Why Is My Join Returning Duplicates?

Why Is My Join Returning Duplicates?

A deep dive into the causes of duplicate rows after SQL joins—and proven techniques to prevent or eliminate them.

Overview

Few things are more frustrating than running a simple JOIN only to discover the result set has ballooned with unexpected duplicates. Understanding why this happens is essential for writing correct analytics queries, optimizing ETL pipelines, and keeping dashboards trustworthy.

Why It Happens

1. One-to-Many or Many-to-Many Relationships

If either table contains multiple rows that match the join key in the other table, every matching combination is returned. This Cartesian multiplication is expected behavior—but can look like duplication when you expected a single row per entity.

2. Non-Unique or Partial Join Keys

Using columns that are not unique identifiers introduces ambiguity. For instance, joining only on first_name and last_name when multiple people share those names will naturally duplicate the parent row.

3. Missing Join Filters

Sometimes a query joins on the correct key but forgets an additional filter (e.g., latest record, active flag, date range). Rows outside the desired slice appear as duplicates.

4. Data Quality Issues

Upstream ETL jobs might have accidentally inserted true duplicates or null keys, amplifying the problem when joins occur downstream.

Detecting the Source of Duplicates

Before reaching for SELECT DISTINCT, diagnose the root cause:

• Count by key: SELECT join_key, COUNT(*) FROM table GROUP BY 1 HAVING COUNT(*) > 1
• Review indexes: Is a unique constraint missing?
• Check data recency: Are historical snapshots mixing with current records?

Strategies to Eliminate or Prevent Duplicates

Choose the Correct Join Granularity

Ensure each side of the join is at the same grain. Aggregate child tables first if you only care about one row per parent:

WITH order_totals AS (
SELECT customer_id, SUM(amount) AS total_spent
FROM orders
GROUP BY customer_id
)
SELECT c.*, ot.total_spent
FROM customers c
LEFT JOIN order_totals ot USING (customer_id);


Use DISTINCT or Window Functions Carefully

SELECT DISTINCT removes duplicates but can hide underlying data issues. A safer pattern is ROW_NUMBER() to pick a canonical record while keeping visibility:

WITH ranked_events AS (
SELECT e.*,
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_time DESC) AS rn
FROM events e
)
SELECT *
FROM ranked_events
WHERE rn = 1;


Add Missing Filters

If you only need the latest status or active records, explicitly filter:

SELECT c.*, s.status
FROM customers c
JOIN status_log s ON c.id = s.customer_id
WHERE s.is_current = TRUE;


Create or Enforce Unique Constraints

Where business logic dictates uniqueness, add constraints or indexes. This not only prevents future duplicate data but helps query planners optimize joins.

Working in Galaxy

Galaxy’s SQL editor surfaces row count diffs after each run, making it obvious when a join unexpectedly explodes in size. You can:

• Use the AI Copilot to explain why duplicates appear and generate a ROW_NUMBER() fix.
• Share the problematic query in a Collection so teammates can endorse the corrected version, eliminating Slack back-and-forth.

Best Practices

• Start every join by asking, “What is the expected grain of this result?”
• Validate uniqueness of join keys with a quick COUNT(*) diagnostic.
• Document assumptions in comments or Galaxy’s auto-generated descriptions.
• Favor window functions over blanket DISTINCT for auditability.

Common Pitfalls—and How to Fix Them

Partial Key Joins

Joining on email alone when multiple accounts share the same address leads to duplication. Include the account_id or enforce uniqueness.

Unfiltered Historical Tables

Warehouse tables often carry versioned records. Always filter to the latest version to avoid duplicate entity rows.

Assuming Left Join Is Safe

A LEFT JOIN can still duplicate parent rows if the right side has multiple matches. Aggregate or deduplicate the right side first.

Conclusion

Duplicate rows after a join are rarely a database bug; they are a signal that the data model or query grain needs attention. By diagnosing the relationship type, validating uniqueness, and applying the right SQL patterns (aggregation, window functions, filters), you can guarantee clean, reliable join results—keeping dashboards credible and pipelines efficient.