分布式关系型数据库Join原理及分片场景下执行逻辑咨询

Great question! Let’s start with the basics: distributed join algorithms build on the core logic of single-machine joins (hash join, merge join, nested loop join) but add extra layers to handle data spread across multiple nodes. The key difference is that distributed systems first need to get related data onto the same node (or nodes) before running the familiar single-machine join logic.

Let’s break down exactly what happens with your specific scenario:

• Users table: Sharded by User_id (so all rows for a given User_id live on one node)
• Comments table: Sharded by Comment_id (so User_id values are scattered across every node)
• Query: SELECT * FROM Users INNER JOIN Comments ON Users.User_id = Comments.User_id

Here are the most common execution strategies distributed databases would use (in order of typical preference):

1. Redistribution (Shuffle) Join #

This is the go-to for large-to-medium sized tables:

• First, the database will re-shuffle the Comments table using the join key (User_id) instead of its original shard key (Comment_id). For every row in Comments, it calculates a hash of User_id and sends the row to the node where the corresponding Users shard lives (since Users is already sharded by User_id).
• Once this shuffle is done, each node has a local subset of Users and the matching subset of Comments.
• Each node then runs a standard single-machine join (like hash join) on its local data.
• Finally, the results from all nodes are collected and returned to the user.

This approach spreads the join work across all nodes, avoiding bottlenecks and leveraging parallelism.

2. Broadcast Join #

If the Users table is small (e.g., a dimension table with only a few thousand rows), this is more efficient:

• Instead of moving all Comments data, the database broadcasts a copy of the entire Users table to every node that holds a Comments shard.
• Each node then runs a single-machine join between its local Comments data and the full Users data it received.
• Results are collected from all nodes and returned.

This avoids the heavy shuffle of a large Comments table, which saves network bandwidth and time.

3. Gather-to-Single-Node Join (Rarely Used) #

Only viable if both tables are tiny:

• The database pulls all Users and Comments data onto one single node, then runs a standard single-machine join there.
• This is almost never the default choice because it creates a massive bottleneck on that one node and wastes the distributed system’s parallelism.

Key Takeaway #

Distributed joins don’t replace single-machine join algorithms—they wrap them with smart data movement strategies. The database optimizer will pick the best strategy based on table sizes, shard keys, and available resources.

内容的提问来源于stack exchange，提问作者Ryn