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?

Practice

1. Which incremental strategy in dbt only adds new rows without modifying existing ones?

easy

A. delete+insert

B. merge

C. append

D. truncate

Solution

1. Step 1: Understand the append strategy

   The append strategy adds only new rows to the existing table without changing any existing data.

2. Step 2: Compare with other strategies

   Merge updates existing rows and adds new ones; delete+insert replaces matching rows; truncate removes all data first.

3. Final Answer:

   append -> Option C

4. Quick Check:

   append = add new rows only [OK]

Hint: Append means add new rows only, no updates [OK]

Common Mistakes:

• Confusing merge with append
• Thinking delete+insert only adds rows
• Assuming truncate is an incremental strategy

2. Which of the following is the correct syntax to define an incremental model using the merge strategy in dbt?

easy

A. config(materialized='incremental', incremental_strategy='append')

B. config(materialized='incremental', strategy='merge')

C. config(materialized='table', incremental_strategy='merge')

D. config(materialized='incremental', incremental_strategy='merge')

Solution

1. Step 1: Identify correct config keys for incremental merge

   dbt requires materialized='incremental' and incremental_strategy='merge' to use merge strategy.

2. Step 2: Check each option for correctness

   config(materialized='incremental', incremental_strategy='append') uses 'append' strategy, not merge; config(materialized='table', incremental_strategy='merge') uses materialized='table' which is not incremental; config(materialized='incremental', strategy='merge') uses wrong key 'strategy'.

3. Final Answer:

   config(materialized='incremental', incremental_strategy='merge') -> Option D

4. Quick Check:

   incremental_strategy='merge' with materialized='incremental' [OK]

Hint: Use incremental_strategy='merge' inside config() for merge [OK]

Common Mistakes:

• Using wrong materialized type
• Misspelling incremental_strategy key
• Confusing strategy key name

3. Given this dbt incremental model snippet using merge strategy:

select id, value from source_table where updated_at > (select max(updated_at) from {{ this }})

What will happen when this model runs?

medium

A. Only new rows with updated_at greater than max in target are added

B. Rows with matching id are updated or inserted based on updated_at filter

C. All rows from source_table replace the target table

D. No rows are added or updated because filter excludes all

Solution

1. Step 1: Understand the filter condition

   The query selects rows from source_table where updated_at is greater than the max updated_at in the target table ({{ this }}).

2. Step 2: Understand merge behavior

   Merge updates existing rows with matching keys and inserts new rows. The filter ensures only changed rows are processed.

3. Final Answer:

   Rows with matching id are updated or inserted based on updated_at filter -> Option B

4. Quick Check:

   merge updates/inserts filtered rows [OK]

Hint: Merge updates or inserts rows filtered by updated_at [OK]

Common Mistakes:

• Thinking it replaces entire table
• Assuming only new rows are appended
• Ignoring the filter condition effect

4. You wrote this incremental model using delete+insert strategy:

config(materialized='incremental', incremental_strategy='delete+insert')

select * from source_table

But the model fails with an error about missing unique key. What is the likely cause?

medium

A. You did not specify the unique_key config required for delete+insert

B. The source_table is empty

C. delete+insert strategy does not support incremental models

D. You must use append strategy instead

Solution

1. Step 1: Recall delete+insert requirements

   Delete+insert requires a unique_key config to identify rows to delete before insert.

2. Step 2: Analyze error cause

   Missing unique_key causes dbt to error because it cannot identify which rows to delete.

3. Final Answer:

   You did not specify the unique_key config required for delete+insert -> Option A

4. Quick Check:

   delete+insert needs unique_key [OK]

Hint: Always set unique_key for delete+insert strategy [OK]

Common Mistakes:

• Ignoring unique_key requirement
• Assuming delete+insert works without keys
• Confusing append with delete+insert

5. You have a large sales table that updates daily with new sales and corrections to existing sales records. Which incremental strategy should you choose in dbt to efficiently update the table without full reloads?

hard

A. merge, because it updates existing rows and adds new ones

B. delete+insert, because it deletes all rows and inserts fresh data

C. append, because it only adds new rows

D. full refresh, because incremental strategies are not suitable

Solution

1. Step 1: Analyze data update needs

   The table has new sales and corrections, so existing rows need updates and new rows need inserts.

2. Step 2: Match strategy to needs

   Append only adds new rows, so it won't update corrections. Delete+insert replaces matching rows but can be costly. Merge efficiently updates and inserts rows incrementally.

3. Final Answer:

   merge, because it updates existing rows and adds new ones -> Option A

4. Quick Check:

   merge = update + insert for corrections and new data [OK]

Hint: Use merge for updates plus new rows efficiently [OK]

Common Mistakes:

• Choosing append and missing updates
• Thinking delete+insert always best
• Ignoring performance of full refresh