Incremental strategies (append, merge, delete+insert) in dbt - Time & Space Complexity

Time Complexity: Incremental strategies (append, merge, delete+insert)

O(n)

Understanding Time Complexity

When working with incremental models in dbt, it's important to understand how the time to update data grows as the data size increases.

We want to know how the cost of appending, merging, or deleting and inserting data changes with bigger datasets.

Scenario Under Consideration

Analyze the time complexity of these incremental strategies in dbt.

-- Append strategy
{{ config(materialized='incremental', incremental_strategy='append') }}

select * from source_table

-- Merge strategy
{{ config(materialized='incremental', incremental_strategy='merge', unique_key='id') }}

select * from source_table

-- Delete + Insert strategy
{{ config(materialized='incremental', incremental_strategy='delete+insert', unique_key='id') }}

select * from source_table
    

This code shows three ways dbt updates data incrementally: adding new rows, merging changes, or deleting and inserting rows.

Identify Repeating Operations

Look at what repeats as data grows.

• Primary operation: Scanning and processing new or changed rows.
• How many times: Once per incremental run, but depends on how many rows are new or updated.

How Execution Grows With Input

As the number of new or changed rows grows, the work to update the table grows too.

Input Size (new/changed rows)	Approx. Operations
10	10 operations (simple append or merge)
100	100 operations (more rows to process)
1000	1000 operations (larger update)

Pattern observation: The time grows roughly in direct proportion to the number of rows changed or added.

Final Time Complexity

Time Complexity: O(n)

This means the time to update grows linearly with the number of new or changed rows processed.

Common Mistake

[X] Wrong: "Incremental updates always take the same time regardless of data size."

[OK] Correct: The time depends on how many rows are new or changed, so bigger updates take more time.

Interview Connect

Understanding how incremental strategies scale helps you explain data pipeline efficiency and design choices clearly in real projects.

Self-Check

What if the incremental strategy used a complex join instead of a simple key match? How would the time complexity change?