MySQL primarily uses nested loop joins, supports hash joins from 8.0.18, and lacks native merge join support but can simulate it via indexing. Nested loop joins work well for small outer tables with indexed inner tables, so indexing join columns, filtering early, and avoiding full scans are key optimizations. Hash joins boost performance for large unindexed datasets, and can be identified via “Using join buffer (hash join)” in EXPLAIN output. Merge joins, though not natively supported, can be mimicked by sorting tables via indexes, using ORDER BY and LIMIT, and avoiding expressions on join keys, helping simulate their efficiency for large sorted datasets.

MySQL has always been known for its performance in handling joins, but understanding how it does it under the hood can help you write better queries and tune your database for better performance. One key thing to know is that MySQL traditionally uses a nested loop join mechanism, but recent versions have added support for hash joins, and while it doesn't support merge joins natively, there are ways to mimic them. Let’s break this down in a practical way.

Nested Loop Join – The Default Workhorse

MySQL’s default join strategy is the nested loop join. It’s simple: for each row in the outer table, it scans the inner table to find matching rows. This works well when the outer table is small and the inner table has an index.

• If you’re joining a small users table with a large orders table, and you have an index on orders.user_id, this can be efficient.
• But if both tables are large and you don’t have proper indexing, performance can drop fast.

Tips to optimize nested loop joins:

• Make sure the columns used in the join condition are indexed.
• Filter early – reduce the size of the outer table with WHERE clauses before joining.
• Avoid full table scans on the inner table by using EXPLAIN to check if indexes are being used.

This is still the most common join type you’ll see in MySQL, especially if you're using InnoDB.

Hash Join – A Game Changer for Large Datasets

Starting from MySQL 8.0.18, hash joins are supported. This is a big deal, especially for data warehousing or analytics workloads where you're joining large tables without good indexes.

The idea is simple: MySQL builds a hash table from the smaller table (or result set) based on the join key, then scans the second table and probes the hash table for matches. This can be much faster than nested loops when dealing with large unindexed datasets.

When to expect MySQL to use a hash join:

• Joining large tables without suitable indexes.
• When the optimizer thinks a hash join will be more efficient than a nested loop.
• When you’re using a join type that can benefit from in-memory operations.

You can check if a hash join is being used by looking at the EXPLAIN output – if you see “Using join buffer (hash join)” in the Extra column, that’s your cue.

Merge Join – Not Natively Supported, But Still Possible

MySQL doesn’t support merge joins directly. A merge join works best when both tables are already sorted by the join key, allowing the database to merge them in a single pass, like how the merge phase of merge sort works.

But you can simulate a merge join by:

• Making sure both tables are sorted by the join key (usually via an index).
• Using ORDER BY and LIMIT to help the optimizer.
• Avoiding functions or expressions on the join columns that would prevent index use.

Merge joins are more common in other databases like PostgreSQL or Oracle, especially for very large sorted datasets. While MySQL doesn’t do this out of the box, understanding this concept can help you structure your queries and indexes more effectively.

So, in short:

• MySQL mostly uses nested loop joins, and optimizing indexes is key.
• From 8.0.18 , hash joins give you a better option for large joins.
• Merge joins aren’t built in, but smart indexing can help simulate some of their benefits.

If you're dealing with joins in MySQL, especially slow ones, start by checking your EXPLAIN plan, then look at indexing and table size. That usually gets you most of the way there.

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