One model per source table rule in dbt - Time & Space Complexity

Time Complexity: One model per source table rule

O(n)

Understanding Time Complexity

When using dbt, we often create one model for each source table. This helps keep things clear and organized.

We want to understand how the time to run these models grows as the number of source tables increases.

Scenario Under Consideration

Analyze the time complexity of this dbt project structure.

-- models/customer.sql
select * from {{ source('sales', 'customer') }}

-- models/orders.sql
select * from {{ source('sales', 'orders') }}

-- models/products.sql
select * from {{ source('inventory', 'products') }}

-- models/payments.sql
select * from {{ source('finance', 'payments') }}

This project has one model for each source table, each simply selecting from its source.

Identify Repeating Operations

Each model runs a query that reads one source table.

• Primary operation: Running a query on a source table.
• How many times: Once per source table (one model per source).

How Execution Grows With Input

As the number of source tables grows, the number of models grows the same way.

Number of Source Tables (n)	Number of Models Run
10	10
100	100
1000	1000

Pattern observation: The total work grows directly with the number of source tables.

Final Time Complexity

Time Complexity: O(n)

This means the total time to run all models grows linearly with the number of source tables.

Common Mistake

[X] Wrong: "Adding more source tables won't affect total run time much because each model runs independently."

[OK] Correct: Even if models run separately, the total time adds up because each model processes its source table once.

Interview Connect

Understanding how your dbt project scales with more source tables shows you can plan for growth and keep your data pipeline efficient.

Self-Check

"What if we combined multiple source tables into fewer models? How would that change the time complexity?"

Practice

1. What is the main reason for following the one model per source table rule in dbt?

easy

A. To avoid using source tables in models

B. To combine multiple source tables into one model

C. To write fewer SQL queries

D. To keep models simple and easy to understand

Solution

1. Step 1: Understand the rule and its benefits

   The rule encourages creating one dbt model for each source table to keep things simple and clear. This approach helps with clarity, debugging, and teamwork by avoiding complex combined models.

2. Final Answer:

   To keep models simple and easy to understand -> Option D

3. Quick Check:

   One model per source table = simplicity [OK]

Hint: One model per source table means simpler, clearer models [OK]

Common Mistakes:

• Thinking it means combining tables into one model
• Believing it reduces the number of models
• Assuming it avoids using source tables

2. Which of the following is the correct way to define a dbt model for a source table named customers?

easy

A. Create a model file named all_data.sql combining all source tables

B. Create a model file named customers.sql with a SELECT from the source table

C. Create a model file named customers.sql but do not select from the source table

D. Create a model file named source_customers.sql without referencing the source

Solution

1. Step 1: Match model name to source table and reference it

   Following the rule, the model file should be named after the source table, here customers.sql. The model should select data from the source table to represent it clearly.

2. Final Answer:

   Create a model file named customers.sql with a SELECT from the source table -> Option B

3. Quick Check:

   Model name = source table name + SELECT from source [OK]

Hint: Model file name matches source table name and selects from it [OK]

Common Mistakes:

• Naming model file unrelated to source table
• Not selecting from the source table
• Combining multiple tables in one model

3. Given this dbt model SQL for source table orders:

select * from {{ source('sales_db', 'orders') }}

What will be the output if the source table has 100 rows?

medium

A. The model will return 100 rows identical to the source table

B. The model will return zero rows

C. The model will return 1 row with aggregated data

D. The model will cause a syntax error

Solution

1. Step 1: Understand the SQL query and predict output

   The query selects all columns and rows from the source table orders. Since it selects all rows without filtering, the output will have the same 100 rows as the source.

2. Final Answer:

   The model will return 100 rows identical to the source table -> Option A

3. Quick Check:

   Select * from source = same rows [OK]

Hint: Select * from source returns all rows unchanged [OK]

Common Mistakes:

• Thinking it returns zero rows
• Assuming aggregation happens automatically
• Expecting syntax errors from correct source() usage

4. You wrote this dbt model for source table products:

select * from source('inventory', 'products')

But dbt throws an error. What is the problem?

medium

A. Model file name does not match source table

B. Wrong source table name

C. Missing double curly braces around the source function

D. Using select * is not allowed

Solution

1. Step 1: Check dbt source function syntax and identify issue

   The source function must be wrapped in double curly braces: {{ source(...) }}. The model uses source(...) without {{ }}, causing a syntax error.

2. Final Answer:

   Missing double curly braces around the source function -> Option C

3. Quick Check:

   dbt functions need {{ }} [OK]

Hint: Always wrap dbt functions in {{ }} [OK]

Common Mistakes:

• Forgetting {{ }} around source()
• Assuming select * is disallowed
• Ignoring model file naming conventions

5. You have two source tables: customers and orders. According to the one model per source table rule, how should you organize your dbt models if you want to create a report showing total orders per customer?

hard

A. Create separate base models for customers and orders, then create a third model joining them for the report

B. Create one model combining both source tables directly without base models

C. Create a single model selecting from both source tables with UNION

D. Create only one model for orders and ignore customers

Solution

1. Step 1: Follow one model per source table for base models and build report model

   Create one model for customers and one for orders separately to keep base models simple. Create a third model that joins these base models to calculate total orders per customer, keeping transformations clear and modular.

2. Final Answer:

   Create separate base models for customers and orders, then create a third model joining them for the report -> Option A

3. Quick Check:

   Base models per source + separate report model [OK]

Hint: Base models per source, then join in separate model [OK]

Common Mistakes:

• Combining sources in one base model
• Skipping base models and joining sources directly
• Ignoring one source table