Choosing the Right DynamoDB Partition Key: The Key to Performance

When you start working with Amazon DynamoDB, you'll quickly realize that it's not like a traditional relational database. The most important decision you will make when designing a DynamoDB table, and the one that has the biggest impact on performance and scalability, is choosing your partition key.

Getting the partition key right is the secret to unlocking DynamoDB's power. Getting it wrong is the most common cause of performance problems.

How DynamoDB Stores Data

Under the hood, DynamoDB stores your data across multiple physical servers called partitions. When you write an item to a table, DynamoDB uses the value of your partition key to determine which partition to store the data in. It does this by passing the partition key value to an internal hash function.

This is why the partition key is sometimes called the hash key.

The Goal: Even Data and Activity Distribution

Your primary goal when choosing a partition key is to select an attribute that will spread your data and your application's read/write activity as evenly as possible across all the partitions in your table.

If a large number of your requests are all directed at a single partition key value, you create a "hot partition." This means all that traffic is hitting a single physical server, which can lead to throttling and poor performance, even if your table as a whole has plenty of provisioned capacity.

Characteristics of a Good Partition Key

A good partition key has two main characteristics:

1. High Cardinality: The attribute should have a large number of distinct values. An attribute like status (with values like "pending", "in-progress", "complete") would be a terrible partition key because all your data would be concentrated on just a few partitions.

2. Uniformly Accessed: The access patterns for your application should be spread evenly across the key's values. If you use user_id as your partition key, but one specific user is responsible for 90% of your application's traffic, you will still have a hot partition.

Common Partition Key Patterns

Let's look at some examples for a table storing e-commerce orders.

Good Partition Keys:

• order_id: This is an excellent choice. Every order will have a unique ID, so the data will be perfectly distributed. This is ideal for your primary access pattern: looking up an order by its ID.
• user_id: This is often a good choice, assuming your traffic is not dominated by a small number of users. It allows you to efficiently query for all orders belonging to a specific user. (This would typically be used in combination with a sort key, like order_date).
• product_id: This could be a good choice if you frequently need to look up all orders for a specific product and your sales are spread relatively evenly across your products.

Bad Partition Keys:

• order_status: Very low cardinality. All your "shipped" orders would be on one partition.
• order_date: While the cardinality might be high, your access patterns would likely be very uneven. All of today's orders would be written to the same partition, creating a massive hot spot for writes, while partitions for older dates would be cold.

What About When You Don't Have a Good Key?

Sometimes, your data doesn't have a single, high-cardinality attribute that works for your access patterns. In these cases, you can use a technique called write sharding or create a composite key.

Example: The Hot product_id

Imagine you have one product that is a massive bestseller and gets 100x more traffic than any other product. Using product_id as the partition key would create a hot partition.

To solve this, you can create a composite partition key. Instead of just product_id, you could make the partition key product_id_{random_number_1_to_10}.

• When you write a new order for this product, you would generate a random number between 1 and 10 and append it to the product ID (e.g., bestseller-product_7). This spreads the writes for your hot product across 10 different logical partitions.
• When you need to read all the orders for that product, you would have to query all 10 partitions (from bestseller-product_1 to bestseller-product_10) and merge the results in your application. This is a common trade-off: you make reads more complex to enable scalable writes.

Conclusion

Choosing the right partition key is more of an art than a science, and it requires you to think deeply about your application's access patterns before you start writing code. By selecting a key with high cardinality that will evenly distribute your workload, you can avoid the dreaded hot partition and unlock the incredible scale and performance that DynamoDB has to offer. Always start by modeling your access patterns, and then choose the key that fits them.