Unique key for merge behavior in dbt - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the role of unique keys in dbt merge operations

In dbt, when performing a merge operation to update a target table from a source, why is defining a unique key important?

A It speeds up the merge by indexing the entire target table automatically.

B It ensures that each row in the source matches exactly one row in the target, preventing duplicate updates.

C It allows dbt to skip the merge step if the source table is empty.

D It automatically creates a primary key constraint on the target table.

💡 Hint

Think about what happens if multiple rows in the source match the same row in the target.

The unique key ensures that each source row corresponds to exactly one target row. This prevents ambiguous updates or duplicate rows during the merge.

❓ Predict Output

intermediate

Output of a dbt merge with duplicate keys in source

Given the following simplified merge logic in dbt, what will happen if the source table contains duplicate rows for the unique key?

merge into target_table as t
using source_table as s
on t.id = s.id
when matched then update set t.value = s.value
when not matched then insert (id, value) values (s.id, s.value)

A The merge will fail with an error due to multiple matches for the same target row.

B The merge will update the target row multiple times, ending with the last source row's value.

C The merge will insert duplicate rows into the target table.

D The merge will ignore duplicate source rows and update only once.

💡 Hint

Consider how SQL merge handles multiple matches on the same key.

SQL merge requires the join condition to uniquely identify target rows. Duplicate keys in source cause multiple matches, leading to an error.

❓ data_output

advanced

Resulting target table after merge with unique key

Consider the following target and source tables before a dbt merge operation:

Target table:
id | value
1  | 'A'
2  | 'B'
3  | 'C'

Source table:
id | value
2  | 'X'
4  | 'Y'

After running this merge:

merge into target as t
using source as s
on t.id = s.id
when matched then update set t.value = s.value
when not matched then insert (id, value) values (s.id, s.value)

What will be the content of the target table?

A 1|'A', 2|'X', 3|'C', 4|'Y'

B 2|'X', 4|'Y'

C 1|'A', 2|'X', 3|'C'

D 1|'A', 2|'B', 3|'C', 4|'Y'

💡 Hint

Think about which rows get updated and which get inserted.

Row with id=2 is updated from 'B' to 'X'. Row with id=4 is inserted. Other rows remain unchanged.

🔧 Debug

advanced

Identify the cause of merge failure due to unique key violation

A dbt merge operation fails with the error: SQL Error: The MERGE statement attempted to UPDATE or DELETE the same row more than once. Given this merge code:

merge into target t
using source s
on t.user_id = s.user_id
when matched then update set t.status = s.status
when not matched then insert (user_id, status) values (s.user_id, s.status)

What is the most likely cause?

A The source table is empty, so the merge cannot find rows to update.

B The target table has duplicate user_id values violating primary key constraints.

C The merge statement is missing a <code>delete</code> clause causing conflicts.

D The source table contains duplicate user_id values causing multiple matches to the same target row.

💡 Hint

Consider what happens if the join condition matches multiple source rows to one target row.

Duplicate keys in the source cause the merge to try updating the same target row multiple times, which is not allowed.

🚀 Application

expert

Designing a unique key for a complex merge scenario

You have a source table with columns user_id, event_date, and event_type. You want to merge this into a target table that stores the latest event per user and event type. Which unique key should you define for the merge to work correctly?

A A composite key on <code>(user_id, event_type)</code> to uniquely identify each event type per user.

B A composite key on <code>(user_id, event_date)</code> to uniquely identify events by date per user.

C A composite key on <code>(user_id, event_date, event_type)</code> to uniquely identify each event occurrence.

D A unique key on <code>event_date</code> only, since dates are unique.

💡 Hint

Think about what combination of columns uniquely identifies each event occurrence.

Using all three columns ensures each event is uniquely identified, preventing merge conflicts and allowing correct updates.

Practice

1.

What is the main purpose of the unique_key in dbt's merge behavior?

easy

A. To delete rows from the source table

B. To sort the data before merging

C. To create new columns in the target table

D. To identify which rows to update or insert during a merge

Solution

1. Step 1: Understand the role of unique_key in merge

   The unique_key tells dbt which rows in the target table correspond to rows in the source table during a merge operation.

2. Step 2: Identify the correct behavior of unique_key

   It helps dbt decide whether to update existing rows or insert new rows based on matching keys.

3. Final Answer:

   To identify which rows to update or insert during a merge -> Option D

4. Quick Check:

   unique_key matches rows for merge = D [OK]

Hint: unique_key matches rows to update or insert [OK]

Common Mistakes:

• Thinking unique_key sorts data
• Believing unique_key creates columns
• Confusing unique_key with deleting rows

2.

Which of the following is the correct way to specify a composite unique_key in a dbt model's merge config?

merge:
  unique_key: ?

easy

A. ['id', 'date']

B. 'id, date'

C. {id, date}

D. id + date

Solution

1. Step 1: Recall syntax for composite unique_key

   dbt expects a list of column names for composite keys, written as ['col1', 'col2'].

2. Step 2: Match syntax options

   ['id', 'date'] uses a list syntax with square brackets and quotes, which is correct.

3. Final Answer:

   ['id', 'date'] -> Option A

4. Quick Check:

   Composite unique_key = list of columns [OK]

Hint: Use list syntax ['col1', 'col2'] for composite keys [OK]

Common Mistakes:

• Using a string with commas instead of a list
• Using curly braces which is invalid syntax
• Trying to add columns with + operator

3.

Given the following dbt merge config:

merge:
  unique_key: 'user_id'

And source data:

| user_id | value |
|---------|-------|
| 1       | 100   |
| 2       | 200   |
| 1       | 300   |

What will happen when merging into a target table with existing user_id 1 and 2?

medium

A. The row with user_id 1 will cause an error due to duplicate keys

B. The last row with user_id 1 will update the target, overwriting previous

C. Both rows with user_id 1 will be inserted as new rows

D. Only the first row with user_id 1 will update, others ignored

Solution

1. Step 1: Understand unique_key behavior with duplicates in source

   dbt uses unique_key to match rows. If source has duplicates, the last row usually overwrites previous ones during merge.

2. Step 2: Analyze the source data effect

   Two rows with user_id 1 exist; the last row with value 300 will update the target row with user_id 1.

3. Final Answer:

   The last row with user_id 1 will update the target, overwriting previous -> Option B

4. Quick Check:

   Duplicate keys in source overwrite last row = C [OK]

Hint: Last duplicate row in source overwrites target row [OK]

Common Mistakes:

• Expecting error on duplicate keys in source
• Thinking all duplicates insert as new rows
• Assuming only first duplicate updates

4.

Consider this dbt merge config snippet:

merge:
  unique_key: ['id']

But the source data has duplicate id values. What is the likely issue?

medium

A. Merge will fail or produce incorrect results due to non-unique keys

B. Merge will ignore duplicates and update only one row

C. Merge will create new columns for duplicates

D. Merge will automatically remove duplicates before merging

Solution

1. Step 1: Identify problem with non-unique keys in merge

   Unique_key must uniquely identify rows. If duplicates exist, dbt cannot correctly match rows.

2. Step 2: Understand merge behavior with duplicates

   Duplicates cause merge to fail or produce wrong updates because dbt doesn't know which row to update.

3. Final Answer:

   Merge will fail or produce incorrect results due to non-unique keys -> Option A

4. Quick Check:

   Non-unique keys cause merge failure = A [OK]

Hint: unique_key must be unique; duplicates cause merge errors [OK]

Common Mistakes:

• Assuming merge ignores duplicates silently
• Thinking merge creates new columns for duplicates
• Believing merge auto-removes duplicates

5.

You want to merge data from a source table into a target table using dbt. The source has columns user_id, event_date, and event_type. You want to update existing rows only if both user_id and event_date match. Which unique_key should you use?

hard

A. unique_key: 'user_id'

B. unique_key: ['event_type']

C. unique_key: ['user_id', 'event_date']

D. unique_key: ['user_id', 'event_type']

Solution

1. Step 1: Identify columns needed to uniquely match rows

   Since updates depend on matching both user_id and event_date, both must be part of unique_key.

2. Step 2: Choose correct unique_key syntax

   unique_key: ['user_id', 'event_date'] correctly uses a list with both columns to uniquely identify rows for merge.

3. Final Answer:

   unique_key: ['user_id', 'event_date'] -> Option C

4. Quick Check:

   Composite key matches both columns = B [OK]

Hint: Use all columns needed to uniquely identify rows [OK]

Common Mistakes:

• Using only user_id when event_date is needed
• Using event_type which is not unique key
• Mixing wrong columns in unique_key