Why incremental models save time and cost in dbt - Performance Analysis

Time Complexity: Why incremental models save time and cost

O(k)

Understanding Time Complexity

Incremental models in dbt help us process only new or changed data instead of the entire dataset every time.

We want to know how this approach affects the time it takes to run and the cost involved.

Scenario Under Consideration

Analyze the time complexity of this incremental model code snippet.

{{ config(
  materialized='incremental',
  unique_key='id'
) }}

select * from source_table
{% if is_incremental() %}
  where updated_at > (select max(updated_at) from {{ this }})
{% endif %}

This code selects all rows from the source table on the first run, then only new or updated rows on later runs.

Identify Repeating Operations

• Primary operation: Scanning rows from the source table.
• How many times: Once for all rows on first run; only for new or updated rows on later runs.

How Execution Grows With Input

When the model runs the first time, it processes all rows, so time grows with total data size.

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

On later runs, only new or changed rows are scanned, so operations grow with the number of changes, not total data.

Final Time Complexity

Time Complexity: O(k)

This means the time depends on the number of new or updated rows (k), not the total data size.

Common Mistake

[X] Wrong: "Incremental models always scan the entire dataset every time."

[OK] Correct: Incremental models only scan new or changed data after the first run, saving time and cost.

Interview Connect

Understanding incremental models shows you can handle large data efficiently, a key skill in real projects.

Self-Check

"What if the incremental model did not have a unique key? How would that affect the time complexity?"