Designing Powerful Filtering With DynamoDB Sparse Indexes

One of the most common challenges with DynamoDB is filtering large datasets efficiently.

DynamoDB’s design is centered around fast lookups by primary keys and predictable query patterns, not arbitrary filtering like in relational databases.

If you try to scan a table and filter results afterward, performance quickly degrades, and costs can spiral out of control as your dataset grows.

This is where sparse indexes come in. By carefully structuring your data model and secondary indexes, you can achieve lightning-fast queries that return only the data you care about, without the overhead of scanning massive tables.

Sparse indexes are one of DynamoDB’s most underrated design patterns, and when applied correctly, they can completely transform how you query and filter data.

What is a Sparse Index?

A sparse index is a secondary index that only contains items meeting specific criteria.

Unlike your base DynamoDB table, which holds every item, a sparse index is populated only by items that have the indexed attribute set.

If an attribute doesn’t exist for an item, DynamoDB does not copy it into the index.

This behavior is key. By using this conditional nature of secondary indexes, you can create purpose-built indexes that contain only the subset of data you want to filter by.

The result is smaller, more focused indexes that are fast to query and inexpensive to maintain.

Let’s take an example.

Imagine you have a table of user accounts, and only a fraction of them are premium members. By creating a sparse index where the indexed attribute is only set for premium users, you instantly have a fast way to query all premium members, without scanning through every single user record.

Creating a Sparse Index

So how do you create a sparse index?

When you write an item to DynamoDB, you can specifiy an attribute, say “accountType” to have one of three possible value:

• Free

• Pro

• Premium

By creating a premium user, you can add the GSI partition key as “premium-users” and the sort key as the user’s ID.

The item will look like this:

pk: "user#123#,
sk: "info",
GSI1PK: "premium-users",
GSI1SK: "user#123",
accountType: "premium"

Now when you need to query all premium users, you can query the GSI directly without requiring any complex filtering: all items are already filtered based on the users account type criteria.

Why Use Sparse Indexes?

Sparse indexes are valuable because they eliminate the need to scan irrelevant items. Here are a few practical benefits:

1. Efficiency: queries against a sparse index return results faster because the index only holds items you care about.

2. Cost Savings: you read fewer items, which lowers your read capacity unit (RCU) usage.

3. Simplicity: they allow you to run queries that would otherwise require complex filtering logic or multiple scans.

4. Flexibility: you can create multiple sparse indexes, each dedicated to a different access pattern, without bloating your main table.

This makes sparse indexes an essential tool when you’re designing for high-performance access patterns in DynamoDB.

Real-World Use Cases

Sparse indexes are versatile and satisfy many different use cases:

• Active vs. Inactive Items: Keep only active users, subscriptions, or sessions in an index.

• Flagged Data: Track flagged posts, comments, or transactions for moderation.

• Workflows: Query only tasks in a specific stage, such as “in progress” or “pending approval.”

• Event Triggers: Maintain an index of items needing processing, then remove them when complete.

In each of these examples, sparse indexes provide a highly efficient way to isolate the exact subset of data you need without wasting capacity on irrelevant items.

Design Considerations

While sparse indexes are powerful, they require careful planning:

1. Write Costs: Remember that each index write consumes additional write capacity units (WCUs). Keep your index set as lean as possible.

2. Index Limitations: DynamoDB allows up to 20 GSIs per table. Use them strategically to support the most critical access patterns.

3. Consistency: GSIs are eventually consistent. If your application requires strongly consistent reads, you’d need to use LSIs or consider another design pattern.

4. Attribute Management: You must ensure the attribute driving the sparse index is set and cleared at the right times, usually through application logic or DynamoDB Streams + Lambda.

Conclusion

Sparse indexes are a subtle but highly effective design pattern in DynamoDB. By indexing only the items that matter for a given query, you can achieve efficient filtering at scale, without resorting to expensive scans.

When combined with DynamoDB’s flexible schema and predictable performance, sparse indexes give you the ability to design data access patterns that are both cost-efficient and lightning fast.

👋 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!