One of the most common challenges users face in DynamoDB is how to efficiently retrieve subsets of data without having high read costs.

Traditional filtering using FilterExpression on a full table scan is expensive and slow, especially at scale.

This is where sparse indexes come in.

Sparse indexes can dramatically help improve read efficiency by indexing only the items that matter for a particular query.

What Is a Sparse Index?

A sparse index in DynamoDB is a Global Secondary Index (GSI) that includes only a subset of items from the base table.

Specifically, only the items that contain the GSI’s partition key attribute.

Items that don’t have that attribute are simply excluded from the index.

This selective inclusion allows you to create purpose-built indexes that include only the data relevant to a specific query pattern.

This reduces index size, improves query performance, and cuts down on unnecessary read costs.

Sparse Index Example

Imagine you have a table with e-commerce orders. Orders can have a boolean value like “isDelayed” if a given order is delayed.

Now, say the customer satisfaction team needs to track only these orders that have been delayed in order to send the customer an email to communicate further.

Rather than querying the entire partition for delayed orders, they would run a query on an index that has the order number and “isDelayed” as the primary keys.

This way any orders that are not delayed will simply not appear on this index, making it an efficient read for delayed orders.

How to Implement a Sparse Index

Let’s walk through how to implement this pattern.

Assume your base table has the following structure:

{
  "PK": "user#101#orders",
  "SK": "2025-01-01T12:00:00",
  "isDelayed": "true",
  "customerID": "c-101",
  "orderID": "201"
}

You create a GSI (let’s call it GSI1) with:

• Partition Key: same PK as base table (user#101#orders)

• Sort Key: use isDelayed (true) and add the orderID

It would look like this:

{
  "GSI1PK": "user#101#orders",
  "GSI1SK": "true#order#201",
  "customerID": "c-101"
}

Now, update only the items you want to appear in the index to include the isDelayed field.

You can exclude isDelayed from items that are not delayed (e.g., processing, delivered, shipped, etc), and they will be omitted from the index entirely.

Now, to get all delayed orders, you query the GSI1 index like so:

TableName: "orders",
IndexName: "GSI1",
KeyConditionExpression: 'GSI1PK = "user#101#orders" AND begins_with(GSI1SK, "true")'

This query will return only the items that are delayed, and only those items will exist in the index.

No wasted read capacity on irrelevant data.

Practical Use Cases

Sparse indexes are useful for many different scenarios:

• Soft deletes: Create a sparse index on isActive = true to fetch only active records.

• Priority queues: Index only items where priority = “high” for urgent processing.

• Published content: Store both draft and published articles, but index only those with status = “published”.

Some Best Practices

• Keep sparse indexes targeted and small to reduce GSI size and improve performance.

• Use string values for GSI keys to maintain compatibility and consistency.

• Don’t over-index, remember that GSIs incur additional write costs. Only create them when they enable a high-value access pattern.

Conclusion

Sparse indexes are a DynamoDB power feature that lets you build efficient, low-latency queries on selective subsets of your data.

By designing your data model to include or exclude attributes used as GSI keys, you can control exactly what appears in the index. This allows you to optimize costs and performance for critical queries.

👋 My name is Uriel Bitton and I hope you learned something in this edition of Excelling With DynamoDB.

📅 If you're looking for help with DynamoDB, let's have a quick chat.

🙌 I hope to see you in next week's edition!