Store test failures for analysis in dbt - Time & Space Complexity

Time Complexity: Store test failures for analysis

O(n)

Understanding Time Complexity

When storing test failures in dbt for analysis, it's important to understand how the time to process grows as the data grows.

We want to know how the time to save and analyze failures changes as the number of tests or failures increases.

Scenario Under Consideration

Analyze the time complexity of the following dbt snippet that stores test failures.

with failed_tests as (
  select *
  from {{ ref('my_model') }}
  where test_result = 'fail'
),

store_failures as (
  select *, current_timestamp as failure_time
  from failed_tests
)

select * from store_failures

This code selects all failed test rows from a model and prepares them with a timestamp for storage or further analysis.

Identify Repeating Operations

Look for repeated actions that affect time.

• Primary operation: Scanning all rows in the model to find failures.
• How many times: Once per run, but it processes every row in the model.

How Execution Grows With Input

The time to find failures grows as the number of rows in the model grows.

Input Size (n)	Approx. Operations
10	10 rows scanned
100	100 rows scanned
1000	1000 rows scanned

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

Final Time Complexity

Time Complexity: O(n)

This means the time to store test failures grows linearly with the number of rows checked.

Common Mistake

[X] Wrong: "Storing failures only takes constant time regardless of data size."

[OK] Correct: Because the code scans every row to find failures, more rows mean more work and more time.

Interview Connect

Understanding how data size affects processing time helps you explain and improve data workflows clearly and confidently.

Self-Check

"What if we indexed the test_result column? How would that change the time complexity?"

Practice

1. What does setting store_failures = true in a dbt test configuration do?

easy

A. It sends an email notification when a test fails.

B. It skips the test execution entirely.

C. It deletes the rows that fail the test from the source table.

D. It saves the rows that fail the test for later analysis.

Solution

1. Step 1: Understand the purpose of store_failures

   This setting controls whether dbt saves the rows that fail a test.

2. Step 2: Identify the effect of setting it to true

   When set to true, dbt stores failing rows in a table for analysis.

3. Final Answer:

   It saves the rows that fail the test for later analysis. -> Option D

4. Quick Check:

   store_failures = true means save failing rows [OK]

Hint: Remember: store_failures saves failing rows for review [OK]

Common Mistakes:

• Thinking it skips tests instead of saving failures
• Assuming it deletes data on failure
• Confusing it with notification settings

2. Which of the following is the correct way to enable storing failures in a dbt test config?

easy

A. config(store_failures == true)

B. config(store_failures: true)

C. config(store_failures = true)

D. config(store_failures = "true")

Solution

1. Step 1: Recall dbt config syntax

   dbt uses Python-like syntax with keyword arguments inside config().

2. Step 2: Identify the correct assignment

   The correct syntax uses equals sign and boolean true without quotes.

3. Final Answer:

   config(store_failures = true) -> Option C

4. Quick Check:

   Use equals and boolean true without quotes [OK]

Hint: Use equals sign and true without quotes in config() [OK]

Common Mistakes:

• Using colon instead of equals sign
• Using double equals (==) instead of single equals
• Putting true in quotes making it a string

3. After running a dbt test with store_failures = true, which SQL query will show the failing rows for a model named orders and test unique?

medium

A. SELECT * FROM dbt_test__orders__unique

B. SELECT * FROM orders WHERE test = 'unique' AND status = 'fail'

C. SELECT * FROM dbt_failures.orders_unique

D. SELECT * FROM test_failures.orders_unique

Solution

1. Step 1: Recall naming convention for failure tables

   dbt stores failures in tables named dbt_test__model__test.

2. Step 2: Apply model and test names

   For model 'orders' and test 'unique', the table is dbt_test__orders__unique.

3. Final Answer:

   SELECT * FROM dbt_test__orders__unique -> Option A

4. Quick Check:

   Failure table = dbt_test__model__test [OK]

Hint: Use dbt_test__model__test naming to find failure tables [OK]

Common Mistakes:

• Assuming failure data is in original model table
• Using incorrect table prefixes
• Trying to filter failures in source tables

4. You set store_failures = true in your test config, but after running tests, no failure tables appear. What is the most likely cause?

medium

A. The test did not find any failing rows, so no failure table was created.

B. You must manually create failure tables before running tests.

C. The store_failures setting only works for schema tests, not data tests.

D. Failure tables are stored in a different schema and need to be queried there.

Solution

1. Step 1: Understand when failure tables are created

   Failure tables are only created if the test finds failing rows.

2. Step 2: Check if test found failures

   If no failures, no failure table is generated.

3. Final Answer:

   The test did not find any failing rows, so no failure table was created. -> Option A

4. Quick Check:

   No failures means no failure table [OK]

Hint: No failure rows means no failure table created [OK]

Common Mistakes:

• Thinking failure tables must be pre-created
• Assuming store_failures only works for some test types
• Looking in wrong schema without verifying failures

5. You want to analyze failing rows from multiple tests on the customers model. Which approach best helps you combine all failure data for analysis?

hard

A. Manually export failure tables and merge them outside the database.

B. Create a union query combining all dbt_test__customers__* failure tables.

C. Run each test again with store_failures = false to avoid duplicates.

D. Use the original customers table and filter rows with errors.

Solution

1. Step 1: Identify failure tables for each test

   Each test creates a failure table named dbt_test__customers__testname.

2. Step 2: Combine failure tables using SQL UNION

   Use a UNION query to combine all failure rows from these tables into one result set.

3. Final Answer:

   Create a union query combining all dbt_test__customers__* failure tables. -> Option B

4. Quick Check:

   Union failure tables to analyze all failures together [OK]

Hint: Use UNION on all failure tables for combined analysis [OK]

Common Mistakes:

• Disabling store_failures loses failure data
• Merging outside database is inefficient
• Filtering original table misses failure details