The Curious Case of the Missing Right Join / Full Outer Join in Broadcast Joins

Ever tried to perform a right outer join/full outer join using a broadcast join and been left scratching your head? You're not alone! This seemingly straightforward combination often throws a wrench in the works, and understanding why can shed light on some fundamental concepts in distributed data processing. What's more, this issue isn't limited to just right outer joins; full outer joins face a similar predicament. Let's dive in!

Broadcast Joins: A Quick Recap

Broadcast joins are a powerful optimization technique, especially when dealing with a large table and a significantly smaller one. The core idea is to replicate the smaller table (the "broadcast" table) across all nodes in your cluster. This allows each node to perform the join locally, avoiding the expensive shuffling of data that would be required otherwise.

Right Outer Joins and Full Outer Joins: Preserving Both Sides

Right outer joins, as we discussed, are all about ensuring that every row from the right table makes it into the final result set. If there's no match in the left table, the corresponding row from the right table is still included, with NULL values filling in for the missing columns from the left.

Full outer joins take this a step further. They aim to preserve all rows from both the left and right tables. If a row has a match on the other side, it's included with the corresponding data. If not, it's included with NULL values for the missing side.

The Conflict

The conflict arises from the way broadcast joins handle data distribution, as we explained earlier. In a typical broadcast join:

• The larger table (usually the "left" table) remains distributed across the cluster.
• The smaller table (the "right" table) is copied in its entirety to each node.

This works well for many scenarios, but it falls short when dealing with right or full outer joins. Here's why:

• Right Outer Join: To guarantee that every row from the right table is included, each node would need to be aware of all rows in the left table. This contradicts the localized nature of a broadcast join.
• Full Outer Join: This join type needs a complete view of both tables to ensure that no rows are lost. Again, this clashes with the broadcast join's distributed approach.

Workarounds and Solutions

The good news is that there are ways to navigate these challenges:

1. Reverse the Join (For Right Outer Joins): The simplest solution for right outer joins is often to switch to a left outer join, reversing the order of your tables. This aligns with the broadcast join's preference for the larger table on the left.
2. Replicate Both Tables: If both tables are relatively small, you can replicate both of them across all nodes. This removes the optimization benefit of a broadcast join but allows you to perform right or full outer joins.
3. Embrace Shuffle Joins: For larger datasets where a right or full outer join is essential, consider using a shuffle join (such as a sort-merge join). While these joins involve more data movement, they are better suited for scenarios where a complete view of both tables is necessary.

Key Takeaways

Understanding the interplay between join types and optimization strategies is crucial for efficient data processing in distributed environments. While right and full outer joins might not be directly compatible with broadcast joins in their typical implementation, there are always alternative approaches to achieve the desired outcome. So, the next time you encounter this situation, remember these workarounds and choose the one that best suits your data and performance requirements.