Handling Concurrent Writes in DynamoDB Global Tables

The Multi-Region Consistency Gap

When deploying a global application, the goal is usually to bring data closer to the user to reduce latency. DynamoDB Global Tables enable this by creating multi-active replicas across AWS regions. However, this introduces a specific engineering challenge: concurrent writes. Because replication is asynchronous, two different users in two different regions can update the same item at nearly the same time, leading to a conflict that the database must resolve automatically.

The takeaway for engineers is that DynamoDB resolves these conflicts using Last Writer Wins (LWW). If your application cannot afford to lose a partial update due to a timestamp difference of a few milliseconds, you cannot rely on the default behavior alone; you must implement application-level versioning or conditional writes.

How Last Writer Wins (LWW) Operates

In a Global Table, every write operation is timestamped. When a change is replicated from Region A to Region B, DynamoDB compares the timestamp of the incoming update with the timestamp of the existing item in Region B. The update with the latest timestamp is preserved, and the older one is discarded.

This mechanism ensures that all regions eventually converge to the same state (eventual consistency), but it does not guarantee that the "correct" business logic won. For example, if Region A updates a user's email and Region B updates the same user's phone number simultaneously, the write with the later timestamp will overwrite the entire item, potentially erasing the update from the other region if the updates were not performed as partial updates.

Implementing a Versioned Update Strategy

To prevent data loss during concurrent writes, you can implement a versioning attribute. Instead of relying on the system timestamp, the application manages a version number. By using ConditionExpression, you can ensure a write only succeeds if the version being updated matches the version currently in the database.

Example: Conditional Update Configuration

Run this command using the AWS CLI from a terminal configured with the necessary IAM permissions (dynamodb:UpdateItem) to update a user profile only if the version matches.

aws dynamodb update-item \
    --table-name GlobalUserTable \
    --key '{"UserId": {"S": "user_123"}}' \
    --update-expression "SET ProfileData = :newData, Version = Version + :inc" \
    --condition-expression "Version = :expectedVersion" \
    --expression-attribute-values '{ 
        ":newData": {"S": "Updated Bio"}, 
        ":inc": {"N": "1"}, 
        ":expectedVersion": {"N": "5"} 
    }'

Expected Result: If the item in the local region has Version = 5, the update succeeds. If another region already replicated an update that bumped the version to 6, this command will return a ConditionalCheckFailedException. Your application should then catch this error, fetch the latest data, and retry the merge.

Trade-offs and Operational Costs

While Global Tables simplify regional failover, they introduce specific overheads that impact the bottom line and performance:

• Write Amplification: Every write performed in one region is replicated to all other member regions. If you have a table in three regions, one write operation costs three times the Write Capacity Units (WCUs).
• Replication Lag: While typically fast, replication is not instantaneous. You can monitor the ReplicationLatency metric in Amazon CloudWatch to determine the window of eventual consistency.
• Conflict Blindness: DynamoDB does not notify you when LWW discards a write. There is no "conflict log" to review; the data is simply overwritten.

Verifying Regional Synchronization

To verify that your Global Table is functioning and to measure the lag, follow this diagnostic flow:

1. Perform a write to an item in your primary region (e.g., us-east-1).
2. Immediately attempt to read that same item from a secondary region (e.g., eu-west-1) using the CLI or a SDK.
3. If the item is missing or contains old data, repeat the read every 100ms until the data appears.
4. Compare this observed time with the ReplicationLatency metric in CloudWatch to ensure your application's timeout settings are aligned with actual network performance.