Multi-source fan-in patterns in dbt - Time & Space Complexity

Time Complexity: Multi-source fan-in patterns

O(n)

Understanding Time Complexity

When combining data from many sources into one place, it is important to know how the work grows as sources increase.

We ask: How does the time to combine data change when we add more sources?

Scenario Under Consideration

Analyze the time complexity of the following dbt model combining multiple sources.

with source1 as (
  select * from {{ source('db1', 'table1') }}
),
source2 as (
  select * from {{ source('db2', 'table2') }}
),
source3 as (
  select * from {{ source('db3', 'table3') }}
)

select * from source1
union all
select * from source2
union all
select * from source3

This code reads from three different sources and combines all rows into one result.

Identify Repeating Operations

Look for repeated work in the code.

• Primary operation: Reading and scanning each source table.
• How many times: Once per source, here 3 times, but could be more if more sources added.

How Execution Grows With Input

As the number of sources grows, the total work grows by adding the work for each source.

Number of Sources (n)	Approx. Operations
3	3 times reading source tables
10	10 times reading source tables
100	100 times reading source tables

Pattern observation: The work grows linearly as you add more sources.

Final Time Complexity

Time Complexity: O(n)

This means the time to combine data grows directly in proportion to the number of sources.

Common Mistake

[X] Wrong: "Adding more sources won't increase time much because they run in parallel."

[OK] Correct: Even if sources run in parallel, total work still adds up, and combining results takes time too.

Interview Connect

Understanding how combining many data sources affects time helps you design efficient data pipelines and explain your choices clearly.

Self-Check

"What if we replaced union all with join operations? How would the time complexity change?"

Practice

1.

What is the main purpose of a multi-source fan-in pattern in dbt?

easy

A. To create indexes for faster queries

B. To split data into multiple smaller tables

C. To delete duplicate records from a single table

D. To combine data from multiple sources into one unified table

Solution

1. Step 1: Understand multi-source fan-in pattern

   This pattern is used to bring together data from different tables or sources.

2. Step 2: Identify the goal of combining data

   The goal is to create a single, unified view that contains all relevant information for analysis.

3. Final Answer:

   To combine data from multiple sources into one unified table -> Option D

4. Quick Check:

   Multi-source fan-in = combine data [OK]

Hint: Think 'fan-in' as merging many into one [OK]

Common Mistakes:

• Confusing fan-in with splitting data
• Thinking it deletes duplicates
• Assuming it creates indexes

2.

Which of the following is the correct syntax to perform a full outer join in dbt's SQL model?

SELECT * FROM source_a a
FULL OUTER JOIN source_b b
ON a.id = b.id

easy

A. SELECT * FROM source_a a FULL OUTER JOIN source_b b ON a.id = b.id

B. SELECT * FROM source_a a INNER JOIN source_b b ON a.id = b.id

C. SELECT * FROM source_a a LEFT JOIN source_b b ON a.id = b.id

D. SELECT * FROM source_a a CROSS JOIN source_b b

Solution

1. Step 1: Identify the join type needed for fan-in

   Multi-source fan-in requires a full outer join to keep all records from both sources.

2. Step 2: Match the syntax for full outer join

   The correct syntax is FULL OUTER JOIN with the join condition.

3. Final Answer:

   SELECT * FROM source_a a FULL OUTER JOIN source_b b ON a.id = b.id -> Option A

4. Quick Check:

   Full outer join syntax = SELECT * FROM source_a a FULL OUTER JOIN source_b b ON a.id = b.id [OK]

Hint: Full outer join keeps all rows from both tables [OK]

Common Mistakes:

• Using INNER JOIN which drops unmatched rows
• Using LEFT JOIN which keeps only left table rows
• Using CROSS JOIN which creates Cartesian product

3.

Given the following dbt model SQL, what will be the output for final_table?

WITH source_a AS (
  SELECT 1 AS id, 'A' AS val
  UNION ALL
  SELECT 2, 'B'
),
source_b AS (
  SELECT 2 AS id, 'X' AS val
  UNION ALL
  SELECT 3, 'Y'
)
SELECT
  COALESCE(a.id, b.id) AS id,
  COALESCE(a.val, b.val) AS val
FROM source_a a
FULL OUTER JOIN source_b b ON a.id = b.id
ORDER BY id

medium

A. [{id:1, val:'A'}, {id:2, val:'B'}, {id:3, val:null}]

B. [{id:1, val:'A'}, {id:2, val:'X'}, {id:3, val:'Y'}]

C. [{id:1, val:'A'}, {id:2, val:'B'}, {id:3, val:'Y'}]

D. [{id:1, val:null}, {id:2, val:'B'}, {id:3, val:'Y'}]

Solution

1. Step 1: Perform full outer join on id

   Ids 1 and 3 appear only in one source, id 2 appears in both.

2. Step 2: Use COALESCE to pick non-null values

   For id=2, COALESCE(a.val, b.val) picks 'B' from source_a since it is first.

3. Final Answer:

   [{id:1, val:'A'}, {id:2, val:'B'}, {id:3, val:'Y'}] -> Option C

4. Quick Check:

   COALESCE picks first non-null val = [{id:1, val:'A'}, {id:2, val:'B'}, {id:3, val:'Y'}] [OK]

Hint: COALESCE picks first non-null value in order [OK]

Common Mistakes:

• Assuming COALESCE picks from source_b first
• Ignoring full outer join rows
• Mixing up values for id=2

4.

Identify the error in this dbt model SQL that attempts a multi-source fan-in:

WITH source_a AS (
  SELECT id, val FROM table_a
),
source_b AS (
  SELECT id, val FROM table_b
)
SELECT
  COALESCE(a.id, b.id) AS id,
  COALESCE(a.val, b.val) AS val
FROM source_a a
JOIN source_b b ON a.id = b.id

medium

A. COALESCE cannot be used with JOINs

B. The JOIN should be a FULL OUTER JOIN to keep all rows

C. The SELECT statement is missing a GROUP BY clause

D. The table aliases 'a' and 'b' are not defined

Solution

1. Step 1: Check join type for fan-in pattern

   Using INNER JOIN drops rows not matching in both tables, losing data.

2. Step 2: Correct join type for full data retention

   FULL OUTER JOIN keeps all rows from both sources, essential for fan-in.

3. Final Answer:

   The JOIN should be a FULL OUTER JOIN to keep all rows -> Option B

4. Quick Check:

   Fan-in requires FULL OUTER JOIN = The JOIN should be a FULL OUTER JOIN to keep all rows [OK]

Hint: Use FULL OUTER JOIN to keep all rows from both sources [OK]

Common Mistakes:

• Using INNER JOIN instead of FULL OUTER JOIN
• Thinking COALESCE is invalid here
• Forgetting to alias tables

5.

You have three sources: sales_2022, sales_2023, and sales_2024. Each has columns order_id and amount. You want a unified table with all orders and amounts, preferring the latest year's amount if duplicates exist. Which approach best implements this multi-source fan-in pattern?

hard

A. Use FULL OUTER JOINs on order_id and COALESCE with order: 2024, 2023, 2022

B. Use INNER JOINs on order_id and SUM amounts

C. Use UNION ALL without any joins

D. Use LEFT JOINs starting from 2022 to 2024

Solution

1. Step 1: Combine all sources keeping all orders

   FULL OUTER JOINs ensure no orders are lost from any year.

2. Step 2: Use COALESCE to prefer latest year's amount

   COALESCE(amount_2024, amount_2023, amount_2022) picks the newest available amount.

3. Final Answer:

   Use FULL OUTER JOINs on order_id and COALESCE with order: 2024, 2023, 2022 -> Option A

4. Quick Check:

   Fan-in with full joins + COALESCE = Use FULL OUTER JOINs on order_id and COALESCE with order: 2024, 2023, 2022 [OK]

Hint: Full outer joins + COALESCE picks latest non-null value [OK]

Common Mistakes:

• Using INNER JOIN loses unmatched orders
• Using UNION ALL duplicates orders without preference
• Using LEFT JOINs misses some data