How To Prevent Hot Partitions from Affecting AWS DynamoDB DAX Performance

Select Language:

When you encounter ReadTimeoutExceptions on DAX during periods of high write activity, it’s important to understand how DAX and DynamoDB work together, especially with hot partitions. DAX acts as a write-through cache, which means that every write, including complex operations like TransactWriteItems and DeleteItem, first gets written to DynamoDB and then to the DAX cache. This process can cause delays that are different from the throttling you see directly in DynamoDB.

DynamoDB automatically adjusts its capacity to handle more traffic in hot partitions—that is, partitions receiving a lot of requests—through its adaptive capacity. But DAX has specific throughput limits at the node level, and if these limits are exceeded, DAX will return a ThrottlingException. This is separate from DynamoDB’s throttling.

If you’re doing a lot of writes to the same items, especially with the same primary keys, this can create a hot partition scenario. Even if DynamoDB manages the load well, DAX might struggle because of a few factors:

– When writing frequently to the same items, DAX needs to constantly invalidate and update those cache entries, adding overhead.
– Each DAX node has its own throughput limits that can be quickly reached during intense write activity.
– The write-through process itself adds complexity, which can lead to contention and delays.

The ReadTimeoutExceptions you’re experiencing are likely due to DAX having trouble keeping up with the frequent cache invalidation and updates needed for your concentrated write pattern. This issue isn’t necessarily with DynamoDB throttling but with DAX’s capacity to keep pace.

To fix this problem, consider the following steps:

– Spread the write load across different partition keys to avoid hot spots.
– Increase your DAX cluster size or capacity by adding more nodes or upgrading the hardware to boost overall throughput.
– Optimize your application to batch writes, reducing the frequency of individual write operations to the same items.
– Review your cache invalidation strategies—try to minimize unnecessary invalidations by structuring your data and access patterns more efficiently.

Implementing these changes can help DAX handle high write loads more effectively, reducing timeout errors and improving overall performance.