Using OR in JOIN Clauses: Why It Hurts Performance and How to Avoid It

When writing queries in Narrative's Data Collaboration Platform using NQL (Narrative Query Language), it might be tempting to join two datasets with an OR in the join condition. For example, you might try to join on one field or another field, like:

SELECT ...
FROM A 
JOIN B 
  ON A.user_id = B.user_id 
     OR A.email = B.email;

On the surface, this seems logical – you want to match records if either of two keys match. However, using OR clauses in JOIN conditions is a bad idea for performance. This article explains why OR conditions in joins can lead to extremely inefficient query execution and even cause queries to run much slower or never finish, and it provides a better alternative using arrays and unnesting to achieve the same result with optimal performance.

Why OR in JOIN Conditions Causes Problems

OR conditions in a JOIN make it hard for the query optimizer to do its job. Database query planners (the engines that decide how to execute your SQL/NQL query) are very good at optimizing single-condition joins (especially equality joins like A.key = B.key). With a single join key, the planner can use efficient algorithms like hash joins or indexed lookups. But when you introduce an OR in the join, the database has to consider multiple join conditions simultaneously, which complicates the execution plan and often prevents these optimizations.

In practice, an ON (condition1 OR condition2) in a join cannot be easily converted into a single, efficient operation. The query planner essentially has to evaluate two separate join conditions for each pair of rows, which often means it can't use a simple index or hash to match rows. Database experts have found that a join condition with an OR is not optimizable to a hash join – the planner isn't "smart enough" to treat it like two separate joins or realize it could split the logic. As a result, the database may fallback to less efficient strategies.

What do these less efficient plans look like? Commonly, the database might perform a nested loop join (or a variation of it) under the covers. This could mean it scans one table and for each row in that table, it checks the other table for matches on both conditions. If neither condition can use an index effectively, this becomes akin to a double nested search – something that explodes in cost as data grows.

Some SQL engines attempt clever workarounds internally, but these often still result in bloated, slow plans. For instance, one SQL Server example showed that an OR in the JOIN confused the planner so much that it created a massive internal spool (temporary data store) of 7 billion rows (112 GB of data!) as part of the execution plan. Essentially, what could have been two separate join operations got squashed into one complex operation, causing the engine to materialize an enormous intermediate result.

Even if your case isn't that extreme, the pattern holds: OR joins tend to disable normal optimizations, often leading to scans, spools, or repeated work that wouldn't be needed with a single-key join. The database might also create internal "constant scans" or multiple join passes to handle the OR logic, which add overhead. In short, the query planner struggles to handle OR in a join, and you pay the price in performance.

Real-World Performance Impact

The theoretical discussion above has very real consequences. Developers have observed dramatic slowdowns and even catastrophic query performance due to OR in join conditions:

• Orders of Magnitude Slower: A straightforward query that should return results quickly might run unbearably slow if an OR join is present. One developer reported a query that was taking over 11 minutes to run while returning only ~40,000 rows, simply because the join used an OR condition. In another case, a query joining tables of around 10k rows each took nearly 4 hours to complete when using a conditional OR in the join, whereas using a single-condition (AND) join on the same data took only a few minutes. The OR caused a runtime explosion far beyond what the data size would normally suggest.
• Inefficient Plan = Slow Query: The slowdowns are directly tied to the inefficient execution plans we discussed. For example, in one scenario a developer noticed that using OR in a join prevented the use of a hash join, forcing the query into a less efficient nested loop plan that ran for hours. In another scenario, the execution plan with an OR join had to handle so much extra work (like the 7 billion-row spool mentioned earlier) that it overwhelmed the system. These are clear indicators that the database is doing much more work than it would with a simple join.
• Comparison: OR Join vs. Proper Alternatives: To truly see how bad an OR join can be, consider cases where the same logic is rewritten without the OR. The difference is startling. In a documented case study, a developer attempted a join with an OR (on two possible user ID fields) and it took 4 minutes 37 seconds to return ~26k rows. When they rewrote the query to split the OR logic into two separate queries combined with a UNION (no OR in the joins), the same result came back in about 12 seconds! In another well-documented performance test, rewriting an OR join as a UNION brought the execution time down from 3 hours 55 minutes to under 2 minutes on the same dataset. In each of these comparisons, removing the OR allowed the query planner to use normal join methods, drastically improving performance.

The takeaway from these examples is that an OR in the join condition can wreck your query performance, turning a query that should be quick into one that barely limps to completion (or doesn't finish at all in reasonable time). For any non-trivial amount of data, this pattern is untenable on the Narrative platform (or any SQL-based system).

Better Approach: Single-Column Joins via Array Combination and Unnesting

The good news is that you don't need to use OR in your JOIN to achieve the same logical result. There are alternative query patterns that are much more performance-friendly. One approach we recommend in NQL is to combine the join keys into an array, then flatten (unnest) that array to simulate the OR logic — while still using a single-column join condition.

How Does This Work?

Instead of asking the database to join on two conditions at once, you can restructure the data so that each possible join key generates a separate row entry, and then perform a standard join on those keys. In practice, this means:

1. Combine the keys into an array on each side of the join. For example, if table A has keys key1 and key2 that need to match either field in table B, you create a new array column (say, keys) that contains [key1, key2] for each row of A. Do the same for table B (an array of the two possible keys from B).
2. Unnest (flatten) the arrays to turn them into individual rows. Using NQL's UNNEST (or similar) function, you can expand each array so that each element becomes its own row (while still carrying the rest of the record's data along). After unnesting, table A's data will be transformed such that if a row originally had two possible keys, it now appears as two rows – one with the first key and one with the second key. The same goes for table B.
3. Join on the single key column resulting from the unnest. Now, instead of an ON (A.key1 = B.key1 OR A.key2 = B.key2), you have something like ON A_flat.key = B_flat.key – a simple equality on one column. This is a single-condition join that the query planner can optimize efficiently (using indexing or hashing), just as it would with any normal join.

By doing this, you've essentially transformed the OR logic into a set of rows that can be joined with a single equality. The database no longer has to juggle two conditions at once; it just matches keys as usual.

Example in NQL

Let's illustrate with a simplified example. Suppose we have two datasets: Purchases and Users. We want to join them such that a purchase is matched to a user either by the user_id field or by an email field (maybe the data has two ways to identify a user). Instead of doing JOIN ... ON Purchases.user_id = Users.id OR Purchases.email = Users.email, we can do:

WITH Purchases_flat AS (
    SELECT 
      p.*, 
      key 
    FROM Purchases p 
    CROSS JOIN UNNEST([p.user_id, p.email]) AS t(key)
),
Users_flat AS (
    SELECT 
      u.*, 
      key 
    FROM Users u 
    CROSS JOIN UNNEST([u.id, u.email]) AS t(key)
)
SELECT 
  pf.purchase_id,
  uf.user_name,
  pf.item, 
  pf.amount, 
  uf.email
FROM Purchases_flat pf
JOIN Users_flat uf
  ON pf.key = uf.key;

What's happening here? In the common table expressions (CTEs) Purchases_flat and Users_flat, we take each row and produce new rows for each key in the array. For instance, a single purchase record {user_id: 123, email: "foo@example.com", ...} becomes two records: one with key = 123 and one with key = "foo@example.com". Similarly, each user record with an id and email becomes two records with those as key. We then join on pf.key = uf.key. This join will find matches where either the numeric ID matches or the email matches, but without using OR – we've flattened those possibilities into the data itself.

This approach maintains the correctness of the result (you'll get all the matches you'd expect from the OR logic), but crucially, the join condition is now a simple equality on a single column. That means the query planner can use a normal optimized join strategy. In fact, this is essentially equivalent to performing two separate joins (one on user_id, one on email) and combining the results – exactly what the planner couldn't figure out how to do on its own before. By making it explicit, you avoid confusing the optimizer.

Why This Is Better for Performance

When you join on a single key (like pf.key = uf.key in the example), the database can build a hash table of one side's keys or use an index to rapidly find matches on the other side. It's doing one thing at a time: "match on this key". All the complexity of the OR condition has been handled by us restructuring the data. There's no need for a giant spool or a nested loop checking two conditions per row – it's back to a straightforward join.

Database systems (and NQL's underlying engines) are highly optimized for flat, single-key joins. By flattening the data ourselves, we allow them to do what they're best at. Many SQL experts recommend this approach of rephrasing queries to avoid OR conditions in joins, since it allows the query optimizer to work more effectively.

Additional Considerations and Best Practices

• Union as an Alternative: Another conceptually simple alternative is to perform two queries and UNION their results – one query joining on the first condition, another on the second. This achieves the same outcome as the OR (assuming an inner join scenario) by literally doing the work separately and then combining the outputs. The union approach has been shown to dramatically improve performance in many cases, turning queries that took minutes or hours into ones that complete in seconds. In NQL, you could certainly do the same. The array/unnest method we showed is essentially accomplishing a similar separation of concerns, but within a single query. Whether you use UNION or the UNNEST technique, the key point is to avoid a direct OR in the join. (If you do use UNION, remember that UNION (without ALL) will de-duplicate results, which in many cases is what you want if the two join conditions could produce the same row. The array method may require you to handle duplicates explicitly, for example by using SELECT DISTINCT if needed.)
• Readability vs. Performance: The flattened join approach can look a bit more verbose or complex than a simple OR join condition. However, it's usually worth the trade-off. For beginners, the syntax might seem intimidating at first, but it's essentially just a mechanical transformation of the data. Once understood, it's quite straightforward: generate all possible join keys, then join. In a knowledge-base context or collaborative environment like Narrative's platform, it might even be a common pattern that teammates recognize. Always favor the clearly performant approach over a syntactically shorter but inefficient one – especially when dealing with large datasets.
• Testing and Validation: If you replace an OR join with the unnesting strategy, test the query on a smaller subset of your data first. Ensure it returns the expected results (the logic should match, but it's good to double-check edge cases). Also, monitor the performance. In almost all cases, you should see a significant improvement in execution time, and the query should be able to scale to larger data without the previous issues. The differences highlighted in the earlier examples (hours vs. seconds) underscore how much of a gain you can expect.
• When in Doubt, Restructure: The general principle is to avoid complex join conditions. If you ever find yourself needing an OR, consider restructuring your data or query. Sometimes this might even mean redesigning how data is stored (for instance, having a mapping table that correlates the two keys so that a single join can be used). But without going that far, the query-level techniques discussed (UNION or UNNEST with arrays) are usually sufficient to solve the problem.

Conclusion

Using OR in a JOIN condition in NQL (or SQL in general) is highly discouraged because of the severe impact on query performance. It confuses the query optimizer and often leads to inefficient execution plans that can make a query run dramatically slower – we're talking differences of minutes or hours for the same task. In worst cases, the query might consume huge amounts of resources (memory, temp space) or not finish at all in a reasonable time.

The core issue is that a join with OR isn't a single join at all – it's essentially asking the system to do multiple joins at once and merge the results. Most SQL engines won't handle that gracefully. The solution is to help the planner out by rewriting the query in a form it can optimize. By combining join keys into an array and flattening them (or using equivalent strategies like separate queries with UNION), we turn the problem back into a standard single-key join scenario. The database can then use efficient algorithms (hash joins, indexed joins, etc.) to get the results quickly.

Best practice: Always aim for a single-column join condition. If you have multiple possible match criteria, break them out – either by unnesting arrays of keys or by running separate queries – rather than using an OR in the ON clause. This will keep your queries running efficiently on Narrative's Data Collaboration Platform and prevent the kind of performance pitfalls that can stall your data analysis. Remember, a well-structured query not only runs faster but is also easier to maintain and troubleshoot. Avoid OR in joins, and your future self (and your teammates and systems engineers) will thank you!