DynamoDB Multi-Attribute Global Secondary Indexes — Why They Matter More Than You Think

When AWS announced DynamoDB Multi-Attribute Global Secondary Indexes (GSIs), my first reaction was… “Is this really a big deal?” After all, for years developers have been using tricks like concatenating multiple attributes into a single GSI key to support compound lookups.

But after using Multi-Attribute GSIs in a real application, my perspective changed completely.

This new feature removes a surprising amount of complexity from data modeling and query logic. It makes attribute updates safer, reduces the chance of bugs, and eliminates the constant need to maintain artificially constructed index keys.

Let me explain why.

🚨 The Old Way: Concatenated Keys Everywhere

Before Multi-Attribute GSIs, developers often did something like this:

GSI_PK = `${entityType}#${entityId}#${eventType}#${timestamp}`

This worked, but it came with downsides:

• Every time a source attribute changed, you had to rebuild the concatenated key
• A bug in the concatenation order meant broken queries
• Query expressions became awkward (begins_with(entityKey, 'Stock#123#Created'))
• It polluted your data model with synthetic fields that existed only for GSI usage

And of course, any update meant rewriting the entire GSI key, not just the changed attribute.

✅ The New Way: Multi-Attribute GSIs

With multi-attribute GSIs, DynamoDB does something much more intuitive:

You define multiple attributes as part of your GSI key schema — no concatenation required.

The benefits are immediate:

✔️ Simpler writes

If you update eventType, you only update that attribute. You do not rebuild a combined key string.

✔️ Cleaner queries

You can use native DynamoDB operators like begins_with on actual attributes, not synthetic ones.

✔️ More maintainable data

Your table schema stays true to your domain model instead of forcing index-driven hacks.

Example: Audit Logs Table

Let’s look at an example from our system. We store audit logs for all entity operations (create/update/delete). Each log entry includes:

• entityType
• entityId
• eventType
• timestamp

With Multi-Attribute GSIs, the table definition becomes beautifully simple:

CloudFormation Definition

AuditLogsTable:
  Type: AWS::DynamoDB::Table
  Properties:
    TableName: ${self:service}-audit-logs-${self:provider.stage}
    BillingMode: PAY_PER_REQUEST
    AttributeDefinitions:
      - AttributeName: PK
        AttributeType: S
      - AttributeName: SK
        AttributeType: S
      - AttributeName: entityType
        AttributeType: S
      - AttributeName: entityId
        AttributeType: S
      - AttributeName: eventType
        AttributeType: S
      - AttributeName: timestamp
        AttributeType: S
    KeySchema:
      - AttributeName: PK
        KeyType: HASH
      - AttributeName: SK
        KeyType: RANGE
    GlobalSecondaryIndexes:
      - IndexName: EntityEventTypeIndex
        KeySchema:
          - AttributeName: entityType
            KeyType: HASH
          - AttributeName: entityId
            KeyType: RANGE
          - AttributeName: eventType
            KeyType: RANGE
          - AttributeName: timestamp
            KeyType: RANGE
        Projection:
          ProjectionType: ALL

No composite strings, no data duplication — just clean, functional attributes.

Querying Audit Logs (Much Simpler Now)

Suppose you want to fetch audit logs for a specific entity, and specifically logs that start with a certain event type such as "StockCreated".

With Multi-Attribute GSIs, you can write natural, readable queries:

const result = await docClient.send(
  new QueryCommand({
    TableName: 'stepfunctions-example-audit-logs-vim',
    IndexName: 'EntityEventTypeIndex',
    KeyConditionExpression:
      'entityType = :entityType AND entityId = :entityId AND begins_with(eventType, :eventType)',
    ExpressionAttributeValues: {
      ':entityType': 'Stock',
      ':entityId': '01JDQ7YGN4X8KPWM2VQZS6THRC',
      ':eventType': 'StockCreated',
    },
  }),
);

That’s it. No string parsing. No synthetic keys. No risk of mismatched concatenation order.

Just clean, DSL-like DynamoDB queries.

🎯 Final Thoughts

I underestimated Multi-Attribute GSIs at first. They felt like syntactic sugar for something we already knew how to do.

But in real-world applications — especially audit logging, event sourcing, and entity history models — they eliminate:

• synthetic attributes
• complex update logic
• fragile concatenated keys
• awkward query patterns

The result is a cleaner schema, safer updates, and more intuitive queries.

If you’re building anything with multi-dimensional access patterns, Multi-Attribute GSIs are absolutely worth adopting.