I'm designing a DynamoDB schema for a scenario where each tenant, like a school, can have millions of items—in this case, students. If I use a partition key like `SCHOOL#{id}` and a sort key like `STUDENT#id`, it groups all students under one partition. This could lead to hot partitions, which I'd like to avoid. I'm considering sharding the partition key to spread out the load (like `SCHOOL#{id}#SHARD#{n}`). What are some strategies for deciding the right number of shards, and what's the best way to structure sharding in DynamoDB? I need to support functionalities like listing all students for a school, adding, updating, and deleting individual student records.
5 Answers
Sharding can be a potential strategy, but it hinges on the nature of your use cases. If you're looking at just a few schools, a composite key using both school and student IDs might work better. This way, you can intelligently distribute the load without creating too much complexity in querying.
Today's DynamoDB is pretty good at handling hot partitions. It has features that allow it to automatically split partitions based on traffic patterns. This means as long as you're not overwhelming a specific partition with thousands of requests per second for a single student ID, you should be fine. Just avoid using incrementing sort keys like timestamps because they can seriously backfire under load.
That’s a relief! I was worried about hitting those limits.
You should definitely start by clarifying the access patterns for your data. DynamoDB works best when you design your schema around how you plan to query it. If certain queries are going to be frequent, that can heavily inform how you set up your keys.
Gotcha! I've updated my question to clarify those access patterns.
Consider how likely it is for a single school to generate enough load to cause a hot partition. If you're only listing students under normal usage, it might not be an issue at all. That said, if you do foresee potential issues, setting up sharding from the get-go is advisable, even if it requires some extra effort on your end.
You could use a hashing strategy for partitioning. Take the hash of the student ID, mod it by N, and append that to your partition key. Just be sure to estimate your data volume correctly to determine N. This can give you numerous partitions to distribute student data without congestion.
I'll have many tenants, so this might actually help me manage the load!