good decisions and trust reports.

3. Final Answer:

   To ensure the data is accurate and reliable -> Option A

4. Quick Check:

   Testing = Accurate data [OK]

Hint: Testing checks data correctness, not speed or deletion [OK]

Common Mistakes:

• Thinking tests speed up loading
• Confusing testing with table creation
• Assuming tests delete data

2. Which of the following is the correct syntax to define a uniqueness test on a column user_id in a dbt model's schema.yml file?

easy

A. - model: users
columns:
- name: user_id
tests:
- unique

B. - name: users
columns:
- name: user_id
test: unique

C. - name: users
columns:
- user_id
tests:
- unique

D. - name: users
columns:
- name: user_id
tests:
- unique

Solution

1. Step 1: Check the correct key for model name

   The key to specify the model is name, not model.

2. Step 2: Verify column and test syntax

   Each column uses name and tests are listed under tests as a list.

3. Final Answer:

   - name: users columns: - name: user_id tests: - unique -> Option D

4. Quick Check:

   Correct schema.yml syntax = - name: users columns: - name: user_id tests: - unique [OK]

Hint: Use 'name' for model and column, 'tests' as list [OK]

Common Mistakes:

• Using 'model' instead of 'name' for model
• Writing 'test' instead of 'tests'
• Omitting 'name' for column

3. Given this dbt test defined in schema.yml for the model orders:

- name: orders
  columns:
    - name: order_id
      tests:
        - unique
        - not_null

What will happen if the orders table has two rows with the same order_id and one row with order_id as NULL when you run dbt test?

medium

A. The test will fail because of duplicate and NULL values in order_id

B. The test will pass because only one test can fail at a time

C. The test will fail only for duplicate values, NULLs are ignored

D. The test will pass because NULLs are allowed in unique tests

Solution

1. Step 1: Understand the tests applied

   The tests are unique and not_null on order_id.

2. Step 2: Analyze the data issues

   Two rows have the same order_id (violates uniqueness) and one row has NULL order_id (violates not_null).

3. Final Answer:

   The test will fail because of duplicate and NULL values in order_id -> Option A

4. Quick Check:

   Duplicates + NULLs = test fail [OK]

Hint: Both unique and not_null must pass for success [OK]

Common Mistakes:

• Assuming NULLs are allowed in unique tests
• Thinking only one test failure causes pass
• Ignoring NULL violation in not_null test

4. You wrote this test in your schema.yml file:

- name: customers
  columns:
    - name: email
      tests:
        - unique
        - not_null

But when you run dbt test, you get an error saying Invalid test configuration. What is the likely cause?

medium

A. The test name should be unique and not_null without dashes

B. The indentation of the tests list is incorrect

C. The model name should be under models key in schema.yml

D. The email column does not exist in the model

Solution

1. Step 1: Check the structure of schema.yml

   Tests must be defined under the models: key in schema.yml.

2. Step 2: Identify missing models: key

   The snippet misses the models: root key, causing invalid configuration.

3. Final Answer:

   The model name should be under models key in schema.yml -> Option C

4. Quick Check:

   Missing 'models:' key = config error [OK]

Hint: Always start schema.yml tests under 'models:' key [OK]

Common Mistakes:

• Removing dashes from test names
• Incorrect indentation of tests list
• Not placing model under 'models:'

5. You want to test that the status column in your transactions model only contains the values 'pending', 'completed', or 'failed'. Which test definition in schema.yml correctly enforces this?

hard

A. - name: transactions
columns:
- name: status
tests:
- values_in: ['pending', 'completed', 'failed']

B. - name: transactions
columns:
- name: status
tests:
- accepted_values:
values: ['pending', 'completed', 'failed']

C. - name: transactions
columns:
- name: status
tests:
- accepted_values: ['pending', 'completed', 'failed']

D. - name: transactions
columns:
- name: status
tests:
- unique
- not_null

Solution

1. Step 1: Identify the correct test for allowed values

   The accepted_values test checks if column values are in a list.

2. Step 2: Check correct syntax for accepted_values

   The test requires a dictionary with key values listing allowed values.

3. Final Answer:

   - name: transactions columns: - name: status tests: - accepted_values: values: ['pending', 'completed', 'failed'] -> Option B

4. Quick Check:

   accepted_values with 'values' key = - name: transactions columns: - name: status tests: - accepted_values: values: ['pending', 'completed', 'failed'] [OK]

Hint: Use accepted_values with 'values' list for allowed values [OK]

Common Mistakes:

• Using unique or not_null instead of accepted_values
• Omitting 'values:' key under accepted_values
• Using wrong test name like values_in